Scientia Spring 2021

SCIENTIA A JOURNAL BY THE TRIPLE HELIX AT THE UNIVERSITY OF CHICAGO

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CONTENTS Published by The Triple Helix at the University of Chicago Editors-in-Chief Thea Applebaum Licht & John Naughton Scientia Board Arundhati Pillai, Isabel O’Malley-Krohn & Plash Goiporia Layout and Design Bonnie Hu & Stephanie Zhang

03 ABOUT SCIENTIA INQUIRY

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Professor Jeffrey Hubbell: How A Multidisciplinary Approach To Science Has Led To Innovation In Bioengineering Fields EDUARDO G. GONZALEZ

WORK IN PROGRESS

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Sequence-Dependent DNA Hybridization Kinetics From Molecular Dynamics Simulations Of Ion-Dna Interactions MELODY LEUNG

INQUIRY

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Bringing Pedagogy to the Forefront: An Inquiry with Dr. Kerry Ledoux

CAROLINE MILLER

REVIEW

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The Function and Regulation of BDNF in Synaptic Plasticity OMAR KASSEM

REVIEW

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Enteric Hyperoxaluria: A Rich Horizon for Precision Medicine NAJYA FAYYAZ

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ABOUT SCIENTIA Dear Reader, It has now been over a year since the University of Chicago’s research programs were first interrupted in response to the early days of the COVID-19 pandemic. Since then, despite the upheaval of normal laboratory work and the transformation of academic life, students have worked hard to stay engaged in the scientific community and involved in scientific exploration. The year’s challenges make us all the more excited to share with you this edition of Scientia, featuring student research and inquiry produced in the midst of a transformed academic landscape.

In this issue you will meet student researchers who, in the face of an uncertain academic terrain, demonstrate through their work a passion for scientific inquiry, and professors whose career journeys allow us to reflect on the importance of science and science education. In particular, you will find impressive original research on DNA hybridization kinetics and an extensive literature review investigating the function and regulation of the protein BDNF in neural synapses. We are also proud to feature in-depth interviews with Professors Kerry LeDeux and Jeffrey Hubbel who shared their journeys through STEM and their passions for research and pedagogy. These pieces represent the efforts of an impressive team of writers, editors, and designers with whom we have had the privilege of working.

Scientia is always looking to broaden our scope and expand the reach of our publication. If you are working on a research project you want to see in print, or if there is a profesor performing eye-opening research you would like to share, please consider writing for us! We encourage all interested writers to contact a member of our team, listed in the back. In the meantime, please enjoy this edition of Scientia, presented by The Triple Helix.

Sincerely, John Naughton and Thea Applebaum Licht Editors-in-Chief of Scientia

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INQUIRY

PROFESSOR JEFFREY HUBBELL: HOW A MULTIDISCIPLINARY APPROACH TO SCIENCE HAS LED TO INNOVATION IN BIOENGINEERING FIELDS Eduardo G. Gonzalez The long road that led Professor Jeffrey Hubbell to the University of Chicago was not always clear. From college to Switzerland to the University of Chicago, Professor Hubbell continued to integrate new fields into his work as he became a more versatile scientist. His drive to learn and make an impact is palpable in each stop he made along the way to his current position as a pioneer and integral member of the University of Chicago’s Pritzker School of Molecular Engineering.

Before he knew what he wanted to do, Professor Hubbellfirst learned what he did not want to do.

When he spent two of his summers in college working at Dow Chemical in Texas, he learned about the roles and responsibilities of process engineers. He also found that he disliked the routine lifestyle of the job. Instead, the world of academia called. The professor for whom he worked his junior and senior years inspired him to attend graduate school––to which his father would jokingly ask, “Are you afraid of getting a real job?” After receiving his bachelor’s degree from Kansas State University in 1982, he began graduate school at Rice University.

In graduate school, Professor Hubbell became a “bioengineer by trade, chemical engineer by training” after working with Dr. McIntire, a bioengineer who applied physics concepts like fluid mechanics to the study of vascular biology. Professor Hubbell would follow in the footsteps of his mentor as he wrote his doctoral thesis on how blood flow influences clotting in vascular injury or thrombosis. His work included creating mathematical models of transport processes for factors needed for coagulation, studying the role of cells within hemostasis, and testing coagulation in the presence and the absence of blood flow. He continued his work with vascular biology as an assistant professor at the University of Texas.

Despite incorporating more of the biological sciences into his research, he was not fully satisfied. He sought to also become involved in translational medicine studies.

There began his endeavor to combine his knowledge of chemistry with the field of biomaterials. The Biomedical Engineering Society and Society for Biomaterials fostered his growth, serving as they both served as hubs where he was able to connect with and learn from other professionals. He felt fortunate to be fully fluent in chemistry, which

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cultivated a different perspective from others in the field andallowed him to innovate significantly. After his time at the University of Texas, Professor Hubbell moved on to the California Institute of Technology where he continued his previous work in vascular tissue engineering.

After his time in California, Professor Hubbell moved to Switzerland where he had the opportunity to lead the Institute for Biomedical Engineering at the Swiss Federal Institute of Technology and the University of Zurich. There he tapped into protein engineering (which would later become his forte), a deviation from the more synthetic and chemical approaches he had taken throughout his career. This expansion diversified his repertoire of scientific knowledge. As he puts it, he became “even more biological” as he studied the creation of growth factors and the use of recombinant technology.

He also brought American bioengineering to Swiss scientists, which helped foster significant advancement in the field there.

Afterwards, Professor Hubbell transitioned to a larger role at the École Polytechnique Fédérale de Lausanne in Switzerland, a research institute and university known for its work in engineering and natural sciences.

He was tasked with creating and running a bioengineering department as part of the president’s mission to create a life sciences division (which Professor Hubbell would eventually lead as the dean). There, he delved into regenerative medicine by employing a combination of all the expertise he had attained up until that point. Professor Hubbell considers it a highlight of his career because he “built the department from zero to the best bioengineering department in Europe.”

After thirteen years in Lausanne, Professor Hubbell moved to Chicago to work at the Institute of Molecular Engineering, now named the Pritzker School of Molecular Engineering (PME). When deciding to move, Professor Hubbell had felt that he had accomplished what he had set out to accomplish in Lausanne and was being presented with a new opportunity to build something up once again. At the time, the PME only had about five professors. Matt Tirrell, the dean of the department, asked Professor Hubbell to lead the development of the Institute, a role similar to his work in Switzerland. He

felt that the University of Chicago offered a strategic advantage for his research since the Pritzker School of Medicine and the Biological Sciences Division could create multiple venues for interdisciplinary collaboration throughtheir proximity to the PME .

At the University of Chicago, Professor Hubbell continued his exploration of immunology through both his work with the University and the translational work done by Morpho-Gene, a company he founded. The first area he works in is autoimmunity, which he had begun to tap into while in Lausanne. He developed a treatment for Celiac disease, an autoimmune disorder that causes gluten intolerance, that is currently in clinical trials. He also worked on inverse vaccines––a type of vaccine that creates a tolerance for immune responses in those with autoimmune diseases. The second area he has worked on is immunooncology. He has collaborated with fellow PME professor Melody Swartz on a method to target tumors with immune regulating molecules to create an environment unfavorable to tumor growth. Dr. Cathryn Nagler’s lab has collaborated with Professor Hubbell, employing his chemical approaches in their research on food allergies.

Alongside his other works, Professor Hubbell continues to develop the research in regenerative medicine that he began in Zurich. He is committed to working with diabetic complications because of the propensity for diabetics to develop life-threatening chronic wounds.

His work in this area includes developing treatments that would promote wound healing. Research has been done on mouse models so far, but Professor Hubbell has also been working with Dr. Priscilla Briquez at Morpho-Gene to begin studying diabetic pigs. He hopes to be able to commercialize his work to make it available to diabetic patients.

In addition to research, Professor Hubbell teaches several courses at the University of Chicago. The first is a course named Biological Materials with Dr. Mustafa Guler. It teaches upper level undergraduate and graduate students about materials in the body — like membranes and proteins — as well as how to create materials for medical applications. The second class is Stem Cell Biology, Regeneration, and Disease Modeling with Dr. Priscilla Briquez. The class has historically been about the biology behind regenerative medicine and its applications, though it now includes more stem cell biology as part of its coursework. In the future, he plans to teach a course on quantitative physiology alongside Dr. Melody Swartz.

Professor Hubbell’s main focus is currently to push drugs and treatments he has developed towards clinical testing. This goal is why he founded Morpho-Gene. He stated that “The first paper is the easy part; the hard part is proving that it works. You have to test the limits of your design.” Professor Hubbell’s life and career serves as a prime example of the power of

collaborative and interdisciplinary science as a catalyst for discovery and innovation. The work he has done has furthered the fields in which he has worked, and led to technologies that will make a difference in many people’s lives.

Eduardo G Gonzalez is a third year student at the University of Chicago, majoring in Biological Sciences, Biological Chemistry, and Chemistry. His interests fall primarily on the study of molecular interactions as a framework for understanding more complex processes undertaken by microorganisms and viruses. Besides writing for SISR, Eduardo is an Emergency Medical Responder for UCEMS, a student-run first responder unit, and a chair for both MUNUC and ChoMUN, Model UN organizations that run conferences. In his free time, Eduardo enjoys writing science fiction, being bad at basketball, watching romantic comedies, and discussing cinematic masterpieces like Space Jam, Sharkboy and Lavagirl, and others.

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WORK IN PROGRESS

SEQUENCE-DEPENDENT DNA HYBRIDIZATION KINETICS FROM MOLECULAR DYNAMICS SIMULATIONS OF ION-DNA INTERACTIONS Melody Leung, Mike Jones, Andrew Ferguson Pritzker School of Molecular Engineering The University of Chicago ABSTRACT

DNA hybridization is a key mechanism in biology and nanotechnology, but studying its dynamics at atomic length scales is notoriously difficult. In this work, we use molecular dynamics simulations and auto-associative neural nets to construct kinetic models based on insights from ion-DNA interactions. To construct the model, we first explored relevant coordinates in describing the hybridization process of a 10-base pair DNA duplex in varying ion conditions.

Then, we leveraged auto-associative machine learning methods to learn slow-collective variables of the system and build high-resolution kinetic models. Using our model, we aim to gain a better understanding of the role of ions in stabilizing and facilitating slow transitions in DNA, which could enable novel interpretations of sequence-dependent hybridization theory and better inform kinetically-based DNA nanotechnologies.

