We developed a spatially-tracked single neuron transcriptomics map of an intrinsic cardiac ganglion, the right atrial ganglionic plexus (RAGP) that is a critical mediator of sinoatrial node (SAN) activity. This 3D representation of RAGP used neuronal tracing to extensively map the spatial distribution of the subset of neurons that project to the SAN. RNA-seq of laser capture microdissected neurons revealed a distinct composition of RAGP neurons compared to the central nervous system and a surprising finding that cholinergic and catecholaminergic markers are coexpressed, suggesting multipotential phenotypes that can drive neuroplasticity within RAGP. High-throughput qPCR of hundreds of laser capture microdissected single neurons confirmed these findings and revealed a high dimensionality of neuromodulatory factors that contribute to dynamic control of the heart. Neuropeptide-receptor coexpression analysis revealed a combinatorial paracrine neuromodulatory network within RAGP informing follow-on studies on the vagal control of RAGP to regulate cardiac function in health and disease.

Corals are critical to marine biodiversity and human welfare. Coral reefs cover <1% of the seafloor but support ~1/3 of all marine species. Approximately 1.5 billion people live within 100 km of coral reefs, relying upon them for food, income from tourism, and protection from storms. Their economic value has been estimated at $375 billion annually. The foundation of coral reefs is the intracellular symbiosis between corals and photosynthetic dinoflagellates of the family Symbiodiniaceae. Tropical corals satisfy up to 95% of their nutritional requirements through photosynthesis, and their ability to construct reefs is biochemically coupled to photosynthesis. While permitting corals to thrive, photosymbiosis also increases their exposure to environmental stressors and vulnerability to climate change. Reliance on photosynthesis restricts reef-building corals to shallow, clear, tropical waters, where they experience higher temperatures and UV exposure. The generation of reactive oxygen species by the symbiont also exposes corals to greater oxidative stress. The symbiosis is particularly sensitive to climate change: all of the mass coral bleaching events have occurred since 1982, driven by elevated ocean temperatures. Molecular cross-talk between host and symbiont impacts resilience of the coral holobiont and resistance to bleaching. Unfortunately, we know little about how photosymbiosis impacts expression or activity of coral genes. Tropical corals engage in an obligate symbiosis with Symbiodiniaceae, so we cannot study their gene expression in a stable aposymbiotic state. However, the northern star coral, Astrangia poculata, engages in a facultative symbiosis with Symbiodiniaceae. I used RNA sequencing to investigate how symbiosis impacts gene expression in A. poculata, focusing on genes implicated in photosymbiosis: antioxidant enzymes (specifically superoxide dismutases) and the NF-κB signaling pathway. From an improved transcriptome assembly, I recovered core elements of a primitively simple NF-κB signaling pathway and a rich complement of SOD proteins. 273 coral transcripts—many associated with protein metabolism and vesicle-mediated transport— were differentially expressed in symbiotic versus aposymbiotic corals. Unlike in the facultatively symbiotic sea anemone Exaiptasia, symbiosis was not associated with depressed NF-κB transcript levels. IKKε, a potential positive regulator of NF-κB activity, was strongly up-regulated, as was one particular superoxide dismutase.

Malignant tumors remain one of the leading causes of mortality with over 8.2 million deaths worldwide in 2012. Over the last two decades, high-throughput profiling of the human transcriptome has become an essential tool to investigate molecular processes involved in carcinogenesis. In this thesis I explore how gene expression profiling (GEP) can be used in multiple aspects of cancer research, including prevention, patient stratification and subtype discovery. The first part details how GEP could be used to supplement or even replace the current gold standard assay for testing the carcinogenic potential of chemicals. This toxicogenomic approach coupled with a Random Forest algorithm allowed me to build models capable of predicting carcinogenicity with an area under the curve of up to 86.8% and provided valuable insights into the underlying mechanisms that may contribute to cancer development. The second part describes how GEP could be used to stratify heterogeneous populations of lymphoma patients into therapeutically relevant disease sub-classes, with a particular focus on diffuse large B-cell lymphoma (DLBCL). Here, I successfully translated established biomarkers from the Affymetrix platform to the clinically relevant Nanostring nCounter© assay. This translation allowed us to profile custom sets of transcripts from formalin-fixed samples, transforming these biomarkers into clinically relevant diagnostic tools. Finally, I describe my effort to discover tumor samples dependent on altered metabolism driven by oxidative phosphorylation (OxPhos) across multiple tissue types. This work was motivated by previous studies that identified a therapeutically relevant OxPhos sub-type in DLBCL, and by the hypothesis that this stratification might be applicable to other solid tumor types. To that end, I carried out a transcriptomics-based pan-cancer analysis, derived a generalized PanOxPhos gene signature, and identified mTOR as a potential regulator in primary tumor samples. High throughput GEP coupled with statistical machine learning methods represent an important toolbox in modern cancer research. It provides a cost effective and promising new approach for predicting cancer risk associated to chemical exposure, it can reduce the cost of the ever increasing drug development process by identifying therapeutically actionable disease subtypes, and it can increase patients’ survival by matching them with the most effective drugs. / 2016-12-01T00:00:00Z

