Scientia - Winter 2013 by The Triple Helix at UChicago

Scientia

A Journal by The Triple Helix at the University of Chicago

Winter 2013 Issue II

he cover of this second issue of Scientia features a detail of Arnaldo Pomodoro’s bronze “Grande Disco” (“Large Disc,” in English) sculpture, captured by Executive Board Member, photographer Andrew Kam. Andrew’s photo shows the center of a disc that stands about ten feet tall, and despite its size, can rotate on its installation post. Our Chicago Disco is part of a group of six sister figures throughout the world, each with their unique touches – much like our chapter, a proud member of The Triple Helix, Inc. (TTH) international consortium. The piece, which cuts a dramatic silhouette between our campus bookstore and the University’s Brain Surgery Research Pavilion, is divided diagonally from its core into five pieces – and we might, with a smile, say that it represents our chapter’s five divisions. Print, E-Publishing, Marketing, Production, and Events each stand admirably, on their own, and we all come together in complex and unique ways, as this cover depicts, to advance the TTH mission. But beyond its design, we knew we had found the right image for this cover in the Pomodoro piece when we read the sculptor’s own description of Disco. He writes: “Our life today is one of crisis...of movement...of tension. We do not know what our world will become. I try to say something about this uncertainty in my work. I try to communicate a sense of vitality and connection with the movement of life today...and to be a part of its movement. The social challenge of art today, in my opinion, is to start a dialogue with the people. I hope that is what happens here with the Grande Disco.” We hope that this issue of Scientia, along with the issues to come, can serve in a similar respect. In short, The Triple Helix at the University of Chicago and its Scientia staff have created an issue we find to be a relevant, thought-provoking, and supportive outlet for the original research of undergraduates on our campus – plus some extras. We hope you enjoy it – you might even read your issue alongside the Grande Disco before it gets too cold.

Scientia 4

Scientia Inquiries: Losing More Than Just Sleep Dr. Gilbert’s Gut Feeling The Elusive γδ T-Cells

Cost as a Function of Competition for Medical Reimbursement Benjamin Dauber

Can Bispectral Monitoring Protocols Accurately Predict Awareness With Potential For Recall?

Grace Tobin and Carolyn Wiest

Myosin and Actin in vitro Motility Assay and the F-actin “Inch-Worming” Model Adam De Jesus

A Tripartite Mechanism for Menstrual Migraine Adam Shuboy

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Produced by The Triple Helix at the University of Chicago Layout and Design by Andrew Kam, Christina Chan, Charles Pena, Jong Chung & Shelby Winans Cover Photograph by Andrew Kam, Director of Production Cover Letter written by Luciana Steinert, Director of Marketing Scientia Team: Patrick Delaney, Khatcher Margossian, Luizetta Navrazhnyk, & Michael Begun

Winter 2013

About Scientia Dear Reader, The Triple Helix, Inc. (TTH) at the University of Chicago is proud to present the second issue of Scientia, our most recent venture as a chapter. Since the positive reception of its first issue, Scientia has solidified itself as a respected journal of original undergraduate research on campus and is expanding rapidly. Over the past six months, the Scientia team of managers and editors has grown and developed, and we are committed more than ever to showcasing the innovative research that undergraduates pursue every day. Scientia now includes shorter investigative pieces, called Scientia Inquiries, which introduce the scientists and lab directors behind some fascinating UChicago discoveries. Additionally, our next issue will contain research abstracts that we believe will greatly increase the accessibility of publishing to all independent student researchers. We are excited to present this issue to you, both as an example of excellent work and as a resource for our research community. Do not wait - dive in and get involved today! Patrick M. Delaney Editor in Chief, Scientia

Scientia

About The Triple Helix

at The University of Chicago

The Triple Helix, Inc. (TTH) is the world’s largest completely student-run organization dedicated to evaluating the true impact of historical and modern advances in science. Of TTH’s more than 25 chapters worldwide, the University of Chicago chapter is one of the largest and most active. We, TTH at The University of Chicago, are extremely proud of our chapter’s 2012 accomplishments, which are perhaps best summed by our title as the William J. Michel Registered Student Organization of the Year. We continue to work closely with an ever-increasing number of faculty members, and have notably acquired the generous support of the founding Pritzker Director of Chicago’s Institute for Molecular Engineering, Matthew Tirrell, and his department. We have expanded our local organization so that now, we can confidently say that there is a place here for each and every one of our fellow college students. We have consciously and dramatically increased the size of our production, marketing and events teams, and have watched our group of talented writers and editors grow at unprecedented levels. In fact, we have further expanded the intellectual diversity of our chapter, with TTH members having declared for more than 30 of the University’s different majors and minors. Finally, we are absolutely thrilled to present the second issue of our journal of original University of Chicago undergraduate research, Scientia. Over the years, TTH UChicago members have found themselves in research positions around campus, taking advantage of the hundreds of opportunities we are lucky to have here. We found ourselves wishing, however, that there was an outlet where we could reach out to our peers on campus, to share our projects and project ideas – and to hear or read about their work as well. So last year, we decided to create that outlet ourselves. Our solution, the journal Scientia, turned out to be a smashing success – so much so that we were able to instate a directorial board specific to Scientia, led by inaugural Editor-inChief Patrick Delaney. The rest of our Chapter Executive Board is amazed by the work they have put into this production, and we hope that you are equally impressed. Welcome to a new age of intellectual connectedness at The U of C. Luciana C. Steinert Director of Marketing

Winter 2013

Scientia Inquiries Losing More Than Just Sleep: The Detrimental Effects of Sleep Deprivation on Metabolism Y. Hanna Huang and Feenalie Patel

ore than six decades ago, Dr. Nathaniel Kleitman at the University of Chicago discovered rapid eye movement sleep and demonstrated its involvement in dreaming and brain activity. Research since then has shown the importance of sleep to proper daily functioning. Not only does sleep deprivation have detrimental effects on daily performance and alertness, but it is also linked to increased risk of mood and behavioral problems, memory and cognitive impairment, injury, heart disorders, obesity, and a poorer quality of life. College students are among the most sleep-deprived age group in the United States. Given the disrupted sleep patterns of college students in the face of various demands and stresses, sleep deprivation can disturb circadian rhythm and alter caloric intake, resulting in detrimental effects on metabolism. Recent transdisciplinary studies by fat biologist Matthew Brady, Associate Professor in the Department of Medicine; Eve

Van Cauter, Frederick H. Rawson Professor of Medicine and Director of the Sleep Metabolism and Health Center; and Esra Tasali, Assistant Professor of Medicine, have found strong evidence that skipping out on a full night’s sleep— independent of food consumption behavior— may have detrimental consequences on metabolism and may increase the risks for obesity, insulin resistance, and type 2 diabetes. In order to investigate the effects of disrupted sleep on metabolism, seven young and healthy participants were given a full night’s sleep and sleep-restricted for four consecutive nights. The participants in this study were between the ages of 18 and 30 years old with normal glucose tolerance, healthy body-mass indices averaging 22.8 kg/m2, and self-reported nocturnal hours of sleep between 7.5 and 8.5 hours. In this experiment, a “full night’s sleep” was considered 8.5 hours of sleep, while participants slept for an inadequate 4.5 hours

Scientia

Matt Piron in the Brady Lab, plates adipocytes in media containing varying concentrations of additives. Photo and caption by Adam Shuboy

during sleep restriction. Participants’ caloric intake and sedentary environment during waking hours of the study were strictly regulated and identical in both sleep conditions. On the fourth day in both conditions, following an overnight fast, a sample of subcutaneous fat tissue was collected from below the skin near each participant’s navel and tested for insulin sensitivity. In order to test metabolic activity, this study evaluated changes in insulin signaling within the fat tissue, as well as wholebody glucose levels. Insulin signaling in the fat tissue was investigated by measuring the activation of Akt, a key insulin signaling protein regulating glucose uptake and storage in tissue. Decreased Akt activation reflects decreased insulin signaling and metabolic activity. To evaluate total body insulin sensitivity, whole-body glucose levels were measured. To do this, blood samples were first drawn from each participant on the fourth day after the overnight fast in both conditions. Fifteen minutes after these blood samples were drawn, participants were injected with glucose, at which time blood samples were collected again, and then injected with insulin twenty minutes later, followed by additional blood sample collections. Glucose levels were measured in these blood samples to evaluate participants’ basal glucose levels, as well as how effectively insulin cleared glucose from the bloodstream. Elevated glucose levels along with slower glucose clearance from the blood stream over time References 1. Brady, MJ and Tasali E. et al. Impaired Insulin Signaling in Human Adipocytes After Experimental Sleep Restriction. Annals of Internal Med. 16 October 2012;157(8):549-557. 2. Johnson CM et al. Sleep Patterns of College Students at a Public University.

suggest systemic insulin insensitivity. The results of the study were dramatic: after just four nights of restricted sleep, fat cells exhibited approximately 30% decrease in insulin sensitivity, while whole-body insulin sensitivity decreased by about 16%. Fat cells from all participants revealed a marked reduction in Akt activation after sleep deprivation compared to normal sleep, suggesting reduced insulin sensitivity. Glucose levels in the blood samples revealed less efficient glucose clearing from the blood stream, also reflective of reduced systemic insulin sensitivity. The decreased metabolic activity found in the subjects after four nights of sleep deprivation resembles the insulin insensitivity in obese and diabetic patients, and results in a metabolic profile equivalent to the aging of fat cells by ten years. Despite the small sample size, Brady and his colleagues were surprised to observe such a strong correlation between insufficient sleep and decreased insulin sensitivity: “One of the huge advantages of this study was that each individual served as their own internal controls. We got the biopsy before the sleep restriction and after the sleep restriction, so we are really able to directly compare what’s going on between the two sleep conditions for each subject. With just seven subjects we were able to get [statistical] significance, which underlines the robustness of the effect.” These results have notable implications for the wider metabolic effects that sleep deprivation has on the body. This study suggests the significant role of sleep alone in regulating metabolism in peripheral tissues, in which sleep deprivation makes peripheral tissues more insulin resistant and increases the likelihood that they develop type 2 diabetes. Dr. Brady notes how management of sleep could be important for regaining insulin sensitivity: “In overweight and obese patients with obstructive sleep apnea, it would be interesting to see if improvements in sleep quality and/or duration would have any impact on systemic or cellular insulin sensitivity.” Although the effects of sleep deprivation may not immediately manifest as diabetes or other metabolic disorders, this study provides compelling evidence for what could be in store for the future. Given the irregular schedules and caloric intake of college students, this study underscores the need for adequate sleep as a part of a healthy lifestyle. So if you are thinking about sleeping early tonight, go ahead—it will pay off in the long run. Journal of American College Health. Mar-Apr 2008; 56(5):563-5. 3. Kirkland JL et al. Fat Tissue, Aging, and Cellular Senescence. Aging Cell. October 2010;9(5): 667–684. 4. Prichard JR et al. Sleep Patterns and Predictors of Disturbed Sleep in a Large Population of College Students. Journal of Adolescent Health. February 2010;46(2):124-132.

Winter 2013

Dr. Gilbert’s Gut Feeling Riva Letchinger

“D

o you have a healthy microbiology?” asks Dr. Jack Gilbert, believing that everyone should know their personal answer to this question. Most of us do not know bacteria are minuscule and thus are often forgotten in our macroscopic lives. However, there are more bacteria than there are plants and animals combined, and their overwhelming presence makes them the base of nearly every food chain on earth. Unsurprisingly, bacterial activity is critical to the functioning of all ecosystems (even our homes, our gardens, and our bodies) through increasing or decreasing their productivity, preventing or causing diseases, and recycling essential nutrients, especially nitrogen. Bacteria allow us to lead more productive and efficient lives; taking an interest in them will allow us to gain a fuller and better understanding of our own health and lives. Dr. Gilbert is a preeminent researcher of these tiny prokaryotes. Dr. Gilbert began his career as a rock musician in a London band. He had not been interested in studying bacteria past a degree in Marine Biology from King’s College London until, at 22, he was presented with a research offer that he could not resist: working with the Australian National Antarctic Research Expedition to catalogue Antarctic lake bacteria. There, he found a life more

interesting beyond that of a musician, realizing a passion for bacterial research. In the decades that followed, Dr. Gilbert devoted his career to studying not just bacteria, but the entire microbial systems that are built upon them. His fascination is not surprising – bacteria run the world. In the Antarctic years ago, Dr. Gilbert simply categorized and counted these creatures. Today, with the power to type the genome of each species, he delves into the minutiae of their lives, using individual genetic information to determine macroscopic effects that impact entire ecosystems. He collects genetic information from countless bacterial samples around the world, utilizing a system of metagenomic data from thousands of ecosystems. Dr. Gilbert takes an unorthodox approach to the organization of his data and the synthesis of his research. Rather than approaching the genomic data with a specific search in mind, he instead allows the microorganisms themselves to eventually reveal what is genetically important – that is, he looks not for specific, pre-identified genetic information, but for correlations among multiple genomes to determine important functions. He emphasizes that “we don’t have a specific experiment with a specific hypothesis, we just look at the environment [and] can thus ask a lot of very specific as well as broad questions.” The crux of his method is to “disentangle questions from vast resources,” which takes advantage of having a uniquely large and varied data pool – the natural world – from which he can dissect and analyze individual pieces of genetic information. “If I sequence thousands of genomes,” he says, “I am able to start saying what role the functions I find in those genomes have in the ecosystem. I can build up statistical probabilities from a large quantity of data…[and] identify correlations.” Within the

Scientia genetics of any microbial creature, he sees the potential for environmental control. Dr. Gilbert’s extraordinary range of data is sourced from a wide variety of projects. He co-leads or works with the Earth Microbiome Project, the Home Microbiome Project, and “American Gut,” each of which approaches the study of the microbiome at a different level and generates different types of data from vastly different spheres of bacterial activity. The Earth Microbiome Project hopes to create what Dr. Gilbert calls “a Gene Atlas” of microbial communities around the world. It began when Dr. Gilbert was in England, researching the English Channel, but has since expanded globally. “Your particular location of study put in the context of other locations gives you an incredible power to understand ‘why’ that location is,” he notes. Relying on the cooperation of scientists around the world and making his own data public, all samples are processed in a standardized way so as to make consistent comparisons. Dr. Gilbert does not limit his research partners to just PhD-certified scientists. He also relies on what he calls “citizen scientists,” non-scientists who are interested in better understanding their environments. This engaged citizenry is key to both the Home Microbiome Project and “American Gut.” The former applies microbiology to

contained spaces such as homes and hospitals, observing the interactions between people and the surfaces that they touch. Dr. Gilbert is looking for what he calls their “microbiome signature” – the bacterial stain that people leave on their surroundings (and that their surroundings leave on them). “American Gut” inquires even further into the relationships between an individual and his bacteria, asking people to donate fecal samples (and other excretions) for analysis. It seeks to characterize “the microbial diversity of the American public” for scientific knowledge by encouraging Americans to “take an interest in their own microbiology,” says Dr. Gilbert. It brings the scientific world directly to the public. Dr. Gilbert believes that understanding our global environment requires us to be fully aware of the impact of bacteria on our entire functionality. His (and our own) growing bacterial mastery will aid in that quest. He is seeking to identify the links that connect a bacterium, a microbe, an individual, a community, an environment, and an ecosystem. His ultimate goal is to apply all of this knowledge to helping people in the world. Bacteria are resourceful, varying, adaptive creatures, existing everywhere and doing everything, and understanding them is key so that in the future we might share in this level of environmental omnipotence.

References 1. Gilbert, Jack A. Telephone interview. 3 Dec. 2012.