INTRODUCTION

DNA is central to biology––not only for storing, processing, and regulating genetic information, but also as an integral part of nanotechnology and molecular self-assembly [1]. DNA hybridization can be used to link together nanoparticles or molecules, and its reversibility and specificity, enabled by the formation of hydrogen bonds between complementary strands, is integral to assembling intricate structures demanded in nanotechnology [2]. Since nucleic acids are highly negatively charged, their resulting high charge density generates strong repulsive forces that must be reduced through electrostatic screening by water and counter-ions in order for DNA to hybridize [3]. Nucleic acid-ion interactions provide large amounts of free energy and are critical to developing a complete description of DNA folding dynamics. However, these interactions are not easily described due to their high mobility, volatility, and dependence on specific DNA sequences [4].

X-ray diffraction and solution NMR have been used to experimentally examine the local ion environment around DNA [4]. However, solution NMR lacks the resolution to fully describe the local environment, and the crystals used in X-ray diffraction may not be representative of the ionic environment in solution [5]. While ion distributions around intact DNA duplexes have been characterized by molecular dynamics (MD) studies, the role and mechanism of ion-DNA interactions during structural changes such as hybridization of DNA have not been explored [6].

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Markov modeling has allowed long-timescale kinetic behavior to be inferred from shorter MD simulations [7]. The first step of this method involves transforming the simulation coordinates into structural features such as intermolecular distances and torsion angles [8]. Then, dimensional reduction is performed by State-free Reversible VAMPnets (SRVs), a deep learning architecture, so that a few non-linear slow collective variables can be learned from the coordinates. The resulting dimensionally-reduced space is clustered into discretized microstates, and further arranged into a transition state matrix with a coarse-graining of the estimated Markov state model (MSM).

In this work, we aim to develop an ion-based SRV-MSM pipeline for improved qualitative and quantitative understanding of ion interactions during DNA oligonucelotide hybridization. We explore whether ion coordinates can predict higher order dynamical modes of DNA, especially in bringing resolution to intermediate fraying modes. We then compare the SRV-MSMs generated by ion-only coordinates and backbone-only coordinates, as well as correlations to physical coordinates. As finding the optimal coordinates is crucial in minimizing model error, we spend the majority of the study exploring candidate feature sets. We also show that SRVs trained on ion coordinates can resolve higher order modes similar to those of backbone coordinates but are more sensitive to random movements of the DNA.

METHODS DNA Modeling and Simulation.

We used coarse-grained molecular dynamics simulations to explore the ion distribution around a 10-base pair DNA duplex with a nucleotide sequence of 5’-ATATGCATAT-3’ during hybridization and de-hybridization.

Our in silico model uses a 3-site-per-nucleotide (3SPN.2) description of DNA, with each sphere centered at the center of mass of the phosphate, deoxyribose sugar, and nitrogenous base [9]. The duplex, shown in Figure 1, was embedded in a periodic 77.74 Å x 77.74 Å x 77.74 Å cubic box filled with 18 neutralizing Na+ ions, 100mM NaCl, and varying concentrations of 50mM, 100mM, and 150mM MgCl2 . The simulation box was large enough to prevent the DNA from interacting with itself from any periodic image of the simulation box. Subsequent analysis was performed with 150mM MgCl2, since modeling more magnesium ions provided more resolution and ion data for SRV-MSM construction.

Figure 1. Three-dimensional representation of the double-stranded DNA oligonucleotide, the periodic box, and the sodium (dark blue), magnesium (green), and chlorine (light blue) ions around the DNA molecule (150mM MgCl2 ). This snapshot was taken after the hybridization event and the figure was rendered using VMD.50 [10].

This DNA sequence was selected because previous molecular dynamics simulations and experimental data of this duplex revealed distinct fraying dynamics, which offers better sampling and resolution of DNA hybridization pathways beyond simple two-state behavior. The simulations were performed using the LAMMPS molecular dynamics package [11].

Featurization.

To perform slow collective variable discovery, we first defined the set of features derived from each instantaneous configuration of the molecular system that will be used to represent the trajectory in the learning algorithm.

We aim to capture the most relevant ion interactions that

influence DNA folding by visualizing different coordinates and comparing them to known physical coordinates. Two featurization methods were considered: the first captures the local ion environment surrounding each phosphate of the DNA backbone, whereas the second better accounts for ion bridging effects between complementary DNA strands. At every frame, all ion-phosphate distances were calculated using the MD traj software package [12]. For the first method, ions of the same type were sorted according to their distance to each of the phosphates, and the inverse distances of a number of the closest Mg2+, Cl−, and Na+ to each phosphate were compiled at every frame as candidate input features for the SRVs. The second method first involves averaging the distance from an ion to a phosphate on one DNA strand with the distance from the same ion to the corresponding phosphate on the complementary strand for each type of ion to each nine phosphate pairs, then sorting by the averaged ion-phosphate-pair distances for each ion type. From the sorted distances, varying numbers of the closest averaged distances were selected as candidate features. A variety of combinations of different numbers of each type of ion-phosphate distance were tested, corresponding to different fractions of the total number of coordinates. After initial testing, a narrower range of fractions were re-tested to reflect the best performing sets of feature coordinates.

The number of ion-phosphate distances included in each feature set for each type of ion was optimized with a Variational Approach for Markov Processes (VAMP) that generates VAMP-2 scores measuring the cumulative kinetic variance of the system [13]. The VAMP-2 score is the sum of the squared eigenvalues of the transfer operator, with a higher score corresponding to a more kinetically accurate model [8, 14]. Feature sets were also built from ion-base pair distances employing the above two methods and compared with the ion-phosphate feature sets to obtain the optimal input features for constructing Markovian models of DNA hybridization dynamics. The candidate feature sets were also passed into SRVs and cross validated with a Pearson’s correlations analysis using SRV coordinates generated from reciprocal permutation-free backbone coordinates. A Granger causality test was also performed to analyze causal relations between the ion time series data and the intermolecular backbone distances. The statistical definition of Granger causality is based on the premises that (i) a cause occurs before its effect and (ii) knowledge of a cause improves prediction of its effect [15]. Thus, a time series X Granger causes another time series Y within a certain lag time if inclusion of the history of X improves prediction of future values of Y (within a certain lag time) over knowledge of the history of Y alone.

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Dimensionality Reduction using State-free Reversible VAMPnets.

From the feature set, we then reduce the dimensionality of the system to both remove the redundant information stored in the atomic coordinates and provide a framework for accurate clustering of conformations.

State-free reversible VAMPnets (SRVs) are a neural network-based approach to directly learn nonlinear collective variable (CV) approximations to the leading eigenfunctions of the transfer operator [8]. The VAMPnet-based framework learns the best low dimensional representation of input features by training twin-lobed networks to minimize a VAMP-r loss function, which then generates the optimal eigenvector approximations [16].

The resulting orthogonal CVs are continuous, explicit, and differentiable functions associated with the slowest dynamical processes in a system. These orthogonal CVs are directly used to interpret kinetic information (such as physical correlations and timescales) and employed as a feature set to construct Markov State Models (MSMs) (described in more detail below). Compared to other models, MSMs constructed from SRV coordinates exhibit faster time scale convergence with higher temporal resolution and state decomposition [17, 8]. The SRVs were trained using the default architecture of two hidden

layers with 100 neurons each, implemented by Keras and Tensorflow libraries [18, 19]. Each of the candidate feature sets were transformed into low dimensional SRV basis sets using the same optimized hyperparameters: a batch size of 5000, a learning rate of 0.01, and a lag time of 10. We ran each model for 50 total training epochs with Keras early stopping, which restored model weights from the epoch with the lowest validation loss to prevent over-fitting for a validation split of 0.2. Three SRVs were constructed for every feature set for comparison, with outputted slow modes of 3, 4, and 5 respectively.

Construction of Markov State Model.

Markov State Models can describe the long-time statistical dynamics of a system from large amounts of shorter timescale molecular dynamics simulation data [17, 20]. This powerful tool addresses the central sampling problem in molecular dynamics and provides quantitative comparisons to experimental data. Constructing MSMs involve (i) discretizing the conformational space into kinetically similar microstates and assigning each conformation in the MD trajectories to the closest microstate, (ii) constructing a transition probability matrix by calculating the conditional probabilities between all microstates within a certain lag time, and (iii) coarsening the MSM

Figure 2. Averaged inverse phosphate-ion distances at each time frame across all 25 trajectories.

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microstates to macrostates in order to obtain an interpretable model [18]. SRV eigenfunctions are optimally suited as an MSM basis because SRVs translate simulation features into their lowest kinetic representations, which is especially pertinent when examining ion coordinates due to their variability. This advantage is evident in the consistently higher SRV-MSM VAMP-2 scores and lower variance compared to that of TICA-MSMs, which is based on a linear approach [8, 14]. SRV-MSMs also exhibited faster implied time scale convergence, enabling shorter lagtimes and thus higher resolution kinetic models.

Our SRV-MSM framework was built with the PyEMMA MSM pipeline [21]. After passing in SRV coordinates, we perform k-means clustering so that each frame was assigned to 200 microstates that were pre-clustered with the backbone SRV coordinates. We then conduct Bayesian MSM construction, and PCCA+ hierarchical macrostate assignments into 5 macrostates. The SRVMSM lag time was selected based on implied timescale convergence.

RESULTS AND DISCUSSION A. Featurization Kinetically-relevant input coordinates are crucial to constructing a good SRV-MSM model with minimal modeling errors. Figure 2 presents the inverse average distance between the closest 30% of each type of ion to each phosphate on both DNA strands centered on the hybridization event and averaged across all 25 trajectories. Since ions are highly mobile and volatile, a smoothened coordinate, calculated by averaging the 5 timesteps before and after each frame, is shown in bold in order to better visualize the overall trend.

The hybridization event is represented by the number of base pairs bound, and the graph shows the 1000 frames centered on the fraying event. The average magnesium-phosphate distance increases significantly after the hybridization event, whereas the average chlorine-phosphate distance decreases. There is a more subtle increase in sodium-phosphate distances. These trends are consistent with the expected electrostatic interactions between ions and the phosphate backbone.