abstract: Aromatic compounds have traditionally been generated via petroleum feedstocks and have wide ranging applications in a variety of fields such as cosmetics, food, plastics, and pharmaceuticals. Substantial improvements have been made to sustainably produce many aromatic chemicals from renewable sources utilizing microbes as bio-factories. By assembling and optimizing native and non-native pathways to produce natural and non-natural bioproducts, the diversity of biochemical aromatics which can be produced is constantly being improved upon. One such compound, 2-Phenylethanol (2PE), is a key molecule used in the fragrance and food industries, as well as a potential biofuel. Here, a novel, non-natural pathway was engineered in Escherichia coli and subsequently evaluated. Following strain and bioprocess optimization, accumulation of inhibitory acetate byproduct was reduced and 2PE titers approached 2 g/L – a ~2-fold increase over previously implemented pathways in E. coli. Furthermore, a recently developed mechanism to allow E. coli to consume xylose and glucose, two ubiquitous and industrially relevant microbial feedstocks, simultaneously was implemented and systematically evaluated for its effects on L-phenylalanine (Phe; a precursor to many microbially-derived aromatics such as 2PE) production. Ultimately, by incorporating this mutation into a Phe overproducing strain of E. coli, improvements in overall Phe titers, yields and sugar consumption in glucose-xylose mixed feeds could be obtained. While upstream efforts to improve precursor availability are necessary to ultimately reach economically-viable production, the effect of end-product toxicity on production metrics for many aromatics is severe. By utilizing a transcriptional profiling technique (i.e., RNA sequencing), key insights into the mechanisms behind styrene-induced toxicity in E. coli and the cellular response systems that are activated to maintain cell viability were obtained. By investigating variances in the transcriptional response between styrene-producing cells and cells where styrene was added exogenously, better understanding on how mechanisms such as the phage shock, heat-shock and membrane-altering responses react in different scenarios. Ultimately, these efforts to diversify the collection of microbially-produced aromatics, improve intracellular precursor pools and further the understanding of cellular response to toxic aromatic compounds, give insight into methods for improved future metabolic engineering endeavors. / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2019

Indiana University-Purdue University Indianapolis (IUPUI) / Regulatory protein families such as transcription factors (TFs) and RNA Binding Proteins (RBPs) are increasingly being appreciated for their role in regulating the respective targeted genomic/transcriptomic elements resulting in dynamic transcriptional (TRNs) and post-transcriptional regulatory networks (PTRNs) in higher eukaryotes. The mechanistic understanding of these two regulatory network types require a high resolution tissue-specific functional annotation of both the proteins as well as their target sites. This dissertation addresses the need to uncover the tissue-specific regulatory networks in development and disease. This work establishes multiple computational genomics based approaches to further enhance our understanding of regulatory circuits and decipher the associated mechanisms at several layers of biological processes. This study potentially contributes to the research community by providing valuable resources including novel methods, web interfaces and software which transforms our ability to build high-quality regulatory binding maps of RBPs and TFs in a tissue specific manner using multi-omics datasets. The study deciphered the broad spectrum of temporal and evolutionary dynamics of the transcriptome and their regulation at transcriptional and post transcriptional levels. It also advances our ability to functionally annotate hundreds of RBPs and their RNA binding sites across tissues in the human genome which help in decoding the role of RBPs in the context of disease phenotype, networks, and pathways. The approaches developed in this dissertation is scalable and adaptable to further investigate the tissue specific regulators in any biological systems. Overall, this study contributes towards accelerating the progress in molecular diagnostics and drug target identification using regulatory network analysis method in disease and pathophysiology.

No description available.

No description available.

