UCLA Undergraduate Science Journal Spring 2016

THE UCLA UNDERGRADUATE

SCIENCE JOURNAL

VOLUME 29 | SPRING 2016 Role of PACAP in Cortical Spreading Depression p. 25

Supervised Classification Algorithm for Seizure Detection in Continuous EEG Recordings p. 46

Investigating Mechanisms of Gender Differences in Immune System Diseases p. 54

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Editors in Chief

Faculty Advisors

Elizabeth Chin

Dr. Tama Hasson

Laura Liu

Dr. Janet Goins

Managing Editor of Research

Asst. Managing Editors of Research

Jennifer Hsueh

Luke Batty Hannah Bell Arielle Tripp Brandon Tsai

Managing Editor of Editorial

Asst. Managing Editor of Editorial

Christine Qin

Linda Ho

Managing Editor of Layout

Asst. Managing Editor of Layout

Sydney Kahmann

Tiffany Taimoorazy

Biological Research Board

Editorial Board

Mahuya Barua

Brock Embleton

Andrea Castro

Jamie Liu

Rebecca Chen

Chayanid (Ing) Ongpipattanakul

Amy Guo

Prasann Ranade

Himani Madnawat

Shayan Saalabi

David Merriott

Vishaka Sriniwasan

Douglas Meyer

Gina Young

Ryka Sehgal Joyce Wang

Layout Board Physical Research Board

Bernadette Chua

Haoxuan Dou

Chia-Ming Lee

Yao Chang Tan

Winston Luhur

Axel (Shiqi) Wang

Mahnoor Saleem

Junyi Wu

Xiwen (Tina) Zhou

Matthew Ye

Emily Vivian Huang

THE UCLA UNDERGRADUATE

SCIENCE JOURNAL VOLUME 29 | SPRING 2016

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Established 1986. The UCLA Undergraduate Science Journal is an officially recognized student organization at UCLA. The UCLA Undergraduate Science Journal is entirely managed and produced by students. Its contributions are the original work of UCLA undergraduates.

PHOTO CREDIT: WINSTON LUHUR

UNIVERSITY OF CALIFORNIA BERKELEY • DAVIS • IRVINE • LOS ANGELES • MERCED • RIVERSIDE • SAN DIEGO • SAN FRANCISCO

SANTA BARBARA • SANTA CRUZ

A Letter from the Editors

Dear Reader, As this letter heads to production, we are in our final quarter as Editors-in-Chief of the UCLA Undergraduate Science Journal. Over this past year, we have seen enormous growth in student interest and curiosity for scientific publishing which is one of the most important aspects of communication of scientific progress. This annual, peer-reviewed journal is a collaborative effort from some of the brightest and most dedicated undergraduates across scientific disciplines. They have come together and tirelessly worked to create the 29th edition of the journal because of an understanding of the importance of science communication – linking research with relevancy in our society and embedding science in the public conscious.

Our mission is to advance the spread of research among UCLA undergraduates and faculty. The diversity of and undergraduate involvement in research at UCLA is what allows us to publish a journal dedicated exclusively to the work of students produced by an undergraduate staff. In this issue alone, Brandon Tsai discusses using systems biology approaches to determine gene networks underlying BPA-induced metabolic syndrome, while Dalar Avadis-Rostamian addresses hippocampal and thalamic differences in schizophrenia and bipolar disorder.

We take great pride in the excellence of all of our articles, and every year, we honor two research articles and one review article with the Vice Provost Award of Excellence. We are excited to award Irvin Lien for his

article Role of PACAP in Cortical Spreading Depression, Shruti Nanivadekar for her article Supervised Classification Algorithm for Seizure Detection in Continuous EEG Recordings, and Brandon Pham for his article Investigating Mechanisms of Gender Differences in Immune System Diseases.

The quality of our journal is due largely to the talent and dedication of our staff and executive board. This year, we had the largest pool of applicants in the history of USJ, and every staff member was carefully selected to ensure each article was peer-reviewed by knowledgeable and intelligent students across scientific fields. Without their hard work and scrupulous attention to detail, the production of our journal would not have been possible.

Our two USJ faculty advisors, Dr. Tama Hasson and Dr. Janet Goins, gave us a tremendous amount of guidance that helped us lead USJ this year. Our discussions about ideas to implement for the journal always led to thoughtful, invaluable feedback – we could not have had better mentors to learn from.

We would also like to thank CTSI for their generous donation towards our journal each year. Without their support, this journal could not have been published.

Thank you for reading the UCLA Undergraduate Science Journal. We hope you enjoy this year’s issue as much as we did creating it!

Sincerely, Elizabeth Chin & Laura Liu

TABLE OF CONTENTS SPRING 2016 | VOLUME 29

01 NEWSBEATS

By USJ Staff

RESEARCH

04 Hippocampal and Thalamic Differences in Schizophrenia and Bipolar Disorder

By Dalar Avadis-Rostamian, Dr. William Grisham

08 Genetic Variation Operates Through Chromatin to Control Heart Failure Susceptibility

By Rachel Lopez, Dr. Emma Monte & Dr. Thomas M. Vondriska

16 Identifying Gene Networks Underlying BPA-induced Metabolic Syndrome

By Brandon Tsai, Qingying Meng, Le Shu, Andrew Mikhail, Helen Luk & Dr. Xia Yang

25 Role of PACAP in Cortical Spreading Depression

By Irvin Lien, Dr. Serapio M. Baca & Dr. Andrew C. Charles

32 Role of Smads1/5/8 in the BMP signaling pathway in the absence of Smad4

By Nancy Morones, Diana Rigueur & Dr. Karen Lyons

37 Altered Molecular Expression in Myofibroblasts: Implications of Cardiac Arrhythmias

By Minu Madhvani, Dr. Thao P. Nguyen

46 Supervised Classification Algorithm for Seizure Detection in Continuous EEG Recordings

By Shruti Nanivadekar, Dr. Richard Staba

REVIEW

54 Investigating Mechanisms of Gender Differences in Immune System Diseases

By Brandon Pham, Dr. Ram Raj Singh

58 Remote Sensing of Vegetation

By Ryan Nicholas Lam

Cover art and image above is an image of a mouse brain courtesy of Luis de la Torre-Ubieta, Geschwind Laboratory, UCLA, Wellcome Images

NEWSBEATS Nosy Sharks: Olfaction As The Key To Ocean Message in a Test Tube: Identifying Elusive Cancer Biomarkers in Blood Navigation In Coastal Sharks Ryka Sehgal

Joyce Wang

For humans, “follow your nose” is merely a figure of speech, but a 2016 study by Nosal et al. proposed that for leopard sharks, olfaction may be the major contributor to open-water navigation. Until now, it has been unclear how sharks spending the majority of their time in the visually consistent Pelagic Zone are able to navigate inland to their nursing grounds. The team captured 26 migratory leopard sharks, blocking the nostrils of 11 of them with cotton wool to render them “anosmic.”

Upon release, only 37% of the anosmic sharks found their way towards shore, compared to 67% of control-group sharks. The anosmic sharks appeared to wander aimlessly, while the control sharks generally swam faster and more directly. These findings suggest sharks likely smell the chemical gradient created by dissolved amino acids and othermarkers of shoreline bioproductivity, helping to explain the shark’s surprisingly large olfactory bulb and navigational prowess.

Advancements in the detection of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) have made liquid biopsies a promising method for charting the progression of tumors and for designing personalized cancer treatments. Liquid biopsies require only a blood draw; however, clinical usage of this procedure has been limited by the difficulties in preserving the integrity of the blood sample. White blood cells start to lyse after only four hours of storage in typical vacutainer tubes, diluting the already low concentration of cfDNA and making it harder to detect their oncogenic markers. Rothwell et al. present a blood collection protocol that can preserve both cfDNA and CTCs for up to 4 days at room temperature using CellSave preservation tubes. Extending the time span for cfDNA extraction would allow a more thorough molecular profiling of cancer and establish the liquid biopsy as a routine clinical procedure.

Reference: Nosal et al. (2016) Olfaction Contributes to Pelagic Navigation in a Coastal Shark. PLoS ONE. 11(1):e0143758. Image source: https://commons.wikimedia.org/wiki/File:Leopard_shark_in_kelp.jpg

Reference: Rothwell et al. (2015) Genetic profiling of tumours using both circulating free DNA and circulating tumour cells isolated from the same preserved whole blood sample. Mol Oncol. 2015.11.006.

Image source: https://commons.wikimedia.org/wiki/File:Spinning_device.jpg

Bowhead Whale Genome Provides Insights into Secrets of Aging Brock Embleton With an estimated lifespan of over 200 years, the bowhead whale is possibly the longest-livingmammal. Despite having such a long lifespan, bowhead whales remarkably exhibit a disease incidence that is much lower than that of humans in old age. Although the cellular, molecular, and genetic mechanisms that allow bowhead whales to be resistant to age-related diseases are unknown, it is evident that these mammals must have preventative mechanisms that combat cancer, neurodegenerative, and cardiovascular diseases. After sequencing and analyzing bowhead whale genomes from different populations, researchers have identified genes under positive- selection and bowhead-specific mutations in genes linked to aging and cancer, as well as gene gain and loss concerning genes associated with DNA repair and cell-cycle regulation. These results further the understanding of mammalian longevity and may lead to future insights regarding the fundamental mechanisms of aging in humans.

Reference: Keane et al. (2015) Insights into the Evolution of Longevity from the Bowhead Whale Genome.

Cell Report. 10.1: 112-122. Image source: https://commons.wikimedia.org/wiki/File:Humpback_whales_ in_Monterey_Bay.jpg

Correlation Between Potatoes and Diabetes During Pregnancy Emily Vivian Huang The third most heavily consumed crop in the world, potatoes have heightened in popularity as a global staple food due to their robust caloric nature and high culinary versatility. However, after conducting a recent ten-year-study, several Harvard University researchers revealed a positive correlation between prenatal consumption of potatoes and risk of gestational diabetes.Tracking the pregnancies of 21,693 women with no history of the disease, the team evaluated participants’ diets quadrennially through questionnaires, afterwards discovering 854 pregnancies to be affected by the condition. Moreover, despite the study’s observational nature, which prevented any cause-and-effect conclusions, the high glycemic index of potatoes, capable of producing “glucose spikes” in the blood post-consumption, corroborated their possible link to gestational diabetes. With the concerning potential of gestational diabetes to cause chronic cardiometabolic problems, standard dietary recommendations may consequently be modified to consider likely health risks associated with the potato, especially for pregnant women.

Reference: Newman et al. (2016) Potatoes and pregnancy: a recipe for diabetes? MNT. 1:304963.

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NEWSBEATS Thinner Than Tin Foil Matthew Ye Research on 2D materials, crystalline layers only one or two atoms wide, has rapidly garnered interest since the discovery of graphene and its unique electronic properties. Zhu, et al. from Shanghai Jiao Tong University have reported the first synthesis of the tin analog of graphene: stanene. Zhu et al. used a technique called molecular beam epitaxy to vaporize high-purity tin and then deposit a biatomic layer on a bismuth telluride (Bi2Te3) substrate. They used reflected high-energy electron diffraction (RHEED) and scanning-tunnelling microscopy (STM) to measure thickness. Indeed, stanene has a graphene-like structure with atoms arranged hexagonally in an ultrathin sheet. The fabrication of stanene could be a huge breakthrough in materials science as stanene is theoretically predicted to conduct electricity without any heat loss, a property known as topological superconductivity. This new method of stanene production enables future investigation of its potentially remarkable properties on both quantum and macroscopic scales.

Reference: Rothwell et al. (2015) Genetic profiling of tumours using both circulating free DNA and circulating tumour cells isolated from the same preserved whole blood sample. Mol Oncol. 2015.11.006.

Image Source: https://commons.wikimedia.org/wiki/File:Graphen.jpg

Fat but Healthy: Brown Bear Microbiota May Help Obese Patients Christine Qin Brown bears can eat a lot, gain weight, hibernate, and still remain fat and metabolically healthy. If bears can gain weight but not suffer from obesity or type II diabetes, studying brown bear hibernation may be a means of developing treatments for overweight individuals. By comparing summer and winter feces of free-ranging brown bears, scientists discovered that a larger diversity of gut microbiota were found in the summer feces while the diversity levels dropped in winter feces. To test whether changes in microbiota diversity were associated with the bears’ health or just a reflection of their eating habits, scientists transferred bear microbiota to mice.

Sommer et al. determined that the greater weight gain in summer-microbe mice did not impair glucose tolerance, which is associated with insulin resistance and diabetes in humans. Thus the microbiota may be linked to healthy obesity phenotype in brown bears and constitute a potential treatment of obesity.

Reference: Sommer, F. et al. (2016) The gut microbiota modulates energy metabolism in the hibernating brown bear ursus arctos. Cell Reports. 14:1-7. Image Reference: https://commons.wikimedia.org/wiki/ File:Brown_bear_eating_fish_in_river.jpg

Drastic Increase in Suicide Rate Found Amongst Concussion Victims Shayan Saalabi

ITOP: A 3D Printer for Human Tissues Andrea Castro The Integrated Tissue and Organ Printing System (ITOP), created by scientists at the Wake Forest Institute for Regenerative Medicine in North Carolina, has successfully produced stable and clinically relevant tissue constructs from adult human stem cells. The tissue printing system incorporates data from CT and MRI scans to make custom tissue for patients.The feasibility of the printed tissue to survive was demonstrated by the implantation of bone fragments into mice. After five months, the bone fragments had become vascularized and integrated. This new method of 3D printing is an important advance and could potentially be used to print entire tissues and organs for use in surgical procedures.

Reference: Kang et al. (2016) A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat Biotechnol. doi:10.1038/nbt.3413. Image source: http://pop.h-cdn.co/assets/16/07/1024x512/landscape-1455548325-ear.jpg

In a comprehensive study of over 235,000 concussion victims published in theCanadian Medical Association Journal, researchers have discovered a threefold increase in suicide rate amongst those who have suffered at least one mild concussion. Excluding cases that required hospitalization, the study reported a suicide rate of roughly 31 deaths per 100,000 persons, a rate more than triple the Canadian average. Researchers stated that the results were not accentuated within certain social demographics; however, a greater suicide risk existed amongst those who had suffered a concussion on the weekend, mostly in recreational settings.

The underlying connection between concussions and suicides remains unclear, but the study does shed light on an issue that has been brought under further scrutiny due to increases in long-term brain damage and cognitive deterioration in concussion victims, particularly professional athletes and military personnel.

Reference: Fralick et al. (2016) Risk of suicide after a concussion. Can Med Assoc J: cmaj-150790.

Image source: https://upload.wikimedia.org/wikipedia/commons/a/ad/Yadier_Molina_with_a_concussion_in_ June_2008.jpg

UNDERGRADUATE SCIENCE JOURNAL | 02

NEWSBEATS The Discovery of Gravitational Waves from a New Technology to Address Antibiotic Binary Black Hole Merger Resistance of S. aureus Haoxuan Dou, Junyi Wu

Laura Liu

On September 14, 2015 gravitational waves caused by a binary black hole merger weredetected by both Laser Interferometer Gravitational-wave Observatory (LIGO) detectors. Thanks to the 2015 Advanced LIGO upgrade, this discovery is made by measuring the small disturbance in space-time that will compress or lengthen objects as the gravitational wave passes through earth. The gravitational wave shows the merging of two black holes of 36 and 29 masses of Earth into a bigger black hole of 62 masses of Earth, while 3 masses of Earth is radiated in the form of gravitational waves.This is the first direct detection of gravitational waves 100 years after Einstein predicted its existence from the field equation of general relativity in 1915. This discovery confirms predictions of general relativity for nonlinear dynamics of highly disturbed black holes, illustrating the possibility of utilizing gravitational waves for further research and industrial purposes.

Staphylococcus aureus, or S. aureus, is a bacterium often found on the nostrils and skin of healthy individuals, which facilitates its transition to becoming infectious. S. aureus is known for producing many antibiotic resistant strains of the bacterium, which have become a growing concern in hospitals and communities. Scientists have struggled to produce an effective solution to this problem but researchers at Genentech have recently been able to develop a novel approach. The approach is to use an antibody-antibiotic conjugate (AAC) that specifically targets intracellular S. aureus rather than planktonic S. aureus (intracellular bacteria have been found to be a more effective spreader of infections.) The rifalogue antibiotic is linked to a monoclonal antibody and is cleaved by an intracellular protease. Although the AAC shows promise for treating S. aureus, it will be interesting to see if a similar concept can be used for other strains of bacteria.

Reference: Abbott et al. (2016) Observation of gravitational waves from a binary black hole merger. Phys Rev Lett.,116:061102(1-16). Image source: https://www.ligo.caltech.edu/image/ligo20160211d, Courtesy Caltech/MIT.LIGO Laboratory.

Reference: Lehar et al. (2015) Novel antibody-antibiotic conjugate eliminates intracellular S. aureus. Nature. 527:323-328. Image source: https://upload.wikimedia.org/wikipedia/commons/d/d3/Staphylococcus_aureus_VISA_2.jpg

Umbilical Blood Immune Cells Kill Cancer Better than Adult Cells Yao Chang Tan When leukemia patients are treated with chemotherapy, their healthy blood cells are often destroyed in the process. To repopulate their circulatory system, doctors have increasingly used donated umbilical cord blood, as they reduce rejection risks.

But a growing concern has been that these cord immune cells are less aggressive to any surviving leukemia, unlike “experienced” adult immune cells. However British researchers have found that the tumors of mice infected with B-cell lymphoma regressed when injected with cord blood immune cells, but kept growing in mice injected with adult cells.When these tumors were examined before being destroyed, it was found that the umbilical cells initiated CD4 cell production, the white blood cells that trigger the immune response. Additionally, these tumors were filled with CD8 cells, the cells that kill cancerous tissues.This suggests that cord blood should continue to be used as the preferred option to prevent leukemia relapse.

Reference: Hiwarkar et al. (2015) Cord blood T cells mediate enhanced antitumor effects compared with adult peripheral blood T cells. Blood. 126(26):2882-2891. Image Source: https://commons.wikimedia.org/ wiki/File:Healthy_Human_T_Cell.jpg

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Wearable Device Analyzes Sweat To Monitor User’s Health Luke Batty In the same spirit as the Fitbit heart monitor, a new scientific breakthrough has led to thecreation of a wearable sweat analyzer. The thin, onboard circuitry is equipped with a sensor array that can detect glucose and lactate by measuring the current produced by immobilized enzymes that oxidize the sweat metabolites, and can be worn as a wristband, headband, or armband. In addition, the array can detect sodium and potassium ions, along with skin temperature. The analyzer broadcasts the data to a smartphone or computer, allowing monitoring in real-time. Currently patent pending, the device could potentially be used to monitor blood-glucose levels, which would be invaluable to those with diabetes, possibly alert an athlete when there is restricted blood flow to muscles, signal the onset of dehydration or electrolyte imbalance, or, more ambitiously, be used as an indicator of active drug levels in patients.

Reference: Gao et al. (2016) Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature. 529: 509-514.

Hippocampal and Thalamic Differences in Schizophrenia and Bipolar Disorder Dalar Avadis-Rostamian, Dr. William Grisham1 Volumetric differences of the hippocampus and thalamus were examined in males and females of ages 6 to 17 with early-onset schizophrenia (EOS), bipolar disorder I without psychosis (BPDwoPsy), and bipolar disorder I with psychosis (BPDwPsy) using MRI scans from the Child and Adolescent NeuroDevelopment Initiative (CANDI). Analysis of covariance (ANCOVA) of FMRIB Software Library (FSL) output data revealed that BPDwoPsy and BPDwPsy adolescents have smaller thalami relative to healthy controls, suggesting that deficits associated with BPD adolescents, such as those in executive function, attention, affect, and sensory processing, are caused by thalamic reduction. ANCOVA also revealed that BPDwoPsy adolescents have smaller hippocampi relative to healthy controls, indicating the role of reduced hippocampal volume in the declarative memory deficits of BPDwoPsy adolescents. Furthermore, greater hippocampal asymmetry in males than females suggests that sexual dimorphism of the hippocampus controls the differential development of EOS and BPD in the two sexes.These findings highlight the importance of processing neuroimaging data using software such as FSL and the role of cortical reduction in explaining EOS and BPD symptoms.

INTRODUCTION Hippocampal (Blumberg et al., 2003; Nelson et al., 1998) and thalamic (Dasari et al., 1999) volume reduction may underlie aberrations in memory, executive functions, affect, attention, and sensory processing in early-onset schizophrenia (EOS), bipolar disorder I without psychosis (BPDwPsy), and bipolar disorder I with psychosis (BPDwPsy). In a manic episode of BPD, the aberrations manifest as symptoms including elevated mood, racing thoughts and speech, attention deficits, and declarative memory dysfunction; depressive episodes involve anhedonia, attention deficit, and psychomotor retardation (National Institute of Mental Health [NIMH], 2012). Adolescents who have hallucinations and delusions in addition to the regular bipolar disorder I symptoms are diagnosed as BPDwPsy (NIMH, 2012). EOS is also marked by psychotic features such as hallucinations, delusions, disorganized thinking, and psychomotor dysfunction. Cognitive deficits affecting executive functioning, attention, and memory as well as affect deficits resulting in anhedonia, flat affect, avolition, and alogia are the two other

domains of EOS symptoms (NIMH, 2009).

Hippocampal reduction explains poor encoding of declarative memory in BPD (Bearden et al., 2006) and impaired recollection of episodic memory in schizophrenia patients (Heckers et al., 1998) because only the hippocampus encodes and retrieves explicit memory.

Disrupted efferent projections to the prefrontal cortex (PFC) and anterior cingulate cortex may also explain executive dysfunctions. On the other hand, thalamic reduction explains SZ and BPD aberrations because thalamic nuclei integrate sensory input, monitor attention by sending burst or tonic mode signals to cortices, and filter cortical input during executive functions (Cronenwett and Csernansky, 2010). For example, anterior and mediodorsal nuclei (MDN) reduction results in insomnia, attention deficit, apathy, hallucinations, and depression (Gallassi et al., 1996). MDN pathology is also linked to psychomotor deficits (Reilly et al., 1992) and working memory deficits arising from disrupted PFC innervations (Sussmann et al., 2009).

Anterior nuclei damage causes executive dysfunction, disorganized thinking, and apathy (Schmahmann, 2003), as these neurons innervate the cingulate gyrus

and modulate mammillary body input.

Pulvinar damage is linked to executive dysfunction and speech deficits (Coscia et al., 2009) due to disrupted inferior parietal lobule communication.

Sexual dimorphism of the hippocampus and thalamus during development (Kimura, 2002) suggests the differential neuropathology of these cortical regions for each sex. Sexual differentiation begins during prenatal brain development as androgens and estrogens exert organizational effects by inducing the production of sex hormone receptors. Sex hormones released during puberty exert activational effects upon binding to these sex hormone receptors, further intensifying sexual dimorphism of the brain (Arnold and Gorski, 1984).

The organizational and activational effects of androgens and estrogens also influence cortical volume. For example, MRI data have revealed that grey matter volume is negatively associated with estradiol levels in pubertal females and positively associated with testosterone levels in pubertal males (Peper et al., 2009). In vivo imaging studies have also showed regionally specific volumetric differences in animal and human models. For example, MRI analysis of

Department of Psychology.

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HIPPOCAMPAL AND THALAMIC DIFFERENCES IN SCHIZOPHRENIA AND BIPOLAR DISORDER rat neurodevelopment has revealed that males have larger thalami and posterior hippocampi than females, but in females, the entorhinal cortex and anterior hippocampus are larger (Spring, Lerch, and Henkelman, 2007).

In this study, volumetric reduction of the hippocampus and thalamus were examined to explain the deficits in memory, attention, sensory processing, and affect found in EOS, BPDwoPsy, and BPDwPsy. These volumetric differences were compared in girls and boys due to recent evidence of sexual dimorphism influencing whole-brain and regionally specific volumes. Neuroimaging data was collected from a recent MRI study (Frazier et al., 2008), which analyzed volumetric differences of limbic structures in male and female adolescents with EOS, BPDwPsy, and BPDwoPsy. Two crucial changes were made to the methodology employed by Frazier et al. The scans developed by Frazier et al. were manually labeled, segmented, and traced using a Cardviews labeling protocol and software (Caviness et al., 1996). Although Frazier et al. ensures high interrater reliability with their method, manual analysis can still lead to human error. To minimize such error, the scans were analyzed using FMRIB Software Library (FSL), a program that segments, traces, and measures volume without any manual involvement, creating images such as those seen in Figure 1. The second change was to control for whole brain volume (WBV), which includes the cerebrum, cerebellum, and brain stem, rather than total cerebral volume (TCV). Including the cerebellum and brainstem may increase the validity of the findings given recent evidence of the under-emphasized role of the cerebellum in neuropsychiatric

studies examining schizophrenia and mood disorders (Konarski et al., 2005; Andreasen and Pierson, 2008) and the role of sexual dimorphism in various cortical regions. It was predicted that FSL analysis of Frazier et al. MRI scans will reveal that females with BPD have the smallest hippocampi and that EOS patients have the smallest thalami.

METHOD Subjects Frazier et al. (2008) neuroimaging data included MRI scans of 103 adolescents in four diagnostic categories: healthy controls (N=29, Mage= 10.5, SD = 2.9), schizophrenia spectrum (N=20, Mage= 13.5, SD= 2.9), BPD I with psychosis (N=19, Mage= 11.6, SD = 2.6), and BPD I without psychosis (N=35, Mage= 10.4, SD = 3.0). The percentages of males in categories HC, SS, BPDwPsy, and BPDwoPsy were 37.1%, 58.8%, 47.4 %, and 55.6% respectively. The subjects of ages 6-17 were recruited through the McLean Hospital and Cambridge Health Alliance programs. All 103 subjects had clinical interviews and diagnoses from boardcertified child psychiatrists using DSMIV diagnostic criteria. The MRI scans of these 103 adolescents were downloaded from the “CANDI Neuroimaging Access Point” project website (Kennedy et al., 2012) for the purpose of the current study.

Experimental Design A 4 x 2 between-subjects ANOVA analyzed the relationship between diagnostic category and sex on WBV. The relationships among diagnostic category, sex, and brain hemisphere and their effects on hippocampal and thalamic volume were analyzed by running a 4 x

2 x 2 mixed-design ANCOVA separately for each structure, covarying age, and brain volume.

Experimental Procedure The T1-weighted MRI scans were downloaded from the “CANDI Neuroimaging Access Point” project website using the FMRIB Software Library v5.0 (Smith et al., 2004). FSL normalized scans to the Talairach space, enabling hippocampi and thalami segmentation for the varying brain sizes of the subjects. The following FSL tools for segmentation were then ran: fslview, Brain Extraction Tool (BET), FIRST, fslstats, and FMRIB’s Automated Segmentation Tool (FAST).

After fslview opened a scan in 3D, BET deleted non-brain tissue, FIRST segmented the structure, and fslmaths defined the segmented region. Finally, fslstat measured the segmentation volume in mm3. FAST was then ran to segment white and grey matter before using fslstats to calculate the WBV. FSL tools were ran in this order for both the hippocampus and thalamus.

RESULTS The effect of diagnostic category on mean WBV was suggested by a 4 x 2 between-subjects ANOVA [F(3, 95) = 3.36, p < .05] and confirmed by Post hoc LSD tests. Post hoc tests showed that BPDwoPsy (p < .05) and BPDwPsy adolescents (p < .001) have smaller WBV than HCs. Analysis of variance (ANOVA) also showed that females have smaller WBV than males [F(1, 95) = 40.57, p < .001] and that BPDwPsy and BPDwoPsy females have the smallest WBV [F(3, 95) = 2.53, p < .05] as shown in Figure 2. No significant

Figure 1. FSL analysis of a T1 weighted scan of a healthy control patient. Here FSL has segmented and measured the volume of the left (blue) and right (red) side of the thalamus.

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DALAR AVADIS-ROSTAMIAN

Mean Whole Brain Volume (mm3)

1400000 1200000 1100000 800000 600000 400000 200000 0

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Figure 2. Mean whole brain volume (WBV) of male and female adolescents in four groups: healthy control (HC), schizophrenia spectrum (SS), BPD with psychosis (BPDwPsy), and BPD without psychosis (BPDwoPsy). Error bars are based on standard error calculations. Between-subjects ANOVA revealed significantly smaller WBV in BPD groups relative to HC; the asterisk (*) indicates LSD post-hoc significance. ANOVA also revealed that males (M) have larger WV than females (F) and a relationship exists between diagnosis and sex such that BPD females have the smallest WBV.

findings for the EOS group were found.

As shown in Figure 3, a 4 x 2 x 2 mixeddesign ANCOVA of the hippocampus, with sex and brain hemisphere as the within-subjects factors and diagnostic category as the between-subjects factor, revealed a significant main effect of diagnostic category on hippocampal volume, [F(3, 93) = 3.50, p < .05]. Post hoc LSD tests showed that BPDwoPsy adolescents have smaller hippocampi than HCs (p < .05), and BPDwoPsy adolescents have smaller hippocampi than EOS adolescents (p = .091). The data also demonstrated that the right side of the hippocampus was larger than the left and the size difference was greater in males, F(1, 93) = .36, p < .05.

The mixed-design ANCOVA) revealed a trend between diagnostic category and

thalamic volume [F(3, 93) = 2.144, p = .100], but post hoc analyses were proceeded with due to a priori indication of a significant main effect of diagnostic category on thalamic volume (Frazier et al., 2008).

LSD tests revealed smaller thalami in BPDwoPsy and BPDwPsy groups relative to HCs (p < .05; p < .05), as shown in Figure 4, and smaller thalamic volumes in SS adolescents relative to HCs (p = .068).

DISCUSSION ANOVA analysis revealed smaller WBV in BPD groups relative to HCs and in females relative to males. Similar to the Frazier et al. study, the current study indicated that BPD females have the smallest WBV, which suggests that Mean Thalamic Volume (mm3)

Mean Hippocampal Volume (mm3)

6000 2500 2000 1500 1000 500 0 BPDwoPsy

BPDwPsy

HC

SS

Figure 3. Hippocampal volume of adolescents in four groups: healthy control (HC), schizophrenia spectrum (SS), BPD with psychosis (BPDwPsy), and BPD without psychosis (BPDwoPsy). Error bars are based on standard error calculations. A 4 x 2 x 2 mixed-design ANCOVA revealed smaller hippocampi in the BPDwoPsy group relative to HCs; the asterisk (*) indicates LSD post hoc significance of this diagnostic category main effect in BPDwoPsy adolescents.

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Figure 4. Thalamic volume of adolescents in four groups: healthy control (HC), schizophrenia spectrum (SS), BPD with psychosis (BPDwPsy), and BPD without psychosis (BPDwoPsy). Error bars are based on standard error calculations. A 4 x 2 x 2 mixed-design ANCOVA revealed a trend between diagnostic category and thalamic volume, but post hoc analyses showed smaller thalamic volumes in BPDwoPsy and BPDwPsy adolescents relative to HCs. The asterisk indicates the LSD post hoc significance of this diagnostic category main effect.

whole brain reduction occurs beyond the cerebrum in female BPD adolescents.

Contrarily, ANCOVA analysis of hippocampal and thalamic volumes did not support Frazier et al. findings. Frazier et al. observed the smallest thalami in EOS patients, but the current study revealed the smallest thalami in BPDwoPsy and BPDwPsy adolescents. Frazier et al. found no diagnostic differences in the hippocampus and a trend indicating greater hippocampal reduction in females with BPD. The current study, however, found smaller hippocampi in BPDwoPsy adolescents relative to HCs and greater hippocampal asymmetry in males than females, independent of diagnostic category.

It is likely that the findings deviated from those of Frazier et al. because FSL was used, in place of manual analysis, to segment and measure the scans. Using FSL, an automatic software, minimizes human errors that can occur during normalization, tracing, segmentation, and volumetric analysis. Furthermore, covarying WBV instead of TCV may have reduced the variance associated with brain size in ANCOVA analysis of the hippocampus and thalamus, given recent evidence of sexual differentiation of brain volume.

The findings may explain the cognitive deficits, affect dysfunction, and sensory processing impairments seen in BPD and EOS patients. Impaired encoding of declarative memory in BPD (Bearden et al., 2006) may be due to hippocampal reduction, which was observed in BPDwoPsy adolescents. Furthermore, thalamic reduction in BPD adolescents suggests that aberrations in executive function, attention, and affect in BPD, as well as sensory processing (hallucinations and delusions) in BPDwPsy, may correlate with reduced thalamic volume.

The link between reduced hippocampal and thalamic volumes and EOS was not as strong as expected. Nevertheless, given the similarity of EOS and BPD symptoms and cortical reductions observed in BPD adolescents, future research may focus on the role of the hippocampus and thalamus in explaining EOS neuropathology.