In the dehybridized state, cations are more evenly-distributed around each negatively charged phosphate, since they are not crowded out on one side by the other DNA strand. Prior buffer equilibration-atomic emission spectroscopy (BE-AES) experiments and MD studies have shown that divalent cations such as Mg2+ dominate the ion atmosphere immediately surrounding the DNA strands, and divalent cations have tighter spacial associations with DNA compared to monovalent cations [4]. The defined increase in Mg2+-phosphate average distance during hybridization is reflective of the exclusion of Mg2+ by WC pairs occupying the DNA core, which has less of an impact on Na+, as it is more loosely bound to begin with. This result is in agreement with existing literature. The sodium and magnesium distances follow a slight inverse relation, consistent with their com-

petitive and repulsive electrostatic interactions and with previous studies [4]. Since the cations are excluded from the core of the bound DNA duplex, and the DNA duplex’s negative charge is more locally concentrated, chlorine ions are less spatially restricted by their repulsion to the phosphate backbone and can move closer towards the cations. The slight increase in the Mg-phosphate inverse distance preceding the hybridization event suggests possible bridging effects, where the Mg2+ could facilitate the initial association of the two highly negatively charged backbones. Bridging effects are further explored below.

Figure 3 shows the Pearson’s correlation value between phosphate-phosphate distances and smoothened ion-phosphate distances. The phosphate-phosphate distance measures the distance between the pair of phosphates corresponding to each WC pair. For the smoothened ion-phosphate distances, the distance of the closest 30% of each type of ion to each phosphate was averaged, and each ion-phosphate distance was averaged with that of the corresponding WC phosphate pair before being smoothened as described above.

(a) Pearson’s Correlation

(b) p-values Figure 3. Pearson’s Correlation and corresponding p-values for phosphate-phosphate and ion-phosphate distances.

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The Pearson’s correlation and p-values for all phosphate pairs reflect the overall trend in Figure 2, and provides more resolution for sequence-dependent correlations. The positive Pearson’s values for chlorine indicates that the chlorine-phosphate distances decrease as the phosphate-phosphate distances decrease. In other words, chlorine ions on average come closer to the DNA duplex after hybridization. The negative Pearson’s values for magnesium and sodium suggest the opposite trend.

Overall, the chlorine-phosphate correlation was the strongest, closely followed by magnesium. Sodium has a lower Pearson’s correlation and higher p-value above the 0.05 significance level, which suggests that the sodium-phosphate distances fluctuate more throughout hybridization without a drastic increase, consistent with Figure 2. The GC-core region of the DNA, represented by phosphates 4, 5, and 6, has the highest Pearson’s value for magnesium, which further supports the hypothesis that ion exclusion could be due to WC pairs occupying the DNA core and thus excluding the ions, such that a stronger WC interaction would correspond to a more sustained exclusion of ions from the DNA core. The slight asymmetry between phosphate pairs 1,2 and 8,9 is likely due to asymmetric fraying, where the lower Pearson’s values for phosphate pair 1 and 2 suggest that Mg2+ ions were not as affected by the exclusion from the DNA core as by the WC pairs on the fraying end, so that the ion distribution during fraying resembles when the DNA was dissociated. This asymmetry is also observed with Chlorine––the negatively charged fraying ends are more spread out in the box, as shown in Figure 4, so that the chlorines are also repelled further away from the DNA strands.

(a) ion time series as predictor

(b) ion time series as predictor Figure 5. P-values for Granger causation null hypothesis significance test at lag times from 0 to 450ps with ion-phosphate-phosphate distances as predictors. 0.05 significance threshold shown in red.

Figure 4. Snapshot of fraying DNA and local ion distribution

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A Granger causality test was performed for the same time series ion coordinates used in Figure 2 versus averaged corresponding phosphate-phosphate distances at every time step. The null hypothesis of the granger test is that the predictor time series X does not Granger-cause another time series Y, so that rejection of the null suggests that X forecasts Y at some lag time. Figure 5 a) shows the p-values against the null hypothesis at various lag times, with the ion-phosphate distance as the predictor, whereas b) shows the reverse causation, with

phosphate-phosphate distance as the predictor.

For magnesium and chlorine, the null is rejected for lag times 1-9 for both prediction directions, indicating bi-directional Granger-causation within 9 lag times.

However, when using ion coordinates as predictors, the p-values for magnesium and chlorine time series is consistently lower and under the 0.05 significance threshold for a larger range of lag times: The Mg2+ time series fails to reject the null hypothesis at 83 lag times, and Cl- does so at 139 lag times, whereas for the reverse direction Mg2+ fails at 11 lag times and Cl- at 10. Conversely, Na+ fails to reject the null even for one lag time when using the Na+ time series as the predictor, while it fails at 31 lag times for the reverse direction. The high lag times at which Mg and Cl ions are still within the threshold suggest that their interactions could predict fraying and hybridization events, whereas Na ions may be less involved.

The stronger correlation for magnesium and chlorine is also supported by Figures 1 and 2. Magnesium and chlorine both exhibited a slight decrease in average distance in the 50 frames preceding the hybridization event, which corroborates the stronger Granger-causation for the ion-prediction direction. This further suggests that magnesium and chlorine directly facilitate hybridization.

Sodium coordinates appear to be uni-directionally Grang-

er-caused by the phosphate coordinates, suggesting a less active role of sodium in facilitating DNA hybridization.

B. Dimension Reduction with SRVs After passing different combinations of inverse ion-phosphate and ion-base pair distances and the corresponding bridging distances into SRVs, ion-base pair bridging distances yielded SRVs with the highest VAMP-2 score, lowest minimum validation loss, and highest Pearson’s correlations with the slow modes of the backbone coordinate generated SRV. Out of all the combinations of number of ion-base pair bridging coordinates, the combination of 9 sodium, 7 chlorine, and 17 magnesium ion-phosphate bridging distances were the most consistent, and the three slow modes generated are shown in Figure 6. Each slow mode of the ion-only generated SRV was compared to the slow modes of the backbone SRV. Physical distances were plotted for reference to the overall hybridization progression.

All SRV slow modes reflect the dehybridized, frayed, and hybridized states clearly. The first slow mode shows the three distinct states most clearly and aligns well with the inverse core distance and backbone distance. The ion-coordinates slow mode exhibits the highest sensitivity, with more pronounced peaks and valleys corresponding to the inverse

Figure 6. The first three slow modes of SRVs generated from ion-only coordinates and backbone-only coordinates compared with physical distances.

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core distance. The second slow mode emphasizes the fraying event and correlates with the inverse fray distance, and the ion SRV strongly correlates with the outer fray distance.

The third slow mode strongly corresponded to the average base pair distances for both SRVs especially before the hybridization event. Overall, the backbone coordinates had slightly higher timescales for each mode, whereas the ion coordinates captured more nuanced dynamics in the dehybridized state, especially leading up to hybridization.

While the deviation between the estimated timescales could be a result of the noise in the simulated data, the similar timescales suggest that the ion coordinates do not have an observable advantage to the backbone coordinates in providing a more robust method. Studies into the similarities between the ion- and backbone- constructed neural nets would be needed to determine if ion-coordinates do provide unique kinetic insights into DNA hybridization.

C. MSM Construction Next, we built an SRV-MSM using the above three SRV modes as a basis and proceeded along the pre-

(a) Ion SRV clusters

viously described pipeline. Figure 7 shows slow mode 1 graphed against slow mode 2 for ion-only and backbone-only SRVs. Trajectory 13, which has the most pronounced fraying dynamics, is overlaid with a gradient from dark to light corresponding to time progression.

We observed that the ion-only SRVs display a large amount of symmetry with the backbone-only SRVs. Vanilla neural networks were used to further explore the isomorphism between the two SRVs. The hybridized and dehybridized states mostly occupy the furthest ends of the SRV and are the most densely populated, as indicated by the heat maps. The k-means clustering for 8 centers also show that the majority of the clusters reside within the hybridized and dehybridized states, with only one or two states in the more sparsely populated fraying states, which is consistent with experimental observations and timescales. The striking isomorphism between the two SRVs suggest that they contain largely similar kinetic information, and that using ion coordinates to construct SRV-MSMs could reveal higher order

(c) Backbone SRV clusters

(b) Ion SRV heatmap Figure 7. Ion vs Backbone Slow Modes 1 and 2.

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(d) Backbone SRV heatmap

(a) Ion SRV-MSM PCCA

(b) Macrostate Physical Coordinates

Figure 8. Backbone SRV-MSM Macrostates and Transition Matrix with histograms of physical coordinates corresponding to each macrostate (0 to 4).

dynamical modes. Meanwhile, the ion heatmaps display a more continuous spectrum from hybridized to dehybridized states, with the fraying region being less isolated, which reflects the larger variance in ion coordinates and perhaps less distinct states.

After visualizing the SRV coordinates with eight k-means cluster centers, we re-clustered the SRV with 200 cluster centers that were pre-assigned using the ion coordinates and used PCCA+ to cluster the microstates into 5 macrostates. The diffusion map and transition matrix for the ion SRV-MSM is shown in Figure 8 along with histograms of physical distances for each macrostate Figure 8 shows the 5 macrostate clusters for the SRVMSM constructed from ion coordinates. Macrostate 0 has the lowest average inverse base pair distance, inverse Mg bridging distance, and inverse Cl bridging distance, representative of the dehybridized state. Macrostate 4 is the hybridized state, as shown by the large inverse distances. Macrostates 1, 2, and 3 represent the frayed states. Macrostate 2 is the most populated frayed state, as indicated by the histograms, and is kinetically closer to the dehybridized state. Macrostate 1 suggests a frayed state that is kinetically closest to the hybridized state, as the probability of the transition is the highest among all other states (0.128), which is corroborated by the histogram of inverse base pair distances. However, its inverse ion bridging distances are not as close to those of the hybridized state, which could suggest that the ion movement throughout the fraying and hybridization process is nonlinear and does not directly correspond to ion-phosphate distance at a single instance. Macrostate 3 is a sparsely populated frayed state with base pairs that are further apart but closer ion bridging distances. The flux from state 0 indicates that there is no direct transition from dehybridized to hybridized conformations, and that most trajectories go through microstate 2.

Figure 9 shows the 5 macrostate clusters for the SRV-MSM constructed from backbone coordinates. The occupation of each macrostate is greater compared to the ion SRV-MSM, with more distinct histograms as well.

Macrostate 3 represents the dehybridized state, with the highest average base pair distances and ion bridging distances. Macrostate 4 is the hybridized state, with the lowest average base pair distances and ion bridging distances. Macrostates 0, 1, and 2 are the fraying macrostates. Macrostate 2 is closest to the dehybridized state with the furthest base pair and ion bridging distances, followed by macrostates 0 and 1. From the dehybridized state, the most likely transition is to macrostate 2, which agrees with the corresponding physical coordinates.

