Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Transcription factors MYOCD, SRF, Mesp1 and SMARCD3 enhance the cardio-inducing effect of GATA4, TBX5, and MEF2C during direct cellular reprogramming.

Transient overexpression of defined combinations of master regulator genes can effectively induce cellular reprogramming: the acquisition of an alternative predicted phenotype from a differentiated cell lineage. This can be of particular importance in cardiac regenerative medicine wherein the heart lacks the capacity to heal itself, but simultaneously contains a large pool of fibroblasts. In this study we determined the cardio-inducing capacity of ten transcription factors to actuate cellular reprogramming of mouse embryonic fibroblasts into cardiomyocyte-like cells. Overexpression of transcription factors MYOCD and SRF alone or in conjunction with Mesp1 and SMARCD3 enhanced the basal but necessary cardio-inducing effect of the previously reported GATA4, TBX5, and MEF2C. In particular, combinations of five or seven transcription factors enhanced the activation of cardiac reporter vectors, and induced an upregulation of cardiac-specific genes. Global gene expression analysis also demonstrated a significantly greater cardio-inducing effect when the transcription factors MYOCD and SRF were used. Detection of cross-striated cells was highly dependent on the cell culture conditions and was enhanced by the addition of valproic acid and JAK inhibitor. Although we detected Ca(2+) transient oscillations in the reprogrammed cells, we did not detect significant changes in resting membrane potential or spontaneously contracting cells. This study further elucidates the cardio-inducing effect of the transcriptional networks involved in cardiac cellular reprogramming, contributing to the ongoing rational design of a robust protocol required for cardiac regenerative therapies.

Posttranscriptional Regulation of Embryonic Neurogenesis by the Exon Junction Complex

The six-layered neuron structure in the cerebral cortex is the foundation for human mental abilities. In the developing cerebral cortex, neural stem cells undergo proliferation and differentiate into intermediate progenitors and neurons, a process known as embryonic neurogenesis. Disrupted embryonic neurogenesis is the root cause of a wide range of neurodevelopmental disorders, including microcephaly and intellectual disabilities. Multiple layers of regulatory networks have been identified and extensively studied over the past decades to understand this complex but extremely crucial process of brain development. In recent years, post-transcriptional RNA regulation through RNA binding proteins has emerged as a critical regulatory nexus in embryonic neurogenesis. The exon junction complex (EJC) is a highly conserved RNA binding complex composed of four core proteins, Magoh, Rbm8a, Eif4a3, and Casc3. The EJC plays a major role in regulating RNA splicing, nuclear export, subcellular localization, translation, and nonsense mediated RNA decay. Human genetic studies have associated individual EJC components with various developmental disorders. We showed previously that haploinsufficiency of Magoh causes microcephaly and disrupted neural stem cell differentiation in mouse. However, it is unclear if other EJC core components are also required for embryonic neurogenesis. More importantly, the molecular mechanism through which the EJC regulates embryonic neurogenesis remains largely unknown. Here, we demonstrated with genetically modified mouse models that both Rbm8a and Eif4a3 are required for proper embryonic neurogenesis and the formation of a normal brain. Using transcriptome and proteomic analysis, we showed that the EJC posttranscriptionally regulates genes involved in the p53 pathway, splicing and translation regulation, as well as ribosomal biogenesis. This is the first in vivo evidence suggesting that the etiology of EJC associated neurodevelopmental diseases can be ribosomopathies. We also showed that, different from other EJC core components, depletion of Casc3 only led to mild neurogenesis defects in the mouse model. However, our data suggested that Casc3 is required for embryo viability, development progression, and is potentially a regulator of cardiac development. Together, data presented in this thesis suggests that the EJC is crucial for embryonic neurogenesis and that the EJC and its peripheral factors may regulate development in a tissue-specific manner.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology.

Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the P(luxI) promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of "hidden interactions" on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context.

Metabolic regulation of T lymphocytes.

T cell activation leads to dramatic shifts in cell metabolism to protect against pathogens and to orchestrate the action of other immune cells. Quiescent T cells require predominantly ATP-generating processes, whereas proliferating effector T cells require high metabolic flux through growth-promoting pathways. Further, functionally distinct T cell subsets require distinct energetic and biosynthetic pathways to support their specific functional needs. Pathways that control immune cell function and metabolism are intimately linked, and changes in cell metabolism at both the cell and system levels have been shown to enhance or suppress specific T cell functions. As a result of these findings, cell metabolism is now appreciated as a key regulator of T cell function specification and fate. This review discusses the role of cellular metabolism in T cell development, activation, differentiation, and function to highlight the clinical relevance and opportunities for therapeutic interventions that may be used to disrupt immune pathogenesis.

Chromatin accessibility mapping identifies mediators of basal transcription and retinoid-induced repression of OTX2 in medulloblastoma.