The Elusive γδ T-cells Alice Ye

ear the end of fall quarter, a few unfortunate students caught the Norovirus, the leading pathogen responsible for acute gastroenteritis (generally called stomach flu). Ironically, it was around the same time that I sat down to interview Dr. Erin Adams, Associate Professor of Biochemistry and Molecular Biophysics, about her work on γδ T-cells. These cells are a rather enigmatic subset of T-cells and the third member of the adaptive white blood cell tripartite (B-cell antibodies, αβ T-cells, and γδ T-cells). γδ T-cells predominately populate the gastrointestinal epithelial tissue, thereby most likely linking these T-cells with the innate (i.e. nonspecific) immune response, a very

surprising hypothesis. It is probable, then, that within the intestines of those ailing students, γδ T-cells were at the metaphorical battlements, jabbing at virus particles in defense of normal gastrointestinal functions. But, after talking with Adams, I discovered that scientists themselves are unsure as to what these cells do and are even more unsure as to what molecules these cells may bind. Before explaining Dr. Adams’ current projects, I will first give a succinct overview of immunology so readers can understand how γδ T-cells fit, or rather how they don’t seem to fit, into the overall picture. Humans and other vertebrates experience two types of immune responses,

Winter 2013 both of which involve white blood cells – the innate and the adaptive. Innate immunity involves nonspecific responses to invading pathogens through phagocytes. Found in the skin and GI tract, phagocytes serve as the first line of defense in the constant battle waged between pathogens and host. These Pac-men of biology roam the body barriers and hunt down anything they deem suspicious. After digesting a pathogen, short strings of amino acids (peptides) are taken from the invader and are presented on proteins called major histocompatibility complexes II (MHCII) that span across the phagocyte’s bilayer membranes. Also called “antigen-presenting cells,” the phagocytes display these foreign peptides (antigens) for the B-cells and T-cells of adaptive immunity. Adaptive immunity involves specific responses to pathogens. B-cells act within the humoral fluids of the body with membrane-bound antibody proteins capable of tagging pathogens for phagocytosis. On the other hand, T-cells can either attack cell-infiltrated pathogens or release chemicals that alarm the immune system to pathogen-induced changes in tissues. Notice that generally T-cells are part of the adaptive immune response but Adams studies a T-cell subset, which are hypothetically involved in the innate immune response. Why and how γδ T-cells are responding like the innate immune response are critical unanswered questions. In fact, it may be that the γδ T-cell response is a mixture of the adaptive and innate immune responses. To figure out the answers, immunologists study how the immune response can differentiate between healthy and diseased tissue. Adams investigates this central question by conducting research in multiple scientific fields ranging from structural biology to evolutionary genetics in hopes of gaining new evidence and immunological perspectives. Specifically, she focuses on ligand recognition by γδ T-cells as well as the role of MHC-like proteins that present lipids instead of peptides. For humans, γδ T-cells account for less than 5% of T-cells while αβ T-cells account for the rest. The difference between them lies in their structure: in T-cell receptors, the repeated molecules on T-cell membranes that

recognize both antigens and MHC molecules on antigenpresenting cells are composed of two protein chains. These chains can either be α and β or γ and δ chains. The majority of immunological research has been focused on αβ T-cells, leaving the γδ T-cells relatively uncharacterized. Although αβ T-cells comprise the majority of T-cells, γδ T-cells are predominant in the peripheral tissues of the gastrointestinal, respiratory, and reproductive tracts. As a post-doc at Stanford, Adams uncovered how a mouse T-cell receptor called γδ TCR G8 binds with a MHC ligand called T22 (2005). B-cells and T-cells bind to antigens and MHC molecules by their complementaritydetermining regions (CDR) 1, 2, and 3 on the variable portion of the γ chain or δ chain. She showed by x-ray crystallography that the G8 TCR can bind to MHC molecule T22 even without the antigen present and binds mainly with its CDR3δ to a very conserved motif across mice G8 TCRs, a motif being a pattern of amino acids. Interestingly enough, if this motif is cut and pasted on to other proteins, these new proteins will bind to T22, showing that binding is dependent on the CDR3δ motif (2008). However, because γδ T-cells are so evolutionarily diverse, this G8-T22 binding model has no known counterpart in humans. In fact, Adams’ lab has examined Photo by Adam Shuboy the genetic diversity among various nonhuman primate species’ TCRs and shown that even within the same genus, γδ T-cells, specifically γ-gene segments, have experienced considerable diversification between primate lineages. Moreover, what G8-T22 binding causes in γδ T-cells is still unknown. What this means is that γδ T-cell studies conducted in mouse models will most likely not contribute to γδ T-cell knowledge in humans (2011). Adams will need human samples in order to study how γδ T-cells work in humans. Since γδ T-cells are epithelium-based, to receive human γδ T-cell samples she plans to work with clinical partners and donors. Adams has wrapped up her work with the mouse model G8-T22 binding and now runs two main projects in her lab. One project explores how Vγ9Vδ2 (V refers to the variable gene, γ9 and δ2 are the types of genes chosen) T-cells are stimulated by phosphoantigens, metabolites

Scientia produced by both bacteria and tumor cells. Once again, no one is quite sure what these Vγ9Vδ2 T-cells do and how they recognize phosphoantigens. Adams is collaborating with Mark Bonneville’s lab in France to figure this out. She suspects that phosphoantigens induce some sort of signaling pathway that leads to Vγ9Vδ2 T-cell stimulation rather than direct binding. Her lab’s second project is exploring MHC-like molecules in collaboration with Albert Bendelac of the Department of Pathology here at the University of Chicago. Specifically, they are studying MHC-like molecules called CD1. These molecules present lipids from pathogens instead of peptides, exploring a different take on how T-cells recognize pathogens. Adams hopes to discover the molecular basis of recognition to CD1. CD1 molecules can present sulfatide lipids (a type of sulfur-containing lipid) that account for 20% of the myelin sheathe in axons. Patients suffering from multiple sclerosis show plaque formation on myelin sheathes in the central nervous system (CNS). Since CD1 is present where plaques accumulate, CD1 seems to have some sort of involvement in the autoimmune disorder. However, it is unknown whether CD1 involvement contributes to multiple sclerosis or is a normal immune response to plaque formation. At this point, the functions and mechanisms of human References 1. Adams’ lab website <http://ejadamslab.bsd.uchicago.edu/Adams_Lab.html> 2. Adams, EJ et al. Structure of a γδ T cell Receptor in Complex with the Nonclassical MHC T22. Science 308, 227 (2005). 3. Adams, EJ et al. An autonomous CDR3δ is sufficient for recognition of the

γδ T-cells and CD1 remain elusive questions. Adams and her lab are working hard to shed light on a few more unknown aspects of these immunological mysteries. What’s interesting is that both of these projects investigate atypical protein roles. T-cells are normally part of adaptive immunity and are not considered prominent in areas like the skin; however, the majority of γδ T-cells are found near epithelial tissue, the place where innate immunity is the first responder to pathogens. A promising human model for γδ T-cell function is the signaling pathway from phosphoantigens to Vγ9Vδ2 cell activation. In addition, the MHC-like molecule CD1, which presents lipids for B and T-cell recognition, seems to play an important role in CNS demyelinating autoimmune diseases, but whether they are protagonists or antagonists is unknown. As scientists like Adams and many others are constantly discovering, the immune system is increasingly complex regulatory system in which the roles we thought cells have are not always kept. Today, immunology is an exciting field filled with questions and challenges, but hopefully in later years, with the roles of γδ T-cell and MHC-like proteins revealed, more effective antivirals and antibiotics will be on the market. Perhaps humans will never have the stomach flu again! nonclassical MHC class I molecules T10 and T22 by γδ T cells. Nature Immu. 9, 777 (2008). 4. Kazen, AR and Adams, EJ. Evolution of the V, D, and J gene segments used in the primate γδ T-cell receptor reveals a dichotomy of conservation and diversity. PNAS 108, 332 (2011).

The Adams lab produces the proteins involved in interactions of interest via isolated, recombinant insect cell lines in vitro. Andrew Sandstrom, PhD candidate in the Adams lab, cultures a growth media suspension occupied by such cells. Photo and Caption by Adam Shuboy

Winter 2013

Cost as a Function of Competition for Medical Reimbursement Benjamin Dauber, Joyce Woo, David Glick Proposals to increase competition among hospitals have been suggested to control rising healthcare costs. Our study sought to determine associations between cost, competition, and different indices of quality. For each hospital, the number of competitors, Medicare per-patient costs, quality measures, and mortality were used to evaluate the relationship between competition, cost, and quality for two medical conditions and two surgical procedures. We found a positive, linear correlation between competition and cost for these medical and surgical procedures. This suggests that classical economic theory cannot be applied to current hospital markets. These results may support the idea that hospital markets are engaging in non-price competition, which may be partially driven by the medical arms race. Finally, our data suggest that qualitative metrics play a greater role in gauging quality than their quantitative counterparts.

Introduction The rapidly increasing cost of healthcare is a growing challenge facing the healthcare industry. Current data show that approximately 16% of the nationâ&#x20AC;&#x2122;s gross domestic product (GDP) is spent on health care, with 52% of this attributed to hospital care and physician services [1]. In the United States, healthcare costs have increased from 9% of GDP in 1980 to 16% in 2006, and are expected to exceed $3 trillion in 2012 [2,3]. Some of the identified contributors to this increase in cost include longer life expectancies, liability-related costs, expensive new technologies, and administrative and regulatory burdens. Reform of the healthcare system to lower rising expenses while improving quality has become an increasing concern for policy makers and healthcare providers. One strategy often discussed to achieve this goal is the pursuit of a competitive health

care environment. Thus, it is important to establish how pricing is determined in the health care market. To better understand the relationship between hospital cost and competition, it is useful to consider the role of classical economics. Theories of market competition state that the supply and demand equilibrium curve determines price and consumers rely on price as a gauge of supply, demand, and quality. Standard economic predictions would state that more competition should result in lower cost [4]. When applied to healthcare, this model indicates that the presence of more providers lowers the price of healthcare. In theory, in a perfectly competitive environment, providers competing on price should reduce prices until they equal the marginal cost of delivering the service. However, hospital markets are eccentric for several reasons. First, patients do not have full information about the product

Scientia (i.e. healthcare services rendered to them). Second, insurance policies separate the consumer from the payer so that decisions may not depend on price. Third, procedures can be elective or non-elective; some nonemergent procedures, such as gall bladder removal, allow the patient to choose the institution for treatment, while other emergent cases, such as acute myocardial infarction (AMI), do not give the patient any choice. In these emergent cases the patient is usually sent to the closest hospital, and price and quality are usually not taken into account. Since these factors differentiate healthcare markets from standard economic models, it may be that hospitals do not necessarily compete on prices [5]. Rather, hospitals may engage in non-price competition. The earliest cost-competition healthcare studies attribute non-price competition to the medical arms race. The medical arms race, based on the term used originally to describe a competition between two or more parties for the best armed forces, asserts that hospitals are competitive based on the clinical services, quality, and amenities that they provide. Therefore, according to this theory, competition leads to increased service rivalry and higher prices to accommodate for those services. Indeed, studies conducted in the 1970s and 1980s found positive relationships between competition and cost. For example, Robinson and Luft studied 5,013 hospitals and found that average costs per admission per day increased with the number of rival hospitals within a 15 mile radius [6]. Additionally, Garnick et al. studied California hospitals and found a positive, statistically significant relationship between competition and cost when examining both individual hospitals and the market as a whole [7]. The medical arms race brought an influx of studies on quality improvement, a topic that is still contentious to this day. Some studies have attempted to define quality in a quantitative manner by developing sets of quantitative metrics [8,9]. Other publications, such as the U.S. News and World Report (USNWR) hospital rankings, rely on both quantitative and qualitative data. Yet with each study that defines quality, there exists another to refute it [10,11]. In short, a universal standard for quality has yet to be established. Some studies have shown that the effect of the medical arms race may have declined over time in favor of selective contracting. Selective contractingâ&#x20AC;&#x2122;s premise is that payment plans such as managed care, a term used to describe a variety of techniques intended to reduce the cost of providing health benefits and improve the

quality of care, or the Prospective Payment System (PPS) only pay for hospital services provided by a contracted provider panel. Selective contracting forces patients to note price in addition to quality, thus pressuring hospitals to lower prices for the insurance market, which then lowers the premiums for purchasers [12]. Robinson and Luft conducted another study using data from 1982 to 1986, and found that, despite what they had found earlier, competition reduced rates of price inflation in five states [13]. Furthermore, Zwanziger and Melnick showed that the introduction of selective contracting in the early eighties led to lower costs per discharge in competitive areas [14]. These data suggest that traditional models of competition, and not the medical arms race, may determine pricing in the hospital market. The current study seeks to examine the relationship between competition and cost in the hospital market with the most current data. Specifically, this study utilizes 2008 data for hospitals in the United States to determine if there is a correlation between Medicare reimbursement and hospital competition for four procedures: heart valve replacement, gall bladder removal, treatment for heart failure, and treatment for acute myocardial infarction (AMI). We sought to determine whether the presence of more providers (hospitals and healthcare providers) lowers the price of healthcare as physicians and hospitals compete to attract more patients. In addition, the study aims to shed more light on the relationship between quality and Medicare reimbursement (i.e. cost). Methods This study was reviewed by the University of Chicago Institutional Review Board and was granted exemption. In order to determine the correlation between competition, cost and quality, data were compiled for the following four procedures: heart valve replacement, gall bladder removal (cholecystectomy), treatment for heart failure, and treatment for acute myocardial infarction (AMI). Two surgical procedures (heart valve replacement and cholecystectomy) and two medical conditions (heart failure and AMI treatments) were investigated based on their thorough representation of elective and non-elective, specialized and non-specialized, and urgent and emergent cases. That is, for example, in the spectrum of cases, heart valve replacement is more elective and specialized, but less emergent than AMI treatment. Proxies for competition and cost data were gathered. Zip codes and provider numbers for each hospital were obtained via the downloadable, publically

Winter 2013 Table 1. The Quality Measures for Each Procedure Procedure

Quality Metrics

Surgical Procedures (Heart Valve, Cholecystectomy)

1. Preventative antibiotics 1 hour before incision 2. Right kind of antibiotic to prevent infection 3. Preventative antibiotic(s) stopped within 24 hours of surgery 4. Doctor ordered treatments to prevent blood clots 5. Treatment to prevent blood clots within 24 hours before and after surgery 6. Patients with controlled blood sugar in days after surgery

Heart Failure

1. Discharge instructions 2. Assessment of LV function 3. ACE inhibitor for LV systolic dysfunction 4. Adult smoking cessation counseling

AMI

1. Aspirin at arrival 2. Aspirin at discharge 3. Beta Blocker at discharge 4. ACE inhibitor for LV systolic dysfunction 5. PTCA received within 90 minutes of hospital arrival 6. Adult smoking cessation counseling 7. Fibrinolytic medication within 30 minutes of arrival

available database Hospital Compare (HC, http://www. medicare.gov/download/downloaddb.asp). The zip codes were then entered into the Blue Cross Blue Shield website (BCBS, http://www.bcbsil.com) to determine the number of competing hospitals. Past studies have used the Herfindahl Index or a 15-mile radius to define competition [7, 15]. In our study, competition was more expansively defined as any hospital within a 20-mile radius that performed the same procedure 10 or more times per year. We did so to expand the sample size and to attain good representation of only hospitals that regularly performed the procedure. Cost data was obtained from the Medicare database (http://www. medicare.gov/download/downloaddb.asp). The average Medicare payment for each Diagnosis Related Group, a patient classification scheme that provides a means of relating the type of patients a hospital treats (i.e., its case mix) to the costs incurred by the hospital, was used as a proxy for cost. The top 50 hospitals for each of the four procedures Gastroenterology, Heart and Heart Surgery were found on the USNWR website (http://health.usnews.com/besthospitals). Additionally, for each procedure, complication rate (percentage of patients that developed problems during treatment), volume (number of patients treated with the procedure), and mortality rate (number of patients who died while undergoing treatment) were obtained from HC using the Care Comparison Tool. Quality metrics were obtained from the BCBS website

and were standardized measures developed by the Centers for Medicare and Medicaid Services (CMS) (Table 1). These quality metrics were scored on a quartile scale, and thus each metric for each hospital was given a score of 1 (top 25%), 0 (middle 50%), or -1 (bottom 25%). Statistical analysis was conducted using Microsoft Excel 2007. Results The mean dollar amounts of Medicare reimbursement (in thousands) for each procedure were: heart valve replacement (M = 41.7, SD = 10.7); gallbladder removal (M = 6.9, SD = 1.9); treatment for heart failure (M = 7.0, SD = 1.6); treatment for AMI (M = 6.0, SD = 1.7). To attain a better understanding of the competitive atmosphere, all four procedures were divided into quartiles where each quartile contained an approximately equal number of hospitals and cutoffs were determined by competition number (Table 2). This was done so that the data did not disproportionately depict high costs at high quartiles due to smaller representation. For each procedure, the fourth quartile, containing hospitals with the highest degree of competition, had the highest mean cost. For all four procedures, the cost gap, defined as the difference in cost between two quartiles, between the third and fourth quartiles was greater than the other two cost gaps. The fourth quartile also contained the highest number of hospitals that ranked in the Top 50 of their specialty by the U.S. News and World Report. Mean costs for USNWR Top 50 hospitals and unranked