However, there is a significant probability of a direct transition from the dehybridized to hybridized state, which was not observed in any of the molecular simulation trajectories and is highly unlikely. The predominant path to hybridization seems to be a transition from fray state 0 to 1, and then to the hybridized state, which directly corresponds to the histogram of physical distances. In the histogram of inverse base pairbasepair distances, there appears to be a bimodal distribution for macrostate 0 that overlaps with the distribution of state 1, which could cause the high probability transition between the two states and overestimate the true transition probability.

Macrostate 0 could be further analyzed and resolved to reflect a more distinct clustering.

For both SRV-MSMs, a range of numbers of macrostates should be further tested by visualizing each macrostate with the physical coordinate histograms. Though both SRV-MSMs show clear dehybridized, frayed, and hybridized states, the population and transition probabilities vary, and more robust models are needed in order to draw more definitive conclusions.

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(a) Backbone SRV-MSM PCCA

(b) Macrostate Physical Coordinates

Figure 9. Ion SRV-MSM Macrostates and Transition Matrix with histograms of physical coordinates corresponding to each macrostate (0 to 4).

LIMITATIONS

Since DNA hybridization and dissociation are rare events, our resolution of the trajectories was limited by computational memory, which is especially pertinent given the more volatile nature of ion movements. Moreover, in order to maximize the ion concentration and chance of the hybridization event, we kept the periodic box size slightly larger than the sum of the maximum end-to-end extension length of a single strand and the force cutoff, which did not allow for ions to reach their bulk conditions and may not accurately reflect conditions in-vivo. A major challenge in ion featurization is that the ion feature set is more than double the size of the backbone feature set, which makes the SRVs harder to train. Further refinement of the ion feature set to fewer key coordinates could be considered in order to create a more computationally-manageable and scalable set of CVs.

CONCLUSION In this work, we employed coarse-grained MD simulations to model hybridization and dissociation behavior of a 10bp DNA oligonucleotide near its melting temperature. We specifically focused on the ion atmosphere during the hybridization event at a high ion concentration by constructing SRV-MSMs based on the ion-base pair distances of many independent and unbiased trajectories. We found high correlations between WC base pair distances and ion-base pair distances, and that ion coordinates alone can predict higher order dynamical modes of DNA hybridization and resolve intermediate fraying modes. The Granger causality test revealed that magnesium and chlorine are strong predictors of base pair distances up to large lag times, suggesting that the two ions may directly facilitate hybridization. Conversely,

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sodium is predicted by the base pair distances rather than driving it, indicating a more passive role in DNA hybridization. VMD renderings of the coarse-grained simulations and tracking of individual magnesium ions also point to bridging effects, where the magnesium ions are shown to bring the phosphate backbones together by occupying the DNA core before escaping as the base pairs flip back in to form WC pairs. Ion bridging distances were then used as input coordinates into the SRV-MSM pipeline, which was compared to SRV-MSMs built from backbone coordinates as well as combined ion and backbone coordinates. The SRV slow modes and heatmaps showed high correlations and symmetries between all three models, with the combined and backbone SRVs having higher timescales than the ion SRV. A closer look at lag times versusverses time scales is needed to further optimize the SRVs. The final MSM construction showed strong correlations to physical coordinates, but both macrostate clusterings need to be further refined to achieve greater resolution. Looking forward, the collective variables used in this study could be applied to accelerated sampling of all-atom simulations, as well as predicting sequence-dependent kinetic properties that could inform nucleic acid kinetics experiments and nanotechnology applications.

REFERENCES Daniel M. H. et al. An experimentally-informed coarsegrained 3-site-per-nucleotide model of dna: Structure, thermodynamics, and dynamics of hybridization. The Journal of Chemical Physics, 139:144903, 10 (2013) Nadrian C. S et al. Dna nanotechnology. Nature Reviews Materials, 3:17068, 1 (2018).

David R. J. et al. Counting the ions surrounding nucleic acids. Nucleic Acids Research, page 1305, 12 (2016).

Jan L. et al. Understanding nucleic acid–ion inter-actions.

Annual Review of Biochemistry, 83:813–841, 6 (2014).

Daniel M. H. et al. Coarse-grained ions for nucleic acid modeling. Journal of Chemical Theory and Computation, 11:5436–544 6, 11 (2015).

Richard L. et al. Analyzing ion distributions around dna.

Nucleic Acids Research, 42:8138–8149, 7 (2014).

Vijay S. P. et al. Everything you wanted to know about markov state models but were afraid to ask. Nature Methods, 52:99–105, 9 (2010).

Hythem S. et al. High-resolution markov state models for the dynamics of trp-cage miniprotein constructed over slow folding modes identified by state-free reversible vampnets. The Journal of Physical Chemistry, 123:7999– 8009, 9 (2019).

Andreas M. et al. VAMPnets for deep learning of molecular kinetics. Nature Communications, 9(1):1–11, (2018).

Wei C. et al. Nonlinear discovery of slow molecular modes using state-free reversible vampnets. The Journal of Chemical Physics, 150:214114, 6 (2019).

Keras @ Github.Com. Martín A. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. (2016) Brooke E. H. et al. Markov state models: From an art to a science. Journal of the American Chemical Society, 140:2386–2396, 2 (2018).

Martin K S. et al. PyEMMA 2: A Software Package for Estimation,Validation, and Analysis of Markov Models.

(2015). .

Melody Leung is a second-year student at the University of Chicago majoring in Molecular Engineering and Physics. She is interested in using machine learning to accelerate materials and drug discovery pipelines, and she plans to attend graduate school to study bioengineering and material science.

Daniel M. H. et al. Coarse-grained modeling of dna oligomer hybridization: Length, sequence, and salt effects.

The Journal of Chemical Physics, 141:035102, 7 (2014).

Humphrey, W. et al. “VMD - Visual Molecular Dynamics”, J. Molec. Graphics, 1996, vol. 14, pp. 33-38. http://www. ks.uiuc.edu/Research/vmd/.

S. Plimpton, Fast Parallel Algorithms for Short-Range Molecular Dynamics, J Comp Phys, 117, 1-19 (1995). https:// lammps.sandia.gov/.

Robert T. M. et al. MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories.

Biophysical Journal, 109(8):1528–1532, (2015). .

Hao W. et al. Variational approach for learning markov processes from time series data. Journal of Nonlinear Science, 30:23–66, 2 (2020).

Hythem S. et al. Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation. Molecular Physics, 118:e1737742, 3 (2020).

Patrick A. S. et al. A study of problems encountered in granger causality analysis from a neuroscience perspective. PNAS, 114:E7063–E7072, 8 (2017).

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INQUIRY

BRINGING PEDAGOGY TO THE FOREFRONT: AN INQUIRY WITH DR. KERRY LEDOUX Caroline Miller

As a first year undergraduate student entering a STEM field, I had one thing on my mind: research.

I, like many others, chose to attend the University of Chicago in hopes of working with some of its world-renowned scientists, at the top of their fields. However, the last three years have taught me that behind all this extraordinary work is the true lifeblood of the university: professors who are not only dedicated to their research, but also to teaching and nurturing the next generation of scientists. Professor Kerry Ledoux’s career as a scientist has largely revolved around her love for teaching, and she has always found a way to keep classroom instruction a part of her work.

Inspired by her lifelong passions for reading and writing, Ledoux knew she wanted to study language as an undergraduate at Clark University. There, she worked in a psychology lab doing research on bird songs. Struck by the contrast between how different species communicate, she began thinking about the uniquely human aspects of language and became interested in studying human communication. After receiving her bachelor’s degree in psychology, with concentrations in biology and sociology, Ledoux received her Ph.D. in Cognitive Psychology at the University of North Carolina at Chapel Hill. Ledoux began her graduate work studying language comprehension and continued in the field of psycholinguistics as a postdoctoral researcher. One of Ledoux’s favorite aspects of her research was creating linguistic stimuli for experiments—a unique opportunity to merge science with her creativity and love of writing.

Throughout her graduate and postdoctoral years, Ledoux always made an effort to make both research and teaching a part of her life. As a graduate student, Ledoux worked as a teaching assistant every single semester, and knew teaching would be an integral part of her career as a scientist. Once she had received her Ph.D. Ledoux became an instructor at the US Air Force Academy in the Department of Behavioral Sciences and Leadership, after which she began postdoctoral studies at UC Davis and Johns Hopkins.

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As a postdoc with few teaching opportunities Ledoux found that she missed her time in the classroom, so she taught classes at a local community college at night. Following her time at Johns Hopkins, Ledoux and her husband moved to Chicago, where they started a family. Unable to fully dedicate herself to both research and teaching, Ledoux chose to continue teaching, and in 2014 she joined the University of Chicago as an Associate Instructional Professor.

The first class Ledoux taught at UChicago was SOSC 18100: Topics in Behavioral and Social Sciences Relevant to Medicine. SOSC 181 is a small discussion-based class for pre-med students, designed to teach how the social sciences inform medicine.

SOSC 181 has provided Professor Ledoux with the unique opportunity to showcase how different fields can connect, and how crucial it is for students entering medical school to understand and appreciate the social sciences. She has taught this class every year since coming to UChicago, and thoroughly enjoys the chance to share her own passion for multidisciplinary approaches to science. In addition, Ledoux regularly teaches in the Mind sequence of the social sciences core, which gives her the opportunity to interact with students of all majors, and approach problems of behavior and psychology from an integrative perspective. Ledoux also teaches courses specifically intended for psychology students, including Psychological Research Methods and Sensation and Perception. Although these courses demand larger class sizes and more lecture-based material, Ledoux enjoys the challenge and works hard to motivate and build relationships with her students. She notes that one of her favorite classes to teach is The Disordered Mind, a seminar course focused on psychological disorders and mental health.

Ledoux’s teaching philosophy has always heavily relied on developing relationships with students that make them feel motivated and connected to the material. She works hard to tailor her approach to fit her students and their goals, and takes every opportunity

to get to know her students better. Ledoux cares not only about her students connecting to the material, but also about helping them develop in ways beyond the course content. With that goal in mind, she often designs assessments that challenge her students’ skills in scientific communication. She speaks fondly of her students, explaining: “It is so incredibly rewarding to see how hard they work, to see how motivated they are…Sometimes they really put themselves out there because they’re just intrinsically motivated. They want to learn, and to do the very best they can, and for many of them the very best they can is so impressive.”

Professor’s Ledoux’s involvement with the Psychology department hardly ends with teaching classes. She regularly works on professional development programs, engages with the honors program, and more; she loves the opportunity to interact with students outside of class and support them in their professional lives.

Ledoux’s future plans with the University will continue to revolve around teaching and forming relationships with students. She would like to continue integrating professional development into her service to the psychology department, and is finding opportunities to work on smaller projects including student research and journal clubs in hopes of continuing to teach through research. Ledoux also has plans to continue developing new electives covering topics such as mental health and psychology and medicine.