Despite an emerging understanding of the genetic alterations giving rise to various tumors, the mechanisms whereby most oncogenes are overexpressed remain unclear. Here we have utilized an integrated approach of genomewide regulatory element mapping via DNase-seq followed by conventional reporter assays and transcription factor binding site discovery to characterize the transcriptional regulation of the medulloblastoma oncogene Orthodenticle Homeobox 2 (OTX2). Through these studies we have revealed that OTX2 is differentially regulated in medulloblastoma at the level of chromatin accessibility, which is in part mediated by DNA methylation. In cell lines exhibiting chromatin accessibility of OTX2 regulatory regions, we found that autoregulation maintains OTX2 expression. Comparison of medulloblastoma regulatory elements with those of the developing brain reveals that these tumors engage a developmental regulatory program to drive OTX2 transcription. Finally, we have identified a transcriptional regulatory element mediating retinoid-induced OTX2 repression in these tumors. This work characterizes for the first time the mechanisms of OTX2 overexpression in medulloblastoma. Furthermore, this study establishes proof of principle for applying ENCODE datasets towards the characterization of upstream trans-acting factors mediating expression of individual genes.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Molecular Insights of CD4+ T Cell Differentiation, Effector Formation and Helper Function

CD4+ T cells play a crucial in the adaptive immune system. They function as the central hub to orchestrate the rest of immunity: CD4+ T cells are essential governing machinery in antibacterial and antiviral responses by facilitating B cell affinity maturation and coordinating the innate and adaptive immune systems to boost the overall immune outcome; on the contrary, hyperactivation of the inflammatory lineages of CD4+ T cells, as well as the impairments of suppressive CD4+ regulatory T cells, are the etiology of various autoimmunity and inflammatory diseases. The broad role of CD4+ T cells in both physiological and pathological contexts prompted me to explore the modulation of CD4+ T cells on the molecular level.

microRNAs (miRNAs) are small RNA molecules capable of regulating gene expression post-transcriptionally. miRNAs have been shown to exert substantial regulatory effects on CD4+ T cell activation, differentiation and helper function. Specifically, my lab has previously established the function of the miR-17-92 cluster in Th1 differentiation and anti-tumor responses. Here, I further analyzed the role of this miRNA cluster in Th17 differentiation, specifically, in the context of autoimmune diseases. Using both gain- and loss-of-function approaches, I demonstrated that miRNAs in miR-17-92, specifically, miR-17 and miR-19b in this cluster, is a crucial promoter of Th17 differentiation. Consequently, loss of miR-17-92 expression in T cells mitigated the progression of experimental autoimmune encephalomyelitis and T cell-induced colitis. In combination with my previous data, the molecular dissection of this cluster establishes that miR-19b and miR-17 play a comprehensive role in promoting multiple aspects of inflammatory T cell responses, which underscore them as potential targets for oligonucleotide-based therapy in treating autoimmune diseases.

To systematically study miRNA regulation in effector CD4+ T cells, I devised a large-scale miRNAome profiling to track in vivo miRNA changes in antigen-specific CD4+ T cells activated by Listeria challenge. From this screening, I identified that miR-23a expression tightly correlates with CD4+ effector expansion. Ectopic expression and genetic deletion strategies validated that miR-23a was required for antigen-stimulated effector CD4+ T cell survival in vitro and in vivo. I further determined that miR-23a targets Ppif, a gatekeeper of mitochondrial reactive oxygen species (ROS) release that protects CD4+ T cells from necrosis. Necrosis is a type of cell death that provokes inflammation, and it is prominently triggered by ROS release and its consequent oxidative stress. My finding that miR-23a curbs ROS-mediated necrosis highlights the essential role of this miRNA in maintaining immune homeostasis.

A key feature of miRNAs is their ability to modulate different biological aspects in different cell populations. Previously, my lab found that miR-23a potently suppresses CD8+ T cell cytotoxicity by restricting BLIMP1 expression. Since BLIMP1 has been found to inhibit T follicular helper (Tfh) differentiation by antagonizing the master transcription factor BCL6, I investigated whether miR-23a is also involved in Tfh differentiation. However, I found that miR-23a does not target BLIMP1 in CD4+ T cells and loss of miR-23a even fostered Tfh differentiation. This data indicate that miR-23a may target other pathways in CD4+ T cells regarding the Tfh differentiation pathway.