Scientia Table 2. Competition Quartiles for Medical and Surgical Procedures Procedure Quartile Degree of Number of Mean Cost SD Competition† Hospitals (Thousands) (Thousands) Heart valve replacement

Cholecystectomy

Heart failure treatment

AMI Treatment

1 2 3 4

0-1 2-4 5-10 11-31

193 149 159 135

37.7 40.8 43.4 46.6

8.3 8.6 12.2 11.6

1 2 3 4

0-1 2-5 6-17 18-101

398 425 437 409

6.2 6.4 6.9 8.0

1.4 1.4 2.0 2.1

1 2 3 4

0-3 4-9 10-24 25-105

153 161 165 160

6.4 6.5 7.1 8.0

0.9 1.1 1.7 1.8

1 2 3 4

0-1 2-5 6-16 17-98

414 441 431 456

5.2 5.5 6.0 7.3

1.0 1.1 1.6 1.9

†The range of competition numbers for each quartile

hospitals were determined (Table 3). It was found that Top 50 hospitals had a higher mean reimbursement value for all four procedures. The highest percent difference in cost between ranked and unranked hospitals was demonstrated by AMI treatment, at 38%, while the lowest percent difference was for heart valve replacement, at 24%. For all four procedures, the cost gap, defined as the difference in cost between two quartiles, between the third and fourth quartiles was greater than the other two cost gaps. The fourth quartile also contained the highest number of hospitals that ranked in the Top 50 of their specialty by the U.S. News and World Report. Mean costs for USNWR Top 50 hospitals and unranked hospitals were determined (Table 3). It was found that Top 50 hospitals had a higher mean reimbursement value for all four procedures. The highest percent difference in cost between ranked and unranked hospitals was demonstrated by AMI treatment, at 38%, while the lowest percent difference was for heart valve replacement,

Procedure Heart valve replacement (n=29) Cholecystectomy (n=28) Heart failure treatment (n=29) AMI treatment (n=29)

at 24%. To determine if a relationship exists between competitive atmosphere and Medicare reimbursement, correlations were determined between the two for all four procedures. Positive, linear correlations were found for all four procedures at highly statistically significant levels (Figure 1). Heart valve replacement demonstrated the least correlation (r = 0.34, p < 0.001), with continuously stronger correlations for more non-elective procedures. AMI treatment possessed the highest correlation between cost and competition (r = 0.54, p < 0.001). To understand another possible mediator of cost, Pearson correlation coefficients were determined for various quality metrics and cost (Table 4). Some hospitals did not report statistics for all metrics, but they were still considered in any calculations where numbers were provided. Both surgical procedures (heart valve replacement and cholecystectomy) possessed the same quality metrics. It is important to note that a negative correlation coefficient between cost and mortality, or cost and complication rate, designates a positive relationship

Table 3. Mean Costs for USNWR Top 50 Hospitals Top 50 Mean Cost Mean Cost of Remaining (Thousands) Hospitals (Thousands) 51.3 ± 8.8 8.7 ± 2.0 9.1 ± 2.2 8.3 ± 1.9

41.3 ± 10.5 6.9 ± 1.9 6.9 ± 1.5 6.0 ± 1.6

Winter 2013 and vice versa. We found that heart valve replacement had the most statistically significant correlations, with complication rate being the only metric with no significance. On the other hand, cholecystectomy demonstrated no statistically significant correlations for the same eight metrics. While some of the correlations for heart failure and AMI treatments were statistically significant (mortality rate and Q2 for heart failure; complication rate, Q2, and Q4 for AMI), they were not all positive. For instance, Q2 for heart failure treatment (assessment of left ventricular function) demonstrated a negative correlation between cost and quality. For AMI treatment, all statistically significant correlations were inversely related to cost: complication rate, Q1 (aspirin at arrival), Q2 (aspirin at discharge) and Q4 (ACE inhibitor for LV systolic dysfunction). Discussion The most recent cost-competition study was conducted by Rivers and Bae in 1999 [15]. Their work suggested a return to non-price competition by showing a positive correlation between the Herfindahl index (a statistic which measures market shares of firms in the industry) within 29 metropolitan statistical areas and cost per admission. To expand upon their work, we defined a less stringent competition index, a larger hospital database, and Medicare reimbursement as a proxy for cost. In this study, we specifically examined the relationship between a hospitalâ&#x20AC;&#x2122;s competitive atmosphere and the Medicare reimbursement that the hospital receives for two medical and two surgical procedures: heart valve replacement, cholecystectomy, treatment for heart failure, and treatment for AMI. Additionally, we examined the relationship between different indices of quality, both quantitative and qualitative, and cost. Positive correlations were determined between competition and cost for all four procedures. Previous studies revealed conflicting data on the relationship between competition and cost. Analyzing data from 2008, we found that hospital costs are not entirely unsystematic; the positive correlation between cost and competition was seen across procedures. Likewise, competitive atmosphere is similar for different medical and surgical procedures, and it is likely that inputs that affect one procedure affect others. Overall, data from this study strongly support the idea that hospitals are once again engaging in non-price competition. However, Pearson coefficients fell with electiveness of procedure, with AMI treatment having the highest followed by heart failure treatment, gall bladder removal,

Figure 1: Competition vs. Cost for Medical and Surgical Procedures A. Heart Valve Replacement

B. Cholecystectomy

C. Heart Failure Treatment

D. AMI Treatment

Scientia Table 4. Pearson Correlation Coefficients (r) for Cost vs. Quality Metrics *

Quality Metric

p-value

95% CI

Mortality Rateâ&#x20AC;Ą Complication Rate Q1 Q2 Q3 Q4 Q5 Q6

-0.12 0.063 0.13 0.23 0.25 0.23 0.19 0.18

0.0021 0.11 0.0010 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

-0.19 to -0.045 N/A 0.054 to 0.21 0.15 to 0.30 0.17 to 0.32 0.15 to 0.30 0.12 to 0.27 0.10 to 0.26

â&#x20AC;ĄBolded metrics indicate statistical significance

Quality Metric

p-value

95% CI

Complication Rate Q1 Q2 Q3 Q4 Q5 Q6

0.029 -0.0054 -0.048 -0.021 0.035 -0.00052 -0.026

0.24 0.83 0.083 0.45 0.16 0.98 0.30

N/A N/A N/A N/A N/A N/A N/A

Quality Metric

p-value

95% CI

Mortality Rate Complication Rate Q1 Q2 Q3 Q4

-0.11 0.051 0.018 -0.086 0.062 0.012

0.0047 0.20 0.66 0.033 0.12 0.77

-0.19 to -0.035 N/A N/A -0.16 to -0.077 N/A N/A

Quality Metric

p-value

95% CI

Mortality Rate Complication Rate Q1 Q2 Q3 Q4 Q5 Q6 Q7

-0.023 0.22 -0.060 -0.11 0.041 0.050 -0.044 0.010 0.059

0.34 < 0.001 0.016 <0.001 0.094 0.043 0.072 0.74 0.24

N/A 0.17 to 0.26 -0.11 to -0.011 -0.16 to -0.065 N/A 0.002 to 0.097 N/A N/A N/A

Winter 2013 and finally heart valve replacement. Furthermore, cost data from the USNWR Top 50 hospitals showed that heart valve replacement had the lowest percent difference between ranked and unranked hospitals in comparison to the other three procedures. These data suggest that selective contracting may still play a role in a non-price competitive market. That is, perhaps for highly elective, specialized, and less emergent procedures such as heart valve replacement, patients have time to choose within the panel of providers made available by their insurance package. In such a case, patients may be more wary of price and may incorporate this statistic into their decisionmaking calculus, forcing hospitals to lower their costs regardless of quality or reputation. Because our data support non-price competition, we hoped to gain further insight on the applicability of the medical arms race theory to current hospital markets. Pearson correlations were conducted between competition and several quality metrics for each procedure. We found that seven of the eight quality metrics correlated positively, at a statistically significant level, for heart valve replacement. This suggests that hospitals that perform heart valve surgery may still compete according to the medical arms race theory. That is, competition leads to higher prices to accommodate for an increase in services offered. On the other hand, none of the quality metrics were statistically significant for gallbladder removal. Furthermore, for heart failure and AMI treatments, those quality metrics that were statistically significant demonstrated both positive and negative relationships with cost. This finding was surprising in light of the fact that negative relationships References 1. World health statistics 2009. World Health Organization [Internet]. 2009 May [cited 2010 July 1]. Available from: http://www.who.int/whosis/whostat/2009/ en/index.html. 2. World Health Statistics 2009. World Health Organization. http://www.who.int/whosis/whostat/EN_WHS09_Full.pdf. Accessed July 25, 2012. 3. The High Concentration of U.S. Health Care Expenditures. AHRQ research in action manuscript 19, 2006. http://www.ahrqu.gov/research/ria19/ expendria.htm. Accessed July 25, 2012. 4. Rosen S. Hedonic prices and implicit markets: product differentiation in pure competition. J Polit Economy. 1974;82:35-44. 5. Robinson JC. Hospital quality competition and the economics of imperfect information. Milbank Q. 1988;66(3):465-81. 6. Robinson JC, Luft HS. The impact of hospital market structure on patient volume, average length of stay, and the cost of care. J Health Econ. 1985;12;4(4):333-56. 7. Garnick DW, Luft HS, Robinson JC, Tetreault J. Appropriate measures of hospital market areas. Health Services Res. 1987;22:69-90. 8. McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, et al. The quality of health care delivered to adults in the united states. N Engl J Med. 2003 Jun 26;348(26):2635-45. 9. Cantor JC, Belloff D, Schoen C, How SKH, McCarthy D. Aiming higher: results from a state scorecard on health system performance. New York (NY): Commonwealth Fund Commission on a High Performance Health System (US); 2007 Feb.

imply that cost increases with decreasing quality. Nevertheless, the data show that overall, quality does not seem to have great impact on the cost of less elective surgical and medical procedures. Since quantitative metrics for healthcare quality did not seem to correlate with cost, we sought to determine if qualitative metrics supported the arms race. The U.S. News and World Report (USNWR) is consistently regarded as an accurate gauge of hospital quality, as they integrate both quantitative and qualitative data. However, studies have shown that the hospital rankings are overwhelmingly determined by reputation, and many dispute the relationship between reputation and patient outcomes [10, 16-19] Nevertheless, we found that for all four procedures hospitals which ranked in the USNWR Top 50 of their field also possessed a higher mean cost, in comparison to unranked hospitals. This implies that qualitative inputs such as reputation play a much more relevant role in hospital pricing in comparison to quantitative metrics. There are still several limitations to our study. First, Medicare reimbursement can be normalized for various inputs that we have not yet considered, such as hospital owner type (Federal, Hospital District/Authority, State, Proprietary, Church Voluntary Non-Profit, Private Voluntary Non-Profit, Other Non-Profit), income per household, and region of the US. Second, data were collected from only 30 states, which may or may not be representative of a national sample. Third, correlation does not imply causation, and thus it cannot be inferred that higher competition leads to higher costs.

10. Apfelbaum SM, Glick DB, Roth SR, Tung A. The value of reputation: correlating hospital compare quality metrics with the US News honor roll. In: Annual Meeting of the American Society of Anesthesiologists; 2009 October 17-21; New Orleans, LA. 11. Wang OJ, Wang Y, Lichtman JH, Bradley EH, Normand SL, Krumholz HM. “America’s best hospitals” in the treatment of acute myocardial infarction. Arch Intern Med. 2007 Jul 9;167(13):1345-51. 12. Morrisey MA. Competition in hospital and health insurance markets: A review and research agenda. Health Serv Res. 2001 Apr;36(1 Pt 2):191-221. 13. Robinson JC, Luft HS. Competition, regulation, and hospital costs, 1982 to 1986. JAMA. 1988;260(11):2676-81. 14. Zwanziger J, Melnick GA. The effects of hospital and the Medicare PPS program on hospital cost behavior in california. J Health Econ. 1988; 7:301-20. 15. Rivers PA, Bae S. Hospital competition in major U.S. metropolitan areas: An empirical evidence. J Socio-econ. 1999;28(5):597-606. 16. Sehgal AR. The role of reputation in U.S. News & World Report’s rankings of the top 50 American hospitals. Ann Intern Med. 2010 Apr 20;152(8):521-5. 17. Woolston C. Validity of hospital rankings: facilities on U.S. News & World Report’s annual list ride their reputations heavily. Los Angeles Times [Internet]. 2010 April 26, [cited 2010 April 26]; Health [3 p.]. Available from: http://www. latimes.com/features/health/la-he-skeptic-20100426,0,5741860.story 18. Wang OJ, Wang Y, Lichtman JH, Bradley EH, Normand SL, Krumholz HM. “America’s best hospitals” in the treatment of acute myocardial infarction. Arch Intern Med. 2007 Jul 9;167(13):1345-51. 19. Miller RH. Competition in the health system: Good news and bad news. Health Aff (Millwood). 1996 Summer;15(2):107-20.

Scientia

Can Bispectral Monitoring Protocols Accurately Predict Awareness With Potential For Recall? Grace Tobin, Carolyn Wiest, David Glick, Michael O’Connor, Avery Tung Unintentional intraoperative awareness is defined as “the experience and explicit recall of sensory perceptions during surgery” [1] for patients undergoing general anesthesia [2]. Such episodes can be traumatic to patients, with up to about 70% subsequently developing post-traumatic stress disorder [3]. Efforts to reduce the incidence of intraoperative awareness have resulted in the development of monitors designed to measure the depth of anesthesia. One such monitor is the bispectral index monitor (BIS). This device produces a value that is intended to indicate the depth of anesthesia. The purpose of this study was to delineate what the BIS monitor can tell us about a patient’s level of sedation and his/her ability to form memories. Instead of looking directly for cases where intraoperative awareness occurred, we examined the relationship between BIS monitor output and awareness levels in patients prior to, or in specific stages of, a general anesthetic (i.e., when subjects were sedated but not unconscious). If BIS scores are accurate predictors of awareness with potential for recall in heavily sedated score-range patients, then they should be accurate in other ranges as well. We examined the ability of the BIS monitors to accurately predict whether patients could recall specific stimuli (presented words, or pre-operative events). Excepting one outlier data parameter, BIS score was not a significant prediction of recollection. This supports the argument against BIS protocol efficacy. Determining whether such a protocol is useful to patients, and worth the cost to hospitals, is an important issue in the improvement of surgical patient comfort and safety.