Opioid use disorder is a chronic, relapsing disease, with more than 85% of individuals experiencing a relapse episode within one year. One common reason patients relapse is due to intense cravings, which are defined as the compulsive urge to use the drug. In fact, craving was recently added to the DSM criteria for substance use disorder diagnosis. Counterintuitively, cravings intensify over the course of extended abstinence, especially in response to drug-paired cues, a phenomenon known as “incubation of craving”. This contributes to difficulty in maintaining long-term sobriety. The mesocorticolimbic reward pathway facilitates self-administration and cue-induced incubation of craving for drugs of abuse and natural rewards, such as sucrose. In particular, the shell sub-region of the nucleus accumbens is a critical brain region involved in context/cue-mediated reward seeking. In the experiments described here, we utilized an incubation of craving model, in which male and female rats self-administered opioids (morphine or heroin) or sucrose for 10 days. Sucrose served as an important control for delineating drug-induced changes from those caused in response to natural rewards, which are not the intended target of potential treatments. Reward delivery was paired with a cue light that was later used to elicit craving. After self-administration, rats underwent brief (one day) or extended (30 days) forced abstinence. One or 30 days later, they were returned to the chambers for a “cue test”, in which responses on the previously reward-associated lever triggered cue presentation, but no contingent reward. We used this model to further delineate behavioral and affective changes that accompany increased opioid craving in late abstinence, as well as molecular alterations underlying craving in rats that did not undergo a cue test. We found an opioid-specific behavioral signature in which peak opioid craving is accompanied by decreased grooming and hyperactivity in both sexes. We tracked the female estrous cycle throughout, as these fluctuations in reproductive hormones (akin to the menstrual cycle) are shown to affect cocaine- and nicotine-related behaviors. We found no differences between females in different phases of the estrous cycle in terms of self-administration, nor craving. RNA sequencing of the nucleus accumbens shell revealed robust changes in gene expression that occurred across extended abstinence, though the genes themselves were altered in a sex- and reinforcer-specific manner. In general, we found many behavioral and molecular changes that were unique to sex and reinforcer (sucrose versus opioids). This is promising in terms of identifying opioid-specific targets that are unlikely to affect the natural reward system in both sexes. Changes in gene expression in the brain are mediated in part by epigenetic processes that influence access of transcriptional machinery to DNA. Acetylation of histone tails, the proteins around which DNA is wrapped and packaged in the nucleus, have been identified as permissive marks that facilitate long-lasting changes in transcriptomics in response to environmental insults. Opioids promote increased acetylation, which may contribute to some of the reported changes here. We tested the efficacy of JQ1, a treatment that interferes with the read-out of opioid-induced acetylated marks, at attenuating heroin self-administration. When administered as an intracerebroventricular microinjection on self-administration day 11, JQ1 had no effect on subsequent heroin taking in either sex, suggesting that it may not be suitable as a systemic treatment at the dose given. These studies lay the groundwork for future studies to administer other treatments throughout abstinence, based on the opioid-specific genes and pathways identified here, to reduce cue-induced heroin craving and the accompanying suite of behaviors in males and females. / Psychology

The chicken bursa of Fabricius is a primary lymphoid tissue important for B-cell development. Our long-term goal is to understand the role of bursal microenvironment in an early B-cell differentiation event initiating repertoire development through immunoglobulin gene-conversion in the chick embryo. We hypothesize that early bursal B-cell differentiation is guided by signals through cytokine receptors. Our theory is based on previous evidence for expression of the receptor tyrosine kinase superfamily members and interleukin receptors in unseparated populations of bursal B-cells and bursal tissue. Knowledge of the expressed genes that are responsible for B-cell differentiation is a prerequisite for understanding the bursal microenvironment’s function. This project uses transcriptomic analysis to examine gene expression across an early B-cell differentiation event. RNA-seq was performed with total RNA isolated from developing B-cells at embryonic day (ED) 16 and ED 19 (n=3). Approximately 90 million high quality clean reads where obtained from the cDNA libraries. The analysis revealed differentially expressed genes involved in Wnt signaling pathway, Jak-STAT pathway, metabolic pathways, tyrosine metabolism, Toll-like receptor signaling pathway, MAPK signaling pathway, and cellhesion molecules. The transcripts for surface receptors, signal transduction and transcription factors identified in this study represent gene candidates for controlling B-cell differentiation in response to bursal microenvironmental factors.

Bacterial pathogens are a major cause of diseases in human, agricultural plants and farm animals. Even after decades of research they remain a challenge to health care as they are known to rapidly evolve and develop resistance to the existing drugs. Systems biology is an emerging area of research where all of the components of the system, their interactions, and the dynamics can be studied in a comprehensive, quantitative, and integrative fashion to generate predictive models. When applied to bacterial pathogenesis, systems biology approaches will help identify potential novel molecular targets for drug discovery. A pre-requisite for conducting systems analysis is the identification of the building blocks of the system i.e. individual components of the system (structural annotation), identification of their functions (functional annotation) and identification of the interactions among the individual components (interaction prediction). In the context of bacterial pathogenesis, it is necessary to identify the host-pathogen interactions. This dissertation work describes computational resources that enable comprehensive systems level study of host pathogen system to enhance our understanding of bacterial pathogenesis. It specifically focuses on improving the structural and functional annotation of pathogen genomes as well as identifying host-pathogen interactions at a genome scale. The novel contributions of this dissertation towards systems biology of bacterial pathogens include three computational tools/resources. “TAAPP” (Tiling array analysis pipeline for prokaryotes) is a web based tool for the analysis of whole genome tiling array data for bacterial pathogens. TAAPP helps improve the structural annotation of bacterial genomes. “ISO-IEA” (Inferred from sequence orthology - Inferred from electronic annotation) is a tool that can be used for the functional annotation of any sequenced genome. “HPIDB” (Host pathogen interaction database) is developed with data a mining capability that includes host-pathogen interaction prediction. The new knowledge gained due to the implementation of these tools is the description of the non coding RNA as well as a computationally predicted host-pathogen interaction network for the human respiratory pathogen Streptococcus pneumoniae. In summary, the computation tools and resources developed in this dissertation study will enable building systems biology models of bacterial pathogens.