Furthermore, sexual dimorphism of the hippocampus may be examined more closely in BPS and EOS patients. Males

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HIPPOCAMPAL AND THALAMIC DIFFERENCES IN SCHIZOPHRENIA AND BIPOLAR DISORDER in the study had greater hippocampal asymmetry than females, suggesting that gender may influence how explicit memory deficits manifest in EOS and BPD adolescents.

Neurodevelopment continues after birth, as shown in synaptogenesis and synaptic pruning (Walker and Bollini, 2002) and the continued activational effects of sex hormones on steroid receptors in sexually dimorphic brain regions (Romeo, 2003). For this reason, future studies should examine the neuropathology of EOS and BPD using longitudinal or cross-sectional designs which include pubertal status. This would allow identification of critical periods of development when intervention may be most successful for children with EOS and BPD. Furthermore, manual analysis of MRI scans should be replaced by software use for tasks such as normalization, tracing, and segmentation. A recent study evaluated the most common deformation algorithms applied to human brain MRI analysis (Klein et al., 2009), an effort which should be continued to assess the relative performance of current brain mapping software.

ACKNOWLEDGEMENTS This research was supported by the University of California, Los Angeles. I would like to thank Dr. William Grisham for his support and expertise in guiding this research and the Psych 116 class for help in gathering the data.

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Genetic Variation Operates Through Chromatin to Control Heart Failure Susceptibility Rachel Lopez, Dr. Emma Monte1, Dr. Thomas M. Vondriska2 Heart failure affects ~5.1 million Americans. During disease, the adult cardiomyocyte reverts to a fetal gene expression program, including upregulation of genes normally silenced during development. The mechanisms by which chromatin is remodeled to induce this fetal gene program during disease are unknown. The hypothesis of this study is that genetic variations in chromatin structureâ&#x20AC;&#x201D;specifically, differences in the abundance of chromatin structural proteins and their modificationsâ&#x20AC;&#x201D;regulate heart failure susceptibility.To test this hypothesis, nuclei isolation was used to measure cardiac chromatin in a mouse strain susceptible (BALB/cJ) and a mouse strain resistant (BUB/BnJ) to isoproterenol-induced heart failure.The abundance of post-translational modifications (PTMs; Histone H3 lysine-9 trimethylation [H3K9me3], H3K9me2, H3K27me3, and H3K4me3) and chromatin structural proteins (High Mobility Group Protein B2 [HMGB2], HMGB3, Linker Histone H1, active RNA polymerase II) were measured by immunoblotting.The BALB/cJ strain had more of the euchromatic PTM H3K4me3 and the chromatin structural protein HMGB2, with no difference in the other proteins measured or in total transcription, showing that HMGB2 and H3K4me3 are potentially affecting gene expression. In addition to testing baseline protein abundances, mRNA levels were measured in a panel of 86 mouse strains that respond differently to isoproterenol treatment. Six histone variants were differentially regulated with isoproterenol treatment.The findings support a model in which disease susceptibility is associated with a global shift towards a more euchromatic environment.

Ongoing work uses mass spectrometry to measure stoichiometry of chromatin structural proteins to comprehensively characterize the role of genetic variation in chromatin accessibility. Finally, future studies include testing the distribution of chromatin proteins across the genome to investigate the role of locus-specific regulation.

INTRODUCTION Cardiovascular diseases (CVD) cause 1 in every 3 deaths in the United States, with heart failure contributing to 1 in every 9 deaths (Mozaffarian et al., 2014).

Heart failure is a debilitating condition that occurs when cardiac output is insufficient to meet the blood supply and oxygen demands of the body (Dickhout et al., 2011). Cardiac hypertrophy is a diseased state that occurs when the heart undergoes stress such as cardiac infarction or hypertension. The stressed heart responds by increasing the cardiomyocyte size and overworking to compensate the decreased outflow of blood. Maintaining cardiac hypertrophy for a prolonged amount of time can lead to heart failure where the blood supply and oxygen demands of the body are no longer met.

Cardiac hypertrophy is characterized by an increase in cell size and protein synthesis (Frey et al., 2004) to compensate for increased afterload due to the strains of

hypertension and/or loss of myocardial cells as a result of myocardial infarction.

In addition, cardiac hypertrophy is often accompanied by reversion to a fetal gene expression program, characterized by a switch to the fetal isoforms of structural proteins (Depre et al., 1998), metabolism, contractile, and calcium-handling genes (Rajabi et al., 2007). The increase in both cell size and protein synthesis along with the reactivation of the fetal gene program support the hypothesis that cardiac hypertrophy requires a coordinated alteration in transcription. Thus, understanding the molecular mechanisms by which chromatin structure is altered to facilitate reactivation of the fetal gene program can contribute to developing therapeutics for this debilitating disease. A therapeutic that can create a heterochromatic environment in the cell could prevent the reactivation of the fetal gene program.

Factors that facilitate transcription in the cell include the availability of transcription factors and accessibility to pro-

moters, via conformational changes in chromatin. Chromatin is composed of nucleosomes (functional units of chromatin) which are comprised of an octamer of four core histone proteins: Histone H2A, Histone H2B, Histone H3 and Histone H4. Factors that affect chromatin structure include abundance and variants of core histone proteins and post-translational modifications (PTMs) on the histones (commonly studied ones being methylation and acetylation). A study using transverse aortic constriction induced heart failure reported changes in the abundance of core histone proteins, histone variants, and PTMs (Franklin et al., 2012). Furthermore, there was a decrease in the ratio of linker Histone H1 to core histones in cardiac hypertrophy that returned to baseline in heart failure (Franklin et al., 2012). Variations in histone proteins were observed with a global shift from heterochromatin (restricted) to euchromatin (accessible) as indicated by the increase in the euchromatic PTM Histone

Department of Physiological Science & Perioperative Medicine, Cardiology and Physiology, Geffen School of Medicine. 2 David Geffen School of Medicine at UCLA.

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GENETIC VARIATION OPERATES THROUGH CHROMATIN TO CONTROL HEART FAILURE SUSCEPTABILITY H3 lysine-4 trimethylation (H3K4me3) and decrease in the heterochromatic PTM H3K9me3 (Franklin et al., 2012; Kaneda et al., 2009). These changes in chromatin accessibility imply that gene expression during disease is in part regulated by chromatin structure through histone protein levels and PTMs in the cardiomyocyte. Thus, increased abundance of euchromatin may be influential in heart disease susceptibility, allowing for the cardiomyocyte to easily shift into the fetal mode due to an increase in chromatin accessibility.

In addition to being regulated in disease, chromatin structure is also affected by genetic diversity (Chen et al., 2016).

There is significant variability in the incidence of heart failure within human populations, due in large part to varying genetic backgrounds. It is appreciated that genetics plays a large role in common forms of CVD, however, neither the mechanism nor combination of specific mutations are known (Cirino and Ho, 2013).

Not only do humans show variations in susceptibility to CVD, but different mouse strains also exhibit differential CVD susceptibility. Data from the hybrid mouse diversity panel (HMDP), a panel of 86 inbred mouse strains (Bennett et al., 2010), suggest that BUB/BnJ mice are resistant to heart failure induced by isoproterenol whereas the BALB/cJ strain is susceptible (Rau et al., 2015). Isoproterenol activates beta-adrenergic signaling. This enhances protein synthesis and proto-oncogene expression, elevates oxidative stress, and stimulates mitogen-activated protein kinases and phosphatidylinositol-3 kinases, all of which contribute to the development of cardiac hypertrophy (Chowhury et al., 2013). Both strains showed an increase in left ventricular mass after isoproterenol treatment suggesting there is cardiomyocyte hypertrophy after comparing the control mice with the mice treated with isoproterenol. However, the BALB/cJ mice had a higher morbidity rate, higher fibrosis, and large ventricles with thinned walls—all phenotypes that indicate these animals have transitioned from cardiac hypertrophy to heart failure.

Therefore, it is hypothesized that differences in chromatin structure regulate susceptibility to heart failure and that strains with the aforementioned dichoto-

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mous phenotypes would be a useful tool to dissect the role of chromatin structural proteins globally and at individual loci.

Specifically, that genetic differences will lead to chromatin differences between strains which will in turn alter gene expression, thereby leading to the different phenotypes. The chromatin differences could include changes in the expression and perhaps localization of chromatin structural proteins, histone marks and transcription factors. Genetically different mouse strains were used to investigate how genetic variation leads to changes in chromatin protein and post-translational modifications as a molecular mechanism to explain the phenotypic differences.

This approach addresses the observation that genetic variation across strains often occurs in non-coding regions. Thus, the phenotypic spectrum across the HMDP cannot be explained by nonsynonymous mutations in protein coding genes that in turn cause complications in cardiac function. Genetic variability, much of which is intergenic, operates through chromatin to influence gene expression and phenotype.

Gene regulation may occur via differences in euchromatin in the basal state leading to increased accessibility of genes for transcription. As a result, the isoproterenol-induced signaling may facilitate the activation of disease-associated gene expression in the susceptible strain.

If this model were true, after isoproterenol treatment, euchromatin would be further increased in the susceptible mouse strain allowing increased transcription in heart disease. It is predicted that global differences in chromatin will be due to changes in the abundance of histone proteins along with post-translational modifications (PTMs) and histone variants. This hypothesis is tested by measuring cardiac chromatin in mouse strains known to be susceptible and resistant to isoproterenol-induced heart failure. By this, key proteins to further study and find a mechanism by which susceptibility to heart failure is conferred can be identified.

MATERIALS AND METHODS Acid-Extraction & Nuclei Isolation Healthy, adult, female mice aged 8 weeks from both BALB/cJ (9 mice; The Jackson Laboratory; strain JAX000651)

and BUB/BnJ (9 mice; The Jackson Laboratory; strain JAX000653) strains were used to obtain data that can be comparable with previous studies conducted (Rau et al., 2015). All the protocols involving animals conformed to the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the UCLA Animal Research Committee. Published acid-extraction and nuclei isolation protocols were followed (Franklin et al., 2011). Whole hearts were extracted and processed as individual replicates. Hearts were homogenized in a glass dounce with 2 mL of lysis buffer (0.15% Nonidet P-40, 10mM Tris pH 7.5, 15mM NaCl, 10mM sodium butyrate, 0.1mM phenylmethylsulfonyl fluoride, 0.2mM Na3VO4, 0.1mM NaF, protease cocktail pellet [REF04693159001; Roche]).

The lysate was poured through a cell strainer and centrifuged to extract a crude nuclei pellet that was then resuspended in cold lysis buffer. The pellet was further purified via centrifugation over a 1M sucrose pad (10mM Tris pH 7.5, 15mM NaCl, 24% sucrose [w/v], 10mM sodium butyrate, 0.1mM phenylmethylsulfonyl fluoride, 0.2mM Na3VO4, 0.1mM NaF, protease cocktail pellet). The resulting pellet was rinsed in cold 0.1% EDTA in PBS. For nuclei isolation, the nuclear pellet was resuspended in a Tris/SDS/EDTA buffer (50mM Tris pH 7.4, 10mM EDTA, 1% SDS, 10mM sodium butyrate, 0.1mM phenylmethylsulfonyl fluoride, 0.2mM Na3VO4, 0.1mM NaF, protease cocktail pellet). For acid-extraction, the nuclear pellet was resuspended in a Nonidet P-40 lysis buffer (20mM HEPES pH 7.6, 7.5mM MgCl2 0.2mM EDTA, 30mM NaCl, 1M Urea, 0.15% Nonidet P-40, 10mM sodium butyrate, 0.1mM phenylmethylsulfonyl fluoride, 0.2mM Na3VO4, 0.1mM NaF, protease cocktail pellet). It was then vortexed and allowed to sit on ice for 5 minutes. The solution was centrifuged for 5 minutes at 4ºC at 16,200 x g to separate the nucleoplasm from the chromatin. The chromatin pellet was gently rinsed with a 0.1% EDTA in PBS solution and was resuspended in 400 µL of 0.4 N sulfuric acid and homogenized in a glass dounce until the chromatin pellet dissolved into the solution. The solution was incubated at 4˚C on a rotator overnight. The solution was centrifuged at 16,000 x g at 4ºC for ten

RACHEL LOPEZ minutes, the nuclear debris was removed and the supernatant was transferred to a new centrifuge tube. Dropwise, 132 µL of trichloroacetic acid was added to the supernatant, inverted to mix the solution and incubated on ice for 30 minutes. The solution was further centrifuged for 10 minutes at 16,000 x g at 4ºC and the resulting pellet contained histone proteins.

The histone pellet was washed with icecold acetone, centrifuged at 16,000 x g at 4 ºC for 10 minutes and washed again with ice-cold acetone. After air-drying, the pellet was resuspended in a Tris/SDS/ EDTA buffer. For both the nuclei isolation and acid-extraction, the solution was sonicated using the Active Motif EpiShear Multi-Sample Sonicator for 9 minutes at 20% amplitude. The sample was centrifuged at 16,200 x g for 5 minutes to collect proteins in the supernatant and pellet out any nuclear debris. The

samples were analyzed by Western blot.

Isoproterenol Treatment Healthy, female adult mice aged 8 weeks from both BALB/cJ and BUB/BnJ mouse strains were used. Prior to isoproterenol treatment, M-mode echocardiography was performed on all 20 mice for baseline physiological analysis of the mice. The control mice were three BALB/ cJ and three BUB/BnJ untreated mice.

Seven BALB/cJ and seven BUB/BnJ mice underwent surgery and an AZLET osmotic mini-pump was inserted in the lower abdominal region. The pump delivered 20 mg/kg/day of isoproterenol to the mice. One week after the surgery, M-mode echocardiography was performed on all 20 mice. Following the echocardiography, the mice were sacrificed and the protocol for acid-extraction was performed on the isolated whole

hearts. Additionally, 10 BUB/BnJ and 10 BUB/BnJ mice were administered 20 mg/kg/day of isoproterenol via a pump for 3 weeks. Prior to the surgery for isoproterenol pump placement, M-mode echocardiography was performed on the mice. After the surgery M-mode echocardiography was performed on the mice every week for 3 weeks.

Bicinchoninic acid (BCA) Protein Assay After acid-extraction or nuclear extraction the protein concentration of the samples was determined using a BCA Protein Assay Kit (23227; Thermo Scientific) following the manufacturer’s protocol. The standard curve was made using Bovine Serum Albumin at a concentration of 1 mg/mL using the quantities of 0 µL, 2 µL, 4 µL, 6 µL, 8 µL, and 10 µL of protein.

The concentration of the samples was determined using the Beckman DU 530 Life

A)

B)

C)

Figure 1. Isoproterenol induces hypertrophy after one week of treatment and heart failure in the susceptible mouse strain after 3 weeks of treatment. Basal mice were untreated and ISO mice were treated with isoproterenol for 3 weeks. A) M-mode echocardiography was used prior to treatment and one week after treatment on ISO-treated mice and untreated (basal) mice. Both strains showed an increase in myocardium size after the treatment, thus indicating that the mice were in hypertrophy. B) The heart weight to body weight ratio was measured in both mice strains. There was a significant increase in heart mass when comparing the basal (grey) to either the ISO treated BALB/cJ (red) or ISO treated BUB/BnJ (blue) mouse strains. C) Ejection fraction was measured through echocardiography prior to and at the end of treatment. Ejection fraction increased in both mouse strains with the isoproterenol treatment for 1 week. However, after 3 weeks, the susceptible BALB/cJ mouse strain had a significantly decreased ejection fraction. (n=10/strain; *P<0.05; **P<0.01;Vertical bars represent standard deviation).

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GENETIC VARIATION OPERATES THROUGH CHROMATIN TO CONTROL HEART FAILURE SUSCEPTABILITY Science UV/Vis spectrophotometer.

Western Blot A Western blot was done to measure the abundance of proteins after they were isolated. Fifteen µg of protein (or 30 µg for measuring RNA Pol II phosphorylated at serine 2) were run through a 12% acrylamide gel or 8% acrylamide gel.

The gel was transferred onto a nitrocellulose membrane and immunoblotted for the following proteins: Linker Histone H1 (1:400; ab4269; Abcam), Histone H3 (1:10,000; ab1791; Abcam), RNA Polymerase II phosphorylated at serine 2 (1:500; ab5095; Abcam), Heterochromatin Protein 1 alpha (1:1000; ab7756; Abcam), High Mobility Group Protein B1 (1:1000; ab28427; Abcam), High Mobility Group

A)

C)

Protein B2 (1:1000; ab67282; Abcam), High Mobility Group Protein B3 (1:1000; ab75782; Abcam), Actin (1:1000; sc-1616; Santa Cruz) and post-translational modifications H3K4me3 (1:1000; ab8580; Abcam), H3K9me3 (1:1000; ab8898; Abcam), H3K9me2 (1:1000; ab1220; Abcam) and H3K27me3 (1:400; ab6002; Abcam). The ladder used for all gels was BioRad Precision Plus Dual Color Standard (#1610374, BioRad) and the control used was a whole cell lysate of HeLa cells or 293 HEK cells at a concentration of 3 µg/µL. Hybrid Mouse Diversity Panel (HMDP) Analysis Microarray data was shared from the labs of Drs. Yibin Wang and Jake Lusis at UCLA. The HMDP mice (86 inbred and

B)

D)

recombinant strains) were treated with isoproterenol and microarray was used to compare gene expression levels before and after isoproterenol treatment. Data and protocol are publically available (Rau et al., 2015). Female mice aged 8 weeks from HMDP inbred strains (n=4/strain) were implanted with an osmotic pump to provide 30µg/g/day of isoproterenol.

Isoproterenol treated mice and control mice (n=2/strain) were echocardiographically evaluated before treatment and weekly, for 3 weeks post implantation.

Pre-treatment heart weight was determined by normalizing to control mice.

At 3 weeks, mice were sacrificed and the heart was harvested and weighed. RNA from 2 mice of each strain and treatment combination were isolated from left ventricular sections using the miRNeasy kit (Qiagen) following homogenization of the sample, and pooled together. RNA integrity was confirmed using the Agilent 2100 Bioanalyzer. Microarray expression profiles were generated using the Illumina MouseWG-8 2 BeadChips (Illumina, San Diego, CA) by the Southern Genotyping Consortium at UCLA. Statistical Analysis Western blot images were quantified using ImageJ to measure the relative intensities of the bands. The data was normalized to Actin or Histone H3. A two-tailed unpaired Student’s T-test in Excel was used to analyze data. The threshold for significance used was p < 0.05. Error bars represent standard deviation, and was calculated using all biological replicates.

RESULTS

Figure 2. Chromatin structural protein High Mobility Group B2 (HMGB2) and euchromatic post-translational modification H3K4me3 is found in higher abundance in the susceptible mouse strain. Representative Western blots are shown and each lane represents a biological replicate. A) The protein HMGB2 is more abundant in the susceptible BALB/cJ strain. The proteins HMGB3, Linker Histone H1, and Histone H3 showed no difference between the strains. RNA Polymerase II phosphorylated at serine 2 showed inconclusive results. B) Chromatin structural proteins were normalized to Actin. HMGB2 was 38% more abundant in the susceptible BALB/cJ mouse strain. C) The PTM H3K4me3 is slightly less abundant in the resistant BUB/BnJ strain. The PTMs H3K9me3, H3K9me2, and H3K27me3 showed no difference between the strains. D) The PTMs were normalized to Histone H3 to ensure differences are due to the amount of PTM rather than the amount of Histone H3 and they were normalized to Actin. H3K4me3 was 30% more abundant in the susceptible BALB/cJ mouse strain. The other measured H3 PTMs showed no difference between strains. (n=4/strain; *P<0.05).

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Cardiac function analyses of BALB/cJ and BUB/BnJ mouse strains reveal isoproterenol-induced hypertrophy In the mice treated with isoproterenol for a week, the chamber wall thickness of the left ventricle increased in size, independent of the mouse strain, and resulted in a reduction of the internal chamber size (Figure 1A). Both mouse strains exhibited an increase in heart mass and ejection fraction after 1 week of isoproterenol treatment (Figure 1B, C), demonstrating both strains have cardiac hypertrophy after isoproterenol treatment. However,

RACHEL LOPEZ

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B)

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Figure 3. Isoproterenol-treatment increases HMGB2 mRNA expression and protein levels in Balb/cJ mouse strain. Representative Western blots are shown and each lane represents a biological replicate. Actin was the loading control and 293T HEK cells the positive control. A) The chromatin structural proteins HMGB1 and HMGB2 both revealed a decrease in protein levels in the BALB/cJ strain with isoproterenol treatment, while there was no change in the protein HMGB3.There was an increase in the proteins HMGB1, HMGB2, and HMGB3 with the isoproterenol treatment in the BUB/BnJ strain. B) The HMGB proteins were normalized to Actin and further solidified the trend observed in the Western blots. Each HMGB protein was run on individual gels. C) Both the strains BALB/cJ and BUB/BnJ showed an increase in HMGB1 mRNA levels with isoproterenol treatment. The protein HMGB2 mRNA expression decreased in the BALB/cJ mouse strain and increased in the BUB/BnJ strain. There was a decrease in HMGB3 mRNA expression levels with the treatment in both mouse strains. (n=2/strain).

treating the BALB/cJ mouse strain with isoproterenol for 3 weeks displayed a significant (p-value: 0.0124) decrease in ejection fraction, indicating these mice are susceptible to heart failure. Further analyses focused on mice treated with isoproterenol for one week. This one-week time point will help identify key changes in proteins that occur prior to the devel-

opment of heart failure.

The proteins HMGB2 and H3K4me3 were more abundant in susceptible mouse strain Chromatin structural protein abundances were measured for possible differences that may underlie altered chromatin structure by performing acid-extraction

A)

B)

Figure 4. Individual histone variants determine phenotype susceptibility. A) All histone variants for each of the core histones were combined across all mouse strains in the HMDP. The mouse strains were categorized based on the condition of the heart after isoproterenol-treatment (white is unassigned, green is resistant, blue is hypertrophy, red is failure). The abundance of histone proteins between the basal (light) and the isoproterenol-treated mice (dark) revealed no significant difference. B) Six histone variants were differentially regulated by isoproterenol between phenotype groups, all of which showed greater up-regulation in the resistant mice, with the exception of H2afb1, which was down regulated in resistant mice.

and nuclear extraction on whole hearts.

Of the different chromatin structural proteins tested (Figure 2A), High Mobility Group Protein B2 (HMGB2) was 38% more abundant in the susceptible mouse BALB/cJ strain than in the resistant BUB/BnJ mouse strain (p-value: 0.04, n: 4/strain). Other chromatin structural proteins, HMGB3, Linker Histone H1, Heterochromatin Protein 1 alpha (HP1Îą), Histone H3, and active RNA Polymerase II phosphorylated at serine 2 showed no difference between the susceptible BALB/cJ mouse strain and the resistant BUB/BnJ mouse strain (Figure 2B). Analyzing the basal state of these two mouse strains revealed no difference in the abundance of chromatin structural proteins with the exception of the DNA-binding protein HMGB2.

In addition to testing chromatin structural proteins, basal chromatin differences between the two mouse strains were also tested through PTMs associated with either heterochromatin or euchromatin.

Specifically, methylation of established residues on the core nucleosome component, Histone H3 was examined (Figure 2C). The PTMs were normalized to total H3 to ensure changes observed were not a result of altered abundance of the protein Histone H3 (Figure 2D). The PTMs were additionally normalized to Actin to ensure changes observed were not due to global protein changes in the cell (Figure 2D). The heterochromatic PTMs H3K9me3

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GENETIC VARIATION OPERATES THROUGH CHROMATIN TO CONTROL HEART FAILURE SUSCEPTABILITY (p-value: 0.12), H3K27me3 (p-value: 0.83), and H3K9me2 (p-value: 0.36) showed no difference between the susceptible BALB/ cJ mouse strain and the resistant BUB/BnJ mouse strain. The euchromatic histone PTM, H3K4me3, was 30% more abundant in the susceptible BALB/cJ mouse strain (p-value: 0.01 n=4/strain).

Heart disease susceptibility is associated with altered levels of the chromatin structural protein HMGB Microarray data was used to focus on the HMGB family of chromatin structural proteins (HMGB1, HMGB2, HMGB3) based on previous work from our lab suggesting a role for these proteins in hypertrophic gene expression (Franklin et al., 2012). There was a decrease in HMGB1 protein levels in the isoproterenol-treated BALB/cJ mouse strain whereas HMGB1 increased in the isoproterenol-treated BUB/BnJ mouse strain (Figure 3A, B).

These differences appear to be controlled at the post-transcriptional level, as microarray data revealed an increase of HMGB1 mRNA expression in both mouse

A)

strains (Figure 3C). HMGB2 protein levels decreased in the isoproterenol-treated BALB/cJ mouse strain but increased in the isoproterenol-treated BUB/BnJ mouse strain. Contrary to HMGB1, HMGB2 levels of mRNA studied through microarray in these strains matched protein levels found by western blot. There was no change in protein abundance of HMGB3 with the isoproterenol treatment in the BALB/cJ mouse strain, however there was an increase in HMGB3 levels in the isoproterenol-treated BUB/BnJ mouse strain. By microarray, there was a decrease of HMGB3 mRNA expression in both mouse strains where it was more prominent in the BALB/cJ, susceptible mouse strain (Figure 3C).

Isoproterenol regulates a small group of histone variants in HMDP To elucidate the relationship between chromatin and disease, HMDP microarray data obtained from the isoproterenol-treated mice was analyzed. The HMDP microarray data was used to analyze gene expression levels of 86 mouse

B)

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Figure 5. Hypothesized model by which HMGB2 and H3K4me3 potentially increase transcription. The euchromatic PTM H3K4me3 was found to be more abundant in the susceptible BALB/cJ mouse strain than in the resistant BUB/BnJ mouse strain. The PTM H3K4me3 is associated with active and poised promoters in the genome.

There was no measured difference in the abundance of active RNA Polymerase II. A) One possible explanation to no changes in the other chromatin structural proteins can be that the proteins are distributed differently between the two strains.The proteins are distributed more equally in the susceptible mouse strain genome while it is more localized in specific regions of the genome in the resistant mouse strain. B) In the susceptible mouse strain, HMGB2 and H3K4me3 can be working together to maintain euchromatic regions of the genome silenced and C) in the resistant mouse strain these proteins are not required to maintain these regions of the genome silent.

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strains. The mouse strains were categorized into four distinct categories dependent on their response to isoproterenol.

For this categorization, multiple phenotypes were measured on the entire HMDP including histological, echocardiography and molecular and consulted with cardiologists Drs. Jessica Wang and James Weiss regarding distinct manifestations of heart disease in humans, data not shown. 40 were unassigned, 13 were in heart failure, 9 exhibited no change (deemed “resistant”), and 22 were in cardiac hypertrophy. An analysis performed on all the variants for each of the core histones across the different mouse strains revealed no differences in histone abundance between phenotypic groups (Figure 4A). However, 6 histone variants were differentially regulated by isoproterenol between groups: H2afb1—an H2A variant, Hist2h3b—an H3 variant, Hist1h2bk—an H2B variant, Hist1h4f, Hist1h4i, and Hist1h4k—all H4 variants.

All histone variants were up-regulated in the resistant mice with the exception of H2afb1, which was down regulated in resistant mice (Figure 4B). Global changes in histone abundance were not observed in the HMDP microarray data analysis, but a small cluster of histone variants were differentially regulated by isoproterenol treatment.

DISCUSSION With isoproterenol treatment, some mice develop hypertrophy after a week and heart failure after three weeks—other strains develop pathology more rapidly, whereas still another class seems oblivious to the stress (Rau et al., 2015).

The physiological parameters including ejection fraction, fibrosis, mortality and left ventricular diameter in diastole were used to identify mice as in hypertrophy, heart failure, unchanged (or resistant), and unassigned. From these groups, the BALB/cJ mouse strain was considered as susceptible and the BUB/BnJ mouse strain was considered as resistant. The abundance of chromatin structural proteins and euchromatic and heterochromatic PTMs was examined to determine if the susceptible and resistant mouse strains displayed differences in chromatin structure. For a majority of structural

RACHEL LOPEZ proteins studied (HMGB3, active RNA Polymerase II phosphorylated at serine 2, Linker Histone H1, HP1Îą, Histone H3) and heterochromatic PTMs (H3K9me3, H3K27me3, and H3K9me2) there were no differences between the resistant and susceptible mouse strains. However, the chromatin structural protein HMGB2 and the euchromatic PTM H3K4me3 were revealed to be more abundant in the susceptible BALB/cJ mouse strain. The PTM H3K4me3 is a modification associated with poised and active promoters in the genome making it a mark indicative of euchromatin (Zhou et al., 2011; Kaneda et al., 2009). The higher levels of the PTM H3K4me3 in the susceptible strain suggest increased euchromatin and thus more active genes, supporting the hypothesis that susceptible mouse strains have increased euchromatic DNA. However, there was no difference in the abundance of active RNA Polymerase II. Therefore the two strains would be predicted to have similar levels of mRNA elongation, as quantified by total RNA polymerase II phosphorylated at serine 2, a marker of transcriptional elongation. It is inferred that both mouse strains have similar levels of global transcriptional activity.

One possible explanation is that both strains have a similar number of actively transcribed genes that are marked with H3K4me3. However, in the BALB/cJ strain, active genes and promoters contain more H3K4me3 than the active genes in the BUB/BnJ strain. If so, it is possible that in hypertrophy, when the cell is stimulated to upregulate expression, the more heavily marked promoters of the BALB/ cJ strain will be better poised for increasing transcription. Another explanation could be that the higher abundance of H3K4me3 in the susceptible strain is the result of a greater number of genes being marked by H3K4me3 in that strain.

However in this model, the H3K4me3 would also be found at inactive genes.

In this case, the inactive genes would have been marked by H3K4me3 by past transcriptional activity, but are no longer being utilized by the cell. If the cell were to hypertrophy and the transcription factors regulating these genes were now expressed, then it is possible that these genes would be poised to turn on.

The PTMs H3K9me3, H3K9me2, and

H3K27me3 are modifications associated with silent regions of the genome (Zhou et al., 2011). There was no change between these marks suggesting there are similar amounts of heterochromatin between the two strains.

In addition to these key PTMs, the role of the chromatin structural proteins HMGB2 and HMGB3, both proteins from the same family responsible for DNA bending, was investigated. The protein HMGB2 increases during hypertrophy and heart failure in a mouse model of pressure overload. However, HMGB2 knockdown in isolated cardiomyocytes induces hypertrophy (Franklin et al., 2012). So, it is hypothesized that HMGB2 is upregulated during disease as a mechanism to maintain heterochromatin and/ or to counteract the relaxing of chromatin that occurs to facilitate transcription during disease. Therefore, a higher abundance of HMGB2 in the susceptible BALB/cJ mouse strain is necessary to maintain silent regions in the genome since the genome is euchromatic in select loci due to the higher abundance of H3K4me3 (Figure 5A, B). The chromatin structural proteins Linker Histone H1, HP1Îą, and Histone H3 showed no difference in relative abundance between the two mouse strains.

While slight differences were observed between the two mouse strains, the only significant differences were in the protein HMGB2 and euchromatic PTM H3K4me3.

This data supports other evidence that HMGB2 and H3K4me3 regulate chromatin accessibility in the setting of heart failure and indicates that one mechanism by which genetic variability leads to phenotypic variability is by altering chromatin packing. Furthermore, the lack of significant differences in the abundance of the other chromatin structural proteins examined does not rule out the possibility that these proteins exhibit a different pattern of localization across the genome due to the genetic variation between the strains, thereby altering chromatin structure and contributing to susceptibility to heart failure (Figure 5C). The distribution of these structural proteins may be localized in regions of the genome that permit heterochromatin in the resistant mouse strain and more evenly dispersed in the susceptible mouse strain allowing more

genes to be accessible.

Differences in protein and mRNA levels of HMGB1, HMGB2, and HMGB3 were found after isoproterenol treatment.

The amount of mRNA of a certain gene does not necessarily indicate the amount of protein produced by the cell. The levels of mRNA for a gene might be high, but the cell might not translate it due to lack of need, translational machinery, or even elongation regulation through codon usage (Nie et al., 2006). Using the microarray data, a bioinformatics analysis on the histones and histone variants between the different mouse strains belonging to the HMDP (data not shown, manuscript in preparation) was performed. While the histones do not determine susceptibility to heart failure, certain histone variants were associated with susceptibility to heart failure.

To confirm that the results in this study are reflective of a general effect of genetic variability in populations, these experiments are being repeated on two other sets of mouse strains considered either susceptible or resistant (Susceptible: A/J and C3H/HeJ; Resistant: C57/BL6J and CE/J) to heart failure. These experiments will also be repeated after isoproterenol treatment on the BALB/cJ and BUB/BnJ mouse strains to observe any differences in how the mice remodel their chromatin in disease.