Introduction Imagine being immobilized in a cold room, strapped to a table and surrounded by shadowy figures half hidden behind oversized monochromatic jackets. Paralyzed, you are awake but unable to move, breath, or even scream as you feel the scrapes of a scalpel. This stuff of nightmares is how recent B-list horror movies have depicted the experience of waking up during surgery. The phenomenon known clinically as intraoperative awareness has lately captured the attention of Hollywood, as well as of the public. The idea of being awake throughout an operation, unbeknownst to the doctors who assume complete

anesthetization, is frightening on a primal level. However melodramatically intraoperative awareness is portrayed in such films, it actually does occur in thousands of patients undergoing surgery every year. A multicenter study evaluating nearly 20,000 patients found a 0.13% incidence of intraoperative awareness with recall under general anesthesia [5]. Though the overall incidence of awareness is therefore low in the general population, this translates to an estimated 20,000 to 40,000 cases of intraoperative awareness in the United States each year [2]. The incidence rate has been shown to be even greater, as high as 1%, in populations at high risk for

Winter 2013 intraoperative awareness [4, 5, 6]. Groups at risk for intraoperative awareness include patients: undergoing cardiac surgery, with a history of awareness, with a history of difficult intubation, or with a history of long term use of opiates, benzodiazepines, or anticonvulsants, among other factors [4, 6, 7]. According to some estimates, up to 71%of patients who experience intraoperative awareness subsequently develop post-traumatic stress disorder [3]. Frequently reported descriptions of awareness include auditory recollections, sensations of not being able to breathe, paralysis, panic or pain [5]. In response to heightened levels of patient fear, various protocols and monitoring devices have sprung up purporting to decrease the potential for intraoperative awareness. One such monitoring device is the Bispectral Index (Aspect Medical Systems, Norwood, MA). The Bispectral Index (BIS) uses a proprietary algorithm to monitor electroencephalographic data and produces a dimensionless number from 0 to 100. A score of 0 corresponds to electroencephalographic silence (brain death), while 100 corresponds to the fully awake state. If patients are kept within a target range of values (40 to 60 for the BIS monitor) [4]. while anesthetized for surgery, it is suggested that awareness could be prevented without unnecessarily large doses of anesthetic agents [8]. This could potentially also decrease recovery time, as excessive doses of anesthesia can increase risk of postoperative complication [8]. Some studies have also suggested that using a BIS monitor protocol can decrease the incidence of intraoperative awareness [4, 9]. One such study was the 2004 B-Aware Trial, which found that BIS-guided anesthesia reduced the incidence of intraoperative awareness by 82% in high risk patients [4]. However, some critics claim that simple yet rigorous protocol-based interventions (basic training and heightened awareness to the potential for intraoperative awareness) can decrease the incidence of intraoperative awareness better and more cost-effectively than using specialized devices like the BIS monitor [1]. It has been proposed that these protocol-driven anesthetics could show similar success rates to the checklist approaches used to decrease other intraoperative risks and complications [10, 11, 12]. The 2011 BAG-RECALL clinical trial concluded that the superiority of the BIS protocol was not established, that, in fact, fewer patients in the standard practice (end-tidal anesthetic-agent concentration, the typical measure used by anesthesiologists) group experienced intraoperative awareness than the patients in the BIS protocol group [1]. One study’s cost estimation for preventing a single case of awareness in a population

of high risk patients using BIS monitoring was $2200 [4]. If, as suggested by the results of the BAG-RECALL study, specific checklists or guidelines can significantly decrease the incidence of awareness, then the monetary expense of the BIS monitors (and the adhesive strips that connect the patients to the monitors), the inconvenience for the patient, as well as the time expense for the hospital staff is not worth the protocol implementation. The goal of this study was to examine the relationship between BIS monitor output and awareness levels in patients prior to a general anesthetic (i.e., when subjects were sedated but not unconscious). Thus, this study focused on the correlation between a BIS reading and a patient’s potential to form memory (awareness with recall). The study was intended to mimic the situation during a general anesthetic when the BIS score rose above 60. This was a way of modeling the accuracy of the BIS in patients who were sedated but had a high likelihood of remembering presented stimuli. New data on BIS accuracy was provided, and was accomplished in a way that yielded greater quantities of data than if we searched specifically for patients (within a massive population) who experienced intraoperative awareness. The patients’ recall of events was quantified by their ability to remember simple words given at pre-specified time points, as well as by their ability to remember an event (being moved from the pre-operative area to the operating room). The ability to remember stimulus words/travelling to OR was then correlated with the BIS reading at the time the words were given/event occured. A score of 100 corresponds to completely awake while lower scores indicate less wakefulness and brain activity. Thus the hypothesis was that the higher the BIS score, the more able, and likely, patients would be to form memories. Methods This observational study was approved by the University of Chicago Hospitals institutional review board. For this study, patients were chosen randomly with the criteria of being over the age of 18 and undergoing general anesthesia. Patients’ risk factors for awareness during anesthesia were recorded, but they did not account for qualification of the study. This data will potentially be used in additional follow-up analyses. Procedure Each patient had a BIS Vista™ Monitor (Aspect Medical Systems, Norwood, MA) attached to his or her forehead via a BIS Quatro™ (Aspect Medical Systems) adhesive monitoring strip. BIS scores (0-100) were recorded when

Scientia Time:

Baseline

0 min

2 min

4 min

5 min*

6 min

Event:

Record Score

“Dollar” and Record Score

“Paper” and Record Score

“Toast” and Record Score

“Plant” and “Cat” and Record Record Score Score

8 min

Traveling to OR

“Coffee” and Record Score

Record Score

Table 1. Timeline of words administered and BIS scores collected. The words administered and the BIS scores are the events on the timeline. * indicates midaz administered

the patient was attached to the monitor in the pre-operative area (to establish baseline) and then five minutes, three minutes, and one minute before any midazolam (a benzodiazepine administered to slightly relax and sedate patients before the administration of general anesthetic) was given. At each of these three times, a unique word was given to the patient to remember. Words were chosen from the Psycholinguistic Database generated at the University of Western Australia (UWA Psychology: MRC Psycholinguistic Database). Words were chosen for this list based on the criteria for similar imagery, ease of remembering, and depth of meaning. Then 1-2 mg of midazolam was administered intravenously. One minute after, and three minutes after the medication was administered, another word was given and the BIS reading was recorded with each word. Additional BIS scores were recorded as the patient was taken back to the OR. Within 2 hours after completion of the surgery (specific time was dependent on the case, some patients take longer to become conscious after surgery), the patients were interviewed. Patients were asked if they recalled any of the words given before surgery, if they remembered traveling to the OR from the pre-op area, and if they could Cued Word

Recalled Word

identify their last memory before going under anesthesia. Patients were subsequently interviewed 24 hours after the procedure, with the same questions. Additionally, patients were given a list of words in a sealed envelope before they left the hospital, and asked to open and look over the list of words during the follow-up phone call/visit. The list contained cued words as well as decoy words. The purpose was to see if patients could recall cued words prior to viewing the list, and then to see if they could recognize additional words that they were previously unable to recall. Results Seventy-one patients were enrolled in the study between May and August 2012. One patient was excluded because the case failed to meet general anesthetic criteria, and 6 additional patients were excluded due to lack of adequate data (data not collected because of preoperative time constraints or BIS equipment malfunction). Thus, data from 64 patients was included in the analysis. It should be noted that not all of the 64 patients included had complete data sets, but if valid points were collected they were included in the analysis. Twenty-nine (45.3%) Did Not Recall Word

P value <

Mean BIS Score

Standard Deviation

Mean BIS Score

Standard Deviation

Dollar

4.61

1.73

0.078

Paper

2.14

5.15

0.38

Toast

3.86

2.03

0.117

Plant

2.17

2.78

0.926

Cat

2.42

5.77

0.003

Coffee

2.56

5.81

0.004

Table 2. Recollection within 2 hours after surgery. Comparison of mean BIS scores between patients able to recall and patients unable to recall the same cued word. Two-tailed independent t-tests for equal variance were used to calculate p values, with p < 0.05 being statistically significant.

Winter 2013 out of the 64 patients were male, and 35 (54.7%) were female. The age range was 18 to 79, with a mean age of 52.5 years old (SD 15.1). The comparison of mean BIS scores between patients who recalled a word within 2 hours post operatively and patients who did not can be seen in Table 2. No statistically significant difference was found between the mean BIS scores of patients who recalled either “Dollar,” “Paper,” “Toast,” or “Plant,” (words 1-4) and those who could not recall those words. However, the mean BIS score was found to be significantly different between patients recalling and not recalling for the words “Cat”(word 5) (p< 0.003) and “Coffee” (word 6) (p<0.004), with the averages for the unable to recall group lower than those of the able to recall group. Table 3 shows the same comparison of mean BIS scores, but for the data collected 24 hours after surgery Cued Word

(the second interview). In this case, only the difference in mean BIS score between patients who were able and unable to recall “Cat” was significant (p<0.006), the mean score being lower in the group who did not recall. Similarly, Table 4 compares mean BIS scores in patient samples recognizing and not recognizing each word 24 hours after surgery. No p value was calculated for “Toast,” as a limited amount of data (N=1 for patients unable to recognize “Toast”) did not allow for accurate calculation. Again, “Dollar” and “Plant” did not have significantly different mean BIS scores, and neither did “Coffee.” However, the difference in BIS means for “Paper” (p<0.0128) and “Cat” (p<0.0092) were significant. The mean BIS score for “Paper” was higher in the group who were unable to recognize versus those who could recognize, and the opposite was true for the mean scores for “Cat.” “Cat” is the only word that had a significant reduction

Recalled Word

Did Not Recall Word

P value <

Mean BIS Score

Standard Deviation

Mean BIS Score

Standard Deviation

Dollar

4.74

1.87

0.423

Paper

2.23

3.92

0.498

Toast

3.09

2.66

0.800

Plant

2.00

2.70

0.673

Cat

2.67

5.65

0.006

Coffee

5.07

6.42

0.254

Table 3. Recollection 24 hours after surgery. Comparison of mean BIS scores between patients able to recall and patients unable to recall the same cued word. Two-tailed independent t-tests for equal variance were used to calculate p values, with p < 0.05 being statistically significant.

Cued Word

Recognized Word

Did Not Recognize Word

P value <

Mean BIS Score

Standard Deviation

Mean BIS Score

Standard Deviation

Dollar

3.95

2.65

0.9452

Paper

2.20

0.50

0.0128

Toast

2.42

Inconclusive

Plant

2.45

2.55

0.8623

Cat

2.99

6.25

0.0092

Coffee

5.46

6.51

0.0951

Table 4. Recognition 24 hours after surgery. Comparison of mean BIS scores between patients able to recognize and patients unable to recognize the same cued word. Two-tailed independent t-tests for equal variance were used to calculate p values, with p < 0.05 being statistically significant.

Scientia

Figure 1. Mean BIS values for each word, connected by lines corresponding to the condition group (memory/no memory) as well as the interview group (2hr recall, 24hr recall, 24hr recognition).

in mean BIS values between patients able to recall and unable to recall in all three interview categories. “Coffee” had a significant reduction between groups in the 2 hour post-operative interview category (Table 2), and “Paper” had a significant increase between groups in the 24 hour post-operative recognition category (Table 4). Figure 1 shows the distribution of mean BIS scores over the sequence of cued words for each of the conditions. The trend is inconsistent until word 5 (“Cat,” given after administration of midazolam), and then decreases

noticeably (though not always significantly in relation to predicting memory, see previous tables). The third word (“Toast”) was given to patients 1 minute prior to receiving a dose of midazolam, the fourth word (“Plant”) was given as midazolam was being administered, and the fifth word (“Cat”) was given 1 minute after midazolam administration. Figure 2 shows the distribution of BIS values, the mean BIS value, the number of patients, and the standard deviation for each of these words. Out of 53 patients with a BIS score recorded for

Figure 2. BIS values at administration of word 3 (“toast,” which was given before midaz), word 4 (“plant,” which was given as midaz was being administered), and word 5 (“cat,” which was given after midaz). Plot distribution of scores, including mean score for each category. Two-tailed independent t-tests for equal variance were used to calculate P values, with P < 0.05 being statistically significant.

Winter 2013

Recollection of Traveling to OR Total Patients With BIS Data

Within 2 Hrs PostOp

24 Hrs PostOp

Patients Who Recall Travel

Patients Who Do Patients Who Not Recall Travel Recall Travel

Patients Who Do Not Recall Travel

Average BIS Score

95.23

94.89473684

96.8

94.97142857

95.64705882

P < 0.134290171

P < 0.599071994

Table 5. Recollection of traveling to the Operating Room from the Preoperative Area, 2 hours and 24 hours after surgery. Comparison of mean BIS scores between patients able to recall and patients unable to recall traveling. Two-tailed independent t-tests for equal variance were used to calculate p values, with p < 0.05 being statistically significant.

“Toast” (word 3, given prior to midazolam), the mean score was 95.79 (SD 3.27). For 56 patients with a score recorded for “Plant” (word 4, at midazolam administration), the mean score was 96.57 (SD 2.39). For 58 patients with a BIS score for “Cat” (word 5, given after midazolam dose), the mean was 94.26 (SD 5.12). The difference in mean BIS score between “Toast” and “Plant” was not statistically significant (p<0.157), nor was the difference in mean between “Toast” and “Cat” (p<0.0655). However, the difference in mean BIS score between “Plant” and “Cat” was a statistically significant decrease (p<0.00267). The minute period following the administration of midazolam displays a significant decrease in average BIS score. This also correlates to the findings of Tables 1, 2, and 3, which indicated a significant reduction in average BIS score for patients recalling/recognizing “Cat” (word 4) and not recalling/recognizing the word. Table 5 reveals the difference in mean BIS values for patients recalling traveling to the Operating Room from the pre-operative area, and those who did not (for the 2 hour post-op interview and the 24 hour post-op interview, respectively). Curiously there was an increase in average BIS score for the patients who did not recall travel, as compared to those who did, in each case. However, the differences were not statistically significant. Discussion The data collected in this study provide little evidence that there is an appreciable difference in BIS score values between patients who could remember a word and those who could not. The exception is the word “Cat,” which was given to patients 1 minute after the administration of the relaxant midazolam. “Cat” showed a significant reduction in mean BIS values (patients able to recall had higher mean BIS scores than patients unable to recall) across all three interview conditions. This reduction in BIS

score corresponds to a reduction in ability to form memory (and thus later recall or recognize a word), which is in line with the claims of the BIS monitor product. However, no such pattern was seen across the other 5 words. For the other words, a higher BIS score was not linked to a higher incidence of recall/recognition, except in “Coffee” (singularly in the 2 hour post-operative interview category), and “Paper” (singularly in the 24 hour post-operative recognition category). These results were not consistent within the interview conditions, and in fact had opposing trends. “Coffee” in the 2 hour group displayed a reduction in BIS score with reduction in recall, but “Paper” in the 24 hour recognition group displayed an increase in BIS score with reduction in recall (quite the opposite of the expected outcome). Thus these anomalies are potentially due to random error, and would need to be further investigated with a larger sample size before conclusions could be drawn. A correlation between higher BIS scores and a higher incidence of recalling traveling to the Operating Room was also not found. In fact, the recorded mean BIS scores were higher for patients not remembering the trip than for patients remembering the trip (though no statistical significance was found for either trend). These results raise interesting questions about the effectiveness of the BIS monitor. On the one hand, the scores do not seem to accurately predict whether or not a person will form a memory (of a cued word, or of the amalgamation of experiences that comprises moving from the preoperative to operative areas). However, the BIS scores were significantly related to the incidence of remembering a cued word in the minute after midazolam was administered. This effect was not seen before injection (words 1, 2, and 3), during injection (word 4), or a few minutes after injection (word 6 and recollection of traveling). The lower BIS scores thus appear to be an effective

Scientia indicator of lower ability to form memory for this brief period after injection. Whether or not this occurs because of the character of midazolam’s suppression of memory formation, or because the sedated state itself produces memory impairment is not clear. In Figure 2 it is evident that word 5 “Cat” has a larger range of BIS score values (77 to 98 as opposed to 84 or 86 to 98) as well as a lower mean score. The trend that can be observed in Figure 1, however, is that “Coffee” is associated with a lower mean BIS score than “Cat” even though remembering “Coffee” was not significantly correlated with BIS score (unlike with “Cat”). This would seem to indicate that the BIS monitor does not, in fact, become more efficacious as level of awareness decreases. This could mean that a state of deeper sedation is not automatically correlated with a decrease in ability to form memory, or that midazolam’s inhibitory effect on memory is independent of its known sedative effect. Potentially the midazolam causes memory impairment directly, instead of as a secondary effect of decreasing the level of awareness. Additional research is required before any such conclusions can be definitively drawn. However, if this were indeed the case, then the BIS monitor would appear to only be effective at predicting awareness in individuals with already drug-compromised memory function. A drug impairment to memory might theoretically level the field of individual differences (in memory ability, distraction level, etc.) in patients, leading to a correlation with decreased BIS score. Any potential theory would need further data. Our next step will be to determine whether the result correlating “Cat” with BIS score had to do with the timing of the word, or was simply an anomaly. Firstly, a different word will be swapped in with “Cat,” to rule out the word itself being functionally distinct in forming memory. Additionally, we will continue to collect data in order to increase our sample size. The order of the References 1. Avidan MS, Jacobsohn E, Glick D, et al. Prevention of intraoperative awareness in a high-risk surgical population. N Engl J Med 2011;365:591-600. 2. The Joint Commission. Sentinel event alert: preventing, and managing the impact of anesthesia awareness. Issue 32. October 6, 2004. (http:// www.pmhd.org/Medical-Staff/documents/SentinelAlertIssue32_ Preventingandmanagingtheimpactofanesthesiaawareness_Joi.pdf.) 3. Leslie K, Chan MT, Myles PS, Forbes A, McCulloch TJ. Posttraumatic stress disorder in aware patients from the B-Aware trial. Anesth Analg 2010;110:823-8. 4. Myles PS, Leslie K, McNeil J, Forbes A, Chan MT. Bispectral index monitoring to prevent awareness during anesthesia: the B-Aware randomized controlled trial. Lancet 2004;363:1757-63. 5. Sebel PS, Bowdle TA, Ghoneim MM, Rampil IJ, Padilla RE, Gan TJ, et al. The incidence of awareness during anesthesia: A multicenter united states study. Anesth Analg. 2004 Sep;99(3):833-9. (Accessed July 5, 2012, at http://www. anesthesia-analgesia.org/content/99/3/833.long). 6. Ranta S, Jussila J, Hynynen M. Recall of awareness during cardiac anaesthesia: Influence of feedback information to the anaesthesiologist. Acta

stimulus words will be switched around, to see if the timing within the sedation process plays a role in prediction of recall. Potentially, we will also test recollection of other stimuli (for example, words given in the post-operative area- where patients tend to be more sedated than in the pre-operative area). Further research might examine the role of sedative/anesthetic drugs other than midazolam on the ability to form memory, and whether BIS scores were accurate predictors in these cases. Overall, the BIS monitor was not found to be an accurate predictor of memory-forming in patients. This supports the notion that BIS monitoring will be ineffective as a preventative measure for intraoperative awareness. The exception to this finding was seen in patients in the first minute of receiving a sedative. Additional study needs to be completed to determine the relationship between apparent BIS accuracy and this specific period of sedation. For the same cued word, patients remembering the word did not have a significantly higher BIS score than the patients who did not remember the word. However, it could be observed that as the BIS score dropped after drug administration (words 5 and 6 had lower mean BIS scores), the percentage of patients who remembered the word also decreased (Tables 1, 2, and 3). Additionally, BIS scores was not significantly related to recollection of traveling to OR. Though a single score does not appear to be an accurate predictor of whether a patient will remember a stimulus (especially when looking at the traveling to OR data), a broader score range might be related to a lower general incidence of memory formation. This did not appear to affect the statistical significance of the findings, perhaps due to the wide individual variety from patient to patient. This hypothesis would need to be investigated further to see if potential ranges of scores were more accurate for predicting recollection.

