Patched Together: cis-Regulatory Logic of the Hedgehog Response.

Understanding the processes that control how we develop from a fertilized embryo to a functional adult is paramount for treating the diseases that result when these processes are disrupted at any stage of life. My dissertation investigates the cis-regulatory logic underlying how cell signaling pathways utilize the genome to create and maintain the wide variety of cell types and tissues needed for proper development and survival. Surprisingly few cell signaling pathways are used throughout embryonic development; I have chosen to focus on Hedgehog (Hh) signaling, a pathway used in such diverse cellular contexts as digit specification, brain development, lung function, and reproductive maintenance. Disruption of this pathway results in developmental defects and cancer. It is essential to understand the mechanisms by which Hh signaling functions to treat these diseases more effectively. One relatively unexplored mechanism of Hh function is how its signal is transduced at the level of DNA, specifically through the regulation of gene expression. In this thesis, I explore the mechanisms that mediate tissue-specific, Hh-dependent gene regulation, and uncover an ancient cis-regulatory logic shared between flies and mice that has significant implications for the maintenance and evolution of cellular communication. I experimentally demonstrate that multiple enhancer elements, which control tissue-specific gene expression, rely on sub-optimal DNA sequences for binding of GLI proteins, the transcriptional effectors of Hh signaling. These sequences are essential to control gene expression in response to Hh and can influence the function of the pathway in a variety of cellular contexts. I also characterize several new transcriptional regulators of Hh signaling and introduce new tools to the field that allow for in depth analysis of the regulatory landscape of Hh target genes at any stage of development. My work presented here addresses a significant gap in our knowledge of how the Hh signaling pathway functions at the cis-regulatory level and describes a framework by which new advances can be made on this topic in the future.PHDCellular & Molecular BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135811/1/dslorber_1.pd

Discovering new drug-drug interactions using data science: Applications to drug-induced Long QT Syndrome

Commonly prescribed small molecule drugs can have net-positive and well-understood safety profiles when prescribed individually, but unexpected consequences when taken at the same time. Detection of these drug-drug interactions (DDIs) continues to be a critical and unmet area of translational research. The Centers for Disease Control and Prevention (CDC) estimate that one third of Americans are concurrently taking two or more prescription drugs, and DDIs are estimated to be responsible for 17% of all drug adverse events. The consequences of DDIs can be relatively minor (headache, skin rash) or much more severe (bleeding, liver toxicity). At a cellular level, DDIs can occur as a result of both drugs competing for metabolism (known as pharmacokinetic interactions) or targeting the same protein target or biological pathway (pharmacodynamic interactions). Clinical trials typically focus on the effects of individual drugs, leaving DDIs to usually be discovered only after the drugs have been approved. One of the most carefully studied drug adverse events is long QT syndrome (LQTS), an unexpected change in the heart's electrical activity that can lead to a potentially fatal ventricular tachycardia known as torsades de pointes (TdP). Some patients have genetic mutations that lead to congenital forms of LQTS, while drug-induced LQTS typically occurs via block of the hERG potassium channel (KCNH2) responsible for ventricular repolarization. After a number of high profile drugs were withdrawn from the market due to discovered risk of TdP, the FDA issued guidelines so that pharmaceutical companies could anticipate and test for this side effect before a new drug is approved. These recommendations have helped prevent new QT-prolonging drugs from entering the market, but nonetheless over 180 approved drugs have been associated with drug-induced LQTS. While information on individual QT-prolonging drugs is thus readily available to clinicians, little has remained known about DDIs (QT-DDIs). There are many more commonly prescribed drugs that are safe when given individually but could increase TdP risk when administered together. This troubling situation is compounded by the fact that traditional post-market surveillance algorithms are poorly equipped to sensitively and specifically detect DDIs. Data science – the application of rigorous analytical methods to large datasets – offers an opportunity for predicting previously unknown QT-DDIs. Some biomedical datasets (such as drug-target binding affinities and experiments to determine protein-protein interactions) have been collected explicitly for research, while other valuable datasets (such as electronic health records) were initially recorded for billing purposes. Each data modality has its own important set of advantages and disadvantages, and integrative data science approaches can incorporate multiple types of data to help account for these limitations. In this thesis we develop new data sciences techniques that combine clinical, biological, chemical, and genetic data. These approaches are explicitly designed to be robust to biased and missing data. We apply these new methodologies to (1) predict new QT-DDIs, (2) validate them experimentally, and (3) investigate their molecular and genetic mechanisms. We exemplify this approach in the discovery of a previously unknown QT-DDI between ceftriaxone (cephalosporin antibiotic) and lansoprazole (proton pump inhibitor); importantly, both drugs have no cardiac indications and are safe when given individually. The clinical data mining, drug target prediction, biological network analysis, genetic ancestry prediction, and experimental validation methods described in this thesis form the basis for a comprehensive pipeline to predict QT-DDIs rapidly and robustly. They also provide an opportunity for further enriching our understanding of LQTS biology and ultimately enabling the design of safer drugs