It is hypothesized that a higher abundance of core histone proteins is indicative of more nucleosomes and therefore more heterochromatin. The amount of nucleosomes will be measured by determining the number of bound and unbound histone proteins in the nucleus. To test the abundance of chromatin, a Micrococcal nuclease assay will be conducted in addition to measuring heterochromatic PTMs. It is predicted that there will be a higher number of histone dimers bound together and lead to more heterochromatin in the nucleus.

Protein levels are being further studied using mass spectrometry before and after isoproterenol treatment for an unbiased analysis of protein level differences between the mouse strains. This allows us to identify other potential proteins that are not being tested by Western blot.

Knowing which proteins are differentially regulated in the different strains will

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GENETIC VARIATION OPERATES THROUGH CHROMATIN TO CONTROL HEART FAILURE SUSCEPTABILITY help focus on them and prioritize them for follow up experiments. These proteins are potentially key regulators of the gene expression remodeling that occurs during heart failure. Knock down and/ or overexpression of these key proteins will help understand the mechanism by which these proteins regulate chromatin structure and gene expression.

Alternatively, alterations in protein abundance may not be the only mechanism contributing to pathology; rather, altered distribution across the genome may also be an important mechanism for some of the chromatin proteins. It is hypothesized that the proteins are distributed in a specific manner across the genome to either prevent the progression of disease or facilitate it. This hypothesis can be tested by conducting Chromatin Immunoprecipitation sequencing, which will help identify where on the genome these proteins are found and if this specific localization corresponds with genes that are implicated in heart failure. Furthermore, the data supports the hypothesis that chromatin structure regulated by structural proteins play an essential role in the progression of heart disease. Understanding the underlying mechanisms that lead to heart failure will facilitate the development of future therapeutics that prevent this disease.

ACKNOWLEDGMENTS Thank you to the members of the Vondriska Lab and Drs. Jessica Wang, Christoph Rau, James Weiss, Jake Lusis and Yibin Wang (UCLA) for helpful discussions and support of this project. The Vondriska lab is supported by National Institute of Health grants HL-105699, HL-115238, and HL129639, American Heart Association grant IRG18870056, Thermo Fisher Scientific and the Department of Anesthesiology and David Geffen School of Medicine at UCLA. Emma Monte (PRE14430015) and Rachel Lopez (UFEL20130049) received AHA fellowships. Rachel Lopezâ&#x20AC;&#x2122;s training has also been supported by the Maximizing Access to Research Careers (MARC) program (T34 GM008563) at UCLA. REFERENCES 1.

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Depre et al. (1998) Unloaded heart in vivo replicates fetal gene expression of cardiac hypertrophy. Nat Med. 4: 1269-1275.

Dickhout, J.G., Carlisle, R.E., and Austin, R.C. (2011) Interrelationship between cardiac hypertrophy, heart failure, and chronic kidney disease: endoplasmic reticulum stress as a mediator of pathogenesis. Circ Res. 108: 629-642.

Franklin et al. (2012) Quantitative analysis of the chromatin proteome in disease reveals remodeling principles and identifies high mobility group protein B2 as a regulator of hypertrophic growth. Mol Cell Proteomics. 11: M111.014258.

Franklin et al. (2011) Specialized compartments of cardiac nuclei exhibit distinct proteomic anatomy. Mol Cell Proteomics. 10: M110.000703.

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Kaneda et al. (2009) Genome-wide histone methylation profile for heart failure. Genes Cells. 14: 69-77.

Mozaffarian et al., (2014) Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation. 127: e6-e245.

Nie, L., Wu, G., and Zhang, W. (2006) Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis. Genetics. 174: 2229-2243.

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Identifying Gene Networks Underlying BPA-induced Metabolic Syndrome Brandon Tsai, Qingying Meng, Le Shu, Andrew Mikhail, Helen Luk, Dr. Xia Yang1 Metabolic syndrome (MetS), a major risk factor for complex metabolic diseases such as cardiovascular disease, type 2 diabetes, and obesity, can be affected by both genetic and environmental factors. Obesogens, exogenous chemicals from the environmental sources, such as diet and industrial chemicals, can induce MetS. Bisphenol A (BPA) is one of the most influential obesogens due to its wide exposure in the general population and strong epidemiological and experimental evidence implicating its MetS association, especially when exposure occurs early in life. It has been previously shown that pregnant mice treated with BPA produce offspring that later develop MetS.To investigate the molecular mechanisms of BPA-induced MetS, the transcriptomes in the hypothalamus and liver were profiled using high-throughput RNA sequencing (RNA-Seq). When sacrificed at the 3-week weaning age, offspring prenatally exposed to BPA displayed 101 differentially expressed genes in the hypothalamus and 810 differentially expressed genes in the liver, compared to control offspring, at the statistical cutoff p-value<0.001. Genes significantly perturbed by BPA were related to metabolic and cellular functional categories such as lipid metabolism, biological oxidation, cholesterol biosynthesis, and the extracellular matrix. Significant overlap was found between the MetS genes uncovered from the BPA signature genes and human genome-wide association studies. Gene network modeling analysis identified tissue-specific liver and hypothalamus networks delineating critical gene-gene interactions and key network regulators, including Hmgcr, Lss, Mvd, and Insig1 in the liver, and Cdh3, Msx1, Steap1, and F5 in the hypothalamus. This study provides systems-level insights into the molecular mechanisms of prenatal BPA exposure and its contributions to MetS. The results reveal potential novel therapeutic targets at the key network regulators for addressing the rising concern of environmentally induced MetS. INTRODUCTION Metabolic syndrome (MetS) is characterized by the combination of cardiometabolic risk factors that contribute to the development of highly complex and interlinked diseases, including obesity, type 2 diabetes, and cardiovascular diseases (Smith and Ryckman, 2015). Consistent evidence supports that both genetic and environmental risk factors, as well as their interactions, influence the development of MetS (Grun and Blumberg, 2006). One category of environmental risk factor is obesogens, defined as exogenous chemicals that promote adipogenesis and obesity through metabolic disruption (Janesick and Blumberg, 2012).

Bisphenol A (BPA) is a widely studied obesogen due to its prevalence in the manufacturing of everyday products.

BPA is used in the production of synthetic polymers, including epoxy resins

and polycarbonates. Due to its good mechanical and structural properties, such as high strength, durability, and thermal stability, BPA is widely used in the manufacturing of everyday goods, including water pipes, food containers, bottles, toys, medical equipment, thermal paper, and electronics (Tsai, 2006). With production exceeding six billion pounds annually, BPA has become one of the most commonly produced compounds in the world. Production continues to increase, raising concern as exposure has been confirmed in the majority of the population and may pose widespread health risks (Kwapniewski et al., 2008).

Both human epidemiology and animal model studies have revealed association of BPA exposure with obesity (Bhandari et al., 2013), diabetes (Ahmadkhaniha et al., 2014), and cardiovascular disease (Ahmadkhaniha et al., 2014). Since 1988, the safety standard set by the U.S. Environmental Protection Agency (EPA)

and the Food and Drug Administration (FDA) reference dose has been 50 μg/kg body weight/day. This continues to be at this level despite accumulating evidence of MetS phenotype development at this dose (Angle et al., 2013).

Early fetal exposure to obesogens increases susceptibility to diseases, such as cardiovascular disease, obesity, and cancer, later on in life (Boekelheide et al., 2012). Developmental BPA exposure at doses below the suggested human safety level have resulted in intrauterine growth retardation and associated phenotypic abnormalities, including low birth weight (Miao et al., 2011), elevated adult body weight (Miyawaki et al., 2007), and altered glucose homeostasis (Alonso-Magdalena et al., 2010). By linking the associated health consequences and the high abundance of BPA, it is hypothesized that BPA and other obesogens are associated with the rise of metabolic diseases worldwide over the last few decades

Department of Integrative Biology and Physiology.

1

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BRANDON TSAI, QINGYING MENG, LE SHU, ANDREW MIKHAIL, HELEN LUK (Heindel, 2003). However, there is a lack of understanding of the mechanistic connections between BPA and MetS. In order to understand the molecular events through which BPA confers MetS predisposition, a systems genomics study was conducted, examining mouse offspring prenatally exposed to BPA in order to characterize the genomic impact of BPA exposure in key metabolic tissues.

In particular, the hypothesis that early prenatal BPA exposure induces genomic reprogramming in the offspring by affecting the organization and functions of tissue-specific gene networks involved in metabolic homeostasis was tested.

This hypothesis is based on mounting evidence in recent literature supporting that genes operate in highly complex tissue-specific regulatory networks; perturbations of specific sections of the networks, termed “subnetworks”, by genetic and environmental factors are responsible for the initiation and development of complex metabolic diseases (Meng et al., 2013). In this study, MetS-related phenotypes in mice prenatally exposed to BPA were assessed.

The transcriptome of the hypothalamus, responsible for central control of metabolism, and the liver, responsible for peripheral control of metabolism, was profiled. Using integrative genomics and systems biology approaches, this information was integrated with data from independent, large-scale molecular data sets to model the dynamics of genegene regulatory networks and identify key subnetwork regulators.

Furthermore, the findings from this animal study were intersected with human genome-wide association studies (GWAS) to assess the relevance of the mouse genomic signals to human disease pathology. This approach provides novelty in the integration of “-omics” data and large-scale molecular data sets, allowing both broad screenings and focused analysis of genes and gene networks to obtain a more comprehensive understanding of the molecular processes upon which BPA acts.

The identified key points of perturbation within specific regulatory subnetworks can serve as the basis for the development of targeted therapeutics. These key regulators may be targeted to counteract the obesogenic effects of BPA as well as to serve as biomarkers to evaluate responses 17 | UNDERGRADUATE SCIENCE JOURNAL

to such therapeutic interventions (Silver and Loscalzo, 2012).

sermann, NJ, USA) with VetACE chemistry analyzer (Alfa Wassermann).

MATERIALS AND METHODS

Tissue Collection Following phenotypic assessment, the mice were sacrificed at the respective 3-week weaning age and 30-week post-weaning age. After sacrifice, hypothalamus and liver tissue were collected and flash frozen in liquid nitrogen, then stored at –80°C

Mouse Model and Treatment Inbred C57BL/6 mice were maintained on special diet 5V01 (LabDiet), certified to contain less than 150ppm estrogenic isoflavones, and housed under standard housing conditions (room temperature 22–24°C) with 12:12 hr light:dark cycle before mating at 8-10 weeks of age. From 1 day post-conception (dpc) to 20 dpc, BPA (Sigma-Aldrich) dissolved in 1mL corn oil was administered to pregnant female mice via oral gavage at 5mg/ kg body weight/day, a dose at the “no observed adverse effect level (NOAEL) according to toxicity test guidelines, but shown to induce metabolic abnormalities (Angle et al., 2013). Negative control mice were fed the same amount of empty vehicle. BPA exposure was restricted to experimental manipulation through the use of polycarbonate-free water bottles and cages. Offspring from each treatment were divided into two groups: 3-week weaning age (short-term) and 30- week post-weaning (long-term) and were maintained on chow diet. For animals in the short-term study, body weight was measured daily from day 5 to day 9 and at 3-week weaning age. For animals in the longterm study, body weight was recorded every two weeks starting from the weaning age.

Phenotyping Adiposity was measured monthly via NMR nuclear spectroscopy (Brucker Minispec). Glucose tolerance was also evaluated monthly by measuring glucose levels at 0, 15, 30, 60, 90, and 120 minutes after intraperitoneal injection of 20% glucose in the volume of 10ml/kg body weight. Blood was collected from the tail vein after overnight fasting, and glucose levels were measured by a glucometer (Bayer’s Contour Meter). At weaning age and before sacrifice, serum samples were collected through retro-orbital bleeding.

Insulin levels were measured by an ELISA kit (Millipore, MA, USA). Total cholesterol, HDL cholesterol, unesterified cholesterol, triglyceride, and free fatty acid levels were measured by ACE Reagents enzymatic colorimetric assays (Alfa Was-

RNA Sequencing (RNA-Seq) A total of 12 RNA samples were isolated from hypothalamus and liver tissue (n=3 per group per tissue) from male offspring using AllPrep DNA/ RNA Mini Kit (QIAGEN GmbH, Hilden, Germany). Samples were processed for library preparation using Tru Seq RNA Library Preparation Kit (Illumina) for poly-A selection, fragmentation, and reverse transcription using random hexamer-primers to generate firststrand cDNA. Second-strand cDNA was generated using RNase H and DNA polymerases, and sequencing adapters were ligated using the Illumina PairedEnd sample prep kit. Library products of 250- 400bp fragments were isolated, amplified, and sequenced with Illumina Hiseq2500 System.

RNA-Seq Data Analyses to Identify Genes with Differential Expression and Alternative Splicing Quality control was performed using FASTX-Toolkit FASTX-Toolkit (Hannon Lab). No trimming was necessary for the RNA-Seq reads. Paired-end RNA-Seq reads were analyzed using the Tuxedo tool package (Trapnell et al., 2012), the pipeline consisting of TopHat (Kim et al., 2013), Bowtie (Langmead et al., 2009), Cufflinks, Cuffmerge, and Cuffdiff (Trapnell et al., 2013). TopHat2/Bowtie2 was used to map reads to the mouse genome (UCSC Genome Reference Consortium GRCm38) and discover transcript splicing sites through splice junction alignment, allowing default two mismatches, and disabling coverage based search for junctions. Cufflinks and Cuffmerge were then used for transcript assembly of the aligned reads and then ran with bias detection and initial estimation to improve accuracy of transcript abundance estimates and

IDENTIFYING GENE NETWORKS UNDERLYING BPA-INDUCED METABOLIC SYNDROME weight of reads for multiple location mapping. Parameters were also set to count all fragments, including those noncompatible to the reference transcript to be included in the calculation of the number of mapped hits, and were also normalized by the upper quartile of fragments mapping to individual loci rather than the total number of fragments.

Transfrags separated by less than the default 50bp were overlapped. Lastly, Cuffdiff was used to compare aligned reads between the BPA and control groups to identify differentially expressed genes, run with initial estimation to improve weight of multiple location reads and normalization by the upper quartile of the number of fragments. Multiple testing correction was accounted for using the Benjamini-Hochberg method, estimating the minimum false discovery rate (FDR) for the tests to be considered significant.

Genes demonstrating differential expression or alternative splicing at p<0.001 were chosen as the “signature” genes for further biological pathway analysis, network analysis, and human genetics data integration.

Functional Annotation of Signature Genes Genes defined as signatures from the RNA-Seq analysis were categorized by their biological functions and pathways using the Molecular Signatures Database (MSigDB) (Subramanian et al., 2005). Gene sets were used from the Kyoto Encyclopedia of Genes and Genomes (KEGG), BioCarta, Reactome, and Gene Ontology Consortium (GO).

Fisher’s exact test was used to calculate enrichment p-values for each biological function and pathway, and the Bonferroni-corrected p<0.05 was used as the significance level.

SNP Set Enrichment Analysis To assess the relevance of the signature genes to MetS in humans, they were intersected to candidate genes of MetS from the human GWAS catalog (Hindorff et al., 2009) for overlap. Then, they were evaluated for functional polymorphism enrichment with a modified version of the SNP Set Enrichment Analysis (SSEA) method (Zhong et al., 2010) to demonstrate disease association strength in humans. The hypothalamus

and liver signature genes were mapped to expression single nucleotide polymorphisms (eSNPs) discovered from previous expression quantitative trait loci (eQTL) studies curated for the corresponding tissues to obtain the representative eSNP sets (Makinen et al., 2014). Briefly, each eSNP set was filtered by linkage disequilibrium (r2>0.7) to eliminate redundancy in eSNPs and intersected to human GWAS MetS datasets for disease association p-values for the filtered eSNP members, termed Pgeneset. Pgeneset was tested for enrichment of low disease association p-values against random expectation derived from 10,000 randomly selected eSNP sets of matching size to derive statistical enrichment p-values PK-S (KomogorovSmirnov test) and PF (Fisher’s exact test).

Significant enrichment at Bonferronicorrected p<0.05 implied that multiple genetic variants of the human orthologs of our BPA signature genes collectively contribute to MetS association in humans.

Network Construction Bayesian networks (BN) of liver and brain were constructed from genetic and transcriptomic data from several large-scale mouse and human studies (Emilsson et al., 2008) using a previously establishedvmethod (Zhu et al., 2008). BN are directed acyclic graphs with edges defined by conditional probabilities characterized by the distribution of states of each gene given the state of its parents. Network topology defines a partitioned joint probability distribution over all network genes. Bayes’ formula was used to determine the likelihood of a BN network model with given observed transcriptomic data. For each data set, 1000 BN with different random seeds were reconstructed using the Monte Carlo Markov Chain simulation and the model with the best fit for each network was determined using the Bayesian Information Criteria (Madigan and York, 1995). In the resulting set of 1000 networks, edges appearing in over 30% of the networks were included in a consensus network. Causal direction was inferred between genes with genetic information as priors for the network (Zhu et al., 2004). For each tissue, the union of nodes and edges from BNs of

multiple mouse and human studies were used as tissue-specific networks. Proteinprotein interaction (PPI) networks from the Human Protein Interaction Database (HPRD) were also collected and used for analysis (Keshava Prasad et al., 2009).

Gene subnetworks perturbed by BPA were derived using the BPA signature genes as seeds to extract the top most connected subnetworks as previously described (Yang et al., 2009). Cytoscape was used for network visualization (Smoot et al., 2011).

Key Driver Analysis (KDA) The previously developed KDA method (Yang et al., 2010) was used to identify key regulatory genes, termed key drivers (KDs). KDs are network nodes whose surrounding subnetworks are significantly enriched for disease-related genes. Briefly, KDA takes an input of gene set G (i.e. BPA signature genes) and directional gene network N (i.e. BNs). For every gene K in network N, neighboring genes within 3-edge distance were tested for enrichment of genes in G using Fisher’s exact test. Network genes that reached Bonferroni-adjusted p<0.05 were reported as KDs. For each BPA signature, KDs were calculated in BNs and PPI networks separately. KDs were ranked by composite score calculated based on consistency of KDs across multiple networks and statistical significance using the formula (CKD+∑ CKD i=1RankKDi )/Nnetwork ; where CKD is the count of networks in which the KD was identified.

RESULTS The tracking of additional animals until adulthood to examine long-term impact is currently taking place; all data shown are from the short-term experiment.

Prenatal Exposure to BPA Induces Metabolic Dysregulation Both male and female offspring from the BPA group show significantly lower body weight, indicative of growth retardation at the 3-week-old weaning age (Figure 1A). In the male offspring, there were significant decreases in serum lipid parameters and an increase in serum glucose level at weaning age (Figure 1B).

Female offspring showed no significant changes in lipid and glycemic traits.

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BRANDON TSAI, QINGYING MENG, LE SHU, ANDREW MIKHAIL, HELEN LUK

A)

were identified in the hypothalamus and 812 DEGs in the liver. Also, 48 alternatively spliced genes, determined by differential usage of exons, and 66 differentially expressed isoforms were identified in the hypothalamus, and 64 genes with alternative splicing and 532 differentially expressed isoforms were found in the liver. All genes with gene-, splicing-, and isoform- level changes were pooled together for each tissue and defined tissue-specific lists of unique gene “signatures” to be used for downstream integrative analysis, giving a list of 215 gene signatures from the hypothalamus, including Ercc2, Xist, Ttr, Apba3, and Slc4a5, and 1408 gene signatures from the liver, including Mup9, Ccl28, Cyp2b13, Cyp4a12b, and H19 (Table 1).

B)

Figure 1. Significantly altered metabolic parameters in the plasma of BPA-treated mice at weaning age. A) Body weights of male and female pups treated with vehicle or BPA were measured daily between 5 and 9 days and at 3-week weaning age, showing significantly lower body weight in BPA-treated group in both males and females. Diamond, square, triangle, and circle represent control female, control male, BPA female, and BPA male, respectively. Ctrl, control; M, male; F, female. B) Serum lipid levels and glucose levels of male and female pups treated with vehicle or BPA were recorded, showing significant decreases in serum lipid levels and increase in glucose levels in males. TG, triglyceride; Tot chol., total cholesterol; HDL, high-density lipoprotein; UC, unesterified cholesterol; FFA, free fatty acid. *p<0.05, **p<0.01, ***p<0.001 by two-sided Student’s T-test.

Large-Scale Transcriptomic Changes in Hypothalamus and Liver RNA-Seq was used to measure the whole transcriptome of individual hypothalamus and liver tissue samples, allowing for the comparison of gene-, transcript-, and exon-level expression between the control and 19 | UNDERGRADUATE SCIENCE JOURNAL

BPA groups. Using RNA-Seq, hundreds of genes and transcript isoforms with altered expression levels and/ or exon usage were identified in both the hypothalamus and liver of male offspring at weaning age, revealing robust BPA-induced transcriptomic alterations (Table 1). At p<0.001, 101 differentially expressed genes (DEGs)

Functional Categorization of Signature Genes The transcriptome data was analyzed for potentially affected biological pathways by assessing the enrichment of each signature for known biological pathways and functional categories curated in various databases. This analysis revealed significant enrichment of many pathways in the liver signatures.

The over-represented liver pathways identified are relevant to drug, steroid, lipid, and cholesterol metabolism, PPAR signaling, and the extracellular matrix (ECM), all of which have been implicated in cardiometabolic diseases (Table 2).

Analysis of the hypothalamus signatures defined at p<0.001 only revealed two significant pathways. Because brain tissues tend to have lower levels of gene expression, tests of lower statistical power were required to detect gene signatures.

Consequently important pathways may have been missed when using the small number of genes defined at the stringent cutoff p<0.001. Indeed, when the statistical cutoff was loosened to p<0.05 to include 1660 hypothalamus signatures, many more metabolic pathways were identified and overlapped with those detected in the liver signature (Table 2).

The detection of these signals supported the critical roles of hypothalamus and liver tissue in regulating metabolic homeostasis and substantiated the observed changes in metabolic parameters in the BPA-treated animals.

IDENTIFYING GENE NETWORKS UNDERLYING BPA-INDUCED METABOLIC SYNDROME

Tissue

Level of analysis

Signature genes

Liver

Hypothalamus

Ercc2 Gene Splicing Isoform Total

Gene Splicing Isoform Total

Excision repair cross-complementation group 2

8.345

Xist

X inactive specific transcript

6.519

Ttr

transthyretin

-5.026

Apba3

amyloid beta (A4) precursor proteinbinding, family A, member 3

-4.711

Slc4a5

solute carrier family 4 (sodium bicarbonate cotransporter), member 5

-3.280

Mup9

major urinary protein 9

-5.262

Ccl28

chemokine (C-C motif) ligand 28

-4.989

Cyp2b13

cytochrome P450, family 2, subfamily b, polypeptide 13

4.605

Cyp4a12b

cytochrome P450, family 2, subfamily a, polypeptide 12B

-4.290

H19, imprinted maternally expressed transcript

3.949

101 48 66 215

812 64 532 1408

log2 fold change (BPA/control)

Top affected genes

H19

Table 1. RNA-Seq analysis revealed robust BPA-induced transcriptomic alterations at the gene, splicing, and isoform levels at p<0.001. The top five genes with the greatest magnitude of fold change from each tissue are shown to demonstrate strong transcriptomic alteration due to BPA treatment.

Hypothalamus

p<0.001

p<0.05

Pathway highly calcium permeable postsynaptic nicotinic acetylcholine receptors

Gene overlap

Adjusted p-value

3

4.53E-02

Relationship Between BPA Signature Genes and MetS To further assess the relationship between the BPA signature genes and MetS in the mouse model, the signatures were tested for Pearson correlation with the measured MetS traits: body weight, free fatty acids, total cholesterol, HDL, triglycerides, blood glucose, and insulin level. At p<0.05, hundreds of the signatures genes were correlated with one or more phenotypic traits (Table 3). For example, Lama1 (a top gene linked to T2D and sudden cardiac arrest in humans and a key gene involved in ECM and focal adhesion) was found to correlate with insulin levels (p=0.01) and free fatty acids (p= 0.03).

Relevance of BPA Signature Genes to Human Metabolic Diseases

Liver

Pathway metabolism of lipids and lipoproteins

Gene overlap

Adjusted p-value

62

3.84E-14

presynaptic nicotinic acetylcholine receptors

3

4.53E-02

biological oxidations

31

9.30E-13

neuroactive ligand receptor interaction

39

3.58E-06

collagen formation

18

5.24E-10

highly calcium permeable postsynaptic nicotinic acetylcholine receptors

7

1.95E-04

12

2.02E-09

neuronal system

35

1.96E-04

drug metabolism cytochrome p450

19

2.05E-09

cholesterol biosynthesis

9

2.28E-04

focal adhesion

30

1.49E-08

acetylcholine binding and downstream events

7

7.31E-04

retinol metabolism

17

1.49E-08

extracellular matrix organization

16

8.20E-04

extracellular matrix organization

19

4.17E-08

GABA receptor activation

12

9.13E-04

integrin cell surface interactions

18

5.37E-08

transmembrane transport of small molecules

43

9.84E-04

DNA replication

12

2.58E-07

presynaptic nicotinic acetylcholine receptors

6

1.03E-03

phase II conjugation

16

3.36E-07

collagen formation

12

2.10E-03

phase I functionalization of compounds

16

3.36E-07

metabolism of lipids and lipoproteins

46

2.63E-03

DNA strand elongation

10

3.57E-06

ECM receptor interaction

14

4.69E-03

fatty acid triacylglycerol and ketone body metabolism

23

4.28E-06

bile salt and organic anion SLC transporters

5

5.62E-03

NCAM1 interactions

11

5.25E-06

vascular smooth muscle contraction

16

9.79E-03

cell cycle mitotic

33

1.20E-05

signaling by PDGF

16

1.55E-02

axon guidance

28

1.32E-05

class B 2 secretin family receptors

13

1.58E-02

NCAM signaling for neurite out growth

12

1.03E-04

gap junction

13

1.85E-02

PPARA activates gene expression

15

1.86E-04

NCAM1 interactions

8

1.85E-02

signaling by PDGF

16

2.99E-04

glucagon signaling in metabolic regulation

7

3.08E-02

small cell lung cancer

13

3.02E-04

adipocytokine signaling pathway

10

3.83E-02

steroid biosynthesis

6

4.36E-04

lipid digestion mobilization and transport

8

3.83E-02

steroid hormone biosynthesis

10

5.98E-04

calcium signaling pathway

19

3.88E-02

ORC1 removal from chromatin

11

6.59E-04

hemostasis

39

3.96E-02

PPAR signaling pathway

11

8.33E-04

lipoprotein metabolism

6

4.13E-02

fatty acid metabolism

8

1.96E-03

dilated cardiomyopathy

12

4.28E-02

lipid metabolic process

38

2.16E-03

cholesterol biosynthesis

p<0.001

Table 2. Functional annotation revealed biological or functional categories over-represented in the hypothalamus and liver gene signatures. Functional annotation from Molecular Signatures Database (MSigDB), including gene sets from the BioCarta, Kyoto Encyclopedia of Genes and Genomes, Reactome, and Gene Ontology Consortium databases. Gene count indicates the number of gene signatures from either the hypothalamus or liver that were related to each pathway. Fisher’s exact test and Bonferroni-corrected p<0.05 was used to determine significance.

UNDERGRADUATE SCIENCE JOURNAL | 20

BRANDON TSAI, QINGYING MENG, LE SHU, ANDREW MIKHAIL, HELEN LUK

Tissue

Counts of significantly associated traits Birth weight, Weaning weight

Fat/Body Weight

Fat/Muscle

30

15

19

23

30

Insulin

Triglyceride

Total cholesterol

Unesterified cholesterol

Hypothalamus (n=101)

HDL

Free fatty acid

Glucose

14

8

27

18

7

Birth weight, Weaning weight

Fat/Body Weight

Fat/Muscle

Free fatty acid

Glucose

519

146

170

518

471

Liver (n=812)

HDL

Insulin

Triglyceride

Total cholesterol

Unesterified cholesterol

171

132

486

279

58

Table 3. Pearson correlation testing revealed high number of signatures genes significantly correlated with one or more metabolic syndrome-related phenotypes. P<0.05 was used to determine significance.

Disease trait

Count

Cholesterol

4

CD36, KCNE2, LCAT, NCAN

Blood pressure

1

PLCE1

Metabolite levels

1

BCL6

Cholesterol

13

ABCG8, ADAMTS7, ARL15, CD36, CUX2, FADS1, LIPG, LPL, LRP1, MLXIPL, MMAB, PSD3, TCF21

Type 2 diabetes

13

A1BG, ADAMTS9, ARL15, CUX2, FADS1, FOXA2, GCK, IGF2BP2, LAMA1, MSMO1, NOTCH2, SGSM2, ST6GAL1

Metabolic traits

10

ACADM, CYP4A11, EDC4, FADS1, GCK, LPL, MLXIPL, NAT8, OPLAH, PSD3

Obesity

10

ADAMTS9, ARHGAP11A, BDNF, COL4A1, FAIM2, KYNU, LEPR, PACS1, RPL27A, ZNF608

Cardiovascular disease

8

Metabolite levels

7

ACADM, BCL6, FADS1, FEN1, GCK, LPL, SLC1A4

Blood pressure

6

ARHGAP42, CYP17A1, FES, LPL, MOV10, TBX3

Lipid metabolism

6

ABCG8, FADS1, LIPG, LPL, MLXIPL, PLA2G7

Triglycerides

5

FADS1, LPL, LRP1, MLXIPL, PSD3

Liver

Hypo

Tissue

Genes

COL4A1, ESR1, FADS1, FNDC3B, LAMA1, LPL, PHLDA1, VRK1

Table 4. Human genome-wide association studies (GWAS) revealed high number of signature genes overlapped with GWAS candidate genes for metabolic disorders. GWAS candidate genes based on the National Human Genome Research Institute GWAS catalog.

GWAS Disease

Hypothalamus

Liver

Data Source

Trait

KS p-value

Fisher p-value

KS p-value

Fisher p-value

Dyslipidemia

GLGC

HDLC

9.43E-01

8.56E-01

2.88E-05

2.12E-08

Dyslipidemia

GLGC

LDLC

9.41E-02

8.33E-01

1.93E-01

6.89E-05

Dyslipidemia

GLGC

TC

3.31E-01

3.83E-01

4.79E-04

4.29E-06

Dyslipidemia

GLGC

TG

7.53E-01

9.98E-01

7.73E-03

6.65E-03

CARDIoGRAM

CAD

8.02E-01

8.56E-01

2.02E-03

2.48E-03

Hypertension

CHARGE

DBP

9.51E-01

8.45E-01

1.43E-01

6.49E-01

Hypertension

CHARGE

SBP

9.76E-01

9.67E-01

2.17E-01

5.29E-01

GIANT

BMI

5.80E-01

9.10E-01

7.49E-03

1.44E-02

T2D

DIAGRAM

T2D

4.80E-01

7.08E-01

2.60E-01

7.57E-01

T2D

MAGIC

Glucose

3.05E-01

6.81E-01

9.25E-01

6.35E-01

T2D

MAGIC

HbA1C

9.85E-01

6.10E-01

2.65E-02

1.25E-01

T2D

MAGIC

Proinsulin

1.18E-01

1.65E-01

6.36E-06

3.18E-03

CAD

Obesity

Table 5. Single nucleotide polymorphism set enrichment analysis revealed enrichment of signature genes in full genome-wide association studies for metabolic syndrome. HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; CAD, coronary artery disease; DBP, diastolic blood pressure; SBP, systolic blood pressure; BMI, body mass index; T2D, type 2 diabetes; HbA1C, hemoglobin A1c test; GLGC, Global Lipids Genetics Consortium; CARDIoGRAM, Coronary ARtery DIsease Genome wide Replication and Meta-analysis; CHARGE, Cohorts for Heart and Aging Research in Genomic Epidemiology; GIANT, Genetic Investigation of ANthropometric Traits; DIAGRAM, DIAbetes Genetics Replication And Meta-analysis; MAGIC, Meta-Analyses of Glucose and Insulin-related traits Consortium; KS, Kolmogorov-Smirnov test; Fisher, Fisher’s exact test. Both Kolmogorov-Smirnov test and Fisher’s exact test were used to determine significance at Bonferronia-corrected p<0.05. Significant values shown in bold.

21 | UNDERGRADUATE SCIENCE JOURNAL

To infer the potential causal role of BPA gene signatures and evaluate the relevance of the signals from the mouse model to human pathology, large-scale GWAS, which have uncovered scores of genetic loci and potential causal genes for human diseases, were used. 120 genes from both tissue samples, such as BDNF, GCK, IGF2BP2, LEPR, LPL, and TCF21, overlapped with the top causal candidate genes detected in human GWAS for MetS-related diseases, including obesity, dyslipidemia, and diabetes (Table 4). To formally test the statistical significance of the overlap between BPA signatures and human GWAS, SSEA and the full sets of GWAS results were used (all ~2 million genetic variants tested, not only top hits as in the direct overlap analysis).