Anaesthesiol Scand. 1996 May;40(5):554-60. 7. Phillips AA, McLean RF, Devitt JH, Harrington EM. Recall of intraoperative events after general anaesthesia and cardiopulmonary bypass. Can J Anaesth. 1993 Oct;40(10):922-6. 8. Recart A, Gasanova I, White PF, Thomas T, Ogunnaike B, Hamza M, et al. The effect of cerebral monitoring on recovery after general anesthesia: A comparison of the auditory evoked potential and bispectral index devices with standard clinical practice. Anesth Analg. 2003 Dec;97(6):1667-74. 9. Punjasawadwong Y, Boonjeungmonkol N, Phongchiewboon A. Bispectral index for improving anaesthetic delivery and postoperative recovery. Cochrane Database Syst Rev 2007:CD003843. 10. de Vries EN, Prins HA, Crolla RM, et al. Effect of a comprehensive surgical safety system on patient outcomes. N Engl J Med 2010;363:1928-1937. 11. Haynes AB, Weiser TG, Berry WR, et al. A surgical safety checklist to reduce morbidity and mortality in a global population. N Engl J Med 2009;360:491-499. 12. Pronovost P, Needham D, Berenholtz S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med 2006;355:2725-2732[Erratum, N Engl J Med 2007;356:2660.]

Winter 2013

Myosin and Actin in vitro Motility Assay and the F-actin “Inch-Worming” Model:

Elucidating the role of buckling in F-actin gliding dynamics along artificially reconstituted bipolar myosin filaments Adam De Jesus, Tood Thoresen, Michael Murrell, Yvonne Beckham, Margaret Gardel

Cytoskeletal motor proteins, such as myosin and actin, play a fundamental role in force generation and motility at the cellular and organismal level (1,2). In in vivo and in vitro conditions, these motor proteins can polymerize to form filaments composed of several hundred myosin proteins, called myosin thick filaments (Figure 2). The motor head groups of myosin thick filaments are arranged in a bipolar fashion with one population of myosin motors oriented oppositely to the other population (Figure 2). In this study, skeletal muscle myosin-II proteins were reconstituted into filaments similar to myosin thick filaments and used in an in vitro motility assay to observe F-actin gliding. The gliding of F-actin was found to vary in speed depending on its position along the length of the myosin filament. After gliding across approximately half the length of the myosin filament, the speed of the F-actin decreased (119.18±34.15 nm/s to 36.1±14.10 nm/s). Buckling of F-actin was observed and measured along several myosin filament lengths, with the radius of curvature increasing in proportion to myosin filament length. In some instances of F-actin gliding, buckling occurred in an “inch-worming” motion. F-actin buckling during translocation is thought to play an important role in describing F-actin gliding in in vitro motility assays using reconstituted myosin filaments. This study contributes a new model for understanding F-actin gliding across the un-physiologically oriented portion of a myosin filament by attributing buckling as an additional mechanism for the observed forward motion of F-actin.

Introduction Movement is one of the most fundamental features of life. Organisms have evolved ways to harness chemical energy to produce mechanical force at the sub-cellular level, as well as at the organismal level [1,2,3]. Interest in biological motion has blossomed due to the understanding that much of the behavior and architecture of cells relies on directed transportation of macromolecules, membranes, or chromosomes within the cytoplasm, all of which depend on molecular motors [4,5]. Some of the most captivating proteins associated with the

cytoskeleton are the molecular motors. These proteins bind to polarized cytoskeletal filaments and use chemical energy harnessed from ATP hydrolysis to move along them. There are a multitude of different motor proteins that exist in eukaryotic cells, differing in the type of filament they bind, the direction in which they move along a filament, and the type of cargo they carry [6]. Kinesin related proteins compose a large family of motors that move along microtubules (long hollow cylindrical filaments composed of tubulin proteins) toward the plus end of a microtubule. Dynein belongs to a class of large

Scientia motor proteins that move in the opposite direction (towards the minus end) of Kinesin [7]. In this study, the primary focus is on myosin motors and the actin filaments they bind to; in particular we focus on skeletal muscle myosin-II. Myosin is a super family of ATP-dependent molecular motors that utilize the chemical energy in ATP to move along actin filaments [8]. Myosin typically consists of three distinct regions: the motor or head domain, which binds actin and is involved in ATP hydrolysis; a neck domain that binds light chain proteins involved in structural and regulation support named the essential and regulatory light chains (ECL and RCL respectively); and a tail (heavy chain) responsible for binding cargo or for binding with other myosin proteins (Figure 1) [6,8]. Monomeric myosin proteins can undergo tail-tail interactions which lead to the formation of large bipolar “thick filaments” containing hundreds of myosin heads, oriented in opposite directions at either end of a myosin thick filament (Figure 2, A). Actin proteins are found in organisms in the form of as globular (single) actin (G-actin) proteins, which can polymerize together to form long filaments, aptly named filamentous actin (F-actin). F-actin has a pointed end and a barbed end, with myosin proteins preferentially moving toward the barbed end (Figure 2, B). Muscle cells are specialized for a single function, contraction, and are the prototypical cells for studying cellular and molecular motility [7]. Skeletal muscle cells are responsible for all voluntary movement, and their main contractile components are present in a highly organized striated pattern optimized for contraction in muscle cells. Skeletal muscles are composed of muscle fibers formed by the fusion of several individual cells during early development. The cytoplasm of these cells is dominated by myofibrils, which are small cylindrical bundles of thick filaments of myosin (~15 nm in diameter) and thinner filaments of actin (~7 nm in diameter) [7]. The myofibrils are further organized as links of contractile parts named sarcomeres that create the characteristic striated facade of skeletal muscles (Figure 3). Force generation is accomplished through the myosin protein’s power stroke along F-actin. The power stroke is the essential (ATP-dependent) mechanism for actomyosin force generation in the cytoskeleton; especially muscle cells. The cleavage of ATP is the initiating step that holds myosin in a “relaxed” (unbent) state. The cleavage of ATP to produce ADP and inorganic phosphate cause a conformational change to a “bent” structure. This bent conformation then binds to an actin filament and the subsequent release of inorganic phosphate snaps myosin

back to its unbent (rigor) state. This motion pushes the myosin protein along the actin filament and when finished the remaining ADP is replaced with a new ATP, which reduces myosin’s affinity for F-actin and allows detachment so as to repeat the cycle on a different section of the F-actin polymer (Figure 4) [9, 10]. Understanding the intricate mechanical details of myosin filament locomotion is essential to developing a more complete picture of cell physiology at a fundamental level. An understanding of the detailed properties of normal (wild type) actin and myosin in these various isoforms contributes to a better understanding of mutant forms of myosin that cause disease. For instance, several mutations of the MYH9 gene that encode myosin IIA cause an autosomal dominant disease, and in mice, elimination of this gene is embryonically lethal [11]. Additionally, Shaker-I is a recessive mutation that causes deafness, hyperactivity, head-tossing and circling behavior due to a dysfunction of the sensory hair cells that leads to a neurosensory degeneration of the inner ear [12]. Myosin-1c isoform plays and essential role in the adaptation of the hair cell for mechanoelectrical transduction (sound recognition in the ear) and disruption of this motor protein is believed to be a key factor in Shaker-I mutation and disease [13]. Lastly, the mutant beta-myosin protein has been shown to be present in certain skeletal muscle through Western blot analysis and was further shown to translocate actin filaments slower than normal controls through myosin and actin using in vitro motility assays [14]. To truly appreciate and scientifically substantiate pharmacological, biological, or biomedical investigations it is necessary to have a fundamental framework on which to stand [15,16]. In vitro motility assays of myosin and actin have been pivotal to our understanding of the actomyosin molecular basis of motion. Several variations of the in vitro motility assay have been developed to allow visualization of the movement of fluorescently labeled actin filaments over different surfaces coated with myosin proteins [1720]. In vitro motility assays can be used to measure the sliding speed of actin filaments moving across a bed of individual myosin heads adhered to the surface of a glassor nitrocellulose-treated coverslip [21]. An adaptation of this motility assay uses artificially reconstituted myosin thick filaments, formed by diluting monomeric myosin in low salt concentrations, and adhered to a glass cover slip with the addition of actin filaments to measure sliding velocities [22]. Artificially reconstituted myosin filaments can range in size from .5-1.5 microns in length for skeletal muscle myosin-II (in 100-150 mM KCl), and lengths of up to tens of microns have been observed for molluscan

Winter 2013 EGTA; Wash Buffer: 20 mM MOPS, pH 7.4, 50 mM KCl, 4 mM MgCl2, 0.1 mM EGTA; Assay Buffer (AB Buffer): 20 mM MOPS, pH 7.4, 100 mM KCl, 4 mM MgCl2, 0.1 mM EGTA, 1% methylcellulose, 2 mM ATP, 0.25 mg/ml glucose, 0.25% β-ME, 0.25 mg/ml glucose oxidase, 35 μg/ml catalase

Figure 6. The flow chamber is created simply by applying two strips of double sided tape to a glass slide and sticking down a glass coverslip to create a narrow volume to flow solutions in. The glass coverslip and slide are placed face down on an objective lens and laser light hits the glass coverslip surface to image actin motility over myosin filaments adhered to the nitrocellulose surface.

smooth muscle myosin thick filament [23,24]. Variations of the motility assay include laser trap experiments, inverted motility assays, and quantum dot tracking [25,26,27]. Previous studies using this motility assay set up have addressed the bi-directionality of reconstituted myosin filaments, in particular the speed changes during actin translocation across long myosin filaments [27-29]. In these studies there is an indication of changes in speed as an actin filament (starting from the very tip of a myosin filament) moves across a myosin filament from fast speeds (as it traverses the correctly oriented end of the bipolar myosin filament) to a low speed (after it crosses the center of the myosin and enters into the unphysiologically oriented end of the myosin filament) [30]. In this experiment, fluorescently labeled skeletal myosinII proteins were artificially reconstituted using a dilute salt concentration, close to physiological conditions, and adhered to a nitrocellulose glass coverslip. Filamentous actin was subsequently added and observed to glide and buckle across myosin filaments. These buckling events elucidate many qualitative mechanical dynamics of filamentous actin and lead to a new postulate for F-actin motility across myosin thick filaments. Materials and Methods Myosin Storage Buffer: 50 mM HEPES, pH 7.6, 0.5 M KCl, 1 mM DTT; Myosin Spin-Down Buffer: 20 mM MOPS, pH 7.4, 500 mM KCl, 4 mM MgCl2, 0.1 mM EGTA, 500 μM ATP; G Actin Buffer: 2 mM Tris-HCl, pH 8.0, 0.2 mM ATP, 0.2 mM CaCL2, 0.2 mM DTT, 0.005% NaN3; F-actin Buffer (F-Buffer): 10 mM imidazole, pH 7.0, 1 mM MgCl2, 50 mM KCl, 2 mM

Myosin protein preparation Rabbit skeletal muscle myosin-II was purchased from Cytoskeleton, Inc. and labeled with fluorescent dye by Todd Thoresen. Skeletal muscle myosin-II proteins were fluorescently labeled using Oregon Green (OG) 488 maleimide dye (Molecular Probes, Invitrogen). The myosin was labeled with a ratio of 3.6 dye per myosin dimer. The myosin is concentrated using Amicon Ultra-15 centrifugal filters (Milipore, 100 kDa cutoff) to 18 mg/ml in Myosin Storage Buffer, then drop frozen in liquid nitrogen and stored at -80°C. Reconstitution of myosin filament The drop frozen aliquots of labeled myosin are thawed and subsequently run through a spin down protocol to remove any myosin filaments that bind to F-actin with abnormally high affinity (i.e., dead myosin motors). The dimeric myosin motors are mixed with phalloidin-stabilized F-actin at a 1:5 myosin to actin molar ratio in SpinDown buffer and centrifuged for 30 minutes at 100,000g (SORVALL, Discovery, M120 SE Thermo Electronics corp). The resulting supernatant ideally contains a large fraction of the low F-actin affinity motors, and the pellet ideally contains a large fraction of the high affinity myosin motors [5]. Since the spin down procedure changes the molarity of the solution, a spectrophotometer (Ultrospec 2100 pro, UV/visible spectrophotometer, Amersham Biosciences) was used to locate the fluorescent dye (~488nm) on the myosin. An estimated molarity was obtained using the linear fit to a graph of various known myosin concentrations labeled with 488nm fluorescent dye [5]. Spin-down was done two hours before an experiment and prepared fresh every day an experiment was conducted. Artificial reconstitution of myosin filaments was prepared using AB Buffer to dilute the dimeric myosin solution in various salt concentrations (25, 150, and 250 mM KCl). A myosin concentration of .5 μM was added to AB Buffer to a total volume of 10 μL and allowed to form filaments over the course of 1 hr in an ice bucket. Low salt concentrations of the AB buffer create a low ionic environment similar to physiological conditions, which facilitate myosin tail-tail binding to form filaments. After 1hr, filaments of myosin ranging from .8-1.5 μm were present and immediately used for the day’s experiments (used no

Scientia longer than 5-6 hours after reconstitution of myosin filaments). Preparation of actin filaments and fluorescent labeling Actin was purified by Melanie Nordstrom from rabbit skeletal muscle acetone powder, frozen, and stored at -80°C [33,34]. Actin polymerization was done fresh the day it was used. Polymerization was accomplished by mixing 10 μM G actin, 1mM ATP, 1 μL 10x F Buffer and brought to a final volume of 10 μL using G buffer. The addition of KCl and MgCl2 initiates the polymerization process, which typically takes 45min-1hr to fully polymerize. The F-actin is labeled with rhodamine phalloidin in order to make the actin translocation visible under fluorescence microscopy (Cytoskeleton, Inc). Rhodamine phalloidin has a dual purpose: the phalloidin molecule binds to actin and stabilizes the filament form (making depolymerization unfavorable) and it is a common flurophore that is excited by green light, and fluoresces in the red region of the spectrum, at about 561nm. After 45 minutes of polymerization, 1.5 μM of phalloidin was added to 1 μM F-actin and placed on ice to use for the days experiments.