Returning to her interest in psycholinguistics, she would also like to bring more opportunities to UChicago students to not only study the psychology of language, but learn for themselves how to integrate their own creative passions for reading and writing with the natural sciences.

Caroline is a third-year in the College majoring in neuroscience and psychology. On campus, she studies octopus limb regeneration in the Ragsdale lab and hopes to get her PhD after college.

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REVIEW

THE FUNCTION AND REGULATION OF BDNF IN SYNAPTIC PLASTICITY Omar Kassem ABSTRACT Brain-derived neurotrophic factor (BDNF) is a key molecule that fulfills diverse functions across different brain tissues.

BDNF has emerged as a regulator for neural circuit development, function, and plasticity. Although the role of BDNF in long-term potentiation (LTP) at glutamatergic synapses has been extensively studied over the last few years, several mechanistic features of BDNF signaling at synapses remain elusive. This review summarizes the mechanisms of BDNF release at synapses, BDNF-mediated structural plasticity, protein synthesis regulation, and the tight regulation of the BDNF gene across different brain tissues. Moreover, I discuss the pathologies arising from BDNF dysregulation, especially psychiatric disorders that involve memory distortions. Finally, I explore outstanding questions for future research.

1. INTRODUCTION

2. BDNF RELEASE AT SYNAPSES

The formation of long term memories is a fundamental function necessary for survival. Researchers have been trying to understand the biological mechanisms underlying this process since Pavlov’s investigations in the 1920s.

Our current understanding of memory formation remains dominated by the idea that synapses are the principal site of information storage in the brain. Activity-dependent synaptic plasticity, such as long-term potentiation (LTP), long-term depression (LTD), and homeostatic scaling, has been studied for elucidating the molecular mechanisms of memory formation. LTP at glutamatergic synapses is the most studied form of synaptic plasticity and its mechanistic features are well characterized. LTP is defined as a long-term increase in synaptic response following a brief, high-frequency stimulation or other induction protocol. The main mechanistic features that contribute to LTP are structural plasticity and actin remodeling, modification of glutamate receptors and increase in AMPAR surface expression, and a presynaptic component involving an increase in release probability and size of vesicle pools. LTP is not a unitary process, glutamatergic synapses have been shown to utilize distinct LTP mechanisms in different brain tissues.

Brain-derived neurotrophic factor (BDNF) is a peptide secreted at synapses, and it plays a significant role in stimulating LTP formation. Through differential expression and cellular localization of its tropomyosin receptor kinase B (TrkB) receptor, BDNF can elicit diverse cellular functions in different tissues. Even though BDNF is one of the most extensively studied neurotrophic factors, there are many unknown mechanistic features of BDNF. This review discusses the role of BDNF/TrkB signaling in stimulating the aforementioned LTP processes, the complex regulation of the BDNF gene, the pathologies emerging from BDNF dysregulation, and the currently unexplored aspects of BDNF function.

Mature BDNF and its pro-peptide are stored at glutamatergic synapses and released in response to LTP inducing stimulus bursts. The release of BDNF in synapses has been shown to be dependent on both the synaptic input and postsynaptic spikes. Tanaka et al (2008)[1] used a pairing protocol where they paired two-photon uncaging of glutamate at a single spine with generation of postsynaptic action potentials in the postsynaptic neuron. Results showed that synaptic stimulation paired with postsynaptic spikes induced a gradual long-term enlargement of spine heads that is mediated by BDNF and dependent on protein synthesis. In contrast, synaptic stimulation alone was not sufficient to trigger BDNF secretion, even though it induces a significant increase in the intracellular calcium concentration of spines via NMDA receptors. These results suggest that the secretion of BDNF is responsive to the synchrony of synaptic input and postsynaptic spikes.

Yet, the specific roles and importance of BDNF release from each synaptic partner are not fully understood. There are multiple levels to the complexity to this consideration; there are TrkB receptors on presynaptic and postsynaptic neurons which can be activated by local BDNF release from the same neuron or the other partner. There is evidence that BDNF acts as a retrograde signal as well. Finally, there are presynaptic and postsynaptic LTP modules that could be regulated differently. A recent study used a viral-mediated approach to delete BDNF or TrkB specifically in CA1 and CA3 regions of the Schaffer collateral pathway [2]. This approach revealed that presynaptic BDNF is involved in LTP induction, while postsynaptic BDNF contributes to LTP maintenance. Similarly, loss of presynaptic or postsynaptic TrkB receptors leads to distinct LTP deficits, with presynaptic TrkB required to maintain LTP, while postsynaptic TrkB is essential for LTP formation. It has also been shown that presynaptic release of BDNF is required for the presynaptic

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components of LTP, such as increasing release probability and size of rapidly recycling vesicle pool, but not the postsynaptic component [3,4]. Furthermore, postsynaptic BDNF synthesis and release mediates retrograde signaling that enhances presynaptic function [5].

On the postsynaptic neuron, BDNF binds to TrkB receptors and exhibits its postsynaptic effects through downstream effectors. BDNF/TrkB signaling is involved in various LTP processes such as: increasing the opening probability of ionotropic glutamate receptors; mediating structural plasticity; and regulating protein synthesis through transcriptional and translational pathways. The processes regulated by BDNF/TrkB signaling in the postsynaptic neuron are currently more understood than the significance of the origin of release. The role of presynaptic and postsynaptic BDNF release is still controversial and there are conflicting results in the field One possible source of conflict in the field could be the variation in the processes of LTP and functions of BDNF across different tissues and in response to different stimuli or induction protocols; this variation means that results obtained from a specific system under certain conditions might not be consistent with results obtained in different experimental conditions.

3. ROLE OF BDNF DURING LTP BDNF/TrkB signaling plays a critical and well-established role in the postsynaptic component of LTP. BDNF/ TrkB signaling is transduced through various downstream effectors and pathways to trigger processes essential for LTP formation, such as structural plasticity and cytoskeletal regulation, channel and glutamate receptors modification, and protein synthesis regulation through transcriptional and translations effects (figure1). The following section will discuss the BDNF/TrkB signaling pathway that allows it to facilitate various LTP processes, as well as limitations of the current understanding of the mechanism through which BDNF mediates its functions.

etal regulation significantly important for LTP and demonstrate the need for tight regulation. This section will discuss evidence that BDNF signaling regulates spine F-actin in LTP. In CA1 pyramidal cells of acute hippocampal slices, exogenous BDNF triggered inhibition of the actin-severing protein cofilin in spines, while application of TrkB-Fc or an F-actin destabilizing drug inhibits F-actin formation and development of late TBS-LTP [9], which asserts the role of BDNF in cytoskeletal regulation. The two main final points of signaling output for actin remodeling are Arp2/3 and cofilin; Arp2/3 is an actin nucleation factor while cofilin is an actin severing factor. Actin polymerization during LTP formation is enhanced by activating the Arp2/3 complex and inhibiting cofilin, through phosphorylation. BDNF triggers TrkB phosphorylation at the juxtamembrane region by cdk5, which recruits a Rac-specific guanine nucleotide exchange factor, Tiam1. Activated Rac1 then enhances Arp2/3 mediated actin polymerization through the wave complex [10]. Rac1 also acts through PAK on the cofilin kinase, LIMK1. BDNF/TrkB signaling inhibits cofilin activity by activating the RhoA-Rock-LIMK1 pathway and increasing the expression of activity-regulated cytoskeleton-associated protein (ARC) through the MAPK pathway [11]. BDNF/TrkB-mediated protein synthesis regulation is essential for actin remodeling as well as many other LTP-related processes [12].

3.1. STRUCTURAL PLASTICITY AND ACTIN CYTOSKELETAL REGULATION Synaptic strengthening during LTP is facilitated by the accumulation of AMPA receptors at synapses. To create more slots for synaptic AMPARs, dendritic spines undergo actin-mediated spine enlargement. Actin polymerization also facilitates LTP by creating additional tracks for transporting AMPAR, and other key LTP proteins, to the PSD. In addition to facilitating protein trafficking, F-actin has been shown to act as a synaptic tag, in both LTP and LTD, for protein trafficking and capture at synapses [6,7]. An alternative model for selective cargo trafficking to active synapses proposes that dendritic shaft machinery is responsible for capturing, sorting, and correctly targeting cargo to active synapses [8]; the evidence that proposes F-actin as a synaptic tag could still be consistent with the hypothesis that F-actin is part of the dendritic shaft machinery responsible for selective trafficking. All of these functions make cytoskel-

Figure 1. BDNF/trkB signalling regulates various plasticity related processes including actin polymerisation mediated structural plasticity, protein expression at synapses, and permeability of ion channels.

Panja, D. & Bramham, C. R. BDNF mechanisms in late LTP formation: A synthesis and breakdown. Neuropharmacology 76 Pt C, 664–676 (2014).

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3.2. BDNF-MEDIATED PROTEIN SYNTHESIS REGULATION Transcriptional Effects BDNF affects the synapse proteome through both transcriptional and translational effects. Several studies have shown that BDNF induces the expression of genes encoding regulators of synaptic activity, such as Arc [13]. Furthermore, LTP induction by exogenously applied BDNF is blocked by transcriptional inhibitor actinomycin D [13]. The transcriptional regulation properties of BDNF are MAP kinase dependent,[13,14] and the effect of MAPK activation on L-LTP involves translation and transcription.[13,15] MAPK activates two major transcription factors CREB and Elk-1. After activation of CREB and Elk-1, transcription factors bind to target regions in IEG (Immediate early genes) and upregulate transcription of genes involved in LTP formation, such as AMPAR subunits GluR1 and GluR2, or other transcription factors like Zif268 that would act directly on LTP-related genes.

BDNF-mediated transcriptional regulation is also calcium-dependent, and phosphorylation of CREB by CaMKII and CaMKIV is responsible for this calcium dependency.

Translational Effects Translational control is another critical BDNF-mediated protein regulation activity; inhibition of protein synthesis and BDNF-TrkB signaling has been shown to eliminate late LTP with similar kinetics, which suggests that BDNF’s ability to regulate translational machinery is one of its most important functions during LTP [1].

TrkB receptors promote translation initiation through activating ERK/MNK1 and PI3K-Akt-mTORC1 pathways.