The BPA signatures from the liver tissue were significantly more likely to harbor genetic risks for many of these metabolic diseases or traits (Table 5). The results from both direct GWAS intersection and SSEA suggested that genes affected by BPA have also been causally linked to human cardiometabolic diseases via genetic association.

Identification of Key Drivers and Gene Subnetworks To explore the gene-gene relationships between the signature genes and to identify key points of perturbation, predefined PPI networks and datadriven BNs were used, capturing wellknown interactions between proteins and revealing gene-gene regulatory relationships in the hypothalamus and liver. BPA signatures were mapped onto PPI and BNs networks, and multiple key regulatory genes were identified using the KDA analysis (Table 6). The top five key drivers from each tissue formed central network nodes connecting many BPA signature genes in the gene subnetworks (Figure 2A and 2B). Other wellestablished MetS-related genes, such as Hmgcr, were also found to be key drivers (Figure 2C).

DISCUSSION This study integrated phenotypic assessment for MetS traits, systematic profiling of the transcriptome of metabolic tissues, association of gene sets to human studies, and network analysis to systematically uncover insight on the

IDENTIFYING GENE NETWORKS UNDERLYING BPA-INDUCED METABOLIC SYNDROME Tissue

Key driver Steap1 F5

Hypothalamus

Sostdc1 Msx1

Score

Signature

six transmembrane epithelial antigen of the prostate 1

0.619

x

PPI x

coagulation factor V

0.571

x

x

sclerostin domain containing 1

0.524

x

x

homeobox, msh-like 1

0.476

x

x

Cdh3

cadherin 3

0.429

x

x

Cox8b

cytochrome c oxidase, subunit VIIIb

0.381

x

x

Ddc

dopa decarboxylase

0.333

x

x

Krt8

keratin 8

0.333

x

x

solute carrier family 4, sodium bicarbonate cotransporter, member 5

0.333

x

x

Slc4a5 Fdft1

farnesyl diphosphate farnesyl transferase 1

0.516

x

x

proline-serine-threonine phosphatase-interacting protein 2

0.410

x

x

Mvd

mevalonate (diphospho) decarboxylase

0.351

P

x

Lss

lanosterol synthase

0.350

P

x

Insig1

insulin induced gene 1

0.322

P

x

Nsdhl

NAD(P) dependent steroid dehydrogenase-like

0.313

P

x

isopentenyl-diphosphate delta isomerase

0.233

P

x

Hmgcr

3-hydroxy-3-methylglutaryl-Coenzyme A reductase

0.177

x

x

Fmn1

formin 1

0.104

x

x

Adfp

Plin2, perilipin 2

0.098

x

x

Prelp

proline arginine-rich end leucine-rich repeat

0.080

x

P

thymidine kinase 1

0.068

x

x

laminin, beta 3

0.067

x

P

fibrillin 1

0.063

P

P

Pstpip2

Liver

molecular mechanism of BPA-induced MetS. Hundreds of genes involved in diverse biological functions, many associated with metabolic diseases and traits, were identified. Furthermore, the association of the similar sets of genes identified in the BPA-treated mouse model with MetS was also found in humans.

Network analysis was conducted to derive gene subnetworks capturing the regulatory relationships among the BPA signature genes and their potential key regulatory genes. These system-level results help fill the gaps of knowledge in understanding the mechanism by which BPA affects metabolic homeostasis.

Despite previous data suggesting development of obesity upon BPA exposure (Bhandar et al., 2013), the phenotypic data showed that BPAexposed offspring have decreased body weights at and before weaning age compared to control offspring, indicative of growth retardation during early development (Figure 1A). Although this appeared to be counter-intuitive, low birth weight has been linked to obesity development in adulthood (JimenezChillaron et al., 2009). Therefore, the offspring with BPA-induced low birth weight were expected to develop MetS later in life in the 30-week long-term experiment. While male offspring demonstrated significant decreases in serum lipid parameters and an increase in serum glucose level at weaning age, female offspring did not. These results supported previous studies demonstrating sex bias towards stronger BPA effects in male mice (Liu et al., 2013).

The large numbers of genes, isoforms, and alternative splicing events found to be affected by BPA supported the broad impact of BPA on the genome.

The detection of alternative splicing and differentially expressed isoforms in both tissues supported that BPA alters the levels or induces different forms of gene products. Compared to the hypothalamus, the effect of BPA on the liver appeared to be much stronger, as demonstrated by the larger number of signature genes detected (Table 1). This may be a result of liver being a more homogenous tissue and hence having higher statistical power.

However, considering the generally lower gene expression levels and higher heterogeneity in the brain compared to

Idi1

Tk1 Lamb3 Fbn1

Table 6. Key driver analysis revealed top key drivers in the hypothalamus and liver. Top key drivers of the hypothalamus and liver gene networks include several gene signatures and overlap between Bayesian networks and protein-protein interaction networks. The composite score was calculated based on consistency of KDs across multiple networks and statistical significance.

peripheral tissues like the liver, the fact that many transcriptional events can be observed in the hypothalamus supported this brain region as an important site of action for BPA. The BPA-affected genes were involved in diverse biological processes, including lipid, cholesterol and steroid metabolism, the PPAR, glucagon, and adipokine signaling pathways, as well as pathways implicated in cardiomyopathy, the ECM, and cell adhesion. All of these processes have been implicated in cardiometabolic diseases (Meng et al., 2013). The detection of multiple metabolic and signaling pathways in both tissues supported the critical roles of these tissues in metabolic homeostasis. In particular, pathways such as lipid and cholesterol metabolism, ECM, NCAM1 interactions are shared between tissues. These multi-tissue pathways may play more important roles in mediating the effects of BPA on disease development.

The results from both direct human GWAS intersection (Table 4) and SSEA

(Table 5) suggest that genes affected by BPA have also been causally linked to human cardiometabolic diseases via genetic association, thus supporting the causal role of prenatal BPA exposure in the development of MetS and related diseases in humans. Among the shared genes between the BPA signatures and the human GWAS candidates are notable obesity genes such as BDNF and LEPR, diabetes genes such as IGF2BP2, and cardiovascular genes such as LPL and TCF21. This attests strongly to the potentially shared mechanisms between environmental risks (e.g., BPA) and genetic risks of MetS based on the perturbation of common sets of risk genes. The agreement between our mouse model study and human GWAS also supports the relevance and translatability of the animal studies to human pathophysiology.

To pinpoint the potential regulators, or KDs, of the large numbers of genes affected by BPA, directional gene networks derived from genome-scale UNDERGRADUATE SCIENCE JOURNAL | 22

BRANDON TSAI, QINGYING MENG, LE SHU, ANDREW MIKHAIL, HELEN LUK

A)

B)

C)

Figure 2. Tissue-specific subnetworks of select top key drivers. Large square nodes are key drivers. Diamond nodes are BPA signature genes. Circles are non-KD and non-signature genes in the networks. A) Network of top 5 hypothalamus key drivers. B) Network of top 5 liver key drivers. C) Network of liver key driver Hmgcr, a well-known metabolic responsible for cholesterol biosynthesis, a human GWAS gene for cholesterol traits, and the target of lipid lowering statin drugs.

data were utilized. These networks significantly enhanced the unbiasedness in capturing both known and hitherto unknown biological interactions and regulatory relations between genes. KDs, when perturbed, could lead to a cascade of changes in a large number of genes due to their core positions in the network’s topology, and thus have the potential of eliciting strong impact on disease development. Some of the liver KDs were well-known metabolic regulators such as Hmgcr and Insig1. Hmgcr is responsible for cholesterol biosynthesis and is the

23 | UNDERGRADUATE SCIENCE JOURNAL

target of statins, a group of drugs highly effective in lowering blood lipids and preventing heart diseases (Sharpe and Brown, 2013); Insig1 is responsible for cholesterol synthesis and regulation (Dong and Tang, 2010). Perturbation of these KDs and their downstream subnetworks by BPA may explain the observed lipid dysregulation in BPAtreated mice. Others, such as the liver KD Pstpip2 (protein interaction) (Drobek et al., 2015) and the hypothalamus KD Steap1 (a cell surface antigen in cellcell junctions) (Gomes et al., 2012), were potential novel mediators of the BPA effect identified using this datadriven approach. Steap1 represented an interesting finding. Despite the literature’s limited information on its function in the brain, in the hypothalamus subnetwork, Steap1 was tightly connected to several genes related to CVD, including Kcne2 – a human GWAS gene implicated in myocardial infarction and coronary heart disease (Kathiresan et al., 2009); Sgk1 – a known hypertension gene (Lang et al., 2009); Ncan – a human GWAS gene for total cholesterol, LDL, and triglyceride synthesis (Jeemon et al., 2011); F5 – a coagulation factor (Maitland-van der Zee et al., 2009). These key regulators of the hypothalamus subnetwork, along with the presence of multiple CVDrelated pathways in the hypothalamus signature (Table 2), supported a perhaps previously under-appreciated role of hypothalamus in CVD. In summary, this systems genomics study of prenatal BPA exposure in a mouse model revealed a broad and acute effect of BPA on a large number of genes in two key metabolic tissues. The dosage of 5mg/kg body weight/day produced results consistent with previous reports, demonstrating early prenatal exposure to BPA leads to metabolic perturbations and genomic reprogramming in C57BL/6 offspring, affecting the organization and functions of metabolically relevant tissue-specific gene networks. The perturbed genes are involved in diverse biological processes directly relevant to MetS and cardiometabolic diseases, such as cholesterol and lipid metabolism, glucagon signaling, ECM, and cardiomyopathy. Furthermore, many of the top BPA signature genes were also GWAS candidate genes, which when

genetically perturbed, were associated with metabolic diseases in humans. The integration of human genetic association information with the results of mouse model analyses enabled indirect causal inference for the BPA signature genes. The fact BPA altered similar sets of diseaseassociated genes as discovered in human GWAS supported a causal role of BPA in inducing MetS and cardiometabolic diseases. The network key drivers identified were likely important mediators of the broad BPA effects.

Some of the known KDs such as Hmgcr helped to partially explain the metabolic changes observed in the offspring with BPA exposure. Additionally, the novel KDs may serve as novel targets for MetS warranting future experimental validation. These findings have farreaching implications because of the prevalent exposure of BPA in the general population and the remarkable sensitivity of the developing fetus to BPA exposure at both genomic and phenotypic levels.

Precautions and control of BPA exposure during pregnancy may be necessary to reduce the transmission and spread of MetS in future generations.n and spread of MetS in future generations.

ACKNOWLEDGEMENTS I would like to thank the members of the Yang Lab for their continual guidance and Zhe Ying for technical assistance.

I also thank the UCLA Undergraduate Research Scholars Program and Amgen Scholars Program for financial support.

The study is supported by the UCLA Faculty Career Development Award, the UCLA CTSI Grant UL1TR000124, and the Hellman Fellows Award to X.Y. Mouse studies were conducted in accordance with and approved by the Institutional Animal Care and Use Committee at University of California, Los Angeles.

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Role of PACAP in Cortical Spreading Depression Irvin Lien, Dr. Serapio M. Baca1, Dr. Andrew C. Charles2 Pituitary adenylate cyclase-activating peptide (PACAP) is a polypeptide that stimulates adenylate cyclase and increases cyclic adenosine monophosphate (cAMP) levels in target cells. This process has been found to be involved in insulin secretion, cellular metabolism, gastrointestinal motility, anti-inflammatory response, and blood vessel vasodilation. Intravenous infusions of PACAP in healthy humans produce migraine-like symptoms by inducing long-lasting vasodilatory events that activate nociceptive sensory endings. Modulating PACAP receptors may reduce headache and/or other symptoms of migraine. To investigate this, an animal model called cortical spreading depression (CSD) was used. CSD, the putative mechanism of visual migraine aura, is a propagating wave of neural and glial hyper-excitation followed by a marked inhibition. Multi-phasic changes in cortical vasculature, tissue oxygenation, and blood flow accompany the cell activity changes in CSD. CSD is readily elicited by focal injection of potassium chloride (KCl) applied to the cortical surface. Given the human infusion results and the function of PACAP as a potent vasodilator, we hypothesized that eliminating PACAP would alter cortical blood vessel and flow dynamics during CSD, and PACAP dysregulation might also affect the electrical activity normally observed in CSD. To test this hypothesis, CSD was triggered in both wild-type and PACAP knock-out (PACAP-KO) mice. Optical intrinsic signal (OIS) imaging and local field potential (LFP) recordings were used to quantify changes in CSD. OIS images were used to quantify changes in blood vessel caliber. Preliminary electrophysiological and OIS data indicate that the electrical changes of CSD are affected with slower recovery rates for PACAP-KOs compared to wild-type. PACAP-KO mice also showed pronounced and prolonged vasoconstriction following CSD that suggest compensatory vasodilatory mechanisms are unable to compensate for lack of PACAP.These results indicate a potentially important role for PACAP-mediated processes in CSD and thus provide further support for PACAP as a potential therapeutic target for migraine.

INTRODUCTION Migraines and Cortical Spreading Depression Migraines are a common and debilitating neurological disorder that impact roughly 17% of females and 6% of males worldwide. Symptoms of migraine vary widely but may include intense head pain, dizziness, visual distortions (i.e., visual aura), confusion, and aphasia (Lipton et al., 2007). Although it is known that headaches are mediated by the activation and possible sensitization of the trigeminovascular pain pathway, the exact pathophysiology of migraines remains undetermined (Levy, 2009).

Visual auras occur in approximately 28% of cases of migraines (Stewart et al., 1991). Migraine visual auras are thought to be the result of a slow wave of excitation with later inhibition that

propagates across the visual cortex. Magnetic resonance imaging (MRI) studies support this link and show slowly propagating changes in visual cortex activity coincident with verbal report of an aura.

This propagating wave shares many of the same features as cortical spreading depression (CSD) in rodents and CSD has been observed in humans following stroke. In both humans and rodent models, CSD manifests as a slowly propagating wave of neural and glial hyper-excitation followed by a marked inhibition with multi-phasic changes in cortical vasculature, tissue oxygenation, and blood flow (Hadjikhani et al., 2000).

Migraines may be triggered by different stimuli including flickering lights, loud noises, and strong odors such as perfume. Similarly, CSD events can be triggered by a number of different stimuli such as intravenous KCl injections, pin-

Department of Neurology 2Director of UCLA Headache Research and Treatment Program.

1

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prick damage to the brain, low oxygen level, and ischemic events. Injections of small amounts of KCl into human brain slices can evoke strong electric field potentials (Berger et al., 2008). At the cellular level, CSDs arise from the cellular influx of sodium and calcium and the efflux of potassium. This process is self-perpetuating and the high level of potassium ultimately initiates the regenerative depolarizing and propagating wave of CSD (James et al., 2001).

PACAP One component underlying headaches may be the dilation of the dural vasculature, particularly the middle meningeal artery and cerebellar artery, which leads to the consequent activation of nociceptive receptors. The highest concentration of peptide receptors responsible for vasodilation in the middle meningeal artery

IRVIN LIEN and cerebellar artery are the PACAP receptors (Syed et al., 2012). These receptors are thus potentially critical in mediating headache. Further clarification of the role of PACAP in the initiation and or termination of CSD may justify targeting PACAP as a new target for both migraine and headache therapy (Lars, 2015).

PACAP was initially studied for its role in increasing the production of cyclic adenosine monophosphate (cAMP) and its similarity to the structure of intestinal vasodilators. PACAP acts in a cascade, targeting G-protein coupled receptors, increasing serum cAMP levels and PKA activity, and ultimately leading to vasodilation (Boron, 2005). Because PACAP receptors are highly expressed in the cerebral cortex and middle meningeal artery, PACAP may be an important mediator of neurovascular function (Murthy et al., 1998).

As a mediator of neurovascular function, PACAP may act as a neuroprotective factor in response to neuronal damage resulting from various insults. Radioimmunoassay results have indicated that there are high concentrations of PACAP receptors in the hypothalamus and the cerebral cortex. Additionally, intracerebral injections of PACAP have been noted to induce an increase in cerebral blood flow, strongly suggesting that PACAP may play a role in moderating blood flow throughout the cerebrum (Jakab et al., 2004). In prior experiments, PACAP injections have increased membrane depolarization and increased the frequency of neuronal action potentials (Arimura, 1998). Thus PACAP appears to play a complex role in both neuronal activity and vascular dynamics.

The action of PACAP receptors has also been shown to play an important role in pain sensation. The PACAP receptors promote the functional coupling of neuronal nitric oxide synthase to N-methyl-D-aspartate (NMDA) receptors, which causes late-onset, transcriptional, and activity-dependent central sensitization to pain (Mabuchi et al., 2004). Following an inflammatory and nociceptive stimulus, PACAP-KO mice show decreased nociceptive behavioral responses. Thus, PACAP may be an intriguing link between vasodilation and dural activation of nociceptive sensory endings (Jongsma

et al., 2001). Migraine attacks are thought to be associated with vasodilation and constriction of the cerebral cortex vessels. The lack of vasodilation and constriction over the cortical cerebral cortex in PACAP-KO mice suggests that dural nociceptors might be a cause of headaches during migraine attacks (Schytz et al., 2010). For example, in healthy men with no history of migraines, intravenous injections of PACAP induced migraine-like aura headaches, decreased cerebral artery blood flow, and increased superficial temporal artery dilation (Schytz et al., 2008).

Mice as Model Organisms The structure of PACAP has been highly conserved over the process of evolution, from proto-chordates to mammals, which strongly suggests that PACAP is an essential peptide involved in the vertebrate phylum. The neurological effects of PACAP receptors have remained consistent through the vertebrate phylum, thus making mice excellent model organisms for further study (Hashimoto et al., 2001).

Positron emission tomography (PET), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) studies of migraine patients show that brain activity, blood flow, and propagation of spreading depolarizations are similar between humans and mice. (Brennan et al., 2007). The link between CSD and migraines is also strengthened by the observation that thresholds for CSD are lower in female mice than male mice, which is consistent with higher rates of migraine in women (Schwedt, 2009). Migraine preventives in humans have been shown to reduce CSD frequency in the mouse model. Thus, the animal model of CSD appears to be a viable platform for

use in screening therapeutic drugs and may share some of the same mechanisms that occur in patients with migraine (Maagdenberg et al., 2004).

METHODS Mice and Cortical Window Preparation Female PACAP-KO mice and female wild-type mice, both strain C57BL/6, were used. Mice were between 3-6 months of age and between 25-30g in weight. Mice were housed in groups of four and given food and water freely. Mice that had hair loss, were clearly stressed, were overweight, or that were kept too long past 6 months were not included in the study.

Out of the approximately 12 PACAP-KO mice and 8 wild-type mice prepared, only 7 PACAP-KO mice and 3 wild-type mice were included. Prior to experiments, all proper training was completed and all guidelines were followed in accordance with the regulations of the Institutional Animal Care and Use Committee.

For each experiment, the mouse was given anesthesia using a mixture of nitrogen/oxygen (2:1) gas and 2.0% isoflurane within an induction chamber.

Once anesthetized, the animal was secured onto a stereotaxic frame and given 1.25% isoflurane to maintain anesthesia.

Isoflurane was adjusted throughout the experiment to keep the mouse breathing unassisted at a rate between 1-2 times a second. Respiration was monitored by a PhysioSuite (Kent Scientific, Torrington, CT). The scalp of the mouse and then the overlying membrane above the skull were carefully removed with forceps and surgical scissors. With a Micromotor Drill (SHIYANG-III with SDE-H37L1 Handpiece, Shiyang Co, Jang-Dong, Ko-

Figure 1. Example of Kymograph used in study. A cross section of a vessel is analyzed on ImageJ for change of diameter over time. This shows us the change in vasodilation following a CSD event. The course of time recorded is a total of 6 minutes, 1 minute prior to CSD event and 5 minutes after the CSD event. A) (Top image) Kymograph of a moderately close to the origin of the CSD. Wild-type from 12/19/12. B) (Bottom image) Kymograph of a moderately close to the origin of the CSD. PACAP knockout from 11/07/13.

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ROLE OF PACAP IN CORTICAL SPREADING DEPRESSION rea), the right parietal bone was thinned to make a transparent “window” to visualize the cortical surface. This cortical window was approximately 4mm by 4mm and allowed visualization of the cortical surface and brain vasculature.

Mineral oil was applied over the cortical window to enhance transparency.

Two penetrating burr holes were drilled through the skull to enable electrode insertions to deliver KCl and to record local field potentials.

Optical Intrinsic Signal Imaging Optical intrinsic signal (OIS) imaging was acquired via a high-definition charge coupled device (CCD) camera (1392x1040 pixels) at 2 frames per second (fps) with a conventional binocular zoom microscope with side camera port (Nikon SMZ 1500, Nikon Instruments Inc., Melville, NY) focused on the cortical surface. Once the mouse was stable, the overhead lights were turned off and black curtains were placed around the Faraday cage to reduce all stray sources of light. The cortical window was illuminated by a single light-emitting diode (LED; 535nm). The images were recorded and then loaded into ImageJ with filters applied to make the CSD wave front easier to track. For example, to obtain the “differenced” image stacks of the CSD, an earlier image was subtracted from later images with a fixed interval of 2 seconds to essentially provide a lowpass filter of the image stack.

Kymographs Analyzing how PACAP is involved in vasodilation during CSD was important to further define the difference between PACAP-KO mice and wild-type mice.

The diameters of cortical cerebral vessels were measured using the acquired OIS images and processed with ImageJ. The kymograph plug-in in ImageJ produced a space-time plot to depict the change in diameter of a given cortical vessel. Kymographs (Figure 1) were created from vessels with a consistent diameter that were at increasing distances from the site of CSD initiation (e.g., the burr hole where KCl was delivered). The diameter of the vessel over a set time interval was recorded, and measurements were taken more frequently at the time of a CSD to

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capture the faster changes in vessel diameters during that period.

Physiological Parameters Throughout the experiment, the physiological condition of the mice was continuously monitored and recorded. A Kent Scientific Physiosuite Monitor recorded body temperature, peripheral oxygen saturation, heart rate, and blood perfusion through an optical sensor placed over a hind paw. A heating pad was set to maintain a body temperature of 37°C. A rectal thermometer was placed to constantly record body temperature, and the heating pad was adjusted automatically to maintain the body temperature. Maintenance levels of isoflurane were adjusted to keep respiratory rate constant.

Figure 2. Normalized LFP of a CSD recovery for wildtype mice. The LFP is recorded following a CSD induced

by KCl injection. The peak of each is event is normalized to the value -1 at t=0, which corrects for variance of gain.

Graph starts at t= -200s to show stability of baseline LFP. Experiment Date

OIS Recovery Time (s)

11/7/2013

146

11/8/2013

126

12/10/2013

150

12/12/2013

172

1/31/2014

156

2/7/2014

132

2/11/2014

136

Experiment Date

OIS Recovery Time (s)

4/27/2013

80

4/30/2013

126

5/17/2013

108

Stimulating and Recording Electrodes Glass electrodes were formed from heated and refined glass capillary tubes.

They were placed into the burrholes, with one in each hole, and used to trigger CSD and to record local field potentials, respectively. The first electrode contained a 1.0 M potassium chloride (KCl) solution which was connected to a custom pressure ejection system, which injected the KCl solution using a TTL trigger into the cerebral cortex for 100ms at 5psi.

The second electrode was a 2 MΩ tungsten metal wire (508 µm diameter tip), which was positioned 300-500 µm below the cortical surface to measure electrical activity. A grounding wire was also inserted proximally to the ear. The electrophysiological readings were amplified and filtered by an AC/DC differential amplifier (AC/DC Differential Amplifier, Model 3000, A-M Systems).

These readings were digitized using a National Instruments A/D board and captured using Signal Express software at 1kHz. All background noise from surrounding electronics was minimized through the use of grounding wires and a Faraday cage.

Table 1. CSD recovery in wild-type mice and PACAP-KO mice. The wild-type mice results (above) can be characterized by a linear recovery model, i.e., R(t) = mt – 1, where R is the normalized LFP.The characteristic recovery time Trec is defined as R(Trec) = -e-2. The PACAP-KO mice results (below) is characterized by an exponential recovery model, i.e., R = - e-t/λ. The characteristic recovery time Trec = 2λ is defined as R(Trec) = -e-2.

Cortical Spreading Depression Baseline local field potential (LFP) was established by waiting at least one hour following electrode insertions. After baseline recording had been maintained for at least 15 minutes, a CSD event was triggered by 1.0 M KCl injection into the

Figure 3. Average CSD recovery time for wild-type mice vs PACAP knockout mice vs PACAP-KO excluding potential outliers based on the local field potential with two standard deviation error bar. The average recovery time for wild-type mice, PACAP knock-out, and PACAP knock-out excluding outliers are graphed along with the error bar of two standard deviation (95% confidence). The outliers excluded include PACAP knock-out mice from 2/7/13 and 2/11/13.

IRVIN LIEN CSD were normalized to the mean of the baseline LFP recording and the peak of the CSD. This is to account for differences between the signal acquisitions gains in each experiment. LFPs were further adjusted to account for noise and baseline LFP drift. The peak of the LFPs was arbitrarily set at time t = 0.

RESULTS

Figure 4. Normalized LFP of a CSD recovery for PACAP knockout mice. The LFP is recorded following a CSD induced by KCl injection. The peak of each is event is normalized to the value -1 at t=0, which corrects for variance of gain. Graph starts at t= -200s to show stability of baseline LFP.

Figure 5: Optical Intrinsic Signal (OIS) imaging during a CSD in wild-type mice, 4/30/13. Images were obtained at 2 fps.The montage shows the progression of the OIS aura in experiment done on 4/30/13 over the course of 4 minutes.

Each image shows progression every 12 seconds (or every 24 frames). Visualized aura from a CSD is induced following cortical KCl injection. Images were obtained over the left hemisphere of the mouse brain. The orientation of the mouse in regards to the OIS imaging is indicated.

cerebral cortex and the OIS signal was measured for changes to ensure CSD had occurred. CSD was observed both as a large DC deflection in the LFP recordings and as a slowly propagating brightening wave originating from the first electrode that expanded in a nearly concentric manner in the video recordings.

Normalizing the LFP from the CSD The electrophysiology readings of the

CSD Recovery Time in LFP Recordings was Significantly Longer in PACAP-KO Mice Compared to Wild-Type Mice Following CSD for wild-type mice (Figure 2), the repolarization back to baseline, i.e., the recovery, is nearly linear in time. A linear model was used to represent the time evolution of the normalized LFP during the recovery for wild-type mice using the equation R(t) = m(t)-1. Here, R is the normalized LFP, t is the time relative to the beginning of the recovery when the normalized LFP recovered from its lowest value, and m = dR/dt is a constant quantifying the rate of recovery. The estimates of the recovery rate m have a mean of 0.030 s-1 and a standard deviation of 0.015 s-1 (Table 1). The characteristic recovery time Trec was defined as the elapsed time for the normalized LFP to change from -1 to the equation R(Trec) = -e-2 = -0.135. The observed characteristic recovery time for wild-type mice Trec had a mean of 45.33 s with a standard deviation of 31.22 s (Figure 3).

For PACAP-KO mice, the normalized LFP recovered at a much slower rate in a near exponential pattern (Figure 4). An exponential model was used to describe the time evolution of the normalized LFP during the recovery for PACAP-KO mice, i.e., R(t) = - e-t/λ, where λ was an exponential recovery time scale. Note that the rate of recovery, dR/dt = -R/λ, depended on the magnitude of normalized LFP. Following the above definition, the characteristic recovery time scale Trec = 2λ such that R(Trec) = -e-2 = -0.135. The characteristic recovery time Trec for the PACAP-KO mice had a mean value of 745.87 s with a standard deviation of 304.09 s (Table 1). However, the standard deviation was reduced significantly to 89.01 s and the mean is 916.34 s if the last two experiments in the PACAP-KO study were excluded from the analysis (Figure 3). The

last experiments had peak DC deflections that remained at the peak value for much longer than what is usually observed in the other wild-type or PACAP-KO trials.

CSD Recovery Time in OIS was Longer but Non-Significantly So in PACAP-KO Mice Compared to Wild-Type Mice The CSD was visualized by changes in OIS following the cortical surface injection of KCl (Figure 5 & Figure 6). The time for CSD recovery was calculated by determining the number of frames it took for the OIS to return to baseline levels (Table 2).

The mean CSD recovery time for wildtype mice was 104.6 s with a standard deviation of 23.18 s. In contrast, the mean CSD recovery time for the PACAP-KO mice was approximately 145.43 s with a standard deviation of 15.74 s. The differ-

Figure 6: Optical Intrinsic Signal (OIS) imaging during a CSD in PACAP knockout mice, 11/08/13. Images were obtained at 2 fps.The montage shows the progression of the OIS aura in experiment done on 11/08/13 over the course of 4 minutes. Each image shows progression every 12 seconds (or every 24 frames).Visualized aura from a CSD is induced following cortical KCl injection. Images were obtained over the left hemisphere of the mouse brain.The orientation of the mouse in regards to the OIS imaging is indicated.

Figure 7. Average recovery seen on optical intrinsic signaling (OIS) with two standard deviation error bar.

The recovery time of the migraine aura in each experiment can be analyzed on ImageJ. The average recovery time for all the wild-type mice with two standard deviation is graphed against the average recovery time of all the PACAP knockout mice with two standard deviation.

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ROLE OF PACAP IN CORTICAL SPREADING DEPRESSION ence between the average mean recovery times was 40.83 s (Figure 7). It is important to point out that there was no appreciable difference in the OIS of the last two experiments as found previously in the LFP. LFPs are recorded from a single region of the cortex whereas OIS recovery is a more global measure taken over the cortical surface.

Vasodynamics are Altered in PACAP-KO Mice Compared to Wild-Type In wild-type mice, the vascular response to a CSD event was immediate, typically within a few seconds. On average, vascular contraction during CSD was at a maximum at 5 s, when small diameter cerebral vessels constricted to 61.2% of baseline diameter. Following vasoconstriction, vessels gradually increased in diameter, and vasodilation was maximized approximately 90 s after the induction of a CSD. On average the maximum vasodilation was a 37.9% increase in diameter. After maximum dilation, the vessels slowly decreased in size and sustained approximately 15% above baseline diameter (Figure 8).

In PACAP-KO mice, there was a slower and smaller constriction of vessels after the CSD event. Vasoconstriction following induction of a CSD was at a maximum at 10 s into the CSD and averaged only 79.05% of the baseline diameter. Following vasoconstriction there was a very slow return back to baseline with no more consistent return to baseline diameter by 30 s. At most, the vasculature only returned to 96.66% of the original baseline diameter (Figure 9).

of recovery and vascular dynamics were affected by PACAP, supporting its importance in neurovascular dynamics. This is evident by the significantly slower LFP recovery time and decreased vasodilation seen in PACAP-KO mice in comparison to the wild-type mice. The slower recovery in PACAP-KO mice suggested that a reduction in PACAP might also result in a decrease in CSD frequency in the continuous KCl paradigm (i.e., fewer CSDs elicited following continuous KCl application). This would be consistent

with other compounds that are known to be effective migraine treatments and that inhibit CSD induction in rodent models.

When comparing the LFP recovery between the wild-type and PACAP-KO mice, the PACAP-KO mice had longer CSDs because of a slower recovery. While the wild-type mice recovered quickly, with repolarization along a linear pattern, the recovery for the PACAP-KO mice appeared to have an exponential repolarization pattern. This difference in repolarization pattern suggested that PA-

Experiment Date

CSD Characteristic Recovery Time Trec (s)

Exponential Constant λ (s)

11/7/2013

895.2392

447.6196

11/8/2013

821.3366

410.6683

12/10/2013

876.661

438.3305

12/12/2013

929.0024

464.5012

1/31/2014

1059.4758

529.7379

2/7/2014

405.0316

202.5158

2/11/2014

234.3456

117.1728

Experiment Date

CSD Characteristic Recovery Time Trec (s)

Constant Recovery Rate m(s-1)

4/27/2013

21.6

0.047

4/30/2013

80.7

0.018

5/17/2013

33.7

0.026

Table 2. Recovery time based on resolution of visualized migraine aura in wild-type mice and PACAP-KO mice.

Images from the OIS imaging is analyzed on ImageJ. The initial onset of the migraine aura is associated with brightening aura and resolves once cortical surface returns to baseline intensity. Wild-type mice recovery is given above, while PACAP-KO recovery is given below.

DISCUSSION PACAP might play an important role in migraine pathophysiology. If true, then further understanding of the functions of PACAP might lead to PACAP and/or its receptors becoming targets for the therapeutic alleviation of migraine attacks. We hypothesized that there would be significant differences in vasodilation between PACAP-KO mice and wild-type mice during CSD. The above preliminary data support the hypothesis insofar as a lack of PACAP in PACAP-KO mice greatly affected CSD, the putative mechanism for migraine aura. Both electrical measures

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Figure 8. Average percentage change in vasodilation/vasoconstriction of kymographs for wild-type mice by experiment. Kymographs are obtained from the OIS imaging. The diameter of the vasculature at 6 different locations are measured at t = {-30, 0, 2, 5, 10, 15, 30, 45, 60, 75, 90, 120, 180, 240, 300}.The 6 locations are averaged for each experiment.