Visualization and analysis A confocal fluorescence microscope (Nikon Eclipse Ti, inverted microscope system) was utilized to visualize the fluorescence of rhodamine phalloidin (red) F-actin and OG 488 (green) myosin proteins. A 60x (1.4 N.A.) objective lens (CFI Plan Apo VC 60x oil/1.40) was used to image the proteins in the assay (Figure 5). Laser light excitation of 560 nm light (for actin) and 488 nm light (for myosin) was used to image the florescence of the proteins. MetaMorph (Molecular Devices, LLC.) software was used to interface with the microscope system and collect digital images. A digital camera with a charge-coupled device (CCD) (CoolSnap HQ2 photometrics CCD) was used to acquire the digital images (1 pixel=107.5 nm). Time sequenced images were taken using MetaMorph’s time series data acquisition option, which was set to collect images at a rate of one frame every 5 seconds. This option automated the acquisition of both fluorescent images (560 and 488 nm fields) allowing rapid and consistent imaging of the motility assay. Data was stored and transferred to an external hard drive. Data analysis was done using Fiji (NIH, Image J, open

Figure 7. The myosin fluorescence intensity (488 nm) for three KCl salt concentrations were imaged (25, 150, and 250 mM for A,B,C respectively). A myosin filament (~1.5 μm length) with a clear fluorescence dip at the center of the filament is indicative of a bare zone (pixel intensity is taken via a line scan) (D,E).

Winter 2013 source software) and MetaMorph software. Statistical analysis (standard deviations, averages, histograms) and charts and graphs were done using MatLab (R2011a Mathworks inc) and Excel (Microsoft, Excel, Mac 2011). Adobe Illustrator (CS5) was used to create and arrange all images. Motility assay The in vitro motility assay was done according to James Seller and Stephen Kron, but modified by using filaments of myosin instead of dimeric myosin motors [20]. The reconstituted myosin filaments were added to a AB Buffer and labeled M-Myosin (‘M’ for motility). The phalloidin F-actin solution was diluted to a concentration of 0.4 μM with AB Buffer and labeled M-Actin. Nitrocellulose coated glass cover slips (Corning, 22x30 mm) were prepared using glass coverslips dipped in nitrocellulose solution (.1% collodin (EMS 12620-50) in amyl acetate) and dried for a day under a fume hood. The nitrocellulose serves the purpose of binding myosin to the surface more efficiently (higher ionic attraction). The flow chambers are used to hold all the solutions and serves as the platform for myosin binding and actin translocation (approximately 10 μL volume) (Figure 6). The flow chambers are composed of a nitrocellulose glass coverslip fixed to a glass slide atop two strips of double-sided tape aligned vertical to the glass slides smaller length to create a small volume chamber (1-2 mm wide).

All solutions were added to the flow chamber in 10 μL amounts and allowed to incubate for 1-5 minutes. Wash Buffer was first added to the flow chamber, and then M-Myosin was added and incubated for 5 minutes. A BSA solution (10% of 10 mg/ml BSA in 1x AB buffer) was added to the chamber as a blocking agent (preventing non-specific binding of F-actin) and incubated for 5 minutes. M-Actin was then added to the chamber and incubated for 5 minutes. After this incubation time, the 60x objective lens was coated with water and the glass slide was placed face down on the objective lens, so that the nitrocellulose cover slip was in contact with the objective. After locating the plain of the surface, the myosin and actin fields were imaged in a time sequence manner using MetaMorph software (as described in the visualization and analysis section above). Both fields (488 and 560 nm fluorescence) were taken simultaneously and saved as a time series of images that were later visualized as a movie of F-actin translocation over myosin.

Results Myosin filaments of various sizes were reconstituted in vitro (see materials and methods) by varying the salt concentration and were observed using fluorescence microscopy (Figure 7). As the potassium chloride concentration was decreased from 250, 150, and 25 mM the myosin filament length increased (0.59±0.14, 1.2±0.23, and3.5±0.79 μm) (Figure 8, D). Myosin filaments of 150 mM used for the motility assay were classified into three scenarios: the F-actin was found past the center of the myosin central bare zone (estimated by line scan measurements); the F-actin was found to start translocation before the central bare zone (tip-interactions); and the F-actin buckled during movement along a myosin filament. The first scenario is represented by a histogram in Figure 9, and found to have an average speed of 54.21±16.99 nm/s. For the second scenario, the speed profile for nine actin filaments was collected and graphed as instantaneous speed versus distance across the myosin filaments (Figure 10, A). The distance axis in Figure 10 is taken with respect to the center of the myosin filament, with zero representing the centFigure 8. Histograms showing reconstituted myosin filament length distribution is shown above (A-C). er of the filament. The average speed Myosin filament size distribution for 25,150, and 250 mM KCl concentrations were 3.5±0.79, 1.2±0.23 0.59±0.14 μm respectively (D). of the actin filaments before the bare

Scientia zone werewas found to be higher (119.18±34.15 nm/s) curvature on average (Figure 15, A). However, myosin filathan after the bare zone (36.1±14.10 nm/s) of the myosin ments with lengths smaller than .5 μm did not display any filament (Figure 10, B). The average length of the actin fila- F-actin buckling or appreciable gliding of actin filament. ments that were observed to translocate along myosin fil- As the filament size increases from 1-3 μm in length, peraments revealed a large spread (3.21±1.23 μm) and there centage of buckling decreases (Figure 15, B). In figure 8, was no significant difference in the speeds, buckling, or the percentage of actin filaments observed to buckle preferential binding of different actin lengths. over a single myosin filament of varying sizes was collectFigure 4 shows a representative sample of actin movement across a myosin filament. The myosin filament displays a clear dip in florescence intensity at the mid point of the filament as indicated by a line scan of pixel intensity (Figure 11, B). The speed is fast in the initial portion along the myosin filament (89.58±7.82 nm/s) and subsequently slows down (49.02±4.71 nm/s) after entering further along the myosin filament (Figure 11, C). This slowdown in speed occurs after passing approximately half of the myosin filament, which corresponds closely to the dip in fluorescence intensity observed in Figure 11, C. Buckling of actin filaments was also observed during translocation across myosin filaments with the average radius of curvature for all instances of buckling Figure 14. An example of a F actin gliding with evident “inch-worming” behavior occurring is shown plotted in Figure 12 as a histogram. The in figure A. The yellow arrows in frames 1-5 show tracking of the leading edge of the actin filament the white arrows point to F actin buckling (frame 4 and 5). The dotted line in frame 1 indicates the measurements in Figure 12 include in- and line scan shown in figure B. Figure C reveals the instantaneous speed profile of F-actin leading edge stances of actin that translocate in the for- moving across the myosin filament. The distance in C is taken with respect to the myosin center (total ward direction and actin that only buck- myosin length ~1.15μm or 1100nm). led but did not move in the forward direction. Figure 13 ed and binned together using Excel and Matlab software shows a representative sample of actin translocation with for four different size ranges (1-1.5, 1.5-2, 2-2.5, and 2.5-3). buckling occurring (and potentially facilitating) forward The total number of buckling events observed and measmotion along the myosin filament. The motion of actin ured for each of the four myosin length ranges was n=124 begins completely on the myosin filament (past the bare for 1-1.5μm , n=135 for 1.5-2μm, n=122 for 2-2.5μm, and zone) and so is an instance of translocation past the cent- n=83 for 2.5-3 μm and the number of samples observed er of the myosin filament. Figure 14 reveals an instance to buckle were 49, 45, 35, and 10 respectively. of F-actin movement starting from the tip of a myosin filament (before the bare zone) and proceeding to move Discussion across the myosin with buckling occurring (Figure 14, A-4 This study aims to address the question of how F-actin & 5). The intensity profile of the myosin filament shows translocates across reconstituted filaments of rabbit a dip in intensity near the center of the myosin filament skeletal myosin-II proteins in an in vitro motility assay. (Figure 14, B) and the corresponding speed profile shows The speed for nine F-actin myosin tip interactions dean initial fast speed (102.13±5.168 nm/s) and subsequent creased from an initial speed of approximately 120 nm/s slow speed (44.86±9.90 nm/s) (Figure 14, C). to approximately 36 nm/s after passing the center of the The radius of curvature was found to be different myosin filament, i.e. the bare zone (Figure 10). Previous depending on the length of the myosin filament (Figure studies observed similar speed changes of F-actin along 15). Longer myosin filaments were found to have buck- the length of bipolar myosin filaments [27-30]. Myosin filaled-actin filaments with correspondingly larger radii of ments are bipolar and have motor head groups optimally

Winter 2013 oriented for F-actin translocation toward the central bare zone. Continued motion beyond the central bare zone enters a region of un-physiologically oriented myosin heads, which must conformationally change in order to move F-actin filaments. This conformational change in myosin head groups of the oppositely oriented end of the myosin filament has been addressed in previous studies [31,32]. We propose that there is an additional factor to consider in the motion of actin filaments, which may play a role in describing certain aspects of actin translocation along myosin filaments. Buckling of F-actin occurred during its motion across a myosin filament, typically after crossing the bare zone (Figure 13 & 14) [36]. The F-actin buckling relaxes in later frames, followed by movement of the filament further along the myosin. The strain produced by buckling is hypothesized to be the result of tension generation due to the unequal speeds at the tail end and leading edge of the F-actin filament by the bipolar myosin filament ends. Since the heads pushing the F-actin from behind are moving at a much faster speed (~120 nm/s) than the oppositely oriented heads in the front (~36 nm/s), buckling occurs. Eventually, the buckling reaches a limit in which the force of buckling is stronger than the binding affinity of the myosin heads on the leading edge, and the F-actin relaxes in subsequent frames. Relaxation is thought to occur by detachment at the leading edge of the F-actin and reattachment at a further position along the myosin filament. This attachment and reattachment essentially relieves the stress of buckling and moves the F-actin forward at a slower rate. The translocation of F-actin is reminiscent of an “inch-worming” motion. We propose an inch-worming model to help describe F-actin gliding along myosin filaments, which incorporates ideas from previous studies regarding conformational changes of myosin heads and highlights the importance of F-actin buckling as a component of its observed translocation beyond the central bare zone (Figure 16). Additionally, there was a measured dependence between myosin filament length and the radius of curvature of the buckled F-actin (Figure 15). As the length of the myosin filament increased, the radius of curvature also increased at an almost linear rate. The number of myosin head groups along either end of the myosin filament increases with an increase in filament length, thereby increasing the radius of curvature of F-actin buckling. Larger radii of curvature are favorable due to the reduced stress on the F-actin produced by buckling. As the length of a myosin filament is reduced below 0.8 μm, the tension of buckling is larger than the binding affinity of myosin

motors to F-actin. Consequently, buckling is not observed for smaller myosin filament length scales. Conversely, myosin filament sizes beyond 3.5 μm were assumed to be randomly oriented aggregates of myosin motors with no bipolar geometry. Consequently, buckling events were not observed due to excessive random tearing of F-actin. Conclusion F-actin moved along myosin filaments from one end (the correctly oriented end) to the other end (past the central bare zone), where the myosin heads are oriented in the opposite direction (non-ideal for F-actin binding and translocation). Muscle cells are constructed in an organized pattern (sarcomeres) that prevents overlap of the F-actin with the oppositely oriented heads of a myosin thick filament (Figure 3). This in vitro experiment allows visualization of F-actin motion along both ends of bipolar myosin filaments and sheds light on the physiological and un-physiological extremes of these proteins. The combination of buckling and the unfavorable orientation of myosin heads are thought to collaborate to collectively cause the observed motion of F-actin on myosin filaments in in vitro motility assays. The organization of myosin and actin proteins as polymers composing the highly ordered structures of sarcomeres is essential for efficient utilization of tension production in muscle cells. Physiologically, F-actin gliding past the bare zone is not observed. However, as illustrated in this study and in previous work, F-actin translocation beyond the bare zone is possible via a combination of conformationally altered myosin heads and the inchworming motion of F-actin proposed in this study. The radius of curvature dependence on myosin filament length observed is a quantitatively new measurement and may be useful for future analysis of myosin filament structure dynamics. One of the drawbacks encountered in this study was the fact that the speeds measured were approximately ten orders of magnitude lower than what is observed in previous studies. Some potential explanations for this large discrepancy could be due to large numbers of inactive myosin heads, which may systematically slow down the speeds. However, the general trend of fast to slow speeds is observed, and a useful analysis of buckling is possible despite this drawback. Future work on this project will undoubtedly consist of using freshly purified skeletal muscle myosin-II in addition to running repeated trials to gauge whether the speeds observed are in correspondence with literature values. Second, faster data acquisition is necessary in order to capture more speed data for motion before

Scientia and after the myosin filament bare zone. This would add statistical strength to the conclusions presented in this paper about speed changes along reconstituted myosin filaments. Third, different isoforms of myosin should be run using a motility assay to assess differences in cardiac, smooth, and non-muscle myosin filaments to determine whether buckling and inch-worming occur, as is observed in this study for skeletal muscle myosin-II. Acknowledgments I would like to thank Margaret Gardel, Ph.D. for allowing me to conduct research in her lab over the summer, as well as her exceedingly helpful guidance and support both in the lab and with other academic endeavors throughout the past year. She is a phenomenal mentor, and I will be forever grateful for her contagious enthusiasm for science, which has influenced my decision to pursue graduate level research in the future. I also extend a special thanks to Michael Murrell, Ph.D. for his guidance, support, patience, and friendship while in the Gardel lab, especially in helping with understanding and analyzing References 1. Szent-Györgyi, Andrew G.. “The Early History of the Biochemistry of Muscle Contraction.” J. Gen. Physiol. 123, no. (2004): 631-641. 2. Diwan, Joyce J.. “MYOSIN.” 2007.http://www.rpi.edu/dept/bcbp/ molbiochem/MBWeb/mb2/part1/myosin.htm (accessed 7-09-11). 3. Jay, Patrick Y.. “A mechanical function of myosin II in cell motility.” Journal of Cell Science 108, no. (1995): 387-393. 4. Vale, Ronald D.. “The way things move: looking under the hood of molecular motor proteins.” Science 288, no. 5463 (2000): 88. 5. Thoresen, Todd. “Reconstitution of Contractile Actomyosin Bundles.” Biophysical Journal 100, no. (2011): 2698-2705. 6. Cooper GM. The Cell: A Molecular Approach. 2nd edition. Sunderland (MA): Sinauer Associates; 2000. Actin, Myosin, and Cell Movement. 7. Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 4th edition. New York: Garland Science; 2002. 8. Krendel, Mira. “Myosins: Tails (and Heads) of Functional Diversity.” Physiology 20, no. (2005): 239-251. 9. Cooper GM. The Cell: A Molecular Approach. 2nd edition. Sunderland (MA): Sinauer Associates; 2000. Actin, Myosin, and Cell Movement. 10. Hernandez, Olga M.. “Myosin essential light chain in health and disease.” Am J Physiol Heart Circ Physiol 292, no. (2007): H1643-H1654. 11. Goodsell, David. “Myosin.” June 2001.http://www.pdb.org/pdb/101/motm. do?momID=18 (accessed 07-08-11). 12. Block, Steven M.. “Fifty Ways to Love Your Lever: Myosin Motors.” Cell 87, no. (1996): 151-157. 13. Sharona, Even-Ram. “Of Mice and Men: Relevance of Cellular and Molecular Characterizations of Myosin IIA to MYH9-Related Human Disease.” Cell Adhesion & Migration 1, no. 3 (2007): 152-155. 14. Wright , Alan F.. “Myosin diversity and disease.” Trends in Genetics 12, no. 6 (1996): 206-209. 15. Cyr, Janet L.. “Myosin-1c Interacts with Hair-Cell Receptors through Its Calmodulin-Binding IQ Domains.” The Journal of Neuroscience 22, no. 7 (2002): 2487–2495. 16. Cuda, Giovanni. “Journal of Clinical Investigations, Inc. .” Skeletal Muscle Expression and Abnormal Function of Beta-Myosin in hypertrophic Cardiomyopathy 91, no. (1993): [2862-2865]. 17. Lauffenburger, Douglas A.. “Cell Migration: Review A Physically Integrated Molecular Process.” Cell 84, no. 3 (1996): 359-369. 18. Vicente-Manzanares, Miguel. “Non-muscle myosin II takes centre stage in cell adhesion and migration.” Nature 10, no. (2009): 778-790. 19. Harada, Yoshie, Akira Noguchi, Akiyoshi Kishino, and Toshio Yanagida. “Sliding movement of single actin filaments on one-headed myosin filaments.” Nature. 326.23 (1987): 805-808. 20. Kron, Stephen J., and James A. Spudich. “Flourescent actin filaments move on myosin fixed to a glass surface .” Biochemistry. 83. (1986): 6272-6276

results and in preparing this paper. Not only is Michael a terrific source of academic and research-related guidance, he is most definitely a role model for any aspiring researcher. I would like to thank Todd Thoresen, Ph.D. for his preparation of the myosin proteins used in this experiment and his general support throughout the year. I would like to extend my thanks to Melanie Nordstrom, Ph.D. for her preparation of the actin used in this experiment and her kindness, support, and expertise. I would also like to thank Yvonne Beckham, Ph.D., Martin Lenz, Ph.D., Tobias Falzone, Jonathan Stricker, Steve Winter, Patrick McCall, and everyone in the Gardel lab for their support and kindness during my time there. Lastly, I want to thank Magdeline Montoya for her help and unrelenting patience in editing and revising the multiple drafts of this paper. Supplemental Information Supplemental figures can be http://thetriplehelix.uchicago.edu.