A major control point in this process is the assembly of the eukaryotic initiation factor 4F (eIF4F) complex on the 5’ terminal of the cap structure. Erk signals to MAPK interacting kinases (MNKs) which binds to elF4G and phosphorylates its partner elF4E; phosphorylation of elF4E leads to altered translation of a subset of mRNAs [16,17]. RNA translation is repressed by eIF4E-binding proteins (4E-BPs), which in their unphosphorylated state sequester elF4E to block its interaction with elF4G. Phosphorylation of 4E-BP, catalyzed by the mammalian target of rapamycin complex 1 (mTORC1), triggers the release of 4E-BP resulting in eIF4F complex formation and enhanced rates of translation. mTORC also regulates translation through s6 kinase, which directly acts on several components of the translational machinery. MAPK activates the mTORC pathway through calcium-dependent calpain.

Finally, BDNF regulates protein synthesis through miRNA biogenesis; BDNF induces rapid expression of Dicer. Dicer expression results in enhanced processing of pre-miRNA to mature miRNA. This process doesn’t increase the production of all miRNAs as BDNF also enhances the expression of the RNA-binding protein Lin28, which selectively blocks Dicer mediated processing of its

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pre-miRNA substrates [18]. It is noteworthy that calpain, which is activated by MAPK in response to BDNF/TrkB signaling, cleaves dicer. This inhibitory effect could be a negative feedback mechanism that confines dicer-enhanced expression to a limited time window, through fine-tuning of the kinetics of both processes. It’s also possible that MAPK-activated calpain is spatially segregated from Dicer so that they don’t interfere with each other.

The colocalization of Dicer and calpain could be easily examined by tagging both proteins with fluorophores and observing their distribution. Furthermore, a Western blot could be used to examine dicer levels during LTP in the presence and absence of calpain inhibitors.

3.3. CHANNELS AND GLUTAMATE RECEPTORS MODIFICATION BDNF/TrkB signaling leads to the modification of several ion channels and neurotransmitter receptors resulting in an amplified postsynaptic response. Tyrosine kinase-mediated phosphorylation of NR1 and NR2B subunits of the NDMA receptor enhances its conductance [19,20]. Phosphorylation of NMDA receptors is mediated by ERK, Akt, and CAMKII [21]. Similarly, AMPA receptor subunit GluR1 tyrosine phosphorylation is acutely increased in an NMDAR dependent manner [22].

TrkB signaling increases AMPA receptor synthesis and surface expression as well. Increased surface expression of AMPA is achieved through CaMKII and PKC phosphorylation at Ser831 in GluR1[23]; phosphorylated AMPARs then interact with trafficking proteins and are anchored in the membrane. PSD-95 is a key molecule that enhances AMPA trapping in the PSD. TrkB activation is necessary for PSD-95 insertion at synapses. TrkB signaling leads to PSD-95 palmitoylation allowing it to insert at the membrane.

Similarly, BDNF signaling modifies ion channels, Nav1.2 channel conductance is compromised through FYN tyrosine kinase-mediated phosphorylation [24].

Furthermore, phosphorylation of the TrkB receptor at its Tyr816 residue activates the phospholipase C γ (PLC γ) pathway. PLC breaks down PIP2 generating DAG and IP3. DAG binds to TRPC calcium-permeable channels and increases their conductance, while IP3 mobilizes internal calcium stores from the endoplasmic reticulum leading to a significant amplification of the cytosolic calcium levels, which is critical for the regulation of calcium-dependent proteins such as CaMKII and calpain.

4. REGULATION OF BDNF BDNF has multiple roles in diverse brain regions, and even within the same brain region it responds to various physiological conditions. This diversity in the roles of BDNF requires complex regulation on multiple levels.

This section will discuss the regulation of BDNF on epigenetic, transcriptional, and posttranslational levels.

4.1 TRANSCRIPTIONAL REGULATION OF BDNF BDNF is tightly regulated by neural activity. Membrane depolarization initiates an influx of calcium ions through voltage-gated calcium-permeable channels and glutamate receptors, primarily NMDA receptors.

Downstream effectors respond to this influx of calcium ions and trigger the binding of transcription factors such as calcium-responsive transcription factor (CaRF) and CREB to the corresponding regulatory elements of BDNF (figure 2). There are a total of nine promoters in human and rodent BDNF, these promoters are responsive to different physiological conditions and are differentially utilized across diverse brain tissues. Alternative usage of the BDNF promoters, differential splicing, and multiple polyadenylation sites explain how BDNF can achieve such diversity in function and expression [25,26]. Moreover, different spatiotemporal patterns of cytoplasmic Ca2+ increases arising, for example, from Ca2+ influx via activated VGCCs versus NMDARs, may activate specific sets of downstream effectors that initiate BDNF transcription through different promoters [27,28]. The exact mechanism through which differential promoter transcription is achieved is currently unknown. All promoters are mainly transcribed by the same transcription factors, CREB and CaRF. while all CREB target genes contain CRE sequences, it is unlikely to be the case that this CRE sequence functions as a simple switch by which the entire portfolio of CREB-driven genes is coordinately turned on when CREB is phosphorylated and turned off when CREB is dephosphorylated. The differential regulation and expression patterns of BDNF promoters under different physiological conditions, as well as other well-characterized CREB-driven genes [29], argue strongly against such a model. The mechanisms by which this stimulus specificity is achieved are not known. 3’UTR of the BDNF region plays an important role in transcript trafficking.

Studies on rat BDNF gene show that transcripts are generated with either a long 3’UTR or a shorter one. BDNF

Figure 2. Transcriptional regulation of BDNF expression through calcium-responsive transcription factor CREB. Cocco, S., Podda, M. V. & Grassi, C. Role of BDNF Signaling in Memory Enhancement Induced by Transcranial Direct Current Stimulation. Front. Neurosci. 12, (2018).

mRNA bearing the long 3’-UTR has a broad somatodendritic distribution. Truncation of the long 3’-UTR abolishes dendritic expression but retains somatic expression of BDNF mRNA [25].

Tight regulation of the BDNF gene is critical for brain health. A methionine substitution for valine at codon 66 (Val66Met), is associated with alterations in brain anatomy, anxiety behaviors, and memory disturbances especially the ability to extinguish fear memory in both humans and mice [30,31,32].

4.2 EPIGENETIC REGULATION OF BDNF Epigenetic regulation of chromatin structure also affects BDNF transcription; data indicates that the demethylation status of BDNF, acetylated H3 and acetylated H4 at the BDNF promoter in the mPFC were increased

by LTP-inducing high-frequency stimulation [33]. It is known that depolarization-activated CaMKII phosphorylates methyl-CpG-binding protein 2 (MECP2) — a transcriptional repressor that binds to methylated DNA in the BDNF promoter region [34]. This phosphorylation causes the release of MECP2 and its interacting proteins histone deacetylase 1 (HDAC1) and transcription regulator homolog A (SIN3A) from promoter IV, which increases BDNF transcription from this specific promoter.

According to this model, MECP2 deletion should result in enhanced transcription of BDNF. Surprisingly, MECP-2 null mice show a reduced level of BDNF expression. It is possible that MECP2 has a posttranscriptional role in the regulation of BDNF and the loss of that function leads to degradation of transcripts [35].

It is well established that aberrant BDNF epigenetic modifications are linked to psychological stress and multiple psychiatric disorders, especially those involving memory disturbances such as PTSD and fear memory.

Experiments that subjected rats to psychological stress regiment, inducing physiological and behavioral sequelae in rats that are comparable to symptoms observed in traumatized people with PTSD, significantly increased BDNF DNA methylation in the dorsal hippocampus, significantly decreased methylation in the ventral hippocampus (CA3), and caused no changes in BDNF DNA methylation in the medial prefrontal cortex or basolateral amygdala [36]. Another study directly tested the contribution of epigenetic regulation and chromatin remodeling to learning-induced changes in BDNF gene expression [37]. Contextual fear learning induced differential regulation of exon-specific BDNF mRNAs (I, IV, VI, IX) that was associated with changes in BDNF DNA methylation and altered local chromatin structure. These results provide evidence for the significance of BDNF expression in neural circuits as well as the role of epigenetic modifications in achieving differential BDNF expression levels across tissues and in determining the specific promoter utilized.

Many DNA methylation screens performed on humans, using peripheral fluids, established a connection between elevated BDNF methylation and several psychiSPRING 2021

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atric disorders including major depression, bipolar disorder, borderline personality disorder [38,39]. One study conducted on Postmortem brain samples from suicide subjects showed a statistically significant increase of DNA methylation at specific CpG sites in BDNF promoter/ exon IV compared with non-suicide control subjects [40].

Yet, it is important to note that this data doesn’t necessarily mean that BDNF dysregulation was directly responsible for inducing suicidal tendencies in subjects, it is possible that disrupted BDNF expression is involved in a psychiatric or psychological disturbance which in turn increases the risk of suicide.

4.3 POSTTRANSLATIONAL REGULATION OF BDNF BDNF mRNA is translated into a precursor molecule (proBDNF) which is then cleaved to generate mature BDNF. Interestingly, it has been shown that activation of p75(NTR) by proBDNF enhances LTD suggesting a bidirectional regulation of synaptic plasticity by proBDNF and mature BDNF [41].

5. DISCUSSION BDNF is a very well-studied peptide and its role in promoting LTP processes namely structural plasticity, transcriptional and translational regulation, ion channels, and glutamate receptors modification are critical for LTP in many tissues. Furthermore, BDNF exhibits functional diversity across different brain tissue and in response to different stimuli or induction protocols. This functional diversity is tightly regulated through a complicated promoter structure, differential splicing and multiple polyadenylation sites, epigenetic control, and posttranslational regulation. Given the functional significance of this peptide and its fine regulation, the fact that BDNF epigenetic dysregulation is linked to psychiatric disorders and memory disturbances is expected.

Despite the extensive study of BDNF regulation and functions, there many mechanistic features that are unknown. The exact mechanism through which differential promoter transcription occurs remains one of the most interesting phenomena in LTP regulation that aren’t currently understood. As previously discussed, the differential regulation of BDNF promoters, as well as other CREB-driven genes, argues strongly against a simplistic on/off model. The stimuli specific response of CREB could either be a product of modification or regulation of CREB that enhances its affinity for specific sites, or it could be due to a CREB independent factor.

Epigenetic modifications and chromatin state are good candidates that could be responsible for determining which BDNF isoform is expressed. It is also possible that the binding of other proteins could be responsible for the stimulus-specific activity. It is important to note that the

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contribution of CREB can only be added to or removed from a complex collection of other transcription factors, such as CarF, enhancers, repressors, and other regulatory proteins. Thus, it is almost certain that this property isn’t achieved through a simplistic on/off or single protein model but rather a complex interaction between multiple factors.