IRVIN LIEN

Figure 9. Average percentage change in vasodilation/vasoconstriction of kymographs for PACAP knockout mice based by dates. Kymographs are obtained from the OIS imaging.The diameter of the vasculature at 6 different locations are measured at t = {-30, 0, 2, 5, 10, 15, 30, 45, 60, 75, 90, 120, 180, 240, 300}. The 6 locations are averaged for each experiment.

Figure 10. Average percentage change in vasodilation/vasoconstriction of all kymographs for PACAP knockout mice and wild-type mice. Kymographs are obtained from the OIS imaging.The diameter of the vasculature at 6 different locations are measured at t = {-30, 0, 2, 5, 10, 15, 30, 45, 60, 75, 90, 120, 180, 240, 300}. All the kymographs from PACAP knockout mice are averaged against all the kymographs for the wild-type mice. The standard deviation is included to illustrate statistical significance.

CAP deficiency fundamentally changed the recovery mechanism. The mean recovery time for wild-type was 45.33 s with a standard deviation of 31.22 s. In

comparison, the mean recovery time for PACAP-KO mice was 745.87 s with a standard deviation of 304.09 s. The recovery for PACAP-KO mice was nearly

20 times slower than that for wild-type mice. Normal distribution was used to analyze the statistical significance of the data, where Z-score = mean (µ) +/- 2 standard deviation (σ). By comparing the data, there was a significant difference between the recovery of the CSD in wild-type mice compared to PACAP-KO mice, with a 95% confidence interval (P<0.05) or 2 standard deviations. The difference was even more evident when excluding the outlier PACAP-KO data experiments from 2/07/14 and 2/11/14.

Although eliminating samples from our already small sample size is not ideal, the LFP data is qualitatively different compared to the other data sets obtained.

The unusual sustained depolarization in the CSD on both 2/07/14 and 2/11/14 might indicate possible experimental errors such as concurrent CSDs, oversaturation of the KCl injection, or other yet unidentified factors. If these data are excluded from the analysis, the CSD recovery time increases to a mean of 916.34 s and the standard deviation decreases dramatically from 304.91 to 89.01 s. Regardless of the increased variability, all of the PACAP-KO trials have significantly slower recovery curves when compared to the wild-type controls. The significant differences in CSD recovery rate and in the characteristic recovery time supported the notion that PACAP contributed to re-normalizing ionic homeostasis, presumably by restoring blood flow to the area. Although highly speculative, it may be the case that there is an optimal rate of recovery of normal function following CSD that might restore function without activating nociceptive systems.

Analysis of the OIS signal showed a slight difference between the wild-type mice and PACAP-KO mice, though not statistically significant. The OIS recovery time for wild-type was 104.6 s with a standard deviation of 23.18 s. In contrast, the OIS recovery time for the PACAP was approximately 145.43 s with a standard deviation of 15.37 s. Once again, the data was analyzed using z-score = mean (µ) +/- 2 standard deviation (σ), resulting in a statistically insignificant difference.

However, the difference was significant with a 68% confidence or 1 standard deviation. Although the OIS changes occurred reliably with CSD, a shorter

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ROLE OF PACAP IN CORTICAL SPREADING DEPRESSION duration of OIS recovery did not correspond to the shorter CSD recovery seen on the LFP. This is important to note, as the OIS changes should correspond to the LFP recovery. A possible explanation for this phenomenon is that the OIS changes may be too miniscule to reliably measure changes in depolarization or that the changes were better characterized by the LFP because it captured activity from a small region versus the more global OIS signal. We did not examine local changes in OIS that may have tracked with the LFP observations.

As PACAP is a known vasodilator, it was expected that the wild-type mice would have increased vasodilation as compared to the PACAP-KO mice. Analyzing the kymographs confirmed this difference. In the wild-type mice, there was vasodilation over 79.05% increase to baseline cortical vessel diameter (Figure 10). The sustained increase in diameter size was suggestive of the role of PACAP in vasodilation. In stark contrast, there was no vasodilation over baseline in PACAP-KO mice. This was consistent with the fact that there was no activation of the PACAP receptors to dilate the vessels.

The lack of vasodilation following a CSD in PACAP-KO mice suggested that PACAP was the primary activator for vasodilation and that there were not sufficient compensatory mechanisms in the rodent.

Interestingly, the vasoconstriction immediately following CSD was different between wild-type mice and PACAP-KO mice. While there was 37.8% vasoconstriction in wild-type mice, there was only 21.95% vasoconstriction in PACAP-KO mice. Although the difference was small, it indicated that PACAP-KO mice might have reduced vasoconstriction processes as a homeostatic way to compensate for the lack of PACAP-mediated vasodilation.

Overall, the results suggested that PACAP plays a significant role in the kinetics of CSD. The significance of the results further supports the importance of investigating PACAP as a therapeutic target for the treatment of migraines. Headaches are often associated with cerebral vasodilation, and inhibition of PACAP could result in the reduction of vasodilation and a decrease in the activation of nociceptive pathways. This is confirmed

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in our data that have shown PACAP-KO mice have slower LFP recovery and reduced vasodilation.

The slower recovery rate following a CSD in PACAP-KO mice seen in the LFP might also play a role in the reduction of the frequency of CSD events. Migraine attacks are often associated with CSD-like activity. A slower repolarization of a CSD would indicate that more time would be required for a second depolarization. Although this study did not focus on concurrent CSDs, future studies could directly address this issue.

Future experiments can be improved by various methods. Unusable data resulted from several factors including adverse reactions to electrode placement, to anesthesia, or to single CSDs leading to terminal states. Better methods of maintaining the subject at minimal anesthesia, or using awake animals, might better reflect normal neurological function. This can be accomplished by finding a more consistent method of triggering CSDs, such as optogenetics, or a less invasive method for measuring LFPs. The number of successful experiments were relatively low, and increasing the number of successful experiments from 3 wild-type mice and 7 PACAP-KO mice would help to more firmly establish the role of PACAP during CSDs. That said, the delayed recovery is a robust observation as were the changes in vascular dynamics. The preliminary data suggest that increasing the sample size, and using a more reliable and less invasive stimulus to induce CSD in PACAP-KO mice is warranted. Using other genetically modified mice with PACAP receptor subtypes modified or eliminated would more quickly establish results and help better characterize the PACAP involved in CSD pathophysiology.

ACKNOWLEDGMENTS I would like to thank the UCLA Goldberg Migraine Program for access to facilities to complete my undergraduate research. Most importantly, I would like to thank my faculty mentors, Dr. Andrew Charles and Dr. Serapio Baca, for providing me with the resources to independently conduct experiments and for assisting me in better understanding the mechanisms of migraines. I would like

to thank Dr. James Waschek for providing the PACAP-KO mice for our studies.

I would also like to thank Dr. Ren-chieh Lien for assistance with the linear and exponential recovery models.

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Smads1/5/8 in the BMP Signaling Pathway in the Absence of Smad4 Nancy Morones, Diana Rigueur, Dr. Karen Lyons1 Bone Morphogenetic Protein (BMP) signaling plays an essential role in the development and maintenance of bone and cartilage. However, the mechanisms by which BMP signaling pathways regulate proliferation, differentiation, and apoptosis have yet to be completely elucidated. In the canonical BMP signaling Smad pathway, the intracellular receptor proteins Smads 1, 5, and 8 (R-Smads) transduce the signal by translocating to the nucleus with cofactor Smad4 to activate BMP target genes. Smad4 is thus an essential component of canonical BMP signaling in most tissues during development. However, this study provides evidence that Smad1/5/8-dependent, Smad4-independent BMP signaling occurs in vertebrates, and in the context of the cartilage growth plate, may be a major mode of BMP signaling. Cartilage-specific loss of Smads1/5 in mice leads to severe chondrodysplasia, yet cartilage-specific Smad4-knockout mice are viable and display a much milder cartilage phenotype. Based on these findings, it was hypothesized that R-Smads1/5/8 act as transcription factors in the absence of Smad4.

Through immunofluorescence, qRT-PCR, and western blot analysis, targets of canonical and non-canonical BMP pathways, such as phosphorylated R-Smad1/5/8, were shown to be elevated. Indian Hedgehog (IHH), a gene imperative for growth plate development, responded to BMP stimulation in isolated Smad4-deficient primary chondrocytes, albeit at a reduced level. Overall, these data exemplified that Smad4 might be dispensable in cartilage. Additionally, this study calls for a re-evaluation of the putative BMP canonical pathway and the extent of BMP signaling.

INTRODUCTION The Bone Morphogenetic Protein (BMP) signaling pathway plays a major role in guiding skeletal development by mediating cell growth, apoptosis and differentiation (Wang et al., 2014, Chu et al., 2006). It was first discovered to be involved in skeletal development but has since been found in other systems such as cardiac and neuronal development (Yuasa and Fukida, 2008; Leblanc et al., 2013).

During skeletal development BMP signaling is involved in orchestrating complex signals necessary for formation of cartilage and bone. In endochondral development, or long bone development, mesenchymal cells condense and differentiate into chondrocytes. These chondrocytes progress through a program of differentiation, where they form a hyaline model for skeletal elements that develops into a more defined compartmentalized structure called the growth plate. A growth plate has three stratified zones: resting, proliferative chondrocytes, and hypertrophic

chondrocytes (Kronenberg, 2003). Errors in developmental BMP signaling can result in death or disease. One well-known disease attributed to a point mutation in one Smad4 allele is Myhre Syndrome, where the affected individuals display defects in skeletal growth (Feldman, 2006, Caputo et al., 2014). Moreover, improper or unregulated BMP signaling can also affect adulthood in vertebrates, and has been found to contribute to diseases such as osteoarthritis and cancer (Virtanen, 2011). Osteoarthritis is one of the leading causes of disability in the United States, with no clinical cure and little definitive understanding of its causes (Guccione et al., 1994). Therefore, studying the cellular pathways involved in chondrogenesis and bone development, such as Bone Morphogenetic Protein (BMP) signaling pathways, is important to find possible treatments for developmental dysplasias and osteoarthritis.

The BMP canonical signaling pathway becomes activated when the BMP ligand binds to the type I and Type II BMP receptors, which dimerize and

transphosphorylate (Kronenberg, 2003).

Receptor activated transcription factors (R-Smads), such as Smads1/5/8 are needed to transduce BMP pathways, while Smads2/3 mediate TGFβ signaling (Song et al., 2009). The Smad complexes are phosphorylated by the type I receptor, cofactor with Smad4, and translocate to the nucleus to activate BMP or TGFβ target genes (Figure 1).

Signal transduction mediated by Smads is known as the canonical pathway while signaling through TGFβ Activated Kinase 1 (TAK1) is known as the non-canonical pathway (Greenblatt et al., 2010). As per the central dogma of molecular biology, Smad4 should be necessary to transduce BMP canonical signals. However, there is some evidence in Drosophila that R-Smads transduce a limited amount of signaling in the absence of Smad4. Smad1/5dependent, Smad4-independent BMP signaling does occur in vertebrates, and in the context of the cartilage developing growth plate, might be the major mode of BMP signaling (Retting et al., 2009).

Using mice, which are common

Department of Orthopedic Surgery.

1

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NANCY MORONES Smad interactions at the mothers against decapentaplegic (MAD) box domain of the IHH promoter using Smad4 and Smad1/5/8 knockout mice chondrocytes.

It is predicted that Smad1/5/8 might bind directly to palindromic GC-rich domains in the IHH promoter.

METHODS Mouse Strain Smad4 cartilage-specific mutants were generated using the cre-loxP recombination system driven by the Collagen 2 promoter, described by Ovchinnikov et al. and the Smad4 floxed allele described by Chu et al. (Smad4fx/ fx;Col2a1Cre; hereafter referred to as Smad4CKO). All mice were housed and bred according to the National Institutes of Health (NIH) guidelines for care, and the use of the animals was under the approval of the UCLA Institutional Animal Care and Use Committee (IACUC), ARC #10095-018-52A.

Figure 1. Induction of BMP-responsive genes (IHH, IDP1, Noggin, Msx1) transduction pathway may not necessarily depend on Smad4 (adapted from Greenblatt et al., 2010). Once the BMP ligand binds to the receptor, R-Smads 1/5/8 become phosphorylated at the receptor and form the Smad1/5/8 complex. In canonical signaling, they then cofactor with Smad4 and translocate into the nucleus to act as transcription factors for BMP targets. Our suggested model would result in Smad 1/5/8 translocation and transcriptional activity in the absence of Smad4.

model organisms for cartilage and endochondral/long bone development, cartilage-specific loss of Smads1/5 led to severe chondrodysplasia and prenatal lethality, yet cartilage-specific Smad4knockout mice were viable and presented a much milder cartilage phenotype (Retting et al., 2009; Zhang et al., 2005).

Although Smad4 function is generally required in most systems and mammals, it appears to be largely dispensable for BMP signaling in bone development, forcing a reconsideration of Smad4’s role in cartilage and bone development and the extent of BMP signaling (Zhang et al., 2005, Wang et al., 2014).

In this study using Smad4 cartilage33 | UNDERGRADUATE SCIENCE JOURNAL

specific knockout mice, Smad4 was determined to not be required for the canonical BMP signaling pathway in the context of the cartilage growth plate: Smad4 enhances the binding of Smad1/5/8 and therefore induces the BMP signaling pathway. However, how Smad4 enhances Smad1/5/8 activity is not well understood. We hypothesized that Smad4 potentiates Smad1/5/8 recruitment to the Indian Hedgehog (IHH) promoter but might not be necessary for Smad1/5/8 dependent IHH expression. Rather, the Smad 1/5/8 complex might be primarily responsible for the transcription of BMP target genes. This study aims to investigate

Genotyping A mixture of Phenol (Sigma-Aldrich, MO):Chloroform (Amresco, TX):Isoamyl Alcohol (Fisher Scientific, CA) (25:24:1 ratio) and a solution of ethanol (Decon Labs) were used to isolate and extract genomic DNA. Polymerase chain reaction (PCR) was performed using the following cre and Smad4 primers from Invitrogen (Table 1). PCR products were then visualized on 1% or 2% agarose gels (Fisher Scientific).

Quantitative Polymerase Chain Reaction (qPCR) For PCR, SYBR green (SG) PCR reagent was used to detect relative or absolute gene expression levels of Ihh. Reactions were run using a quantitative PCR (qPCR) machine (Agilent Technologies, Statagene MX3005P), with alternating annealing temperatures of 56°C and 55°C for multiple cycles. Fluorescent readings were read on MxPro and exported into excel and then normalized with a wildtype sample and also normalized to a house keeping gene such as tubulin using the Delta Delta cT method to detect fold expression differences.

Immunofluorescence (IF) The mice were sacrificed, and their

SMADS1/5/8 IN THE BMP SIGNALING PATHWAY IN THE ABSENCE OF SMAD4

A)

B)

C)

D)

Figure 2. Western blot analysis shows activation of R-Smad 1/5/8 proteins in the absence of Smad4. A) Western analysis of Smad4, pSmad1/5/8 and pSmad5 expression in primary rib chondrocytes from Smad4fx/fx mice treated with Adeno-GFP or Adeno-Cre and subsequently maintained in the presence or abssence of BMP2. Smad4 is detected in Smad4fx/fx chondrocytes treated with Adeno-GFP, but is ablat-ed in Smad4fx/fx chondrocyte treated with Adeno-Cre, confirming efficient Smad4 depletion. pSmad1/5/8 and pSmad5 are not detected in WT (Smad4fx/fx;Adeno-GFP) chondrocytes are induced by BMP2. In contrast, levels of pSmads1/5/8 and pSmad5 are elevated in both the presence or absence of BMP stimulation in Smad4-deficient (Smad4fx/fx;Adeno-Cre) chondrocytes. B) Quantification of pSmad5 levels using ImageJ analysis of Western blots. C) ImageJ quantification of Smad4 levels in WT and Smad4-deficient (Smad fx/fx; Adeno-Cre) D) ImageJ quantification of pSmad1/5/8 levels. ** p < 0.05 All analyses were performed in triplicate.

hind limbs were harvested and fixed in a 4% paraformaldehyde (PFA) solution.

Tissues were dehydrated in ethanol followed by xylene and then embedded in paraffin blocks. Tissue sections of 5 µm thickness were collected using a microtome. For IF, sections of embedded tissue were heated at 47˚C for 2 hours, left overnight in 100% xylene, and then rehydrated the following day. The slides were placed in citrate buffer (pH 6 Vector Laboratories, H-3300) to boil in the microwave for 1 minute and then at 98˚C for 15 minutes. The slides were then washed with 1x Tris-buffered saline with 2% Tween-20 for 15 minutes, and followed by several water washes before the sections were lined with a lipid pen.

At this point, slides were blocked in 10% goat serum (in Tris-buffered saline with 2% Tween-20) for 40 minutes and left overnight in 1:100 antibody dilution (Invitrogen, A4566) at 4˚C or 4 hours at room temperature. After washing the slides to remove the primary antibody, the secondary biotinylated anti-rabbit antibody (Vector Laboratories, Cat. No. BA-9401) was added for 30 minutes and then washed. The fluorescent Streptavidin Alexa Fluor 488 conjugate (Thermofisher, S11223) was added for 30 minutes while the slides were covered. Slides were washed while covered in a hybridization chamber, mounted with Fluoro-Gel (Electron Microscopy Sciences, Cat. No. 17985-30), coverslipped, and photographed with teal light from a mercury lamp.

Isolation of primary chondrocytes and treatment with Bone Morphogenetic Protein-2

Primary chondrocytes were isolated from Smad4CKO nascent mice and seeded at 1×105 cells/well in 12-well plates.

Cells were maintained in chondrogenic medium for 3-4 days. Because mutants were rare, we opted to use the Smad4 fx/ fx chondrocytes and drove recombination using adenoviral cre infection for the majority of our culture experiments. The primary chondrocytes from Smad4fx/ fx cells were either infected for 24 hours with adenoviral Cre-recombinase (AdCRE) or adenovirus expressing GFP (AdGFP) as a control (University of Iowa Gene Transfer Vector Core, Iowa City, IA) at a multiplicity of infection of 300.

DNA was isolated using aforementioned genotyping methods, and PCR was performed to verify Smad4 excision.

Cells were treated with BMP2 for 2 hours, and lysates were collected for western blot analysis.

Western Blot Lysates were prepared with 2x Tris Base Laemmli Sample buffer with 10% 1M dichlorodiphenyltrichloroethane (DTT). The samples were heated at 90˚C for 5 minutes, run on a 10% SDSPAGE gel, and then transferred onto a Polyvinylidene fluoride (PVDF) membrane. These membranes were then blocked in 5% BSA in Tris Buffered Saline with Tween 20 (0.2%) (TBST) and probed with Anti-pSmad1/5/8 (Abcam, 1:2000, Cat. No.9511) and pSmad5 (Abcam, 1:2000, ab92698) overnight. Membranes were washed and probed by a secondary goat antirabbit HRP-conjugated antibody (1:2000, Bio-Rad, Cat. No. 170-6515) for 30 minutes, followed by a 15-minute

treatment in HRP substrate (Pierce ECL 2, 80196), and then left exposed for 30 seconds using the Bio-Rad-ChemiDoc Imager or film (Amersham Hyperfilm ECL, Cat No. 28906835). Blots were quantified using ImageJ, and averages and standard deviation were measured in Microsoft Excel in triplicates. A t-test (p-value<0.05) was used to validate significant differences in the data.

IHH promoter mutagenesis Primers were used to implement the two point mutations in the 740bp IHH luciferase promoter (Qiagen, CA).

After PCR with this construct and the primers that implemented the nucleotide changes, bacteria were transformed via heat shock at 42˚C, plated, and incubated overnight on LB agar-ampicillin plates.

Ten colonies were picked and grown overnight in LB medium containing ampicillin. Plasmid DNA was purified using Qiagen columns. Incorporation of the targeted mutations was confirmed by sequencing (Laragen, CA).

RESULTS Receptor Smads1/5/8 are both active and translocated to the nucleus in the absence of Smad4 Our first goal was to determine whether or not endogenous levels of Smads 1, 5, and 8 are affected in the absence of Smad4.

Using western blot analysis on Smad4CKO and wildtype littermate lysates, we found that their total levels remained the same.

Next, we wanted to test whether or not they were in activated phosphorylated form. To test whether there were changes in Smad activity in the absence of Smad4, UNDERGRADUATE SCIENCE JOURNAL | 34

NANCY MORONES

A)

A)’

C)

B)

B)’

D)

Figure 3. Immunofluorescence shows decreased IHH and Cyclin D levels. Sections through proximal tibias of E18.5 (A, B) WT and (A’,B’) Smad4CKO littermates immunostained for IHH (A, A’) and its downstream target cyclin D (B, B’) are both downregulated in the absence of Smad4. (C) IHH Luciferase reporter assay performed in WT (Smad4fx/ fx;Adeno-GFP) vs Smad4-deficient (Smad4fx/fx;Adeno-Cre) chondrocytes. IHH-Luciferase activity is increased by BMP in WT cells, but not in Smad4-deficient cells (D) Chromatin immunoprecipitation assay showing that pSmad 1/5/8 is bound to the IHH promoter i-n both WT and Smad4-deficient cells, but that this binding is not induced by exogenous BMP2 in Smad4-deficient cells.

Figure 4. IHH Promoter contains GC-rich regions that may enable Smad 1/5/8 binding. Primer that was made is in grey forward and reverse. The region changed in blue (the palindrome) was mutated to GaaGCCG.

an in vivo immunofluorescence assay for pSmad1/5/8 in the growth plate of Smad4CKO mice was conducted. The assay found that R-Smads were located in the nucleus, as verified by pSmad1/5/8 and DAPI fluorescence dye overlap. However, the assay did not verify a basal level of Smad1/5/8 presence in the nucleus. In order to quantify pSmad1/5/8 levels, we collected whole cell lysates of primary Smad4fx/fx chondrocytes treated with AdenoCre targeting the Smad4 allele for recombination. Activated pSmad1/5/8 from wildtype (WT) (Smad4fx/fx, 35 | UNDERGRADUATE SCIENCE JOURNAL

AdenoGFP) and Smad4-deficient (Smad4fx/fx;AdenoCre) chondrocytes were quantified using a western blot for pSmads1/5/8 in in response to exogenous BMP2. An increase in pSmad1/5/8 levels in the absence of Smad4 was noted in Smad4-deficient chondrocytes, (vv 2, A and D), and this increase was seen in both the presence and absence of exogenous BMP2.

Probing for pSmad5 levels also showed elevated levels in Smad4-deficient chondrocytes, compared with wildtype (Figure 2, A and B). In both cases, Smad4

levels were greatly reduced in our CKO chondrocytes (Figure 2C). Higher levels of both pSmad1/5/8 and pSmad5 were also found in Smad4-deficient mutants than in WT littermates, when comparing active pSmad1/5/8 and pSmad5 levels (Figure 3).

To challenge the dogma of canonical BMP signaling, in which Smad4 is a cofactor within the Smad1/5/8 complex required for R-Smad mediated transcription, the location of Smad1, whether mainly in the cytoplasm or in the nucleus, could suggest the function of R-Smads in the absence of Smad4.

Through immunofluourescence on sections of growth plates of Smad4CKO mutants, the translocation of Smad1 into the nucleus was confirmed and documented by an overlap of Smad1 with DAPI staining in the nucleus.

Immunostaining showed a decrease in IHH expression in Smad4CKO mice, although staining was not abolished.

Unexpectedly, increased levels of pSmads1/5/8 were not due to increased mRNA levels, which were also measured in these mice, suggesting that R-Smads might be activated mainly at the protein level through a different unknown pathway as a compensatory mechanism.

IHH expression does not require Smad4 Next, the transcription of R-Smad target genes in the absence of Smad4

SMADS1/5/8 IN THE BMP SIGNALING PATHWAY IN THE ABSENCE OF SMAD4

A)

B)

Figure 5. Both TAK1 and PPR promoter regions contain GC-rich sites that may allow Smad1 binding at the MH2 region. A) Schematic diagrams of the proximal TAK1 (upper panel) and PPR (lower panel) promoters. Both TAK1 and PPR promoters have either GC-rich or palindromic regions (pal) where Smad1 may bind. B) Primers for both were generated to probe different regions of each promoter.These can be used in future experiments for ChIP experiments to detect Smad binding to various promoter regions of the genes.

was investigated. One well-characterized BMP and Smad1/5/8 dependent target is the IHH gene (Figure 4), an established marker of pre-hypertrophy, which occurs after cells exit the proliferative phase of chondrocyte differentiation. Qualitative analyses of IHH expression patterns along the growth plate were visualized with IF, which showed a slightly smaller domain of expression in the pre-hypertrophic region. Additionally, a decrease in the the domain of expression of Cyclin D1, a transcriptional target of IHH, was also observed (Figure 3). Nevertheless, genome sequences of IHH using Basic Local Alignment Search Tool (BLAST) show potential Smad 1/5/8 binding (Figure 4), and these regions were mutated for future functional assays. IHH might depend on Smad1/5/8 binding at its GCrich palindrome sequences in order to have proper expression; however, further testing may be necessary.

DISCUSSION The BMP signaling pathway has been demonstrated to be pivotal in skeletal development as well as degenerative diseases such as osteoarthritis. The nuanced differences between its canonical Smad pathway, which this study focused on, and all of its noncanonical pathways have not been fully elucidated. In the conventional mechanism for BMP signaling, the interactions of effectors R-Smads1/5/8 with cofactor Smad4 are necessary for the R-Smads to then act as transcription factors. Our findings suggest that IHH may be a target of Smad1/5/8 that can be regulated by BMPs in a Smad4independent manner. Our findings thus challenge the canonical dogma by providing evidence of continued BMP target activation and Smad activity

in the absence of Smad4 in cartilage.

Nevertheless, the mechanisms by which Smads1/5/8 act as transcription factors in the absence of Smad4 remain unclear.

Based on previous analysis of the IHH promoter, R-Smads1/5/8 might directly bind to the GC-rich palindrome region on the IHH promoter (Seki and Hata, 2004). Preliminary ChIP analysis findings indicated Smad1/5/8 binding to the IHH promoter in the absence of Smad4, and similar results were suggested for TAK1 expression (Retting et al., 2009). Other BMP response targets might exhibit similar regulation. We are particularly interested in the PTHrP receptor (also known as Pth1r and/or PPR) which is suspected to be a direct target of BMP signaling and also contains GC-rich regions where Smad1, 5, and 8 may potentially bind (v). Furthermore, it was described to have a signaling loop with IHH expression in chondrogenesis (Kronenberg 2006). Elucidating the mechanisms by which Smad1/5/8 mediate BMP signaling in the absence of Smad4 may help understand the processes required to form and maintain cartilage.

To determine whether other potential targets of BMP signaling also exhibit Smad1/5/8-dependent, but Smad4independent responsiveness to BMPs, the TAK1 gene may be investigated. To gauge whether Smad1/5/8 could potentially bind to the TAK1 promoter region, the ENSEMBL genome browser was used to identify the transcription start site and an upstream 1 kb region near a TATA box in the mouse promoter region sequence from the Santa Cruz genome database (Figure 5). A GCCGnCGC motif homologous to the Drosophila MAD box found in BMP/Dpp target genes as characterized by Kenji Seki and Akiko Hata (2004) was identified. This

sequence is an important site for BMPtargeted IHH transcription mediated by R-Smads1/5/8 (Kim et al., 1997). Sitedirected mutagenesis was currently being conducted on the putative Smad1/5/8 binding sites, the GC-rich palindromes, in the IHH and TAK1 promoters. These constructs could be used to determine whether the identified GCCGnCGC motifs mediate Smad1/5/8-dependent, Smad4-independent BMP signaling.

Our future studies can potentially explain the anomalous abilities of Smads 1/5/8 to mediate BMP responses in tissues such as cartilage and may perhaps also gather insight on other irregular systems such as cancer, that worsen in the absence of Smad4.

ACKNOWLEDGEMENTS This work is funded in part by a National Institutes of Health/National Institute of General Medical Sciences training grant (NIH MARC T34 GM008563, PI: D.D. Simmons) and by NIH R01 AR044528 to K.M. Lyons.

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Altered Molecular Expression in Myofibroblasts: Implications of Cardiac Arrhythmias Minu Madhvani, Dr. Thao P. Nguyen1 Cardiac injury can result in enhanced crosstalk features between cardiomyocytes and myofibroblasts.This can result in increased susceptibility of myocytes involved in the enhanced communication to become arrhythmogenic and can spread the arrhythmogenic quality to adjacent myocytes via propagation. The enhanced interaction between myofibroblasts and myocytes are due to the myocyte-like characteristics that the myofibroblasts develop as a result of fibrosis. These qualities distinguish healthy fibroblasts from myofibroblasts, which may include increased gap junction component expression, specifically Connexin43 (Cx43) and Connexin45 (Cx45), and enhanced paracrine signaling factor secretion compared to healthy fibroblasts. It was hypothesized that if increased expression of Cx43 and Cx45 occurred in hearts subjected to chronic stress, fibrosis would ensue, compared to normal, healthy hearts. It was expected that there would be an increase in paracrine factor signaling, specifically Angiotensin II (Ang II) and Transforming Growth Factor-beta (TGF-ß). It was found that the fibrotic hearts may correlate to higher expression of Cx43 and Cx45. Exceptions, such as the spontaneous hypertensive rat (SHR) that showed less Cx43 expression than the healthy heart, may be due to the degree of fibrosis that occurs with certain stress conditions. Diabetic and aged rat models showed significant Cx43 and Cx45 expression compared to the healthy heart.This increased expression correlated to potential for gap junction formation between myocytes and myofibroblasts, which can provide reasoning behind arrhythmogenesis in fibrotic hearts and none in healthy hearts. None of the rat models of young, SHR and aged showed any TGF-ß secretion, and Ang II levels showed to be relatively the same across all samples.The study concluded that enhanced expression of Cx43 and Cx45 have a relative impact on fibrosis that can be associated with amplified interaction between myofibroblasts and myocytes.

INTRODUCTION Sudden cardiac death due to ventricular tachycardia and/or fibrillation (VT/ VF) is a worldwide health problem, with about 325,000 cases of sudden cardiac death occurring each year in the United States (CNN). Although clinical and experimental findings demonstrate a correlation between VT/VF and myocardial fibrosis, a causal relationship between the two remains elusive. It has been commonly accepted that fibroblasts contribute passively to the emergence of VT/VF by creating structural barriers that interrupt electrical conduction. However, in light of recent in vitro findings of direct coupling between healthy cardiomyocytes and myofibroblasts via connexin (Cx)based gap junctions or nanotubes, a more active role of fibroblasts has come into

question (Vasquez et al., 2010; Dixon et al., 2011). Here, this study aims to provide molecular evidence for the contribution of cardiac myocyte-myofibroblast gap junction coupling as well as for the contribution of paracrine factors secreted by myofibroblasts in the increased arrhythmogenicity of fibrotic hearts.

Following cardiac injury, communication between myofibroblasts and myocytes is enhanced (Vasquez et al., 2010), resulting in increased vulnerability of neighboring myocytes to arrhythmia triggers. Gap junction coupling (Vasquez et al., 2010), (Rook et al., 1989, 1992), (Dixon et al., 2011) and paracrine signaling (Pedrotty et al., 2009) from myofibroblasts have been shown to contribute to the communication between these two cell types. Gap junctions are intercellular connections bridging the cytoplasms of

Department of Cardiology, Cardiovascular Research Laboratory, University of California, Los Angeles.

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two cells, thus allowing rapid exchange of ions and macromolecules. From each cell arises a connexon, or hemichannel, and together the two connexons comprise a gap junction. Each connexon is composed of six Cx subunits, and can either be homomeric (uniform Cx subunit type), or heteromeric (different Cx types).

The connexon is the larger structure composed of Cx and is the main protein complex that makes up the infrastructure of gap junctions. Cx40, Cx43, and Cx45 are the primary Cx subunits found in the heart. The Cx40 subunit is predominantly expressed by atrial myocytes, whereas the Cx43 and Cx45 subunits are expressed by ventricular myocytes. Fibroblasts in the young healthy heart express few to no Cxs. In contrast, myofibroblasts in the fibrotic heart express higher levels of Cxs. Similar to myocytes, myofibroblasts

ALTERED MOLECULAR EXPRESSION IN MYOFIBROBLASTS: IMPLICATIONS FOR CARDIAC REMODELING AND ARRHYTHMIAS express different Cx types depending on their cardiac chamber of origin.