found

online

21. S.J. Kron, Y.Y. Toyoshima, T.Q. Uyeda, J.A. Spudich, Assays for actin sliding movement over myosin-coated surfaces, Methods Enzymol. 196 (1991) 399–416. 22. Seller, James R. . “In vitro Motility Assays with Actin .” Cell biology. (2006): 387-392. 23. Lowey , Susan, Waller Guillermina S., and Kathleen Trybus M. “Function of Skeletal Muscle Myosin heavy and Light Chain Isoforms by an in vitro Motility Assay.” Journal of Biological Chemistry . 268. (1993): 20414-20418. 24. Katsura, Isao, and Haruhiko Noda. “Reconsitituted Myosin Filaments.” J. Biochem. 73. (1973): 245-256. 25. Yamada, Akira, and Naokata Ishii. “Direction and Speed of actin filaments moving along thick filaments isolated from molluscan smooth muscle.” J. Biochem. 108. (1990): 341-343. 26. Kiwamu, Saito, Aoki Takaaki, and Aoki Toshiaki. “Sliding movement of single myosin filaments and myosin step size on an actin filament suspended in solution by laser trap.” Biophysical Journal. 66. (1994): 769-777. 27. Barak, Gilboa, David Gillo, and Oded Farago. “Bidirectional cooperative motion of myosin-II motors on actin tracks with randomly alternating polarities.” Soft Matter. 5. (2009): 2223-2231. 28. Mansson, Alf, Mark Sundberg, and Martina Balaz. “In vitro sliding of actin filaments labelled with single quantum dots.” Biochemical and Biophysical Research Communications, ELSEVIER. 314. (2004): 529-534. 29. Scholz, Tim, and bernhard Brenner. “Actin sliding on reconstituted myosin filaments containing only one myosin heavy chain isoform.” Journal of Muscle Research and Cell Motility. 24. (2003): 77-86. 30. Sellers, James R. , and Bechara Kachar. “Polarity and velocity of sliding filaments: control of direction by actin and of speed by myosin.” Science. 249. (1990): -406-408. 31. Yamada, Akira, and Takeyuki Wakabayashi. “Movement of actin away from the center of reconstituted rabbit myosin filament is slower than in the opposit direction.” Biophys. J. . 64. (1993): 565-569. 32. Yamada, Akira, and Keiichi Takahashi. “Sudden increase in speed of an actin filament moving on myosin cross-bridges of “mismatched” polarity observed when its leading end begins to interact with cross-bridges of “matched” polarity.” J. Biochem.. 111. (1992): 676-680. 33. Spudich, James A., and Susan Watt. “The regulation of rabbit skeletal muscle contraction.” Journal of Biological Chemistry. 246.15 (1971): 4866-4871. 34. Sellers, James R. “In vitro motility assay to study translocation of actin by myosin.” Current Protocols in Cell Biology. 13.2 (1998): 1-10. 35. Stephen W. Paddock. Principal light pathway in confocal microscopy. 20002012. Photograph. Microscopy U, Madison, Wisconsin. Web. 3 May 2012. <http:// www.microscopyu.com/articles/confocal/confocalintrobasics.html>. 36. Kierfeld, Jan, and Petra Gutjahr. “Buckling, bundling, and pattern formation: from semi-flexible polymers to assemblies of interacting filaments.” J. comput. Ther. Nanosci. 3. (2006): 898-911. Web. 5 May. 2012.

Winter 2013

A Tripartite Mechanism for Menstrual Migraine Adam Shuboy Migraine is a neurological disorder characterized by nausea, vomiting, photophobia (and/or phonophobia), and intense, throbbing (and/or pulsatile) encephalic pain. Its duration typically varies from four hours to three days [ICHD-II]. Key to the pathogenesis of menstrual migraine (MM), viz., a migraine which occurs perimenstrually, is the surge and subsequent fall of estrogen which marks the end of the follicular phase and the beginning of the luteal phase. The estrogen efflux has three fundamental downstream effects that together facilitate the onset of MM: I.) ion dyshomeostasis; II.) neuronal hyperexcitability; and III.) trigeminal afferent activation. In this paper I will first argue for a causal relationship between estrogen and MM, and then go on to expand on the three proposed mechanistic components underlying the relationship. Estrogen and its downstream mechanisms significantly further the likelihood of a migraine episode occurrence in susceptible individuals, yet there must be additional facilitator(s), provided the existence of general migraine. Migraines are three times more prevalent in women than in men. Women in their thirties are the most afflicted group, constituting 28% of sufferers (2007 American Migraine Prevalence and Prevention Study (AMPP), conducted by Lipton and colleagues) (n=77,185 males/85,571 females) [1]. The AMPP states that the comprehensive one-year prevalence is 17.1% in women

and 5.6% in men. Interestingly, the loosely three-to-one sex ratio holds despite influencing factors like household income (HHI). In general, migraine prevalence is inversely proportional to HHI. The survey finds that 13.6% of women and 4.2% of males with the highest HHI (>$90,000) are affected with migraine (all types included), and 20.1% of women and 8.8% of males in the lowest income bracket (<$22,500) are affected [1]. The epidemiological data suggest the possibility that hormonal differences inherent to sex are responsible for a disproportionate occurrence of migraines across sexes. Of importance, Lipton et al. report that migraines are significantly less prevalent in pre-menarche and postmenopausal women than in fertile, menstruating women [1]. Before and at the time surrounding menarche (12-17 YO), the prevalence for females is 7.3% compared to 4.9% for males of the same age. In women sixty years of age or older (presumably postmenopausal) the prevalence rate is 6.4% while it is only 2.1% in the corresponding male population [1]. It may seem incongruous that postmenopausal women are still afflicted at a frequency three-fold that of their male counterparts despite the disappearance of the estrogen efflux. A conceivable explanation for such an inconsistency is that the experience of MM during womenâ&#x20AC;&#x2122;s fertile years may pre-dispose them to attacks later in life (see section â&#x20AC;&#x2DC;I.â&#x20AC;&#x2122;

Scientia for further discussion). However, regarding the work of Lipton et al., it is notable that the authors do not state the difference between the number of women and men responders by age; a significant difference in responders by sex could skew the ratio for elders [1]. The onset of MM coincides with and endures two days into menses [ICHD-II]. During that timeframe, the risk of migraine increases 3.4 times in women [2]. It is frequently reported that MM’s are more painful in the first two days preceding menses; this could be attributed to the symptomatic heightened pain sensitivity that precedes menses [2]. The arguments stands, therefore, that female sex hormones (viz., estrogens) are possible facilitators of migraine and so are responsible for the greater frequency of attacks in women over men. It is feasible that estrogens are capable of such action since the brain, like the periphery, is subject to the sudden transient hormonal surges that underlie the menstrual cycle. This is because estrogen, as a steroid—lipophilic and low in molecular weight—readily diffuses through the blood brain barrier (BBB). Thus, the time-dependent serum concentration of estrogen in the periphery and brain are comparable [3]. Current literature strongly suggests that estrogen is the substrate underlying the pathogenesis of MM. Enduring changes in migraine frequency and severity are associated with sexual developmental milestones, i.e.: puberty, pregnancy, lactation, and menopause. In such periods, estrous physiology markedly changes; during menstruation, estrogen levels surge and then fall back to baseline whereas during pregnancy and menopause, estrogen levels initially rise or fall (respectively) but are static thereafter. It has been postulated that the estrogen behavior that triggers migraine is its “withdrawal” in late luteal phase and not merely its high or low serum concentration level [4,5,6]. Thus, the absence of a “withdrawal effect” in pregnancy and menopause results in less frequent and less severe attacks in migraineurs. Silberstein et al. finds that during the second and third trimesters, 75% of pregnant women who have had experience with MM report being migraine free (1993) [7]. Attending to fourteen women volunteers who each had a minimum six-month documented history of MM, Somerville et al. administered a long-acting intramuscular injection of estradiol valerate in oil during their menstrual cycles [8]. This effectively staved off MM given that the withdrawal was delayed with the temporary stabilization of estrogen levels. An injection of short-acting exogenous estradiol was without such effect. Taking from this, the investigators infer that

baseline exposure to estrogen followed by a transient surge and fall is sufficient for a MM episode to occur [8]. The menstrual cycle is complex and multifaceted, driven not only by estrogen but also by progesterone and luteinizing hormone (LH). Upon examination of each of the respective infradian rhythms of these hormones, it is evident that the LH surge and/or the increase in progesterone levels starting at the end of the follicular phase could be the potential cause(s) of MM in lieu of or in addition to estrogen. In the context of MM, little is known about LH. Hence, one cannot with certainty dismiss it as a possible factor in the etiology of MM. Fortunately, the potential role of progesterone is far less ambiguous. In an earlier study, Somerville administered exogenous progesterone premenstrually to eight women (23-45 YO) who had documented attacks for six consecutive cycles prior to the study [9]. The logic was that if progesterone is significantly implicated in the etiology of MM, then advancing the rise in progesterone levels (by three to six days before menstrual flow) to levels typical of mid-luteal phase (i.e., 5-20 ng/ml) would, in effect, advance the onset of MM. However, notwithstanding this hormonal manipulation, five of the six migraineurs participating experienced an attack surrounding when would have been the normal time of menses. That is to say, for five of the women being treated, the progesterone therapy postponed menstrual flow until the cessation of treatment. At that time, none of those women reported experiencing an additional attack. In the subsequent cycle, Somerville controlled for estradiol, administering extended release exogenous estradiol [9]. Progesterone levels were not affected by the exogenous estradiol, as ascertained by carefully comparing progesterone levels to daily serum concentrations measured in a previous unmanipulated control cycle. This treatment effectively delayed menstrual migraine by three to nine days beyond the normal time of menses; after that time period lapsed, with the withdrawal of estrogen, seven of the eight migraineurs suffered an episode [9]. Provided the results, it is highly unlikely that progesterone plays a significant role in the etiology of MM. Presuming that estrogen withdrawal is a major trigger of MM, it follows that hormone replacement therapy (HRT) should alleviate attacks. Theoretically, a pharmacologically-induced normative, stable hormonal milieu in post-menopausal and menstruating women alike prevents falls in estrogen levels and thus migraines. de Ligniere et al. conducted a double-blind placebocontrolled crossover experiment wherein women (n=18) between the ages of 32-53 clinically diagnosed with MM

Winter 2013

21 23 DAYS 1 T 14 FOLLICULAR PHASE LUTEAL PHASE 36.7‘ BASAL BODY TEMPERATURE 36.4’ I HORMONE LEVEL FSH LH ESTROGEN PROGESTERONE I - -ovum OVARIANCYCLE © , O Q ovumnou ! UTERINECYCLE

MENSES PROLIFERATIVE SECRETORY

Migraine episodes have been associated with the fall in estrogen serum levels at the time of menses.

were provided either a week-long daily treatment of placebo or estradiol two days before the earliest expected attack [10]. In the experimental group, only eight MM’s were noted throughout the 26 treated cycles (30.8%). However, the placebo control group had a significantly higher incidence of MM with 26 noted attacks during the 27 cycles (96.3%) monitored [10]. Additionally, the attacks were less severe in pain and shorter in duration in the estradiol treated group. To the contrary, a number of studies report no significant difference between HRT and placebocontrol groups, and even go as far as to postulate that HRT may trigger MM’s [11,12]. Thus, the efficacy of hormone replacement therapy in treating MM is, at best, controversial in the medical sciences. That said, though, the inconsistencies in the literature may in the future be alleviated by controlling for numerous more subtle factors such as dosage, the dosing regimen, the structure of the compound (estrogen preparations consist of natural, synthetic, and conjugated types), and the method of administration. Such factors have been overlooked in the literature, and such factors have been shown to largely impact results [13]. For example, treating three-subgroups of postmenopausal women with different regimens of HRT, Facchinetti et al. observed significant differences in the occurrence of MM

during the study. They concluded that the continuous estradiol hemihydrate regimen was more effective than conjugated estrogen (2002) [13]. In addition, transdermal patches have been noted to be consistently more effective at alleviating MM than estrogen taken orally [4]. In a word, evident confounds do exist in the methodologies that, if addressed, may resolve the divergent findings. Consistent with the withdrawal effect, there is evidence that an estrogen threshold effect exists for MM. The instance of estrogen levels falling below a theorized threshold following a serum concentration spike is essentially a “withdrawal.” Nagel-Leiby et al. observed in clinic that as estrogen levels fell below 40-45 pg/mL (average: 41.6 ± 7.1 pg/mL) during the perimenstrual period, MM ensued; the concentration of the control group was 50.6 ± 8.9 pg/mL [14]. This assessment was made in women suffering from migraine without aura. On the other hand, women who suffered from migraine preceded by aura had roughly double the threshold level at 94.4 ± 28.3 pg/mL [14]. This second postulated threshold suggests the possibility that a “delta effect” or an approximate magnitude of change is at the essence of the MM estrogen trigger, and not merely a decline in concentration levels to some arbitrary point whereat MM is elicited. Perhaps a minimum delta measure—a