Another interesting phenomenon that can’t be explained by our current understanding of BDNF regulation is that MECP-2 null mice show a reduced level of BDNF expression [42]. These results suggest that MECP2 might be regulating BDNF post-transcriptionally as well, and loss of this post-transcriptional activity would lead to reduced BDNF expression. It is important to distinguish whether the decreased expression is due to a lower transcription rate or degradation of the transcripts.

Assays that rely on testing transcription factor binding to the promoter would be ineffective in this particular case as CREB is a zipper TF that is always bound to its target. CREB is regulated through phosphorylation on its kid domain allowing the recruitment of coactivator CBP. It might be possible to precipitate activated CREB using a phosphoCREB specific antibody, or using a two-step precipitation process where CBP specific antibodies are used to precipitate free and phosphoCREB-bound CBP, then CREB specific antibodies could be used to precipitate phosphoCREB. The level of phosphoCREB itself isn’t a good proxy for BDNF transcription as CREB regulates many other targets. A better proxy could be using qPCR to quantify BDNF genes bound to phosphoCREB, a ChIP experiment, and compare the levels of BDNF promoter bound to phosphoCREB in WT and MECP2 null mice.

Standard ChIP is a relative measurement untethered to any external scale in a way that obviates direct comparison amongst experiments, so Internal Standard Calibrated ChIP (ICeChIP) would be more accurate for this comparative analysis. ICeChIP uses a series of barcoded semisynthetic nucleosomes as internal standards to calibrate ChIP, so that data is expressed on a precise, accurate, reproducible, and biologically meaningful scale [43].

Another controversial area in the BDNF literature is the role of proBDNF in inducing LTD. Some studies show that activating p75(NTR) by proBDNF enhances LTD [41] while others show that LTD occurs with normal efficiency in BDNF null environment [44]. The fact that LTD can occur normally in absence of BDNF doesn’t contradict the hypothesis that proBDNF could induce LTD; it is possible that other compensatory molecules or neurotrophins fulfill the same function in absence of proBDNF, in this model proBDNF is sufficient but not necessary for LTD induction due to the existence of parallel pathways.

Finally, the regulatory functions of mature and proBDNF on GABAAR transcription, surface expression, and chloride transporters are well established51, but unlike glutamatergic synapses, the role of BDNF in LTP at GABAergic synapses isn’t well characterized. Also, regulatory

effects of BDNF on the proteome at GABAergic synapses, or lack thereof, are unknown. The effect of BDNF on protein synthesis could be tested using high throughput proteomic techniques such AQUA and SILAC. Another possibility is to compare changes in the proteome at GABAergic synapses in the presence and absence of BDNF. Moreover, a candidate approach could be used to test prime BDNF targets, which had been characterized in glutamatergic synapses. Transcriptomic analysis would be important to determine the regulatory effect on gene transcription independently. It is possible that GABAergic synapses rely on different downstream effectors to regulate BDNF in response to distinct neurophysiological conditions, so the regulatory pathways established in glutamatergic synapses would need to be revisited and tested. Finally, epigenetic regulation of BDNF plays a significant role at glutamatergic synapses and its dysregulation was correlated to various disorders, so it would be interesting to survey BDNF methylation at GABAergic synapses and perform comparative analysis.

Omar is a third year undergraduate majoring in neuroscience and minoring in biology. He is interested in studying the regulation of synaptic plasticity on the cellular and molecular level, and the pathologies that could arise from the dysregulation of this process.

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REVIEW

ENTERIC HYPEROXALURIA: A RICH HORIZON FOR PRECISION MEDICINE Najya Fayyaz Extensive research has been done on the effects of enteric hyperoxaluria (EH) [1-12]. Although there is a heavy presence of research, there is a need for a better understanding of how those patients for whom a particularly promising focused approach won’t work due to their particular genetics and biological system. A primary concern of EH is the increased risk of calcium oxalate (CaOx) kidney stone (KS) formation [1] due to the gut-kidney axis [1], wherein oxalate enters the blood stream through paracellular pathways in the gut epithelia and deposits in the kidneys or urinary tract. Once oxalate is absorbed into the blood, hyperoxalmia, or high blood oxalate, can occur as oxalate can form CaOx crystals that form deposits as stones in organs, including the kidneys, heart, liver and eye [1].

EH can cause hyperoxaluria , or high urinary oxalate, leading to painful KS formation. EH may occur with an oxalate-rich diet. Such a diet elevates oxalate concentrations in the blood and urine. When excess oxalate is filtered by the kidneys it may bind to Ca2+ ions to form CaOx crystals that lead to KS formation. Hyperoxaluria is a major risk factor for KS[1]; moreover, emerging data indicates oxalate is also potentially involved in the progression of polycystic kidney disease (PKD), chronic kidney disease (CKD), and end stage renal disease (ESRD)-associated cardiovascular diseases and endothelial dysfunction [5-10] and is is associated with other conditions, including inflammatory bowel disease (IBD), diabetes mellitus (DM), and obesity [11-12]. Notably, stone formation risk increases with small increases in urine oxalate, even with levels in the normal range (25-30 mg/day) [1].

Prevalence of KS is on the rise, affecting ~1 in 5 men and ~1 in 11 women [1], and recurrence rates remain high [1], indicating current interventions are inadequate. Increasing prevalence and recurrence combined with the significant cost burden of >$10 billion annually in the US highlights the need for a more effective, preventative approach. To date, there are no FDA-approved treatments for hyperoxaluria or KS. OF microbiota have been shown to secrete bioactive factors that are associated with the uptake of oxalate into the gut from the blood (secretion of oxalate) via A1 and A6 Oxalate transporters. This secretion of oxalate back to the gut is highly desirable because it decreases blood oxalate, decreasing the risk of hyperoxaluria

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which allows for oxalate to be taken up by OF or be excreted through feces. Additionally, a low oxalate diet is impractical because oxalate naturally occurs in a myriad of plant and plant-derived foods, such as fruits, vegetables, nuts, grains, legumes, seeds, chocolate, coffee, and tea. The effects of gut microbiota, namely oxalate-consuming Oxalobacter formigenes (OF), on blood and urine oxalate concentrations has been of great interest and heavily studied. This decreases oxalate excretion by the kidneys, which typically excrete 90-95% of oxalate in the body. Excretion via the gut is preferable because oxalate can build up in kidney tissue, forming CaOx crystals that may develop into KS, as approximately 80% of KS’s are composed of calcium oxalate (CaOx). The aim of this review is to explore some of the recent developments of interest regarding enteric hyperoxaluria and KS formation as well as entertain the potential of precision medicine as an approach to strengthening the weaknesses in the field of research as it provides treatment for those who would otherwise be excluded.

First, this review will discuss the transmembrane co-transporter SLC26A6. SLC26A6 (A6) is a crucial subject of interest in elucidating mechanisms of oxalate transport to treat EH in a preventative manner. A6 is imbedded in the apical side (facing the intestine) of gut epithelial cells. It has been characterized as an oxalate transporter implicated in the secretion of oxalate from gut epithelial cells into the gut, where it can then leave the body via feces. Gut excretion is desired because excretion via the kidneys increases risk of CaOx crystal and KS formation. Stimulation of A6 oxalate secretion is under ongoing study as it has much potential for clinical use as a preventative treatment for KS. Furthermore, activation of Protein Kinase A (PKA), which guides the passage of specific chemical signals, has been linked to an increased A6 function as shown by its ability to significantly increase oxalate secretion in human intestine model Caco BBE-2 (C2) cells [2].

Understanding the nature of PKA activation of A6 and various chemical species that can act as agonists for PKA is important since finding the most useful one can lead scientists to a more precise treatment for enteric hyperoxaluria. It was found that intestinal oxalate transport is directly regulated by activation of the Protein Kinase A (PKA) signaling pathway, and PKA, when ac-

tivated with PKA agonist forskolin and IBMX (F/I), significantly stimulates (3.7-fold) 14C-oxalate transport by C2 cells (49% of which is mediated by A6). Additional PKA agonists, such as PTH and oxalate secretagogue, should be studied to characterize which is most effective ex vivo in human organoids and eventually in vivo to get the most realistic understanding of the effects on surrounding biological mechanisms and environments.

Such investigation is imperative, as PKA is implicated as a cell signal pathway for many different independent mechanisms. A key study showed that the effect of agonist-triggered PKA stimulation of A6 can be completely blocked by the PKA reversible-inhibitor H89 (derived from H8), indicating that A6 stimulation is PKA dependent [2]. These findings have great potential for treatment; however, in vivo and ex vivo effects of H8 in relation to prevent A6 stimulation need to be characterized. Moreover, a statistically-significant decrease in oxalate secretion when PKA agonists are present should lead researchers to investigate the optimal mechanism of reversing H8 or H89’s inhibition of PKA’s stimulation of A6. Additionally, other potential PKA inhibitors exist and their effects and reversibility potential should be researched in relation to its effects on A6 secretion. Furthermore, A6 may have direct inhibitors of its own which should be explored and examined, as such inhibitors may be more prevalent in patients with ailments or pre-dispositions that make them more at risk of hyperoxaluria and KS formation.

Tied to studies of A6 are human intestine model cells called C2 cells. It has been established in C2 cells that Oxalobacter formigenes (OF)-derived bioactive factors secreted in a culture-conditioned medium stimulate oxalate transport via a PKA-dependent pathway and subsequent activation of A6 transport activity. Oxalate absorbed by the blood is sent into the gut epithelia cytoplasm via what is believed to be the A1 transmembrane co-transporter in the jejunum and ileum (parts of the small intestine); however, it is unknown whether the transmembrane transporter does this in the duodenum and what species it exchanges for oxalate. It is important to clarify this to better characterize the entirety of the mechanism involved. This is also of clinical significance because there may be patients with a range of mutations on the gene coding for the A6 transporter, so even PKA stimulation of A6 with the intent of increasing oxalate secretion into the gut may not be entirely effective. A better understanding of the transporter that supplies oxalate to epithelial cells, possibly A1, to then be secreted by A6 would better enable scientists to understand how to approach this issue. Perhaps a higher concentration of oxalate within gut epithelia is necessary for an underperforming A6 to secrete oxalate, so a dietary increase of foods high in the species that A1 exchanges for oxalate or in the species that may stimulate A1 from the interior of epithelial cells can enable the A1 transporter to supply

more oxalate for A6 to secrete into the gut. Additionally, since A6 was implicated in >49% of oxalate secretion, at least some portion of oxalate secretion can be assumed to come from SLC26A2 (A2) which is also an oxalate transporter on the apical side of the gut epithelia; A2 mechanisms of stimulation can be studied as an alternative for oxalate secretion when A6 is not an option due to genetic mutations. Conversely, patients may be more prone to hyperoxaluria due to some mutation in the gene coding for, presumably, A1. This would render the method of stimulating A6-mediated oxalate secretion futile because a malfunctioning or underperforming A1 transporter would be the rate-determining step for the secretion of oxalate since it supplies oxalate to the epithelial cells, capping the oxalate secretion ability of A6 at A1’s ability to transport oxalate instead of A6’s normal or stimulated ability.