Dynamics of Internal Models in Game Players

A new approach for the study of social games and communications is proposed. Games are simulated between cognitive players who build the opponent's internal model and decide their next strategy from predictions based on the model. In this paper, internal models are constructed by the recurrent neural network (RNN), and the iterated prisoner's dilemma game is performed. The RNN allows us to express the internal model in a geometrical shape. The complicated transients of actions are observed before the stable mutually defecting equilibrium is reached. During the transients, the model shape also becomes complicated and often experiences chaotic changes. These new chaotic dynamics of internal models reflect the dynamical and high-dimensional rugged landscape of the internal model space.Comment: 19 pages, 6 figure

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods

Background Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematically compared and evaluated the ability of machine learning algorithms and case-control combinations to phenotype acute ischemic stroke patients using data from an EHR. Materials and methods Using structured patient data from the EHR at a tertiary-care hospital system, we built and evaluated machine learning models to identify patients with AIS based on 75 different case-control and classifier combinations. We then estimated the prevalence of AIS patients across the EHR. Finally, we externally validated the ability of the models to detect AIS patients without AIS diagnosis codes using the UK Biobank. Results Across all models, we found that the mean AUROC for detecting AIS was 0.963 ± 0.0520 and average precision score 0.790 ± 0.196 with minimal feature processing. Classifiers trained with cases with AIS diagnosis codes and controls with no cerebrovascular disease codes had the best average F1 score (0.832 ± 0.0383). In the external validation, we found that the top probabilities from a model-predicted AIS cohort were significantly enriched for AIS patients without AIS diagnosis codes (60–150 fold over expected). Conclusions Our findings support machine learning algorithms as a generalizable way to accurately identify AIS patients without using process-intensive manual feature curation. When a set of AIS patients is unavailable, diagnosis codes may be used to train classifier models

The evolution of cooperation and altruism--a general framework and a classification of models.

One of the enduring puzzles in biology and the social sciences is the origin and persistence of intraspecific cooperation and altruism in humans and other species. Hundreds of theoretical models have been proposed and there is much confusion about the relationship between these models. To clarify the situation, we developed a synthetic conceptual framework that delineates the conditions necessary for the evolution of altruism and cooperation. We show that at least one of the four following conditions needs to be fulfilled: direct benefits to the focal individual performing a cooperative act; direct or indirect information allowing a better than random guess about whether a given individual will behave cooperatively in repeated reciprocal interactions; preferential interactions between related individuals; and genetic correlation between genes coding for altruism and phenotypic traits that can be identified. When one or more of these conditions are met, altruism or cooperation can evolve if the cost-to-benefit ratio of altruistic and cooperative acts is greater than a threshold value. The cost-to-benefit ratio can be altered by coercion, punishment and policing which therefore act as mechanisms facilitating the evolution of altruism and cooperation. All the models proposed so far are explicitly or implicitly built on these general principles, allowing us to classify them into four general categories

Differential processing of the direction and focus of expansion of optic flow stimuli in areas MST and V3A of the human visual cortex

Human neuropsychological and neuroimaging studies have raised the possibility that different attributes of optic flow stimuli, namely radial direction and the position of the focus of expansion (FOE), are processed within separate cortical areas. In the human brain, visual areas V5/MT+ and V3A have been proposed as integral to the analysis of these different attributes of optic flow stimuli. To establish direct causal relationships between neural activity in human (h)V5/MT+ and V3A and the perception of radial motion direction and FOE position, we used transcranial magnetic stimulation (TMS) to disrupt cortical activity in these areas while participants performed behavioral tasks dependent on these different aspects of optic flow stimuli. The cortical regions of interest were identified in seven human participants using standard functional MRI retinotopic mapping techniques and functional localizers. TMS to area V3A was found to disrupt FOE positional judgments but not radial direction discrimination, whereas the application of TMS to an anterior subdivision of hV5/MT+, MST/TO-2 produced the reverse effects, disrupting radial direction discrimination but eliciting no effect on the FOE positional judgment task. This double dissociation demonstrates that FOE position and radial direction of optic flow stimuli are signaled independently by neural activity in areas hV5/MT+ and V3A.NEW & NOTEWORTHY Optic flow constitutes a biologically relevant visual cue as we move through any environment. With the use of neuroimaging and brain-stimulation techniques, this study demonstrates that separate human brain areas are involved in the analysis of the direction of radial motion and the focus of expansion in optic flow. This dissociation reveals the existence of separate processing pathways for the analysis of different attributes of optic flow that are important for the guidance of self-locomotion and object avoidance