Atrial myofibroblasts typically express Cx40 (Oyamada et al., 2013), whereas ventricular myofibroblasts primarily express Cx43 and Cx45 (Zhang et al., 2008), (Oyamada et al., 2013). (Myo)fibroblasts are mostly a mixture of fibroblasts and myofibroblasts. Some, not all, change into myofibroblasts depending on the degree of fibrosis.Since the expression of Cxs is unusual for (myo)fibroblasts, this may elucidate why myofibroblasts form enhanced physical interactions with myocytes as a recuperative mechanism but are more deleterious than beneficial to the heart, resulting in the development of arrhythmias.

In a heart that develops fibrosis from natural aging or disease, myofibroblasts arise de novo or through transformation of either activated fibroblasts or mesoendothelial cells. The rate of occurrence is random; as a result of hypertension or diabetic stress on the heart, myofibroblasts originate in the process called fibrosis. In culture, fibroblasts undergo transformation into myofibroblasts. Myofibroblasts are at least ten times larger than fibroblasts and much more metabolically active.

Myofibroblasts express abundant Cx and new proteins during the differentiation, such as alpha-myosin, which allow myofibroblasts to contract as an adaptive mechanism following injury to the heart and consequent loss of myocytes as the fibroblasts develop more myocyte-like qualities, hence the added prefix in this study’s usage of “(myo)fibroblasts.”

Additionally, myofibroblasts secrete abundant paracrine factors, many of which are arrhythmogenic (Vasquez et al., 2010), such as transforming growth factor beta (TGF-β), Angiotensin II (Ang II), and interleukin 33 (IL-33) (Kendall et al., 2014). These original fibroblasts do not affect normal cardiac function until differentiation into myofibroblasts, because the potential for myocytes to physically interact with each other is much higher. TGF-β and Ang II are found in various areas throughout the body in conjunction with wound healing, but the derivation of these factors from cardiac fibroblasts has the potential to interrupt the rhythmic and electrical functions

Day

Day 1

Day 2

Day 3

Average

Blood Glucose Reading

541

567

528

~545

Table 1. Evidence for Diabetic condition: Blood Glucose Readings for ZDF rat before cardiac cell isolation.

Glucose measurements for three consecutive days between the hours of 5 PM and 7 PM PST for ZDF rat that provided fibroblast cell lysate sample for the Western blot. Blood sample for measurement were taken from the tail vein of the ZDF rat. Alpha Trak 2 blood glucose monitoring system meter was used to take measurements and was calibrated to a setting compatible for rats.

of the heart. It is hypothesized that in the fibrotic diseased or aged hearts, fibroblasts respond to stress to promote the emergence of VT/VF by at least one of two mechanisms: (myo)fibroblast direct gap-junction coupling with myocytes and/or paracrine signaling from fibroblast-derived factors. To demonstrate the contribution of each mechanism, the levels of Cx43 and Cx45 expressed were quantified and compared between fibroblasts in healthy young hearts and those expressed by myofibroblasts in rat fibrotic hearts affected by normal aging or cardiovascular diseases, such as hypertension, diabetes, and obesity. It is expected that in the fibrotic diseased hearts with active robust inflammation, Cx43- and Cx45-based gap junctions may exceed those found in the fibrotic aged hearts with low levels of chronic inflammation. This is most likely due to the heart being subjected to prolonged, active exposure to stress, and therefore, can result more extensive fibrosis than in hearts with less active, short amounts of exposure to stress. The results from this study will shed more light on the physical interactions between myocytes and myofibroblasts and possibly contribute to improving the clinical management of arrhythmias in fibrotic hearts.

MATERIALS AND METHODS Animal Models The study was conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the University of California Institutional Animal Care and Use Committee.

Control rats were healthy young adults 5-month-old male Fischer 344. Sexmatched experimental rats were selected from three established and clinicallyrelevant rat models with fibrotic hearts: (1) aged rats (21-month-old) and agedmatched young adult (5-month-old)

rats with common cardiovascular diseases, such as (2) diabetes mellitus Type II (Zucker diabetic fatty rats (ZDF)) or (3) hypertension (spontaneously hypertensive rats (SHR)).

Aged rats were purchased from the National Institute of Aging, and the rest were purchased from Charles River Laboratories.

Only the surgically removed rat hearts were utilized in this study, and not the actual rats themselves. ZDF rats were raised to about 5 months of age and glucose levels were measured via tail vein before sacrifice.

The ZDF rats are available for order at 10 weeks of age and when induced by special high fat diet at 12-15 weeks of age, express Type II diabetes (Purina #50008 diet recommendation). The genetically obese ZDF rat line has a genotype of Fa/ Fa, mutant for a missense mutation in the leptin receptor gene. (Yokoi et al., 2013) As the diabetes expression is epigenetic, the rats aging to at least 12-15 weeks of age was necessary for rat growth and development of diabetes and fibrosis.

To ensure the validity of comparisons between different fibrotic conditions, the Fischer 344 (young) rats and SHR group were both analyzed at the same age.

Around 4 months of age, the rats had a very strong smell in their cage distinct from other, healthy rats, which occurs when there are high glucose levels present in their urine. Glucose measurements for three consecutive days were taken around 5 months of age to ensure diabetic expression (Table 1), a month after initial signs of diabetes as well as a month after the maximum waiting time given by the vendor for diabetes expression. The average of approximately 545 mg/ dL indicated diabetes.

Cardiac Cell Isolation Rat ventricular myocytes and (myo) fibroblasts were isolated from the nonfibrotic hearts of the control group and the

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Isolation of Fibroblast Proteins Cultured fibroblasts transform into myofibroblasts after 48 hours in culture (Masur, 1996), raising two concerns.

Fibroblasts in vitro may not represent those in vivo, and any pathophysiological distinctions between fibroblasts and myofibroblasts are blurred by the conversion of fibroblasts into myofibroblasts in culture. To prevent these possibilities, (myo)fibroblast lysis for protein isolation was performed immediately after centrifugation, which was done within 10 minutes of cell isolation to keep the (myo)fibroblasts as close to the freshly isolated state as possible.

Lysis buffer (200-500 L) was added to the pellet, depending on the size of the pellet so that the lysate would contain an appropriate protein concentration. The lysis buffer was prepared with 1:100 Protease inhibitor (Cell Signaling Technologies) to

A)

radioimmunoprecipitation assay (RIPA) buffer containing 0.1% SDS, 1% TritonX-100, 1% Sodium deoxycholate and (in mmol/L) NaCl 158, Tris-HCl 10, EGTA , sodium orthovanadate 1, phenylmethylsulfonyl fluoride 1 (pH 7.2 adjusted with NaOH and HCl. The cell pellet and lysis buffer solution were left on ice for 30-45 min and spun at 1400 rpm at 4°C for 20-30 min. The cell lysate was stored at -80°C. Further rationale for centrifugation and direct lysing of pellet vs culture, enrichment, and then collection When directly lysed, young Fischer 344 rat fibroblasts showed little to no expression when compared to the same exposure time as the NRVFs that were cultured for a long period and lysed (Figure 1). Thus, after obtaining a mostly pure fibroblast pellet from each heart’s cardiac cell isolate, the cells were directly lysed using a quantity of lysis buffer proportional to each pellet’s size. Since ZDF fibroblast pellets and fibrotic heart rat source pellets were large, the number of rats for protein isolation was one per group. While this sample size may not be statistically representative of a population, it can provide insights to lead further research in a certain direction for a specific population. This, in turn, can provide a basis for further research into a potential aid community and public health.

B)

kDa

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resuspension into fresh media washed the cells. This was done two more times, with the pellets consolidated with fresh media and resuspended into one tube after the third spin. This cell suspension was then spun at 1500 rpm for a final wash. The resulting pellet was then subjected to cell lysis. Each sample consisted of fibroblast lysate sourcing from one rat from the respective fibrotic group.

F ZD

fibrotic hearts of the experimental group.

Rats were euthanized by an IV injection of 1000 units (U) heparin sulfate and sodium pentobarbital (100 mg/kg). Adequacy of anesthesia was confirmed by the lack of pedal withdrawal reflex, corneal reflex, and motor response to pain stimuli by scalpel tip. Using the Langendorff method, isolated spontaneously-beating rat hearts were perfused at a constant rate of 5 mL/min with standard normal Tyrode’s solution at 37oC. The Tyrode’s solution was pH 7.4 after it was adjusted with NaOH and contained 100 mg/L albumin, 125 mmol/L NaCl, 5.4 mmol/L KCl, 1.8 mmol/L NaH2PO4, 24 mmol/L NaHCO3, 1.8 CaCl2, 0.5 mmol/L MgCl2, and 5.5 mmol/L glucose. To separate (myo)fibroblasts from myocytes, freshly isolated cardiac cells were centrifuged at 500 relative centrifugal force (rcf) three times in 5-minute durations. (Myo) fibroblasts were resuspended in 90% Dulbecco’s Modified Eagle Medium (DMEM) containing 1% Penicillin/ Streptomycin, 0.01% gentamycin, .005% Fungizone, and 10% fetal bovine serum, then incubated at 37°C in 5% CO2.

Fibroblasts must therefore not be cultured, as in vitro conditions are conducive to myofibroblast differentiation (Masur, 1996; Santiago et al., 2010) and must be free from cardiomyocytes as much as possible. The pellet from the cell isolation was centrifuged multiple times to purify the fibroblasts and remove contaminating cardiomyocytes.

Immediately after isolation, the isolation product was centrifuged at 40 rcf for 4 minutes. The pellet was discarded and the supernatant was decanted into a new tube. The supernatant was spun down again once more at the same rate, and the pellet was discarded. The supernatant after the second spin was spun at 500 rcf for 5 min. The newly formed pellet contained mostly fibroblasts. Myocytes tended to separate at a low centrifugation speed, hence the low initial spin speed.

To pellet the fibroblasts, a significantly higher speed was used afterwards, which may have incorporated debris into the pellet, but the pellet was immediate lysed, and debris was discarded after collecting the final protein isolate. However, it was important to get as much of a pure fibroblast sample as possible, so the final

1

2

3

4

Figure 1. Evidence for aggregation of connexin subunits: boiled (left) vs. non-boiled (right) samples for Cx45 A. 30-second exposure for anti-Connexin 45 (Santa Cruz Biotechnology, Inc) found at 45 kDa.The left blot contained boiled samples and the right blot contained non-boiled samples. Lane 1 contained protein cell lysate from freshly isolated fibroblasts sourced from one Fischer 344 rat aged 4.6 months. Lane 2 contained protein cell lysate from fibroblasts cultured for a week sourced from one ZDF rat aged 4.5 months. Lane 3 contained protein cell lysate from fibroblasts sourced from neonatal rats cultured for three weeks. Lane 4 contained protein cell lysate from cardiac tissue sourced from one SHR rat aged 8 months. B. 1 minute exposure of same blot.

ALTERED MOLECULAR EXPRESSION IN MYOFIBROBLASTS: IMPLICATIONS FOR CARDIAC REMODELING AND ARRHYTHMIAS

A)

B)

Figure 2. AutoCAD design template for co-culture that achieved closely strict border zone A) Physical barrier design was used to tackle multilayer formation and to distinguish a clear border zone for accurate analysis of Cx expression in gap junctions. Diameter of physical barrier was 33mm in order to fit inside a 35mm dish, and template outlines were 1mm thick. The barrier was thin as possible to minimize the total culture time for fibroblasts.

ARVF=adult rat ventricular fibroblasts. NRVM were plated first in the innermost circle and after approximately 8090% confluence, rat ventricular fibroblasts were plated in the outer semicircles. After 24 hours, the barrier was removed from the 35mm petri dish and the fibroblasts were allowed to grow towards the NRVM to fill in the gap. B) 3-D view of “cookie cutter” template/physical barrier.

Collection of (Myo)fibroblast Medium The fibroblast-conditioned media was collected from fibroblasts isolated from the four different sources and cultured for one week: young rat (~5-month old), SHR rat (~5-month-old), ZDF rat (~5-monthold) and aged rat (23-months-old). One rat source, the 23-month-old rat, or aged group, was subject to fibroblast media containing 1isoprenaline hydrochloride (Sigma-Aldrich), in order to mimic external stress. This external stress was diluted in 20mL of fibroblast media, 5mL of which replaced the week-old media.

A sample of the fibroblast medium was collected after 24 h of fibroblast incubation. After another 24 h of stress, another sample of fibroblast medium was collected. The medium was not replaced with fresh medium after the first 24 h of incubation. Because fibroblasts in a T-25 culture dish would have reached contact inhibition within a week, fibroblasts were passaged and subjected to another 24 h of stress with 5 mL of fresh stress media. Thisrevealed if passaging and space for fibroblast growth would impact the cytokine secretion. Known positive controls for Ang II and TGF-β were mouse kidney tissue lysate and rat intestine lysate, respectively (Santa Cruz Biotechnology and Abcam, respectively).

The mouse kidney was harvested from 4 week old mouse. The rat intestine was harvested from a 26-month-old rat.

Protein Estimation The loading of the samples was

estimated and standardized for equal loading using the Thermo Scientific Pierce BCA Protein Assay Kit. Nine standards were prepared to calibrate the absorbance readings for the samples.

Using the Albumin standard provided in the kit and RIPA buffer as the diluent, standards included the following concentrations in g/mL: 2000, 1500, 1000, 750, 500, 250, 125, 25, and 0. Dilutions of samples for measurement were either five-fold or ten-fold, depending on the accuracy of points on the standard curve.

Preparation of SDS Page Gel For a 10% SDS PAGE separating gel was prepared by combining 10% Acrylamide, 25% 1.5 M Tris (pH 8.8 adjusted with HCl and NaOH), 48% mL of water, 0.01% sodium dodecyl sulfate (SDS), 0.01% ammonium persulfate (APS) stored at -20°C, and 0.1% tetramethylethylenediamine (TEMED).

The separating gel was loaded in between glass plates with a pipette with an inch left for stacking gel. The stacking gel was prepared by combining 4.72% Acrylamide, 11.8%, 0.5 M Tris pH 6.8, 74% water, 0.01% SDS, 0.01% APS, and 0.118% TEMED. The stacking gel was loaded in between the plate with pipette and the comb was added. The gels were stored at 4°C in running buffer for a maximum of two days until they were ready to be used.

Western Blot The samples were prepared by adding sample buffer with a 1:1 sample to sample buffer ratio. They were then boiled at 100°C for 5 min. In a 2X reducing gel, the sample buffer consisted of 10% SDS, 10% glycerol, 0.1% bromophenol blue, Tris-HCl 62.5 mmol/L, pH 6.8, and 5% mercaptoethanol. The wells were loaded with 40g of the protein samples with Bench Mark protein marker (Kaleidoscope, high range) as the ladder.

The gel ran at 50V until the dye passed the stacking gel and then at 90 V until it reached the end. The polyvinylidene fluoride (PVDF) membrane was soaked for 10-15 min in methanol. The apparatus was kept in an ice bucket to protect the transfer process from the heat along with an ice pack added to the transfer gasket.

The proteins then were electrotransferred

at a current of 90V for 90 min in the ice bucket. After transfer, the membrane was rinsed briefly with 0.1% Tween-20 (wash buffer) in tris-buffered saline (TBS).

The blocking solution was added to the membrane and kept at room temperature for approximately 1 hour. Blocking solution consisted of 5% non-fat dry milk in wash buffer. 5% Bovine Serum Albumin (BSA) was also prepared in 1X TBS for secondary antibody dilution and incubation. The membrane was incubated overnight at 4°C in primary antibody diluted in 5% non-fat dry milk blocking buffer and washed with wash buffer for 15 min three times while rocking. The membrane was incubated for 45-60 min at room temperature in secondary antibody and then washed again with wash buffer three times for 15 min. After the final wash with wash buffer, BioRad Clarity Western ECL substrate chemiluminescent reagent was added to the membrane, mixing about 1 mL each of solutions A (peroxide) and B (enhancer/LumiGLO) prior to addition.

The membrane was exposed to an X-ray film or imaged with a digital imager in the dark for different exposure times depending on the protein and intensity of signal. The X-ray film was developed with the developer for 2 min in the dark, water for 1 min in the dark, fixer for 2 min in the dark, and water for at least one minute.

The samples blotted for Cx were not boiled due to the natural tendency of Cx subunits to aggregate, which can lead to inaccurate aggregation with other proteins. Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) was the loading control used for the Cx blots.

Co-culturing Monolayers of Myocytes and (Myo)fibroblasts Using a physical barrier made of 35 mm petri dishes and 3-D printed circular cookie templates (Figure 2), each dish was pre-coated with laminin or collagen (extracellular matrix factor), then plated.

The neonatal rat ventricular myocytes (NRVM) were plated in the center (Figure 2). NRVM were used instead of adult rat myocytes because NRVM have more affinity to attach and grow without concern of loss of cells in culture during routine tasks such as media change

UNDERGRADUATE SCIENCE JOURNAL | 40

MINU MADHVANI and washing or during experimental manipulations that need to be visualized and analyzed afterwards. After the NRVM grew to approximately 80-90% confluence up to the border of the inner circle, the fibroblasts of different sources were plated in the semicircle halves.

Although establishing monolayers for monocultures can be readily achieved, establishing monolayers for co-cultures with clearly defined border zones between the two cell types was more challenging. To address this issue, NRVM were first seeded on the petri dish, then 24 hours later seeded the fibroblasts on topfor a maximum time to culture of 48 hours. As more time passed, the fibroblasts ran the risk of differentiation into myofibroblasts in vitro. (Masur, 1996; Santiago et al., 2010). The fibroblasts fell on top of the myocytes as they were aimed to seed in its appropriate area, but more fell in the gaps between the myocytes since the NRVM would not have had time to form a confluent monolayer. This design served as the border zone mimicking in vivo heterocellular coupling in fibrotic hearts, which was then fixed and subjected to immunofluorescence using confocal fluorescence microscopy to image the cells and the connexin (Rother et al., 2015). Different co-cultures were created with the ventricular fibroblasts from different rat sources, subjected to the same quadruple stain.

Monocultures of NRVM or ventricular fibroblasts from a 5-month old Fischer 344 rat were created to control for antibody effectiveness. NRVM were fixed after two days of isolation and plating and stained for TroponinT and either Cx43 or Cx45. Fibroblasts were fixed after approximately 70-80% confluence, which took two days. Fibroblasts were stained for vimentin, a (myo)fibroblast unique marker. All antibodies worked efficiently and showed clear images of cell markers and proteins of interest.

Alternative method: Co-culturing myocytes and fibroblasts in two dimensions Multilayer formation was a major issue that developed with methods of co-culture, preventing a clearcut border zone from emerging once immunostained. An alternative method

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Figure 3. Enhanced Cx45 expression in fibrotic hearts A. 45-s exposure for anti-Cx45 (Santa Cruz Biotechnology, Inc) found at 45 kDa. Lanes 1 and 2 contained protein cell lysate from freshly isolated fibroblasts sourced from one Fischer 344 rat aged 4.6 months, Lane 3 contained protein cell lysate from freshly isolated fibroblasts sourced from one ZDF rat aged 5 months, Lane 4 contained protein cell lysate from freshly isolated fibroblasts sourced from one SHR rat aged 5 months, Lanes 5 and 6 contained protein cell lysate from freshly isolated fibroblasts sourced from one aged rat aged 21 months, Lane 7 contained protein cell lysate from almost 5 million freshly isolated NRVM. 45 second exposure of same blot for GAPDH (Cell Signaling Technology) found at 37 kDa. B. 1 minute exposure. C. Image analysis and quantification of Cx45 expression; means of young rat and aged rat bands were represented on graph.

to overcome this issue was to form a layer of NRVM on the petri dish and plate a layer of fibroblasts on top. The twodimensional bilayer was then fixed and subjected to immunofluorescence using confocal fluorescence microscopy to image the cells and the connexin (Rother et al., 2015) Different two dimensional cocultures were created with the ventricular fibroblasts from different rat sources, subjected to the same quadruple stain.

Immunofluorescence Staining Co-cultures were quadruple-stained for (myo)fibroblasts (anti-vimentin, host: goat), myocytes (anti-TroponinT, host: mouse), 4’,6-diamidino-2-phenylindole DAPI (nuclei), and either Cx43 or Cx45 (anti-Cx43 or anti-Cx45, host: rabbit).

All primary antibodies were against respective proteins of human origin and specified to be compatible with rats. The cells were washed three times with PBS buffer and fixed with 4% paraformaldehyde (PFA) in PBS for 15 min at room temperature. The cells were washed again with PBS three times and permeabilized with 0.5% Triton-X in PBS for 5 min at room temperature. The cells were washed again with PBS three times and blocked with a blocking buffer containing 5% BSA in PBS for 30 min. The primary antibody was applied overnight at 4°C and diluted with 1% BSA in PBS. The cells were washed three times with PBS and secondary antibody was applied for 45 min at room temperature,

A)

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GAPDH Young

B)

SHR

ZDF

Aged

NRVM

1.2 1 0.8 0.6 0.4 0.2 0

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Figure 4. Enhanced Cx43 expression in fibrotic hearts A. 1 minute exposure for anti-Connexin43 found at 43 kDa. Lane 1 contained protein cell lysate from freshly isolated fibroblasts sourced from one Fischer 344 rat aged 4.6 months, Lane 2 contained protein cell lysate from freshly isolated fibroblasts sourced from one SHR rat aged 5 months, Lane 3 contained protein cell lysate from freshly isolated fibroblasts sourced from one ZDF rat aged 5 months, Lane 4 contained protein cell lysate from freshly isolated fibroblasts sourced from one aged rat aged 21 months, Lane 5 contained protein cell lysate from almost 5 million freshly isolated NRVM. 1 minute exposure of blot for GAPDH as loading control (Cell Signaling Technology) found at 37 kDa. B. Image analysis and quantification of Cx43 expression.

diluted with 1% BSA in PBS with the same ratio as the primary antibody. The cells were kept in the dark while the secondary antibody was applied to avoid activation of the fluorescence. The cells were washed three times and mounting

ALTERED MOLECULAR EXPRESSION IN MYOFIBROBLASTS: IMPLICATIONS FOR CARDIAC REMODELING AND ARRHYTHMIAS

A)

B)

D)

E)

Key Cx43 DAPI TropT Vimentin

C)

Figure 5. Immunofluorescent stains of NRVM-aged rat (myo)fibroblast co-cultures showing Cx expression at border zone A. Co-cultures of NRVM and rat ventricular fibroblasts aged 23 months passage 2 in culture for a total of 17 days triple stained (10x).The fibroblasts were in this specific co-culture with the NRVM for four days: one day for seeding and three days to fill in the 1 mm gap. B. 20X image of quadruple stain capturing fibroblast-fibroblast coupling with prominent presence of Cx43. C. 40X image of quadruple stain capturing fibroblast-fibroblast coupling with prominent presence of Cx43. D. and E. 20X image of fibroblast-NRVM coupling with some Cx43 signal between the cells.

media was added, which also contained DAPI, or DAPI was applied for 5-10 min at a 1:500 dilution in 1% BSA in PBS. Immunostained cells were placed onto the mounting media on the slide and observed. The secondary antibodies used include chicken anti-mouse (Alexa Fluor 488), donkey anti-goat (Alexa Fluor 568), and chicken anti-rabbit (Alexa Fluor 647) (Life Technologies).

RESULTS Cx45 and Cx43 expression among the different groups of rats (Figures 3 and 4).

The healthy Fischer 344 rat showed little to no Cx45 expression, and the SHR and aged rat groups displayed expression levels similar to that of the ZDF rat, which showed the most expression. All fibrotic groups had similar expression levels to that of the positive control, NRVM Cx45. As expected, this supported the relationship between increased Cx expression in myofibroblasts and increased physical coupling between

myocytes and (myo)fibroblasts.

It has been previously suggested that connexins tend to aggregate intrinsically, even when not in their natural environment of forming connexon complexes of gap junctions (Bukauskas et al., 2000).

Boiling the samples prior to blotting affects the results, since boiling may have an opposite effect, causing the connexins to aggregate, including Cx43 and Cx45, resulting in higher bands (Figure 1).

The left half of the blot included boiled sample sand the right half of the blot included unboiled samples. Boiling the samples compared to not boiling the samples before running through SDS PAGE confirmed this expectation. While the bands were found in different areas, both appeared at the highest molecular weight.

Cx45 expression was prominent between the myofibroblasts from the aged rat as well as some areas of myofibroblast and NRVM connection (Figure 5). This provided visual representation and

localization of Cx45, further supporting results of the blot, and correlated with myofibroblasts’ ability to form gap junctions with other myofibroblasts and NRVM in 1-dimension. Controls for the antibodies for their respective marker and controls for connexin expression between NRVM demonstrated the efficiency of the antibodies (Figure 6). During the creation of co-cultures, randomly co-culturing or using tape would problematically result in multilayer formation since fibroblasts would spill over to the NRVM area.

This issue has been advantageous to other studies (Eckermann et al., 2011; Kidamhi et al., 2007; Rother et al., 2015).

The physical barrier in Figure 2 was designed to combat this issue by keeping NRVM and fibroblasts separate while seeding and then allowing the two cell populations to grow towards each other in no more than 48 hours while separated by a thin interface. However, NRVM must be seeded first and grown to a certain confluence before the fibroblasts seed since the different growth rates

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A)

B)

C)

D)

E)

F)

G)

H)

Figure 6. Immunofluorescent stain of monocultures that control and show antibodies to be effective. A. Triple stain of monoculture of NRVM with Cx43 (20X) B. 40X overlay with Cx43 C. Brightfield (40X) of NRVM D. Triple stain of monoculture of NRVM with Cx45 (40X). NRVM kept in culture for a total of two days before fixation. E. Mono stain of 5 month-old fibroblasts cultured for two days before fixation with Vimentin (40X) F. Double stain with DAPI (40X) G. Vimentin stain (10X) H. Brightfield image (10X)

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Figure 7. Non-differential expression of paracrine factors A. 2-minute exposure of Western blot of TGF-ß (13kDa); lane 1 contained media collected from fibroblasts isolated from a 5 month old Fischer 344 rat and cultured for one week. Lane 2 contained media from fibroblasts isolated from a 6 month old SHR rat and cultured for one week. Lane 3 contained media from fibroblasts isolated from a 23 month-old aged rat and cultured for one week. Lane 4 contained media from the same culture flask containing the same fibroblasts as Lane 3 but with fresh stress media (1 µM isoprenaline hydrochloride and incubated for 24 hours. Lane 5 contained the same media as Lane 4 but collected after another 24 hours without replacement. Lane 6 contained media incubated with the same 23 month-old rat fibroblasts but after passaging and incubating with fresh media with same stress for 24 hours. Lane 7 contained mouse kidney tissue lysate from a 4-week old C57BL/6 wild-type mouse. Lane 8 contained rat intestine tissue lysate from a 25 month-old age rat.

B. Western blot for Ang II (60kDa).

can result in multilayer formation, with the fibroblasts overtaking the myocytes.

Despite the use of the barrier, the multiplayer formation and incomplete separation of the two occurred as the fibroblasts filled in the gap to a minimal extent (Figure 5).

Without or without stress, all media samples had ample levels of Ang II, which can correlate with high Ang II secretion that was comparable among all rat sources regardless of degree of stress (Figure 7). TGF-β was unexpectedly

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absent from all media samples, as shown by the lack of bands in all of the lanes of the same blot, except for one band that marked the positive control, the rat intestine tissue lysate (Figure 7A).

DISCUSSION The Western blot analyzing Cx45 showed that all samples sourced from fibrotic rat hearts showed definite presence of Cx45, similar to the expression levels found in myocytes. Thus,

Key Cx43 or Cx45 DAPI TropT

myofibroblasts affected by fibrosis can form gap junctions and physically couple with cardiomyocytes. This, in turn, can lead the heart to become arrhythmogenic, since the repair functions, spearheaded by the presence of myofibroblasts, are an urgent but problematic fix that can lead to more complications. Although the aged rat seemed to have the highest expression level of Cx45, the ZDF rat may actually have the highest expression levels albeit not much different from those of an SHR rat or an aged rat.

Possible explanations are not only the unequal loading but also the finding that the amount of Cx45 expressed in ZDF fibroblasts, despite lesser GAPDH expression, showed that diabetes has a potential correlation with fibrosis. This may result in increased physical coupling between cardiomyocytes and fibroblasts in an injured heart as a consequence of increased Cx43 and Cx45 expression.

Still, at a young age, Type II diabetes along with obesity appear to be correlated with a considerable amount of fibrosis in contrast toa Fischer 344 rat of the same young age. SHR rats of similar age also showed a significant degree of fibrosis and therefore were more likely to harbor more arrhythmogenic triggers than a healthy rat was. Young rat fibroblasts also showed a surprisingly high level of Cx43 expression comparable to the other fibrotic hearts and ventricular myocytes, possibly due to more fibrosis being

ALTERED MOLECULAR EXPRESSION IN MYOFIBROBLASTS: IMPLICATIONS FOR CARDIAC REMODELING AND ARRHYTHMIAS present in a 6-month-old rat prior to cell isolation and protein collection than the 5-month-old rat. Alternatively, Cx43 levels may be greater than C45 levels because Cx43 is more widely present in cardiomyocytes and more commonly found in the gap junctions in between cardiac cells As shown by the loading control, the inequal amounts for GAPDH in the samples could indicate that the results are not strictly accurate but may be accurate to some degree as a result of standardization through BCA protocol (Figure 3). However, they did correlate to higher expression of Cx43 and Cx45 in fibrotic heart sources compared to nonfibrotic hearts. Myocyte contamination in the pellet before cell lysis was also a possibility, since this sample was from a slightly older but similar age range for rats. Fischer 344 rats were still considered to be in young adulthood when in the 5-6 month age range (Nasrullah et al., 1992; Rikans et al., 1993; Rikans et al., 1998).

Immunostaining co-cultures of NRVMs and aged rat fibroblasts twice passaged presented a visualization of the physical coupling in vivo. A defined border zone between the two cell populations was reminiscent of the post-myocardial infarction model in vivo. The patchy localization of fibroblasts was spread throughout the NRVM population or vice versa, depending on the degree of fibrosis.

The mosaic-like appearance resembled fibrosis in diseased models in vivo, such as diabetic and hypertensive individuals.

After culturing the different fibroblasts from the different sources and then isolating the protein, the results for expression of Cx were inconclusive since fibroblasts had a tendency to differentiate into myofibroblasts in vitro. (Masur, 1996; Santiago et al., 2010). Figure 1 showed that even neonatal rat ventricular fibroblasts (NRVFs, lane 3) cultured for a certain period of time showed expression of Cx45, correlating with differentiation into myofibroblasts in vitro.

The level of noise from myocyte contamination was determined to be insignificant as the cell lysate from a young rat directly after isolation still showed little to no Cx45 (Figure 1). The ZDF rat sample from fibroblast lysate, in which most, if not all, of the cardiomyocytes have been removed from the culture via

routine media change, would primarily show results from fibroblasts. Because this sample shows a much thicker band than the young rat direct cell lysate sample, this result supports that the level of noise from myocyte contamination can be negligible. It is characteristic of adult cardiomyocytes to have low affinity to attach in vitro without proper coating of the cell culture dish. Therefore, there was a negligible amount of cardiomyocytes in the pellet, if any, after centrifugation that did not significantly affect the blotting for connexin from fibroblast lysates obtained from cells directly after isolation. In addition, blotting for connexin from these direct cell lysates was more closely representative of conditions in vivo.

In addition, after starting to express diabetes, the rats could not immediately experience fibrosis in the heart, so the rats must live with diabetes and obesity for some time to ensure that they would be appropriate test subjects.

When creating the co-cultures, randomly mixing the two cell populations or using tape to create borders would result in the problem of multilayer formation: the fibroblasts tended to spill over to the NRVM area. Other groups took advantage of this characteristic (Eckermann et al., 2011; Kidambi et al., 2007; Rother et al., 2015). The physical barrier (Figure 2) was designed to combat this issue, physically keeping NRVM and fibroblasts separate during seeding. The two cell populations quickly grew toward each other separated by a thin interface after the barrier was removed. However, NRVM must be seeded first and grown to a certain confluence before the fibroblasts seed due to the different growth rates that can result in multilayer formation.

That is, fibroblasts may overpower the myocytes (Figure 5).

Ang II was correlated with the differentiation of fibroblasts into myofibroblasts as culturing in vitro results in presence of this cytokine in media. Although not all of the fibroblasts in the culture, including the culture for young rat, could have differentiated into myofibroblasts, it was believed that a significant portion of these fibroblasts may have differentiated and thus secreted this molecule as a response to fibrosis as predicted. The type or amount of stress

underwent by these cells did not seem to impact the amount of Ang II released into the extracellular matrix. The stress media was added after incubating the fibroblasts for a week without changing media and again after passaging so that the fibroblasts would have room to grow without facing contact inhibition. Other stresses were contact inhibition and passaging cells. However, all samples showed similar levels of Ang II expression, which may imply that secretion of Ang II was not an unusual activity of cardiac fibroblasts, or that the myofibroblast state was enough to influence the fibroblasts to secrete Ang II, another survival mechanism with repercussions. TGF-β, on the other hand, did not seem to appear in any of the stress-applied situations, and therefore, cannot be correlated as a byproduct of fibrosis or said to have a direct relation with cardiac arrhythmias.