As stated earlier, A1’s involvement in oxalate secretion is contested, as one study in A1-null mice showed that their hyperoxaluria, hyperoxalemia, and lithiasis (calcium-based stones in the urinary tract) indicated that the lack of A1-mediated oxalate transport was responsible, tying A1 to oxalate transport, enabling secretion [12]. However, two following studies in the same A1-null mice set in a different background showed that A1 was not implicated in oxalate secretion [13, 14]. Further study is likely necessary to adequately characterize A1’s involvement in oxalate transport and clarify if there are potential clinical applications for A1 stimulation of oxalate secretion in tandem with A6. This could potentially be accomplished by monitoring oxalate secretion in C2 cells via sRNA knockdown of compared to no knockdown. The aforementioned study [4] of A6 examined the effect of stimulating A6 with PKA agonist F/I while A6 was knocked down by sRNA in comparison to when it was normally expressed to show that PKA-activation of A6 was the source of oxalate secretion, rather than some other underlying mechanism that PKA may have effected, not involving A6.

Additionally, it is known that oxalate absorption (from the gut into the blood) occurs predominantly via paracellular pathways. Paracellular pathways involve transport mainly between cells rather than through, as in transcellular transport[1]. It has been shown that obese patients tend to have increased paracellular permeability; [3] thus, obese patients have a higher risk of hyperoxaluria. In patients with greater paracellular permeability, it is yet to be studied whether or not stimulation of A6 via PKA agonists like F/I or OF secretagogue would allow for enough oxalate secretion to counteract higher levels of oxalate absorption via paracellular pathways such that there still is a net secretion of oxalate. A net secretion of oxalate would reduce urine oxalate to a level that would decrease risk of CaOx KS formation. It is also yet to be studied whether the increased risk of hyperoxaluria in obese patients may be due to other underlying factors, such SPRING 2021

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as a higher than normal concentration of PKA inhibitors, particularly elevated PTH which is often linked to obesity [16]. Further study is crucial for broadening the horizons of knowledge on the matter as well as the applicability of hyperoxaluria treatment in patients with other complications.

Precision medicine, the personalized treatment of each individual’s diseases through an understanding of their specific genetics and those pathways involved, is becoming more of a reality. Individual patient’s genetic mutations should not go ignored, making precision medicine imperative. In addition to specific focus on transporters and agonists, there is promise in treating EH through precision medicine. Literature on hyperoxaluria, PKA mechanisms and various agonists and inhibitors involved are a highly relevant subject matter and should be further studied in their relation to oxalate secretion. Additionally, it is important to acknowledge that there is more to understanding biological maladies on the cellular level than targeting individual signaling enzymes. This is illustrated by the fact that many kinase cascades, which are key to activating enzymes that have large effects on cellular activity, are constrained by scaffolding and anchoring proteins which ensure that signals get transmitted to their precise locations [5]. For example, many distinct genetic lesions have been linked to numerous distinct diseases. Additionally, such lesions can disrupt protein kinase anchoring which has been associated with various pathological responses and may offer potential therapeutic targets. It is possible that a genetic lesion having to do with PKA functionality in cells specific to oxalate absorption can lead to hyperoxaluria due to subsequent malfunctioning of PKA which prevents the stimulation of A6 and its secretion of oxalate.

There are many areas in the field which have been heavily researched and numerous mechanisms characterized with quantified effects, such as the gut-kidney axis, A6 as an important oxalate transporter, and the effects of certain PKA agonists, namely PKA and oxalate secretary factors, in increasing oxalate secretion. However, there still exists a lack of understanding of which treatment should be applied and a need for clarification of the effects and reversibility of other PKA inhibitors and direct A6 or A2 inhibitors. Additionally, there is potential that PKA anchor proteins may be related to genetic mutations or chemical species that perturb their functionality. How this can be navigated to still stimulate A6 has yet to be studied; consequently, patients with these issues will not have access to treatment when a treatment is available and works for others. This would occur, for example, because stimulation of a malfunctioning PKA is not likely to enable A6 to secrete oxalate into the gut to exit the body and relieve the patient of the toxin. Precision medicine is an emerging treatment method in many fields, and hyperoxaluria patients stand to benefit from the expansion

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of understanding of specific pathways of disease and the subsequent tailored treatment for them. Current understanding in the field should be furthered to consider applying precise modes of treatment that takes into account each patient’s individual comorbidities and genetic mutations. Specifically for EH, precision medicine is imperative as there are many mechanisms and species involved in the handling of oxalate in the body, and one single mode of treatment cannot work for them all.

Najya Fayyaz is a second-year student at the University of Chicago majoring in biology. She hopes to attend medical school and become a physician, specializing in genetics and precision medicine.

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Enhanced Gastrointestinal Passive Paracellular Permeability Contributes to the Obesity-associated Hyperoxaluria. American Journal of Physiology-Gastrointestinal and Liver Physiology. 316. 10.1152/ajpgi.00266.2018.

[4] Arvans, Donna & Jung, Yong-Chul & Antonopoulos, Dionysios & Koval, Jason & Granja, Ignacio & Bashir, Mohamed & Karrar, Eltayeb & Roy-Chowdhury, Jayanta & Musch, Mark & Asplin, John & Chang, Eugene & Hassan, Hatim. (2016). Oxalobacter formigenes-Derived Bioactive Factors Stimulate Oxalate Transport by Intestinal Epithelial Cells. Journal of the American Society of Nephrology. 28. 10.1681/ ASN.2016020132.

[9] Gulhan B, Turkmen K, Aydin M, et al. The Relationship between Serum Oxalic Acid, Central Hemodynamic Parameters and Colonization by Oxalobacter formigenes in Hemodialysis Patients. Cardiorenal Med. 2015;5(3):164-74. Epub 2015/07/22.

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[12] Caudarella R, Rizzoli E, Pironi L, et al. Renal stone formation in patients with inflammatory bowel disease. Scanning Microsc.

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[14] Ko N, Knauf F, Jiang Z, Markovich D, Aronson PS. Sat1 is dispensable for active oxalate secretion in mouse duodenum. Am J Physiol Cell Physiol. 7/01/12; 303(1):C52-7.

[45] Smith FD, Omar MH, Nygren PJ, et al. Single Nucleotide Polymorphisms Alter Kinase Anchoring and the Subcellular Targeting of A-Kinase Anchoring Proteins. PNAS. 2018;115(49):E11465-E11474. doi:10.1073/pnas.1816614115

[15] Whittamore JM, Stephens CE, Hatch M. Absence of the sulfate transporter SAT-1 has no impact on oxalate handling by mouse intestine and does not cause hyperoxaluria or hyperoxalemia. Am J Physiol Gastrointest Liver Physiol. 1/01/19; 316(1):G82-G94.

[56] Waikar SS, Srivastava A, Palsson R, et al. Association of Urinary Oxalate Excretion With the Risk of Chronic Kidney Disease Progression. JAMA Intern Med. 2019;179(4):542-51.

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[16] Larsson S, Jones HA, Göransson O, Degerman E, Holm C. Parathyroid hormone induces adipocyte lipolysis via PKA-mediated phosphorylation of hormone-sensitive lipase. Cell Signal. 2016 Mar;28(3):204-213. doi: 10.1016/j.cellsig.2015.12.012.

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ACKNOWLEDGE MENTS The Triple Helix, Inc. (TTH) is the world’s largest student-run organization dedicated to evaluating the true impact of historical and modern advances in science. Of TTH’s more than 20 chapters worldwide, The University of Chicago chapter is one of the largest and most active. Our TTH chapter continues to proudly share some of the most distinct publications and events on campus, engaging the minds and bodies of our institution and the public as “a global forum for science in society.” Our mission, to explore the interdisciplinary nature of the sciences and how they shape our world, remains the backbone of our organization and the work we do. In addition to Scientia, we publish The Science in Society Review (SISR) and an online blog (The Spectrum), while also creating events to discuss the most current, pressing topics at the intersection of science and our society.

WE ALSO THANK THE FOLLOWING DEPARTMENTS AND GROUPS

THE CENTER FOR LEADERSHIP AND INVOLVEMENT UNIVERSITY OF CHICAGO ANNUAL ALLOCATIONS STUDENT GOVERNMENT FINANCE COMMITTEE (SGFC) BIOLOGICAL SCIENCES COLLEGIATE DIVISION (BSCD)

Undergraduates who have completed substantial work on a topic are highly encouraged to submit their manuscripts. We welcome both full-length research articles and abstracts.

Please email submissions to eic.scientia@gmail.com. Please include a short description of the motivation behind the work, relevance of the results, and where and when you completed your research.

If you would like to learn more about Scientia and The Triple Helix, visit thetriplehelix. uchicago.edu or contact us at tth.uchicago.president@gmail

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MEET THE STAFF Editors-in-Chief / Thea Applebaum Licht / John Naughton Managing Editors / Palash Goiporia / Isabel O’Malley-Krohn / Arundhati Pillai Associate Editors / Indira Khera / Josie Brown / Jessica Metzger / Bernadette Miao / Isabella Cisneros /Jenna Nimer / Ananth Panchamukhi

SCIENTIA SCIENCE IN SOCIETY REVIEW

PRODUCTION

EXECUTIVE

SPECTRUM

EVENTS

Writers / Omar Kassem / Caroline Miller / Melody Leung / Eduardo Gonazelz / Nayja Fayyaz

Editors-in-Chief / Allison Gentry / Caroline Kim Managing Editors / Airi Kogishi / Mallory Moore / Nick Ornstein

Directors / Bonnie Hu / Ariel Pan Coordinators / Francesca Chu / Stephanie Zhang

President / Emily Guernsey Vice President / Rohan Kumar

Editors-in-Chief / Pascale Boonstra / Olivia Paraschos Managing Editors / William Cerny / Tanya Cukierman / Avery Rosado

Director / Hannah Dubinski Coordinators / Rosalind Pan / Tania Pena Reyes

SPRING 2020

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THE TRIPLE HELIX AT THE UNIVERSITY OF CHICAGO

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