Scientia threshold, as it were— is necessary to engender a migraine. This hints at one of several possibly subtler aspects of the withdrawal effect that triggers MM. Ion Dyshomeostasis A major downstream effect of estrogen withdrawal is the dyshomeostasis of several ions vital to many neural processes, namely, magnesium (Mg+2) and calcium (Ca+2) divalent ions. This dyshomeostasis may potentially manifest in migraine. Ion dyshomeostasis is one of three mechanistic interfaces between estrogen withdrawal and MM. Magnesium ions, perhaps most of all, are fundamental in facilitating a number of processes relevant in the etiology of MM, e.g.: the mediation of serotonin release, the control of cerebral artery tone, the gating of NMDA receptors, and the biosynthesis and (subsequent) release of cytokines, NO, and other proteins involved in noxious sensation and pro-inflammation [15,16,17]. Studies show that magnesium ion levels are negatively correlated with estrogen serum levels while calcium ion levels are positively correlated [15]. Extracellular deficiency of Mg+2, being symptomatic of the first days of the luteal phase, can feasibly result not only in serotonin dysregulation (and a slew of downstream effects which this alone prompts) but also in the inhibition of vasoconstriction and the induction of neuronal hyperexcitability [16]. In brief, estrogen suppresses Mg+2 levels and such levels are significantly correlated to the incidence of MM [16,17]. Collecting data from a population of 270 women, Mauskop and colleagues measured low Mg+2 serum concentrations (<0.54-0.64 mmol/L) in 45% of women experiencing MM. Only 14% of menstruating women with low serum levels were attack free—a threefold difference. Of those who had low Mg+2 during MM, 15% qualified as having interstitial deficiency [4]. Interestingly, Li et al. observed that androgens have no evident correlation with ion flux as do estrogens [2]. The effects of Mg+2 deficiency suggest it as a mechanistic interface between menstruation (and associated estrogen levels) and MM. Additional ionic dyshomeostasis resulting from the transient estrogen surge principally involves zinc, copper, and iron ions. These dysregulations have also been implicated as possible causal mechanisms of MM [15]. Estrogen levels positively correlate with Cu+2 absorption and estrogen prolongs the half-life of Cu+2 [15]. The zinc ion deficiency frequently noted in migraineurs is caused by the increased Cu+2 concentration, as Cu+2 facilitates digestive malabsorption of zinc. In part, the physiological significance of Zn+2 is that it is essential to the synthesis of melatonin and serotonin. A deficiency

of these neuropeptides has been noted in migraineurs [6]. Melatonin and serotonin function as potent antioxidants in neural as well as in somatic tissues. Moreover, zinc, a non-redox ion, is critical in and of itself as an antioxidant, easily incorporated into microbiological systems [15]. Furthermore, Zn+2 is essential to the synthesis and the functionality of Krox-20 and Krox-24. Krox proteins, being the products of immediate early genes, function as immediate transcription factors (ITF). They are swiftly and transiently activated in response to neural activity, and have many downstream effects potentially pertinent in the etiology of MM. Krox-24 was observed to be most the most reactionary of the more prominent ITF’s (viz., c-Fos, c-Jun, Jun B, etc.) to both chemical and physical noxious stimuli in the thoracolumbar dorsal horns of the spinal cord [15,18]. This suggests that Krox-24 is the principal ITF in the CNS for such neural activity, as multiple forms of noxious stimuli evoked its response. Irregular accrual of iron depositions have been observed in the periaqueductal grey area (PAG) of the brainstem of migraineurs [19]. Excessive ferrous depositions signify a homeostatic breakdown of iron metabolism in a region of the brain. Evidence indicates that Fe+2 oxidative metabolism may be instrumental in the antinociception function of the brain region. Upon administration of morphine, the receptors of transferrin, the principal extracellular transporter of iron ions, are significantly down-regulated (Gomez-Flores R. et al., 1994) [19]. In another study, Welch et al. measured lifestay of migraine affliction against the scale of non-heme iron accrual in the PAG, delineating a positive correlation between the two variables [19]. Overabundant iron depositions are of heavy consequence because Fe+2 is a highly reactive transition metal and, as such, has the potential to generate free radicals (e.g., superoxide anion nitric oxide, hydroxyl radical, etc.) which go on to engender cellular dysfunction and damage [19,20]. Thus, in neural tissues, high iron levels may be indicative of aberrant functionality of the respective brain region. It is probable that the disruption of the PAG’s function in antinociception predisposes one to migraine attack. This neural anatomy of MM noted thus far is consistent with the clinical observations of Rashkin et al. who crossexamined fifteen separate patients who had electrodes implanted in their PAG [21]. After the surgery, each patient reported perpetual chronic, migraine-like pain, and so Rashkin et al. inferred that perturbation in PAG function may engender the migraine [21]. To summarize, ionic dyshomeostasis leads to anti-oxidant downregulation, thereby impeding iron metabolism and enabling free

Winter 2013 radical production, which in turn causes oxidative stress, thus contributing to the onset of MM. That is, the said effects have the potential to disrupt antinociception (i.e., pain processing) in the PAG, a pivotal site in the sensory system that processes what is ultimately perceived as migraine pain (see section ‘III.’ for further elaboration).

such networks that are significantly dampened in their respective activities. It is conjectured that estrogen suppresses these systems [23]. The mechanism of estrogen modulation of neuroinhibitory transmission is genomic in nature and is predominantly localized to the hippocampal and hypothalamic brain structures. Estrogen binds to nuclear membrane bound G-coupled Neuronal Hyperexcitability and CSD estrogen receptors: ERα and/or ERβ [23]. Through binding Neuronal hyperexcitability (NH) is the second proposed to these receptors, estrogen directs the transcription of mechanism by which estrogen triggers MM’s. For one, relevant genes via further downstream transcription NH induces vasodilation. Put simply, heightened neural factors of the signaling pathway. These transcription activity necessitates greater blood flow. Importantly, factors then bind to estrogen response elements on estrogen facilitates the excitatory glutamatergic system promoter regions of the DNA [6]. Estrogen downregulates (GS) [17]. Magnesium ions gate the voltage-dependent brain-derived neurotropic factor, a potent transcription NMDA receptor complexes, allowing or disallowing regulator of GABAergic interneurons, and decreases the inflow of Ca+2 from the extracellular space. Thus, the presence and production of the GABA-synthesizing with the decreased levels of Mg+2 and the increased enzyme, glutamic acid decarboxylase (and, consequently, intracellular levels of Ca+2, the system is rendered GABA levels) [6]. Also, transitory high levels of estrogen more excitable. Neurons involved in glutamatergic facilitate the uncoupling of the GABAB receptor (GBR) neurotransmission are more readily depolarizable, having from the inwardly rectifying GIRK potassium channel. In a higher resting membrane potential. The potentiation of past in vivo studies, twenty minutes of acute estradiol glutamatergic neurons in the rat cerebellum following exposure markedly decreased the ability of GBR agonists microiontophoresis of estradiol onto the Purkinje cells to activate the GIRK channels, and this effect endured for testifies to the influence of estrogen on the GS [22]. an excess of twenty-four hours [24]. Considering that the Estradiol was proven to further the responsiveness of estrogen surge is measured in days, the surge is certainly GS to the ubiquitous neurotransmitter, glutamate, or, in sufficient to produce this effect. Once uncoupled, the other words, to enhance the excitatory tone of the neural ability of GBR’s to hyperpolarize neurons is severely circuit [22]. compromised. So, the surge in estradiol facilitates NH With the onset of menstruation, neuroinhibitory by spurring both the disinhibition and dimming of the networks arrive at a nadir in activity, and so enable NH GABAergic system. [6]. The GABAergic and serotonergic systems are two Neuroimaging techniques, such as MRI-BOLD and PET, have been instrumental in revealing the neurogeography of the migraine [20]. Each technique measures blood-flow as a proxy for brain activity. Weiller et al. utilized PET to measure blood flow during episodic unilateral migraine attacks (n=9) [20]. The investigators observed heightened cerebral blood flow (CBF) of 11% in the brainstem, indicative of heightened neural activity; they measured likewise heightened regional CBF in the cingulate, auditory, and visual associated cortices. However, after an injection of the vasorelaxant Thrubbing poinpain percept associated with MM may arise from the pulsation of dilated vasculture within sumatriptan, only heightened The throbbing hypersensitive, inflamed meningeal tissue. CBF in the brainstem persisted

Scientia undiminished. Such activity was not observed during attack-free periods. Therefore, the authors infer that the superior brainstem is the anatomical seat of initial migraine nociception [20]. These findings are consistent with the conclusions of Ter Horst and colleagues, who examined trigeminal nociception rat models. Utilizing c-Fos expression as a histochemical marker of neural activity, by proxy, they revealed significantly higher neural activity in the trigeminal nucleus, PAG, amygdala, parvicellular nucleus, and the raphe nuclei [25,26]. After juxtaposing the c-Fos expression evoked by normal pain-associated stimulation, the investigators concluded that the trigeminal system is deeply implicated in the pathogenesis of the migraine. NH initiates the cortical spreading depression wave (CSD), which evidence indicates is the spring of the migraine aura [27,28]. Twenty percent of migraineurs suffer from the phenomenon of visual aura. Essentially, the CSD is a wave of transient NH that sweeps through the cortex followed by a more extensive bout of depressed neural activity. CSD also has been observed through imaging technologies such as fMRI, lending credence to its actuality. In the extrastriate cortex, Hadjikhani et al. initially observed a focal increase in the BOLD (bloodoxygen-level-dependent contrast) signal during the experience of visual aura in three subjects. The change in the BOLD reading then slowly advanced, making its way from the occipital cortex at 1.1-3.5 mm/min. This is consistent with the nature of the aura as a visual percept, provided the specific cortex from which it springs. Subsequent to the excitatory wave, the BOLD signal fell below baseline levels, indicating vasoconstriction and the depression of activity in the area(s) [27]. Thus, the pathogenesis of the aura is postulated to arise from NH and, so, too, by implication, from ion dyshomeostasis. Trigeminal Afferent Activation Essentially, the physiological function of the trigeminal sensory complex is the encoding and processing of sensory stimuli of the face and mouth. The trigeminal nucleus, housed in the brainstem, is the initial target of trigeminal sensory afferents. A portion of trigeminal afferents synapse here from the junction between meningeal vasculature and tissue where their dendritic trees are rooted. Like other sensory systems, the trigeminal nervous system modulates the inflammatory response in its respective tissues, and so influences meningeal vasculature [29]. The trigeminal perivascular nerve terminals contain vasoactive neuropeptides that are released upon their

activation. The importance of the vasoactive calcitonin gene related peptide (CGRP) in pathophysiology of the migraine cannot be underrated. Its central role is largely acknowledged in the literature [5,27,29,30]. CGRP is the most potent vasodilator, and CGRP levels rise dramatically in a migraineur experiencing an attack [31]. Correspondingly, CGRP levels drop to near basal levels with the cessation of pain and/or treatment with migraine medications, such as triptans [30]. Moreover, the neuropeptide substance P, which is also released into the tissue following neuronal activation, influences

vasculature permeability [29]. Extravasation essentially means the unmediated influx of blood factors, pathogens, and other macromolecules, into encephalic tissue, which are otherwise strictly regulated. The effect of the continual release of substance P on the protein matrix and tight junctions in the BBB, which is to say, prompted fenestration, can be a grave threat to neuronal function [29]. Ultimately, extravasation can increase the susceptibility and duration of a migraine attack [6,28].

Winter 2013 In brief, the trigeminal nervous system is the primary migraine pain pathway [5]. At a site where a trigeminal sensory afferent innervates meningeal vasculature, there are several micro-peripheral terminals that innervate the vasculature in addition to the main terminal [32]. They branch from the main axonal tract just upstream from the terminal [32]. This intricate branching enables an axon reflex-like arc wherein an impulse, destined for the trigeminal sensory complex, upon passing a fork in the tract is partly reverted in an antidromic direction back to the many perivascular nerve terminals, thereat eliciting activation. This arc, independent of the central nervous system, enables swift and robust local vasodilation and/or inflammation [32,33]. In sum, the following cascade of events precipitates MM: once the headache is triggered (i.e., by way of ion dyshomeostasis, NH, and trigeminal afferent activation), the trigeminal neurons innervating vasculature release CGRP, substance P, and other vascoactive neuropeptides which then initiate vascular permeability, mast cell degranulation, and blood vessel edema, and initiate vasodilation while lowering the threshold for the activation of the trigeminal nerve. In turn, the former effects culminate in meningeal neurogenic inflammation and excessive neuronal firing,

particularly in the dural membrane [5,8,23]. And this inflammation perpetuates the attack for trigeminal neurons subjected to pro-inflammatory agents have been shown in vitro to continue to release greater levels of CGRP in the surrounding tissues as compared to neurons not subjected to such agents (n=36; p<0.001). In fact, pro-inflammatory factors work at the genetic level, therein upregulating CGRP promoter activity [30]. The trigeminal ganglion innervates the intracranial meninges [34]. Indeed, these meningeal tissues are painsensitive, the dural membrane laden with sensory afferents [35]. In research now considered seminal in headache studies, Strassman et al. revealed that meningeal tissues are in fact chemosensitive. The investigators compared the response sensitivity of fifteen mechanosensitive neurons before and after topical application of chemical agents (including an inflammatory agent soup, high/low osmolarity buffers, and ionic solutions) [34]. Five minutes post-treatment, heightened neuronal sensitization was observed, with ten of the fifteen displaying acute responses (p<0.01) [34]. Before treatment, the neurons were insensitive to the normally innocuous stimuli. Provided the heightened neuronal mechanosensitivity of intracranial afferents, it is plausible that the pain

Astroglia end-feet encircle capillaries in neural tissue, adjoining the microvasculature to neighboring neurons. The BBB that characterizes neural vasculature relies on occluding tight junctions which adjoin adjacent endothelial cells. Fenestration and subsequent extravasation across the BBB is thought to be implicated in the pathogenesis of MM

Scientia percepts that characterize migraine are patent cases of tactile allodynia [34]. The throbbing perception typically associated with migraine may arise from the pulse of dilated meningeal arteries, particularly the middle meningeal artery—stimuli that otherwise falls below the regular threshold of sensation, viz. allodynia. This nociceptive information then propagates to the trigeminal nucleus in the brainstem to the thalamus and cortex, where pain associated with the migraine is ultimately perceived [20]. NH, ion dyshomeostasis, and trigeminal afferent activation, the upshots of the estrogen flux inherent to References 1. Lipton RB, Bigal ME, Diamond M, Freitag F, Reed ML, & Stewart WF. Migraine prevalence, disease burden, and the need for preventive therapy. Neurology (2007); 68:343-49. 2. Gupta S, Mehorotra S, Villalon CM, Persuquia M, Saxena PR, & MaassenVanDenBrink A. Potential role of female sex hormones in the pathophysiology of migraine. Pharmacology and Therapeutics (2007); 113:321-40. 3. Oren I, Fleishman SJ, Kessel A, & Ben-Tal N. Free diffusion of steroid hormones across biomembranes: a simplex search with implicit solvent model calculations. Biophys. J., (2004); 87:768–779. 4. MacGregor EA. Estrogen replacement and migraine. Maturitas (2003); 63:51-55. 5. Moskowitz MA. The neurobiology of vascular head pain. Annals of Neurobiology (1984); 16:157-168. 6. Martin VT & Behbehani M. Ovarian hormones and migraine headache: understanding mechanisms and pathogenesis – part 1. Headache (2006); 46:3–23. 7. Facchinetti F, Nappi RE, Tirelli A, Polatti F, Nappi G, & Sances G. Hormone supplementation differently affects migraine in postmenopausal women. Headache (2002); 42(9):924-29. 8. Somerville BW. The role of estradiol withdrawal in the etiology of menstrual migraine. Neurology (1972); 22:355-365. 9. Somerville BW. The influence of progesterone and estradiol upon migraine. Headache (1972); 12:93-102. 10. de Lignieres B, Vincens M, Mauvais-Jarvis P, Mas JL, Touboul PJ, & Bousser MG. Prevention of menstrual migraine by percutaneous estradiol. Br Med J (Clin Res Ed.) (1986); 293:1540-48. 11. Hering R & Rose FC. Menstrual Migraine. Headache Quarterly-Current Treatment and Research (1992); 3(1):27-31. 12. Kornaat H, Geerdink MH, & Klitsie JW. The acceptance of a 7-week cycle with a modern low-dose oral contraceptive (Minulet). Contraception (1992); 45:119–27. 13. Nappi RE, Cagnacci A, Granella F, Piccinini F, Polatti F, & Facchinetti F. Course of primary headaches during hormone replacement therapy. Maturitas (2001); 38(2):157-163. 14. Nagel-Leiby S, Welch KM, Grunfeld S, & D’Andrea G. Ovarian steroid levels in migraine with and without aura. Cephalalgia (1990); 10(3):147-52. 15. Dhillon KS, Singh J, & Lyall SL. A new horizon into the pathobiology, etiology and treatment of migraine. Medical Hypotheses (2011); 77:147-51. 16. Mauskop A, Altura BT, & Altura BM. Serum ionized magnesium levels and serum ionized calcium/ionized magnesium ratios in women with menstrual migraine. Headache (2002); 42:242-248. 17. Sun-Edelstein C & Mauskop A. Role of magnesium in the pathogenesis and treatment of migraine. Expert Reviews Neurotherapy (2009); 9(3):369-379. 18. Lanteriminet M, Isnardon P, Depommery J, et al. Spin and hindbrain structures involved in visceroception and visceronociception as revealed by the expression of fos, jun, and krox-24 proteins. Neuroscience (1993); 55(3):737-753. 19. Welch KM, Nagesh V, Aurora SK, & Gelman N. Periaqueductal gray matter dysfunction in migraine: cause or the burden of illness? Headache (2001); 41:629-637. 20. Weiller C, May A, Limmroth B, Juptner M, Kaube H, Vonschayck R, Coenen HH, & Diener HC. Brain-stem activation in spontaneous migraine attacks. Nature Medicine (1995); 1(7):658-60. 21. Raskin NH, Hosobuchi Y, & Lamb S. Headache may arise from perturba tion of brain. Headache (1987); 27:416-420. 22. Smith SS, Waterhouse BD, & Woodward DJ. Sex steroid effects on extrahypothalamic CNS. I. Estrogen augments neuronal responsiveness to iontophoretically applied glutamate in the cerebellum. Brain Res. (1987);

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Acknowledgements

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