On the other hand, these stress conditions applied in vitro may have insufficient to coerce the myofibroblasts to secrete TGF-β as fibroblasts that are increasingly passaged and most likely differentiated into myofibroblasts express increased amounts of TGF-β. (Santiago et al., 2010).

Future Directions Multilayer formation was still an issue present in the physical barrier method to creating co-cultures, so the next step is to utilize the multilayer formation issue as an advantage. 2-D co-cultures would be created, seeding NRVM first and after 24 hours with the fibroblasts from different sources seeded on top.

Fresh fibroblasts could be used and the fibroblasts would remain in culture for a maximum of 2-3 days. These cultures would then be quadruple stained but imaged with confocal fluorescence microscopy, imaging the different layers separately and getting a clearer picture of the physical coupling between NRVM and fibroblasts. While similar, the results would be clear enough to perform further experimentation and studies, such as wound healing and genetic therapies. Furthermore, this model of patchy areas of fibroblasts would be more representative of the diseased heart model. Also, different ratios of NRVM and fibroblasts could be co-cultured, with 100% NRVM as a control, a 75% to 25%

UNDERGRADUATE SCIENCE JOURNAL | 44

MINU MADHVANI ratio of NRVM to fibroblasts, a 50% to 50% ratio of NRVM to fibroblasts, and a 25% to 75% ratio of NRVM to fibroblasts to analyze if there is a difference between different fibroblast levels.

Future directions for analysis of paracrine factor secretion would be to incubate the other fibrotic heart source fibroblasts for longer periods of time in culture and also under isoprenaline hydrochloride stress. Moreover, the fibroblasts from each rat model source, including aged rat hearts, could be incubated in a different type of stress such as Bay K8644, a calcium channel agonist that can affect the calcium-sodium exchange that occurs in the cardiac cells.

In addition, more straining stresses, such as physical stress, may induce TGF-β secretion.

Furthermore, Fischer 344 rats could be induced with Type I diabetes via injection of drug Alloxan and Streptozotocin, which are toxic to insulin-producing beta cells in the pancreas. This would provide an alternative diabetic model to analyze since these would be diabetic rats but not genetically obese.

The interactions that occur in the heart, both direct physical association and indirect by signaling molecules, at the molecular level are crucial to a better understanding of arrhythmogenic triggers and activity. By resolving questions about the molecular level of communication, the underlying cause of arrhythmias or worsened heart conditions that are conducive to sudden cardiac death would be easier to eliminate. With these initial findings, research can be taken to the next level and translated into clinical treatments that have the potential to be permanent solutions.

ACKNOWLEDGMENTS We would like to thank Scott John and Neha Singh for insightful advice on the project, and we thank Pauline Morand and Shankar Iyer for technical support.

This study was funded by NIH UCLA CTSI UL2TR000122 (TPN) and AHA NCRP SDG (TPN).

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Eckermann et al. (2011) Characterization and modulation of fibroblast/endothelial cell-cocultures for the in vitro preformation of threedimensional tubular networks. Cell Biol Int. 35(11):1097-1110.

Kendall et al. (2014) Fibroblasts in fibrosis: novel roles and mediators. Frontiers in pharmacology.

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(1992) Age-related immunosenescence in Fischer 344 rats: influence of exercise training. J Appl Physiol. 73(5):19321938.

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Rother et al. (2015) Cross-talk of cardiomyocytes and fibroblasts in co-cultures. Open Biology.

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Vasquez et al. (2010) Enhanced fibroblastmyocyte interactions in response to cardiac injury. Circ Res. 107:1011-20.

Yokoi et al. (2013) A novel rat model of Type 2 Diabetes: the Zucker Fatty Diabetes Mellitus ZFDM rat. Journal of Diabetes Research.

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Supervised Classification Algorithm for Seizure Detection in Continuous EEG Recordings Shruti Nanivadekar, Dr. Richard Staba1 Scalp or intracranial electroencephalography (EEG) can record seizures and is an important test for the diagnosis of epilepsy, but seizures are irregular and require long-term continuous EEG monitoring. Computer automated methods using objectively measured features in EEG can detect seizures faster and possibly more accurately than manual detection.This study developed a supervised classification algorithm in Matlab to detect seizures in continuous EEG recordings obtained from lateral fluid percussion-injured rats. The signal features used to characterize seizures were line-length, energy, zero-crossings, and area. Results showed that detection of seizures within a single animal was associated with high sensitivity (85.81%) and accuracy (70.65%) but a low specificity (58.36%). In contrast, when applied across different animals, sensitivity decreased to 44.71%, and accuracy and specificity were respectively reduced to 44.16% and 49.47 %. Manual review of detected events revealed the false positives were not associated with electronic or movement artifact as seen in previous work. Results indicate that compared to manual review, an automated approach could accurately detect seizures within an individual animal.

INTRODUCTION Epilepsy is a serious neurological disease characterized by spontaneous seizures and the neurobiological, cognitive, and social consequences of this condition (Fisher et al., 2005).

Detection of seizures in continuous electroencephalography (EEG) recordings is carried out manually, which is time-consuming and can be inaccurate depending on the experience of the reviewers. Computer-automated methods can detect seizures faster and possibly more accurately. These methods could potentially also be applied to seizure prediction, which could be helpful to control epileptic seizures. For example, prediction algorithms could be implemented in deep brain stimulating devices that are now being evaluated as treatment in patients with drug-resistant epilepsy.

Several algorithms have been utilized to detect epileptic seizures as well as to identify interictal epileptiform (epilepsylike) discharges like sharp waves and spikes in the EEG recordings of animals with chronic epilepsy (Gardner et al.,

2007). One approach involves extracting the line length feature, a measure of distance between consecutive data points, from intracranial EEG data. The line length feature has been used to detect pathological high frequency oscillations (HFOs), which are believed to reflect neuronal disturbances responsible for epilepsy and a potential biomarker of the seizure onset zone (Gardner et al., 2007).

This study utilized features of line length, amplitude and duration as parameters to detect interictal HFOs. Another study compared the existing methods of seizure detection: the power method (amplitude squared), the coastline method (sum of distances between consecutive data points), the spike frequency method, and the autocorrelation method (correlation in the data range combined with spike frequency). These methods identified an unsupervised algorithm that was most predictive and sensitive on long-term intracranial EEG records from injured rats (White et al., 2006). Several of these methods were significantly predictive, but the rate of false detections associated with noisy data was apparent in these studies.

It is possible that a combination of these

methods rather than one alone could reduce or eliminate the false detection of high-frequency and high amplitude events that often occur during epileptic seizures.

With a broader goal of being able to apply this detector over a wide range of data obtained from TBI animals to epileptic human patients, there is an evident need to optimize the seizure detector to account for differences in the data without modifying the training algorithm. This will maintain efficiency and prevent dependence on manual intervention. Therefore, this study tested a signal correlation dependent computerautomated methodology that computed the features that best correlated with the raw data signal and used only those optimized features to train the classifier that detects spontaneous seizures. The results from this automated approach were then compared with results obtained from manual detection of seizures in the same continuous recordings.

METHOD Data Collection and Manual Review Continuous long-term EEG recordings

Department of Neuroscience and Department of Neurology

1

UNDERGRADUATE SCIENCE JOURNAL | 46

SUPERVISED CLASSIFICATION ALGORITHM FOR SEIZURE DETECTION IN CONTINUOUS EEG RECORDINGS FROM TRAUMATIC BRAIN INJURY RATS

Figure 1. Dorsal surface of rat skull illustrating the location and type of electrodes used to record continuous EEG in each animal.

3

Normalized LLF overlaid on original signal

2 1 0 -1 -2 0

Area overlay Normalized Voltage

Normalized Voltage

Figure 2. Graphical representation of the mathematical feature functions derived from the raw EEG signal. In this example, each feature function was superimposed on the ictal EEG discharge (black traces). The blue trace corresponded with the line length feature (LLF); the green shaded area reflected the Area Feature; the yellow trace was the Energy Feature; and the red dots corresponded with the Zero Crossings Feature.

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4 x 106

Figure 3. Example of each of the feature function computed for the same continuous EEG recording (blue traces).

Overlay of Line Length Feature (green). Area Feature (red). Energy Feature (yellow). Zero Crossing Feature (green). Note the change in amplitude of each feature function with respect to time. An increase or decrease in amplitude of one feature function did not correspond with a similar change for other feature functions.

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were obtained from lateral fluid percussion-injured rats that are an animal model of human traumatic brain injury and posttraumatic epilepsy. The rats were handled in accordance with UCLA Policy 990, Use of Laboratory Animal Subjects in Research. Electroencephalography is a technique that allows electric signals from the neurons to be detected by electrodes placed on the scalp or intracranially. EEG data was sampled at 1 kHz from 16 depth electrodes implanted bilaterally in neocortical areas to record spontaneous intracranial activity (Figure 1). An experienced reviewer then independently reviewed unprocessed, continuous EEG data using DataPac 2K2 software and marked the time corresponding to the onset and offset of spontaneous seizures. This labeling was based on standard electrographic features associated with clinical seizures, defined as distinct evolution of EEG in amplitude and spectral frequency across recording sites compared to preceding baseline EEG. Data Processing and Supervised Classification Detection Algorithm 1 hour recordings during 5 different days from a single TBI animal were used.

For each 1 hour recording, the raw data for all the 16 channels was exported from the data collection software to a Matlab array of voltage values. The data, which was obtained in smaller segments of shortduration recordings, were concatenated into single long arrays for each channel.

These five Matlab arrays were labeled “concat05”, “concat07”, “concat08”, “concat10” and “concat13”, where the number corresponds to the post-injury day these data were recorded. Then an epoch between 300 and 400 seconds in duration was selected in each array that contained ictal and interictal regions.

Each of five epochs (epoch05, epoch07, epoch08, epoch10, and epoch13) was then divided into a training set and a testing set. The training set consisted of four of the data epochs and the testing set was the remaining fifth epoch. This was an iterative process with each epoch being tested for detection by the predictive classifier that trained on the 4 training epochs. Manual review of the EEG records showed that channel 7, which

SHRUTI NANIVADEKAR Normalized LLF overlaid on original signal

Normalized Area overlaid on original signal

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Figure 4. Overlay of the 4 Features (green) on the raw signal (blue) demonstrating changes in feature amplitude during the same continuous EEG recording of several seizures with seizure onset denoted by vertical red line and seizure offset or end denoted by vertical black line. Episodes following a black line and before a red line corresponded with an interictal episode. Compared to interictal episodes, the LLF, area, and energy features generally increased while the zero crossing feature decreased during seizures.

was the bipolar recording electrode position nearest to the injury site, was associated with the most prominent in EEG waveform between ictal and interictal regions, making it a preferable starting point for training. Hence, all the epochs in the training set were only taken from channel 7 in order to create an accurate training set. The four features extracted for each of the four training set epochs were line length, area, energy and zero crossings, computed as follows:

Line Length (X) = �i=2 n|Xi=X(i-1)| Area (X) = �i=1 n|Xi| Energy (X) = �i=1 n (Xi)2, and Zero Crossings (X) = �i=2 n((Xi-1 mean (X)<0) AND (Xi-1-mean(X) > 0)) where X represents the respective epoch in the training set (Figure 2) (Litt et al., 2001). Each feature function was then applied to non-overlapping moving windows within one training epoch.

The features served to enhance the electrographic aspects of the signal

that were characteristic of epileptiform activity (Figure 3 and 4). The sample feature values obtained were then interpolated and normalized to get distinct arrays of Line Length, Energy, Zero Crossing, and Area values for each training epoch. These individual feature arrays were then combined into a single sample feature array for each training epoch. The original data for each epoch was then grouped into group 1 (seizure onset and offset), group 2 (ictal

UNDERGRADUATE SCIENCE JOURNAL | 48

2 1 0 -1 -2

Manual Detection Automated Detection

-3

Time (s)

Voltage

Voltage

-4

Figure 5. An example of two seizures detected using the automated approach in a single animal (“tbi12”).

In this figure, a consecutive pair of red lines marked the onset and offset associated with a seizure labeled by the manual reviewer. Paired black lines denoted the onset and offset of each ictal event as detected by the automated algorithm. A) Expanded time base showing the automatic compared to manual detection of the first seizure. In this seizure, the algorithm detected the seizure onset approximately 5 seconds after the manual onset, while the end of seizure was nearly the same between automated and manual methods. B) During the second seizure, both seizure onsets were nearly identical when using automated and manual approaches, but the automated approach missed the end of seizure by more than 3 seconds (black line corresponding with automated seizure offset not shown).

zone), group 3 (interictal zone) based on the manual labeling conducted by the expert reviewer. A classifier object was then created to train the feature array on the grouped data. This would enable classification of a new data value as group 1, 2 or 3 based on only the feature extraction values, and trained the classifier object to define an ictal zone or an interictal zone in terms of some unknown combination of the four feature functions for each data window. The 2 classifiers used for this training were the Naïve Bayes and the Linear Discriminant classifiers that are available in the signal processing toolbox of Matlab.

After creating these 2 classifier objects for each of the 4 training set epochs, the same feature extraction method was applied to the testing set epoch, and the combined array of features was stored as Test_sample. The 8 classifier objects from the 4 training epochs were then each used to predict the labeling for the testing set feature values. The trained classifier used the learned parameters based on feature training to predict the group 2 (ictal), group 3 (interictal) and group 1 (onset-offset) labeling in the testing set

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epoch. Hence, the prediction of seizures in the testing set was based on classifier’s learned parameters for classification from the training set.

Finally, to visualize the detector’s output, the testing dataset epoch was plotted along with marked ictal events as identified by the human reviewer.

The predicted labels for the testing data were utilized to mark the onset and offset regions as predicted by the seizure detection algorithm. The computerautomated detection of seizures was compared to manually detected seizures to evaluate the performance of the algorithm. Using the comparison between these two detections, with the human identification as the gold standard, the specificity, sensitivity and accuracy were computed: Sensitivity = (tp/(tp+fn))*100; Specificity = (tn/(tn+fp))*100; Accuracy = ((tp+tn)/(tp+fn+fp+tn))*100; Where tp = true positives, tn = true negatives, fp = false positives, and fn = false negatives. The seizure detector was trained to separately detect the seizure onset and offset based on a different combination of features as the criteria for detection. The true positive for onset was defined as an onset-detection that appeared anywhere within the region between the onset and offset. The true positive for the offset was defined as an offset-detection that exactly aligned with the manually detected offset. Conversely, the false positive for onset and offset reflected the erroneous detection of an onset or offset respectively, in the interictal region. A false negative for the onset reflected the absence of an onsetdetection anywhere between the onset and offset of the manual. The offset false negative reflected the absence of an offset-detection at the manually detected exact offset. The true negatives for both offset and onset reflected the correct corresponding non-detection during interictal regions.

Feature Optimization Using Correlation Method In an attempt to expand the application of the detection algorithm to data from different rats, the detector was tested on the continuous EEG recordings obtained from 7 different rats labeled “tbi11”,

Normalized Voltage

Normalized Voltage

3

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SUPERVISED CLASSIFICATION ALGORITHM FOR SEIZURE DETECTION IN CONTINUOUS EEG RECORDINGS FROM TRAUMATIC BRAIN INJURY RATS

Figure 6. Representative example of automatic detection in “tbi12” with and without feature optimization.

A) Top: Example of automatic detection that was conducted using the optimal features for this data (Energy and Area) as calculated using the correlation coefficient. Bottom: Example of automated detection that was conducted without the optimization and used all four features (LLF, ZX, Energy, and Area) for classification and prediction. The third seizure (S3) was completely missed (false negative) in the optimized detection, whereas only the offset of S3 was missed by the non-optimized detector.While both optimized and non-optimized methods detected the onsets for S4 and S5, only the non-optimized method detected the offset for S4. The rest of the offsets was not detected by the detector. (False negatives of offset detection). B) Examples of seizure episodes at expanded time base to more clearly show the corresponding ictal and interictal periods detected manually. Each box showed the raw EEG data from channel 5 and 6 during particular ictal episodes in a single animal (“tbi12”). A consecutive pair of red lines marked onset and offset associated with an ictal episode labelled by the manual reviewer. The pair of black lines depicted the onset and offset of each ictal event as detected by the non-optimized form of the automated algorithm.

The pair of green lines depicted the onset and offset of each ictal event as detected by the optimized form of the automated algorithm. Onsets for S1, S2, S4 and S5 were detected by both methods and corresponded to manual detection of onsets. Offset detection was missed for S4 and S5 whereas S3 was completely undetected (both onset and offset) * False detection of possible interictal spike by optimized and non-optimized methods.

“tbi12”, “tbi31”, “tbi32”, “tbi33”, “tbi34” and “tbi48”.

For this analysis, approximately 300 second epochs from the long-term recordings of each TBI animal were used. Manual review of the EEG records showed that channel 5 was associated

SHRUTI NANIVADEKAR

Epoch

tp

tn

fp

fn

Sensitivity

Specificity

Accuracy

epoch05

4

5

0

0

100%

100%

100%

epoch07

8

4

7

0

100%

36.36%

63.16%

epoch08

9

5

5

0

100%

50%

73.68%

epoch10

11

9

6

2

84.62%

60%

71.43%

epoch13

4

5

6

5

44.45%

45.45%

45%

Table 1. Sensitivity analysis of seizure detection algorithm in comparison to manual seizure identification.

Epoch

tp

tn

fp

fn

Sensitivity

Specificity

Accuracy

tbi11

4

4

8

5

44.45%

33.34%

61.53%

tbi12

4

5

1

1

80%

83.34%

81.82%

tbi31

3

3

3

0

100%

50%

66.67%

tbi32

2

1

16

1

66.67%

5.88%

15%

tbi33*

1

2

0

0

100%

100%

100%

tbi34

1

1

6

0

100%

14.28%

25%

tbi48

2

2

10

0

100%

16.67%

28.57%

Table 2a. Sensitivity analysis of seizure detection algorithm in comparison to manual seizure identification in 7 different animals With feature optimization: ONSET Epoch

tp

tn

fp

fn

Sensitivity

Specificity

Accuracy

tbi11

2

4

6

7

22.23%

40%

31.57%

tbi12

2

5

1

3

40%

83.34%

63.63%

tbi31

1

3

2

2

33.34%

60%

50%

tbi32

2

1

15

1

66.67%

6.25%

15.79%

tbi33*

1

1

1

0

100%

50%

66.67%

tbi34

0

2

5

1

0%

28.57

25%

tbi48

1

2

11

1

50%

13.38%

20%

Table 2b. Sensitivity analysis of seizure detection algorithm in comparison to manual seizure identification in 7 different animals With feature optimization: OFFSET

with the most prominent and earliest change in EEG waveform between ictal and interictal regions. The noticeability of the characteristic shift in signal morphology between ictal and interictal periods made this an ideal starting point for obtaining all the epochs in each training set. For each 300 second epoch, the raw data (.edf file) was directly extracted in Matlab using the function that read the .edf file into a Matlab array, making it more consistent with other training data in Matlab.

The raw data from channel 5 was normalized and interpolated. This experiment utilized 7 different testing sets, one from each rat. This method was simply a variation of the previous experiment. That is, the features used to train and predict the classification labels

of the testing set were specialized to the testing set by computing the correlation coefficient between the features and the signal data from the selected channel. The correlation coefficients were computed using the Matlab function corrcoef: FeatureCorrelation= corrcoef (Test_ feature_interp_norm,Test_channel_ interp_norm) Where Test_feature_interp_norm is the array of normalized and interpolated values of a given feature and Test_ channel_interp_norm is the array of normalized and interpolated values of the signal data. These correlation coefficients computed for each feature within each testing set were then compared to find the 2 features that show the maximum correlation with signal data. These 2 features were the only ones used to then

train, classify and predict the ictal and interictal regions in the testing set. This feature optimization was hypothesized to contribute to a lower detection of false positives. To confirm this, the results obtained from the optimized feature extraction (2 best features) and non-optimized feature extractions (all 4 features) were compared.

Spatial Localization of Seizure Spread The developed algorithm was modified to apply the supervised classifier object to channels 3, 7, 12 and 16 to obtain the spread of a particular set of seizures across different regions in the brain based on electrode placement. The resulting seizure spread pattern was obtained by manually analyzing the difference in time of onset or latency of a seizure across channels.

Autocorrelation Metric In an attempt to increase the specificity of the seizure detection algorithm, the Autocorrelation metric (White et. al, 2006) was used as an additional postclassification parameter to reduce the number of false positives. The existing dataset was decomposed into smaller windows of 30 data points. The maximum and minimum values for each 30-point group were computed. These were termed max(Si) and min(Si) where Si represents a group of 30 points. This metric was calculated: HVi = min[max(Si), max(max(Si+1), max(Si+2))] LVi = max[min(Si), min(min(Si+1), min(Si+2))] metric3 = �i=1 100 (Hvi-LVi) where HVi was the ith high value and LVi was the ith low value. Metric 3 was the sum of the difference between these two values.

RESULTS The representative examples (Figure 5) corresponded to different kinds of outputs obtained from the automated seizure detection. Some ictal episodes detected were similar to the manual detection (Figure 5B), whereas other automated detections demonstrated a lag (Figure 5A) or a lead in their detection in comparison to the manual detection.

Figure 1 showed the location and type

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SUPERVISED CLASSIFICATION ALGORITHM FOR SEIZURE DETECTION IN CONTINUOUS EEG RECORDINGS FROM TRAUMATIC BRAIN INJURY RATS

Epoch

tp

tn

fp

fn

Sensitivity

Specificity

Accuracy

tbi11

1

3

5

6

14.28%

37.5%

26.67%

tbi12

5

7

1

0

100%

87.5%

92.3%

tbi31

3

4

1

0

100%

80%

87.5%

tbi32

2

1

16

1

66.67%

5.88%

15%

tbi33*

0

2

0

1

0%

100%

66.67%

tbi34

0

1

7

1

0%

12.5%

11.11%

tbi48

1

1

7

1

50%

12.5%

20%

Table 3a. Sensitivity analysis of seizure detection algorithm in comparison to manual seizure identification in 7 different animals – Without feature optimization: ONSET Epoch

tp

tn

fp

fn

Sensitivity

Specificity

Accuracy

tbi11

2

3

5

6

25%

37.5%

31.25%

tbi12

1

7

1

4

20%

87.5%

61.53%

tbi31

0

4

0

3

0%

100%

57.14%

tbi32

0

1

15

1

0%

6.25%

5.88%

tbi33*

1

2

0

0

100%

100%

100%

tbi34

1

1

6

0

100%

14.28%

25%

tbi48

1

1

8

1

50%

11.11%

18.18%

Table 3b. Sensitivity analysis of seizure detection algorithm in comparison to manual seizure identification in 7 different animals – Without feature optimization: OFFSET

of electrode used to record continuous EEG from the surface or depth within each TBI animal. The following results were obtained using the automated detection algorithm in relation to manual detection for each seizure in each data set. For data set labeled “epoch05”, which contained 4 ictal events and 5 interictal regions, the mean (±SD) latency for automated versus manual seizure onset detection was 2.5±1.3 seconds and the mean (±SD) lead for offset detection was 0.75±0.9 seconds. Epoch07 contained 8 ictal events and 9 interictal regions.

The mean (±SD) latency was 3.4±1.2 seconds and the mean (±SD) lead for offset detection was 0.63±0.99 seconds.

Epoch08 contained 9 ictal events and 10 interictal regions. The mean (±SD) latency was 4.1±0.7 seconds and the mean (±SD) latency for offset detection was 2.83±1.3 seconds. Epoch10 contained 13 ictal events and 14 interictal regions. The mean (±SD) latency was 0.5±1.2 seconds and the mean (±SD) lead for offset detection was 1.9±1.8 seconds. Epoch13 contained 9 ictal events and 10 interictal regions.

The mean (±SD) latency was 2.2±1.01 seconds and the mean (±SD) latency for offset detection was 1.8±1.1 seconds. For

51 | UNDERGRADUATE SCIENCE JOURNAL

most of the seizures there was minimal latency or lead of the detected seizures compared to seizures detected manually.

However, all of the data sets contained several incorrect detections (i.e. false positives) that occurred during interictal episodes, meaning the periods between spontaneous seizures.

Across all the epochs, 43 ictal episodes and 48 interictal episodes were analyzed.

Across the 5 epochs, the mean (±SD) onset detection latency was 2.54 ±1.2 seconds, the mean (±SD) offset detection latency was 2.3±0.5 seconds and the mean (±SD) offset detection lead was 1.1±0.58 seconds. The average sensitivity of the seizure detection algorithm for the 5 epochs was 85.81 % with a standard deviation of about 21% (Table 1). The average specificity of the seizure detection algorithm was 58.36% with a standard deviation of 22% and the average accuracy of the seizure detection algorithm was 70.65% with a standard deviation of 17.8%.

The representative examples (Figure 6A) corresponded to the detection outputs obtained from the feature optimized detection (top) and nonoptimized detection (bottom) in animal

“tbi12.” Corresponding raw data at an expanded time base was also provided to more clearly show the individual seizures and to better compare the seizure onset/ offset between manual, optimized and non-optimized detection (Figure 6B).

The onsets for Seizures S1, S2, S3 and S4 were detected by both the optimized and non-optimized method. The offsets were detected for S1 and S2 but not for S3 – S5 (Figure 6B). Overall, the automated seizure detection in Figure 6B agreed with the manually detected seizure onset but the seizure offset was quite variable.

Analysis of data from this rat showed a high specificity, sensitivity, and accuracy (Table 2). All the ictal episodes except the third seizure (S3) were detected by the optimized detection method.

The average sensitivity of the seizure detection algorithm (onset and offset) with feature optimization for data from the 6 animals was 64.52% (Table 2).

The average specificity of the seizure detection algorithm (onset and offset) with feature optimization for data from the 6 animals was 41.79%. The average accuracy of the seizure detection algorithm (onset and offset) with feature optimization for data from the 6 animals was 46.52%. The average sensitivity of the seizure detection algorithm (onset and offset) without feature optimization for data from the 6 animals was 44.71% (Table 3). The average specificity of the seizure detection algorithm (onset and offset) without feature optimization for data from the 6 animals was 49.47%. The average accuracy (onset and offset) of the seizure detection algorithm without feature optimization for data from the 6 animals was 44.16%.

Automated detection of seizures in signals simultaneously recorded from 4 different channels in animal “tbi12” showed a lot of overlap with manual detection, demonstrating high accuracy.

Based on the earliest appearance of electrographic changes in amplitude and frequency compared to preceding baseline EEG, manual review indicated that channel 7 was the site of seizure onset, while channels 3, 12, and 16 were channels of seizure spread. Channel 3 was an epidural screw in the contralateral frontal cortex. Channel 12 was a depth electrode in the ipsilateral anterior

SHRUTI NANIVADEKAR

for seizure detection led to a reduction in false detections of interictal spiking activity as seizures.

0.6 0.4

DISCUSSION

0.2 0 -0.2 0.4 0.2 0 -0.2 -0.4 -0.6 0.4 0.2 0 -0.2 -0.4

0.2 0.1 0 -0.1 -0.2 -0.3 -0.4

0

2

Figure 7. An example of automatic detection of a seizure recorded on 4 different channels (channels 16, 7, 3, 12) in relation to position of recording electrode shown in Figure 1. Based on the detection across channels for the same epoch, here the seizure first appeared on EEG recording channel 16 positioned in the hemisphere contralateral to the injury site, then was detected on channel 7 within the injury site, followed by channel 3 in the contralateral frontal region, and lastly channel 12 in ipsilateral cortex adjacent to the injury. A consecutive pair of red lines marked onset and offset associated with an ictal episode labeled by the manual reviewer. The pair of black lines depicted the onset and offset of each ictal event as detected by the automated algorithm. The long continuous red line denoted the manually determined onset across channels. The black dotted line represented the automated detection of onset on each of the channels. The yellow arrow represented the latency in automated seizure detection.

hippocampus. Channel 16 was a depth electrode in the contralateral cortex homotopic to lesion core. The choice of analyzing seizure spread from electrodes placed in the lesion core, contralateral frontal cortex, anterior hippocampus and region homotopic to lesion core was based on the manual review of seizure activity in these channels. Figure 7 mapped the trajectory on the brain electrode map to generate a potential spatial seizure spread pattern based on the time of onset in each channel. For this

epoch, it seemed as if the seizure initiated in channel the region homotopic to the lesion core and the lesion core at the same time and propagated to the contralateral frontal cortex and finally to the ipsilateral anterior hippocampus.

The windowing and modification of detection thresholds seemed to have curbed some of the false positives. In accordance to the predicted outcome, preliminary results (not represented in this paper) showed that using the autocorrelation method after the supervised classification

The main results from this study were that a supervised classifier that used a combination of line length, energy, amplitude and zero crossings could detect spontaneous seizures in continuous EEG recordings with high sensitivity within an individual animal. In contrast, the supervised classifier shows a significant decrease in sensitivity analysis values when tested across animals. This suggests that a larger training data set would be needed to accurately detect seizures across different animals due to the large variability of signal features associated with seizures between animals.

The overall sensitivity of this seizure detection algorithm was high except for epoch13, which was an outlier (Table 1).

The low sensitivity for epoch13 could be a consequence of the consistent latency in onset and offset detection.

The high sensitivity for all other epochs, however, was indicative of large number of true positives evident from the plots that showed consistent overlap with manually marked ictal events. However, the specificity was considerably lower due to the number of false positives.

The plots illustrated in Figure 5 showed that most of the false positives were not completely associated with interictal episodes but were within a certain range of delay or precedence of the manually detected seizures. This could explain both the occurrence of false positives and false negatives.

In comparison to other studies that relied on amplitude or frequency factors to classify ictal and interictal episodes, this study used features that are less likely to be associated with noisy data during interictal episodes. Although results using this detection algorithm contained several false detections, they did not always correspond to periods of noisy data (i.e. low signal-to-noise), suggesting possible elimination of the conventional issues associated with artifact. Although there is a notable increase in signal amplitude associated with animal arousal and movement in seizure 5 (S5) (Figure

UNDERGRADUATE SCIENCE JOURNAL | 52

SUPERVISED CLASSIFICATION ALGORITHM FOR SEIZURE DETECTION IN CONTINUOUS EEG RECORDINGS FROM TRAUMATIC BRAIN INJURY RATS 6B), the automated detector does not detect an event (i.e. false positive) during the movement-related noise.

There is a need to determine a minimal amount of allowable lead or latency in the automated detection relative to manually marked events in order to consider it a sufficiently valid detection. These delayed detections could suggest that the training set of this algorithm needs to be more robust and more ictal epochs need to be added to it in order to better define the specific patterns of epileptiform data.

Another possibility is that the detection algorithm was detecting specific or subtle (or both) features about the seizure that were not apparently visible to the reviewer. Higher variability in the deviation of automated seizure detection from manual review might reflect aspects in the EEG that the algorithm detected differently from humans or that are invisible to human identification.

A consistent delay suggested that the code may have a bias due to window length. This issue could be addressed by adjusting the window length, such as implementing overlapping-windows based on a moving average instead of the non-overlapping windowing used in the current analysis.

Contrary to the hypothesized outcome, the correlation method for feature optimization did not significantly reduce the false positive detection or rescue specificity of seizure detection. Interestingly, the sensitivity was relatively enhanced with feature optimization, whereas the specificity was higher in the detection algorithm that used all 4 features. The detection accuracy did not show a significant trend. Despite the feature optimization to improve the statistical performance of the detector, the sensitivity and specificity were lower than desired. There could be other contributors to the low specificity that must be investigated and eliminated by other methodological modifications.

One such method tested in order to reduce the number of false positives was the autocorrelation method which tested for the continuous correlation between consecutive epochs of data as a criteria for discarding interictal spike detections.

The preliminary results from this method showed a significant decrease in false

53 | UNDERGRADUATE SCIENCE JOURNAL

detection; however further sensitivity analysis must be conducted in order to verify these preliminary results.

A caveat of this study was the small size of total number of seizures and interictal regions on which this method was tested. The full benefit of the reduced time and effort with the automated versus manual approach cannot be realized until the number of false positives can be reduced. Otherwise, analyses that require the quantification of total numbers or rates of seizures might be inaccurate.

The criteria for determination of the true and false positives and negatives could be refined based on the desired goals and applications of the detector.

This could also contribute to providing more meaningful values of sensitivity, specificity, and accuracy.

Hence, this study moved one step closer to the ultimate goal of applying this detection algorithm across different rats and eventually implement a detector for seizure prediction and treatment in human epileptic models. However, there are more refinement and rigorous data analysis required to make this method a convincingly effective method of signal optimization for seizure detection.

Further experiments could be done to improve the sensitivity and specificity by creating a more robust training set.

Experiments could also be performed using the differences between channels to localize spatially and temporally the source and progression pattern of the seizures based on the amount of delay in the onset of the ictal events across channels.

ACKNOWLEDGEMENTS I would like to sincerely thank Dr. Richard Staba for his continual mentorship and guidance throughout this project. I would also like to thank Dr. Aylin Reid and Joyel Almajano for providing the data and recordings for the seizure detection analysis and Dr. Negar Memarian for her guidance in MATLAB programming.

REFERENCES 1.

Fisher et al. (2005) Epileptic seizures and epilepsy: definitions proposed by the

2.

3.

International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE).

Epilepsia. 46:470-472.

Gardner et al. (2007) Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings. Clin Neurophysiol. 118(5):1134-1143 White et al. (2006) Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury. J Neurosci Meth. 152(1-2):255-266.

Investigating Mechanisms of Gender Differences in Immune System Diseases Brandon Pham, Dr. Ram Raj Singh1 Males and females differ in their physiological anatomy, psychosocial development, and their susceptibility to certain types of disease. In perhaps what is the most pathologically relevant example, females appear immune-privileged, mounting stronger immune responses to pathogens and vaccination. While this limits females’ susceptibility to infection compared to males, it increases their likelihood of developing autoimmune disorders by as much as 15-fold. Few studies have succeeded in determining the precise cause of gender bias in autoimmune disorders. This bias may be caused by a multitude of factors: hormones, gonads, external environment, habits, sex chromosomes, etc. Although certain methods, such as gonadectomies, have been used in attempt to distinguish between the effects of these confounding variables, they have often been ineffective by themselves.

To determine which of these factors is responsible for creating gender differences, researchers have developed a model of gene-targeted animals whose gonadal sex is independent of their sex chromosome complement. In this “four-core genotypes” (FCG) mouse model, the Sry gene on the Y chromosome has been deleted, resulting in gonadally female XY- mice (XYF) that differ from female XX mice (XXF) in their sex chromosome complement. Additionally, the Sry gene has been translocated onto autosomes, resulting in gonadally male XX Sry mice (XXM) that similarly differ from male XY- Sry mice (XYM) in their sex chromosome complement. By comparing XXF with XYF mice and XXM with XYM mice, this model allows researchers to observe the effect of sex chromosomes in the absence of confounding hormonal and gonadal influences. Although the FCG mouse model has limitations, it promises to provide continued information about the reasons for gender differences.

INTRODUCTION Males and females differ significantly in many physiological and psychological features. While some of these gender differences are highly visible, others are not. It is well known, for example, that males on average tend to have greater upper and lower body strength compared to females (Miller et al., 1993).

On the other hand, males are three to four times more likely than females to develop neurological disorders, such as stuttering or dyslexia (McCarthy et al., 2012). In fact, there are numerous other attributes that differ significantly between males and females, especially in regard to pathophysiology. For example, men are more likely to develop renal and cardiovascular disease than females (Reckelhoff, 2001). In contrast, women are more prone than men to experience heart attack-like symptoms and nausea (Jamison et al., 2006).

Perhaps one of the most notable

physiological differences between males and females is their immune system, which is in part believed to play a role in explaining differences in longevity between females and males; in particular, females tend to live longer than males (Ginter, 2013). In fact, certain studies have suggested that high testosterone can lead to a weakened immune system (Furman et al., 2013). On the other hand, females are more likely to develop autoimmune disorders than males (Fish, 2008). These two facts suggest that females generally mount stronger immune responses than males, which has been reiterated by an overwhelming body of scientific literature (Margaret et al., 2012). In most cases, this stronger immune proves advantageous in protection against harmful infectious agents. However, the stronger immune system can also cause harm if not regulated properly, as is the case in autoimmune disorders.

The precise biological reasons for gender differences are often difficult to

Department of Medicine, Pathology and Laboratory Medicine at UCLA School of Medicine.

elucidate. Studies often have trouble isolating which of these factors cause the gender difference of interest. Researchers cannot control for factors such as gonads, anatomy, or hormones. However, any of these variables could be responsible for the observed gender difference. In animal models, researchers can alleviate this problem by controlling for additional factors. By feeding animals the same diet and raising them in identical conditions, the researcher may ensure that males and females do not differ in their microbial flora and external environment. Researchers can attempt to control for hormonal differences between males and females through gonadectomies and hormonal injections (Devroey et al., 1994). However, the researcher is still unable to dissect the hormonal influences from effects caused by sex chromosome complement— both of which could be causing the gender difference of interest.

This review will discuss an animal

1

UNDERGRADUATE SCIENCE JOURNAL | 54

BRANDON PHAM

Figure 1. Schematic depiction of four-core genotypes (FCG) mouse model. The four-core genotypes (FCG) mouse model was created to identify effects of the sex chromosome complement without the confounding effects of differences in sex hormones.Y—designates a deletion of gene Sry from the Y chromosome, and Sry designates that Sry has been translocated onto an autosome.

Figure 2. Four-core genotypes (FCG) mouse model eliminates confounding variables. Using the four-core genotypes (FCG) mouse model, researchers can compare mice with altered sex chromosome complement and same gonadal sex (e.g. XYM with XXM or XYF with XXF) to assess the effect of sex chromosomes on phenotype.

Researchers can also compare mice with opposite gonadal sex and identical sex chromosome complement (e.g. XYM with XYF or XXM with XXF) to assess the effect of gonadal sex on phenotype.

model, called the “four-core genotypes” (FCG) mouse model that has been used to separate the effects of sex chromosomes from testicular and ovarian hormones, and by doing so, further elucidate the cause of gender differences (Figure 1) (Arnold et al., 2009). The need for the FCG mouse model will first be introduced by describing the prevalence of gender differences whose causes have yet to be fully understood. In particular, it will describe the limitations of certain approaches that have been used before the FCG mouse model. This review will conclude with an examination of the effectiveness of the FCG mouse model and a discussion of future directions needed to ultimately further the scientific literature about the causes of gender differences in humans.

GENDER DIFFERENCES IN IMMUNE FUNCTION Autoimmune disorders, the third most prevalent category of disease in the United States, occur when the body’s 55 | UNDERGRADUATE SCIENCE JOURNAL

immune system mistakenly attacks its own healthy tissue (Lettre and Rioux, 2008). Gender differences in immune function occur because males and females differ in their basic immune response.

Specifically, women exhibit a higher T helper (Th)2 immune response whereas men exhibit a higher Th1 response.

Th1 cells elicit cell-mediated immunity whereas Th2 cells elicit strong antibody responses (Fairweather et al., 2008).

This fundamental gender difference in immune response is believed to explain the increased female bias for autoimmune disorders; an estimated 78% of those affected by autoimmune diseases are women (Rubin et al., 2012). Gender differences in Th1 and Th1 response may also explain why autoimmune disorders tend to cause more organ damage in males than females (Andrade et al., 2007). In particular, men respond to trauma, infection, and vaccination with lower antibody production than women, and women respond with less severe inflammation than men. These findings manifest as increased protection against infection in women and a higher mortality rate in men (Fairweather et al., 2008).

USING GONADECTOMY TO SEPARATE CHROMOSOMAL FROM HORMONAL EFFECTS In order to separate the effects of sex chromosomes from those of hormones, researchers often perform gonadectomies, in which the gonads of mice are removed. Gonadectomies, which are performed on mice at an early developmental stage, allow researchers to observe the effects of sex chromosome complement on gender differences in the absence of confounding gonadal hormonal influences (Iwahana et al., 2008). When comparing gonadectomized (GDX) female mice with a GDX male mice, researchers can safely assume that any observed gender difference is due to differences in sex chromosome complement (XX vs. XY), rather than differences in gonadal hormones since the mice are hormonally identical. For gender differences hypothesized to be caused by differences in hormones, researchers can likewise compare GDX female with GDX male mice to test confirm that

the gender difference disappears upon gonadectomy.

Hormonal add-back injections allow researchers to confirm whether gender differences were in fact caused by differences in hormonal levels (Figure 2).

LIMITATIONS OF GONADECTOMY The central issue with the use of gonadectomy is that the procedure does not normalize for the potentially permanent effects of prenatal exposure to hormones, since there is a delay period between the moment the mouse is conceived and the moment it is gonadectomized. If a particular gender difference, observed between wild-type (WT) males and WT females, is maintained when both sexes are gonadectomized, one cannot automatically assume that sex chromosome complement caused their gender differences. Because the gonadectomy occurred after the mice was born, the effects of the hormones may have played highly important roles in the neonatal and early development of the mouse, both physiologically and psychologically. These effects, termed “organizational effects,” may have permanent consequences on the development of the mice even once the hormones are later removed (Arnold, 2009).

Certain researchers have attempted to perform the gonadectomy as early as possible, to limit the amount of time the mice lives under the influence of its gonadal hormones. However, it is often very difficult, and sometimes impossible, to perform neonatal gonadectomies.

Others have identified peptides that can be given to mice neonatally that inhibit secretion of gonadal hormones; one study in particular identified a neuropeptide that inhibits gonadotropin secretion, called gonadotropin-inhibitory hormone (GnIH) (Ubaka et al., 2005). However, these drugs pose risks in disrupting other biological processes that can confound experimental results. Neonatal gonadectomies and other methods that attempt to normalize for gonadal hormones are therefore not sufficient to separate the effects of sex chromosome and gonadal hormones.

FOUR CORE GENOTYPES MOUSE MODEL

(FCG)

To test whether the gender differences

INVESTIGATING MECHANISMS OF GENDER DIFFERENCES IN IMMUNE SYSTEM DISEASES Four-Core-Genotypes Compare mice with different sex chromosome complement, keep gonadal type constant

XX without Sry (ovaries)

XY without Sry (ovaries)

XY with Sry (testes)

XX with Sry (testes)

Compare mice with different gonadal type, keep sex chromosome complement constant

Figure 3. Gonadectomies can dissect the roles of sex chromosomes from those of gonadal hormones.

By comparing gonadectomized (GDX) male mice with GDX female mice, researchers can determine whether gender differences are caused by altered sex chromosome complement or difference in gonadal hormones.

are due to differences in sex chromosome complement, many researchers use the FCG mouse model. In this model, a gene called Sry that is present on the Y chromosome has been knocked-out (XY). Sry, known as the sex-determining region of the Y chromosome, is required for male development. The resultant mice are XY females (XYF), which differ from XX females (XXF) in their number of X and Y chromosomes; in particular, they have a Y chromosome even though they are gonadally and hormonally female. Similarly, when the Sry gene has been translocated onto autosomes, the resultant mice are XX males (XXM), which differ from Y. Sry—knockout autosome Sry transgenic XY males (XYM) (Arnold et al., 2009).

This model allows researchers to dissect the role of sex chromosomes in the absence of confounding factors such as sex hormones and gonads, by comparing XXF with XYF mice and XXM with XYM (Figure 3). Since both XYF and XXF mice are gonadal females, they both have ovaries that produce the same types and amounts of estrogens. Gender differences observed between XXF and XYF can therefore be assumed to be caused by differences in sex chromosome complement.

Gonadectomies and hormonal injections can be used in conjunction with the FCG mouse model to determine whether or not hormones are responsible for creating the gender differences of interest.

This model has provided support for the hypothesis that female’s “double dosage” of X chromosome leads to increased risk for autoimmune disease

Figure 4. Toll-like receptors (TLRs) recognize pathogens and upregulate transcription of proinflammatory genes.

Toll-like receptors (TLRs) play an important role in the innate immune response by recognizing molecules associated with pathogens called pathogen-associate molecular patterns (PAMPs). To date, ten human TLRs have been characterized (TLR1 to TLR10).

(Libert et al., 2010). Specifically, a class of proteins encoded on the X chromosome, called toll-like receptors (TLRs), has been identified as important to the development of autoimmune disorders. TLRs identify a wide variety of molecules that can cause tissue damage by recognizing pathogen-associated molecular patterns (Akira et al., 2004). As a result, TLRs are highly important in the innate immune response, which provides a relatively nonspecific mode of attack on pathogens (Figure 4). However, the innate immune response can result in damage to healthy tissue if prolonged. That females may express a double dosage of these TLR genes may explain their increased bias for autoimmune disorders. Because this “self-targeting-self” damage is characteristic of autoimmune disorders, TLRs have become a central focus of immune gender bias research (Akira et al. 2004). A recent study found that the dosage of X-linked TLR8 creates gender differences in the development of systemic lupus erythematosus (Umiker et al., 2014). Ultimately, the FCG mouse model has contributed to pinpointing the causes of certain physiological differences between males and females.

LIMITATIONS MODEL

OF

FCG

MOUSE

Although the FCG mouse model may discern whether a gender difference is due to sex chromosome complement, it is limited in its ability to determine whether

a gender difference is caused specifically by presence of a second X chromosome or the absence of a Y chromosome (e.g. when comparing XXM with XYM and XXF with XYF). This problem can be avoided through the use of a Turner’s syndrome mouse model, in which a gonadal female lacks a second X chromosome (genotype is XO) (Sybert and McCauley, 2004). By comparing XO with XX mice, a researcher can deduce whether the gender difference observed was due to presence of double X or absence of Y and therefore better understand the mechanism of gender bias. However, this model elicits other confounding factors, as Turner’s syndrome often leads to other abnormalities that may affect immune function.

Additionally, the FCG has certain inherent flaws. XXM mice, which were initially thought to have only one copy of Sry on one of their autosomes, have several copies of Sry on several different autosomes (Itoh et al., 2015). The Sry gene, when present in multiple copies, may alter physiological and psychological features of the mice. Upon translocation onto an autosome, the Sry gene may also disrupt the open reading frame of an important gene, thereby disrupting gene function (Kleinjan and Heyningen, 2005).

DISCUSSION AND CONCLUSIONS Research about the cause of gender differences promises to provide novel information about the mechanistic UNDERGRADUATE SCIENCE JOURNAL | 56

BRANDON PHAM development of immune system disorders. By researching why females are more likely to develop autoimmune disorders than males, scientists can better understand the root causes of these diseases. This understanding is vital to the development of new treatments. This review discusses the effectiveness and shortcomings of the strategies used to determine whether gender differences arise from hormonal or sex chromosomal influences. In particular, the FCG mouse model has been used widely by a variety of researchers to learn about the causes of specific gender differences, such as more severe lung fibrosis in male mice compared to female mice in systemic lupus erythematosus mouse models (Coultas et al., 1994). When coupled with gonadectomy and hormonal addback injections, the FCG mouse model allows researchers to separate the effects of hormones from sex chromosomes in their analysis of gender differences.

Despite certain limitations, the FCG mouse model shows promise because it may elucidate the cause of gender bias in autoimmune disorders. For gender differences found to originate due to sex chromosome complement, future studies will determine whether they are caused by the presence of double X or absence of Y. Once established, further research will identify the genes on the sex chromosomes involved and their mechanisms of action.

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2.

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Andrade, R.M. et al. (2007) Accelerated damage accrual among men with systemic lupus erythematosus. Arthritis Rheum.

Arnold, A. P. (2009) The organizational– activational hypothesis as the foundation for a unified theory of sexual differentiation of all mammalian tissues. Horm Behav 55.5: 570-78.

Arnold, A. P. and Chen, X. (2009) What does the “four core genotypes” mouse model tell us about sex differences in the brain and other tissues? Front Neuroendocrin 30.1: 1-9.

Coultas, D. B et al. (1994) The epidemiology of interstitial lung diseases. Am J Resp Crit Care 150.4: 967-72.

Devroey, P. et al. (1994) Normal fertilization of human oocytes after testicular sperm extraction and intracytoplasmic sperm injection. Hum Reprod 13.8: 2045-046.

Fairweather, Delisa et al. (2008) Sex differences in autoimmune disease from a pathological perspective. Am J Pathol: 600-09.

Fish, E. N. (2008) The X-files in immunity: sexbased differences predispose immune responses.

Nat Immunol 8.9 (2008): 737-44.

Furman, D. et al. (2013) Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination. P Natl Acad Sci USA 111.2: 869-74.

Ginter, E., and Simko, V. (2013) Women live

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longer than men. Bratislava Medical Journal BLL 114.02: 45-49.

Itoh Y. et al. (2015) Four core genotypes mouse model: localization of the Sry transgene and bioassay for testicular hormone levels. BMC Research Notes 8:69 Iwahana, E. et al. (2008) Gonadectomy reveals sex differences in circadian rhythms and suprachiasmatic nucleus androgen receptors in mice. Horm Behav 53.3: 422-30.

Jamison, Dean T. (2006) Chapter 10: Gender differentials in health. Disease Control Priorities in Developing Countries. New York: Oxford UP, N. pag.

Kleinjan, D. A., and Heyningen, V. V. (2005) Long-range control of gene expression: emerging mechanisms and disruption in disease. Am J Hum Genet 76.1: 8-32.

Lettre, G., and J. D. Rioux. (2008) Autoimmune diseases: insights from genome-wide association studies. Hum Mol Genet 17.R2 Libert, Claude, Lien Dejager, and Iris Pinheiro.

(2010) The X chromosome in immune functions: when a chromosome makes the difference. Nat Immunol 10.8: 594-604.

McCarthy, M. M. et al. (2012) Sex differences in the brain: the not so inconvenient truth. J Neurosci 32.7: 2241-247.

Remote Sensing of Vegetation Ryan Nicholas Lam Remote sensing is a reliable and effective means of conducting Earth science research without having to go into the field. Detecting vegetation is easy with a simple classification index that utilizes visible red and near-infrared wavelengths. In the past, conducting research in large scale areas and remote locations hindered studies in areas like the tropics. Remote sensing has enabled research to be done on regional and global scales with applications in agriculture, deforestation, species richness, and biodiversity.

INTRODUCTION The term “remote sensing” was coined in the 1950s by Evelyn Pruitt of the United States Office of Naval Research as the science of identifying, observing, and measuring an object without coming into direct contact with it (Graham, 1999).

Since all objects emit or reflect back some sort of wavelength, they can be detected via remote sensing.

Remote sensing uses satellites, which have the advantage of seeing the lay of the land from a “bird’s-eye” view. These satellites are equipped with sensors that can detect energy in visible and nonvisible wavelengths, which are used in defining spectral signatures of vegetation.

Hence, remote sensing creates a greater perspective of the world through a larger scope and various lenses to obtain data.

Obtaining real-time data anywhere in the world is practical, economical, and does not require fieldwork. The most popular satellite is the Landsat series with images dating back to the 1970s and low resolution to view large scales.

It costs money to obtain high spatial resolution data, but nowadays it is cheap; for example, the Carnegie Airborne Observatory charges $0.10 per hectare for a 10,000 hectare image (Carnegie Institution for Science, 2011). In 1990, a low spatial resolution Landsat image in 40-inch color print cost $1,500 (Draeger et al., 1997). With over forty years of Landsat data, remote sensing has become an inexpensive, efficient way to do change detection.

Researchers can use remote sensing to study vegetation, which is defined as any characteristic of plant cover in a

mosaic of plant communities (CNVC, 2013). Applications for remote sensing of vegetation go beyond the classification of biomes and the distribution of rainforest in comparison to grassland. Research is geared toward solving practical issues.

For instance, remote sensing can be used to determine plant health. This information can then help increase crop efficiency and yields for maximum output in agriculture and also be used as an argument as to why productive agricultural lands should not be paved over by urban sprawl. Furthermore, countries traditionally self-reported forestry data. Now, remote sensing can offer an impartial look at deforestation, as well as determine the effectiveness of policies, such as the Reducing Emissions through Deforestation and Forest Degradation (UN-REDD) policy. Remote sensing can also discern particular trends or traits of a species, providing valuable information about where to institute reserves while preserving the highest amounts of biodiversity.

THEORETICAL BASIS Remote sensing has opened up a greater “visibility” of the natural world with its ability to see the reflectance and absorption of various wave frequencies.

Satellites operate with multiple windows to transmit specific frequencies through the layers of the atmosphere and down to Earth. For example, ultraviolet rays are absorbed by the ozone in the stratosphere and therefore would not be useful in sensing vegetation. In contrast, microwaves have longer wavelengths

that are not absorbed by water vapor, allowing them to penetrate through clouds and fog to detect the Earth’s surface. Spectral signatures are another key feature to remote sensing of land over a variety of terrains and vegetation types.

The spectral signature plots are based on the reflectance values of different objects over a range of wavelengths (Figure 1) (EUMeTrain, 2010). The sharp change in the reflectance percentage for the conifer at 0.7 µm is known as the red edge and is the backbone of indices to quantify vegetation.

Vegetation Indices Indices aid in understanding remotely sensed images through manipulation or analysis of overlaid image data to obtain the target of interest (The Landscape Toolbox, 2013). They were first developed from simple band ratios, specifying certain spectral properties of vegetation. The most widely used is the Normalized Difference Vegetation Index (NDVI); all other indices for vegetation, such as the Enhanced

Figure 1. Spectral Response. This demonstrates the reflectance values of vegetation across various wavelengths in comparison to water and soil (EUMeTrain, 2010).

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Figure 2. Global NDVI. This map shows the global distribution of NDVI values for June 2015 from Terra (Stockli, 2015).

Vegetation Index, are derivatives of the NDVI. Healthy vegetation absorbs red visible light and reflects near-infrared wavelengths, creating a simple ratio with these two variables. Using this ratio, NDVI measures the greenness of Earth’s surface to determine the density and coverage of vegetation undergoing active photosynthesis as a unitless value (NOAA OSPO, 2016). In the context of biomes, low values, -0.01 to 0.4, are associated with deserts, tundra, or grassland, while high values, 0.4 to 0.9, indicate forests and tropical rainforests (Figure 2).

Sensor Standardization Many satellites have the capability of sensing the spectral signatures of vegetation with bands in the red visible and near-infrared wavelengths.

However, each satellite has its own unique functions and settings that can customize the data. Two popular sensors of vegetation are the Moderate Resolution Imaging Spectrodradiometer (MODIS) and the Advanced Very-High-Resolution radiometer (AVHRR). In a comparison of NDVI values, MODIS recorded slightly higher values than AVHRR in part due to the absorption bands. AVHRR red bands (600-680 nm) encompass a portion of the green spectra (585-600 nm), which skews the NDVI values, resulting in a lower red reflectance reading for MODIS (Ji et al., 2008). However, although their correlation is not perfect, NDVI values are still consistent. This consistency means each satellite offers accurate data, but choosing one over the other offers benefits that should be exploited on a case-by-case basis.

For instance, if the aim is to study the 59 | UNDERGRADUATE SCIENCE JOURNAL

global distribution of biomes, it would be logical to use AVHRR due to its large swaths. However, if remotely sensing a temperate forest, using AVHRR will lower NDVI values due to the green spectra that is included in the visible red wavelength. Therefore, MODIS would provide a more accurate value with its higher sensitivity when dealing with high biomass concentrations. Meanwhile, it would not be wise to use the Landsat satellite to remotely sense a preserve in Brazil due to the satellite’s sixteen-day revisit cycle, with which cloud cover can easily interfere. Instead, researchers can use MODIS, which has a short revisit time and large swath. These examples illustrate that satellites can be used to the user’s advantage. Although each satellite measures the same object using visible red and near-infrared wavelengths, each satellite has different specifications, such as resolution, orbit, and swath size, along with various sensor bands, to work around nature’s parameters.

APPLICATIONS As primary producers harvesting energy from the sun, plants yield usable energy for the natural world, making them the foundation of life. Therefore, distribution and health of vegetation have implications on humans and sustainability. Hence, applications of remote sensing range from agriculture to climate change to understanding biodiversity and species distribution.

Agriculture It took French monks thousands of years to perfect the art of developing

fine wines. Remote sensing has made this process much easier in a shorter time span. In ecology, there are three living zones: the zone of intolerance where plants die, the zone of stress where they are on the verge of dying, and the optimal zone where plants are healthy and most plentiful. In viniculture, however, it is desirable to have the grapevines in the zone of stress. Vines that have excess light, nutrients, and water will turn their efforts toward growth with investment in larger leaves and budding vines.

However, if the plant is only receiving the bare minimum for survival, the grapevine will use the limited resources to reproduce, diverting them to the grape.

Therefore, grapes produced in stressful environments make higher quality wines with more complex flavors (Prichard et al., 2015). Achieving a zone of stress on a grapevine includes environmental factors such as slope, elevation, soil content, light exposure, and water (Weier, 2000). Modifying the topography over a hundred acre vineyard is not a feasible task. Remote sensing serves as a validation tool to measure plant health via NDVI values as indicators of optimal zones and zones of stress to ensure that the necessary adjustments are made.

Agricultural implications are not limited to determining plant health.

In China, urban sprawl is threatening prime farmland. To avoid the politics, researchers Xia Li and Anthony Gar-On Yeh took a scientific approach, using remote sensing in the Pearl River Delta with NDVI as an indicator of productivity.

To zone the fertile land, a slicing threshold was determined based on the number of high NDVI pixels to qualify as a protected zone. Although areas of fertile lands may not be continuous, this remote sensing data was integrated with Geospatial Information Systems (GIS) and cellular automata. This incorporated a number of constraints to allow zoning to be practical with future urban sprawl (Li and Yeh, 2001). This particular method is not restricted to agricultural protection and can be substituted with any environmental constraint, such as the addition of a particular tree species that provides habitats for endangered birds.

The methodology is especially relevant in the future of environmental planning and management.

REMOTE SENSING OF VEGETATION Climate Change and Deforestation Deforestation poses the threat of climate change due to the loss of a major carbon stock. The spatially explicit analysis of forest distribution provides data based on which countries need to reform their forestry practices. Traditionally, countries self-report forest data. However, in order to determine forest cover change, a transparent, consistent, and repeatable approach can be taken through remote sensing. Using data of Landsat 7 ETM+ from 1999 to 2012, 650,000 images were obtained and processed using techniques such as NDVI that were then added to the thematic mapper program to issue a tree cover percentage. This percentage was then tracked using the thirty meter resolution on a pixel by pixel basis to determine whether percentages were going up due to reforestation or down from deforestation (Hansen, 2013). Temperate forests are the major vegetation carbon stocks and have a higher biomass carbon density than the boreal, temperate, or subtropical forests. Little attention is given to these forests, however, because they are in developed countries which already have policies in place to protect them.

On the other hand, tropical rainforests are typically in developing countries, which have few regulations about cutting down trees. For instance, although the southeastern region of the United States had a disturbance rate four times higher than the South American rainforest, 31% of trees were regrown, attributable to sustainable forestry practices. Malaysia and Cambodia—tropical rainforest ecozones and developing countries— boast the highest rate of forest loss (Hansen, 2013).

UN-REDD is an international agreement by the United Nations to enhance forest management in developing countries in an effort to mitigate climate change. In order to assess their progression, there must be a benchmark achieved by quantifying the total carbon stocks in live biomass.

Light detection and ranging, a remote sensing technique, was used to model the spatial distribution of aboveground standing biomass density (AGB) by measuring the vertical profile of the forest. MODIS NDVI values were also used to compute forest area as defined

by 10% tree cover over three continents— South America, Africa, and Asia. Wood density measurements from the field were also taken to determine the amount of carbon tree species actually had. The findings provide data on seventy-five developing countries and improve upon other assessments that are outdated or incomplete (Saatchi et al., 2011).

This information, combined with the spatial distribution of deforestation, can determine the extent of carbon sink lost. That data can then can be put into climate models, which have implications for future distribution of plants with warming temperatures. It can also project the extent of polar ice melt, which has two effects: rising sea levels and reduced range of polar bears. Finally, this study is also relevant to urban planning, commerce, and homeland security because 50% of the world’s population live on coastlines, which are vulnerable to rising sea levels.

Biodiversity Biodiversity is an indicator of ecosystem health as well as sustainability. Tropical rainforests are only 7% of the planet’s total landmass, but they are biodiversity hotspots accounting for about 50% of the world’s species. Development in the tropics through deforestation is a hazard to the species that reside there. Mapping biodiversity is a step toward determining patterns, processes, and environmental factors that can be used to preserve areas proven to be biodiversity hotspots.

Species have a defined niche. This is especially true in the tropics where there are so many species and a limited amount of land. To avoid competition, each species needs to have a set of parameters in which it can thrive, such as insolation availability, soil nutrients, slope, and elevation. The Barro Colorado Island in Panama has a tree inventory of 204,275 species of free-standing woody plants—the ideal site to test remote sensing techniques for individual species (Fricker, 2015). Geoffrey Fricker, a lecturer at UCLA, used airborne LIDAR and the Quickbird satellite to quantify canopy reflectance, canopy structure, and sub-canopy micro-topography across each vegetation level using LIDAR and its penetrating capabilities (Figure 3).

He determined that species richness was

highest in open canopy and medium stature forest, where trees can exist in higher densities. Because less than 20% of sunlight penetrates the canopy floor to reach the understory, topography is more important for small trees than for medium and large canopy trees (Raphael, 2016).

The ability to scan the understory with LIDAR revealed information about the shrubs, young trees, and next generation tall canopy trees, which hold the majority of the diversity. These study results were able to construct a 3-D representation of the rainforest structure while preserving NDVI values, in contrast to generic NDVI distributions (Figure 2) which only display the uppermost layers.

This further differentiates features of particular species.

Overall, the study found that remote sensing data can predict 25-50% of total variance in tree species richness over the sample plot, far exceeding Fricker’s initial expectations, which were set below 25% due to the high density of species in the tropics (Fricker, 2015). It is infeasible for a team of ecologists to catalog every plant in the tropical forest; remote sensing has the potential to achieve this task in a fraction of the manpower and time, making it a highly efficient and economic option.

This study is only one of a handful that has attempted to use remote sensing to classify species. The results are promising for an application of remote sensing that is still in its infancy.

CONCLUSION The power of remote sensing lies in its capability to provide images and large amounts of data of electromagnetic wavelengths—quantifying what the eye cannot. The applications of this technology include analyzing storm systems, sea surface temperature, and glaciation, as well as urban planning and military intelligence. Remote sensing provides eyes in the sky and the ability to view anything, anytime, anywhere on a multitude of spatial scales with available satellites.

In remote sensing, the ideal parameters call for high spatial resolution with small pixels and high spectral resolution with many bands providing higher accuracy for true spectral signature. Current satellites are not able to achieve both, UNDERGRADUATE SCIENCE JOURNAL | 60

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3.

4.

5.

CV=0.19(-2sigma)

CV=0.27(-1sigma)

6.

7.

8.

9.

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CV=0.36(+1sigma)

CV=0.73(+2sigma)

Figure 3. Structure of the Rainforest using Coefficient of Variance. Four vertical cross sections (10 meter depth) of the forest inside the 50 ha plot show Coefficient of Variation (CV) in forest structure. Low CV scores indicate low variation in upper canopy height as a high average canopy high while high CV scores indicate higher variation in upper canopy and low average canopy height (Fricker, 2015).

but rather one or the other. For example, Geo-Eye 1 has one of the highest spatial resolutions in the civilian sector at 0.46 m but has a spectral resolution of five bands. On the other hand, MODIS has a spectral resolution of thirty-six bands and a coarse spatial resolution of 1 km (Gillespie, 2016). Tradeoffs currently exist that make the choice of satellite important depending on the research. However, there is technology in development to eliminate these tradeoffs.

The future in remote sensing vegetation is taking place at the Carnegie Airborne Observatory (CAO) where aircraft as opposed to satellites are used as the platform for their sensor. This sensor uses laser light to attain images of tropical forests to gather data on tree size and NDVI. Although this is similar to Fricker’s project in finding species richness in the tropics, CAO has taken on the mission of identifying the genus of trees. This is achieved with a hyperspectral sensor which has 350 bands at 5 nm each and a spatial resolution of 1 m. Hyperspectral data has enabled remote sensing to tap into the chemistry of plants, as opposed to only the “greenness” that NDVI values represent, with the ability to measure ecosystem biochemical and physiological properties (Asner, 2007). This additional data helps distinguish certain species 61 | UNDERGRADUATE SCIENCE JOURNAL

from others. The revolution behind this technology is that the hyperspectral sensor is able to recreate its surroundings simply by collecting the spectral signature.

The applications of remote sensing are far-reaching. The observations will allow researchers to see the world in better ways by expanding horizons beyond blue, green, and red—and to the entire electromagnetic spectrum.

ACKNOWLEDGEMENTS I would like to thank K.N. Liou, Distinguished Professor of Atmospheric Science at UCLA, and Geoffrey Fricker, Lecturer in the Geography Department at UCLA, for their guidance and expertise through the process of this publication.

Their research is what drove me to delve deeper into remote sensing vegetation for its unparalleled applications towards understanding the environment, and the overall potential of remote sensing.

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