Although the lineage identity and regulatory networks for Tfh cells have been defined, the differentiation path of Tfh cells remains elusive. Two models have been proposed to explain the differentiation process of Tfh cells: in the parallel differentiation model, the Tfh lineage is segregated from other effector lineages at the early stage of antigen activation; alternatively, the sequential differentiation model suggests that naïve CD4+ T cells first differentiate into various effector lineages, then further program into Tfh cells. To address this question, I developed a novel in vitro co-culture system that employed antigen-specific CD4+ T cells, naïve B cells presenting cognate T cell antigen and BAFF-producing feeder cells to mimic germinal center. Using this system, I were able to robustly generate GC-like B cells. Notably, well-differentiated Th1 or Th2 effector cells also quickly acquired Tfh phenotype and function during in vitro co-culture, which suggested a sequential differentiation path for Tfh cells. To examine this path in vivo, under conditions of classical Th1- or Th2-type immunizations, I employed a TCRβ repertoire sequencing technique to track the clonotype origin of Tfh cells. Under both Th1- and Th2- immunization conditions, I observed profound repertoire overlaps between the Teff and Tfh populations, which strongly supports the proposed sequential differentiation model. Therefore, my studies establish a new platform to conveniently study Tfh-GC B cell interactions and provide insights into Tfh differentiation processes.

microRNA Regulation of Cellular Immunity

Immunity is broadly defined as a mechanism of protection against non-self entities, a process which must be sufficiently robust to both eliminate the initial foreign body and then be maintained over the life of the host. Life-long immunity is impossible without the development of immunological memory, of which a central component is the cellular immune system, or T cells. Cellular immunity hinges upon a naïve T cell pool of sufficient size and breadth to enable Darwinian selection of clones responsive to foreign antigens during an initial encounter. Further, the generation and maintenance of memory T cells is required for rapid clearance responses against repeated insult, and so this small memory pool must be actively maintained by pro-survival cytokine signals over the life of the host.

T cell development, function, and maintenance are regulated on a number of molecular levels through complex regulatory networks. Recently, small non-coding RNAs, miRNAs, have been observed to have profound impacts on diverse aspects of T cell biology by impeding the translation of RNA transcripts to protein. While many miRNAs have been described that alter T cell development or functional differentiation, little is known regarding the role that miRNAs have in T cell maintenance in the periphery at homeostasis.

In Chapter 3 of this dissertation, tools to study miRNA biology and function were developed. First, to understand the effect that miRNA overexpression had on T cell responses, a novel overexpression system was developed to enhance the processing efficiency and ultimate expression of a given miRNA by placing it within an alternative miRNA backbone. Next, a conditional knockout mouse system was devised to specifically delete miR-191 in a cell population expressing recombinase. This strategy was expanded to permit the selective deletion of single miRNAs from within a cluster to discern the effects of specific miRNAs that were previously inaccessible in isolation. Last, to enable the identification of potentially therapeutically viable miRNA function and/or expression modulators, a high-throughput flow cytometry-based screening system utilizing miRNA activity reporters was tested and validated. Thus, several novel and useful tools were developed to assist in the studies described in Chapter 4 and in future miRNA studies.

In Chapter 4 of this dissertation, the role of miR-191 in T cell biology was evaluated. Using tools developed in Chapter 3, miR-191 was observed to be critical for T cell survival following activation-induced cell death, while proliferation was unaffected by alterations in miR-191 expression. Loss of miR-191 led to significant decreases in the numbers of CD4+ and CD8+ T cells in the periphery lymph nodes, but this loss had no impact on the homeostatic activation of either CD4+ or CD8+ cells. These peripheral changes were not caused by gross defects in thymic development, but rather impaired STAT5 phosphorylation downstream of pro-survival cytokine signals. miR-191 does not specifically inhibit STAT5, but rather directly targets the scaffolding protein, IRS1, which in turn alters cytokine-dependent signaling. The defect in peripheral T cell maintenance was exacerbated by the presence of a Bcl-2YFP transgene, which led to even greater peripheral T cell losses in addition to developmental defects. These studies collectively demonstrate that miR-191 controls peripheral T cell maintenance by modulating homeostatic cytokine signaling through the regulation of IRS1 expression and downstream STAT5 phosphorylation.

The studies described in this dissertation collectively demonstrate that miR-191 has a profound role in the maintenance of T cells at homeostasis in the periphery. Importantly, the manipulation of miR-191 altered immune homeostasis without leading to severe immunodeficiency or autoimmunity. As much data exists on the causative agents disrupting active immune responses and the formation of immunological memory, the basic processes underlying the continued maintenance of a functioning immune system must be fully characterized to facilitate the development of methods for promoting healthy immune function throughout the life of the individual. These findings also have powerful implications for the ability of patients with modest perturbations in T cell homeostasis to effectively fight disease and respond to vaccination and may provide valuable targets for therapeutic intervention.

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs.

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.

Evolution of networks and sequences in eukaryotic cell cycle control.

The molecular networks regulating the G1-S transition in budding yeast and mammals are strikingly similar in network structure. However, many of the individual proteins performing similar network roles appear to have unrelated amino acid sequences, suggesting either extremely rapid sequence evolution, or true polyphyly of proteins carrying out identical network roles. A yeast/mammal comparison suggests that network topology, and its associated dynamic properties, rather than regulatory proteins themselves may be the most important elements conserved through evolution. However, recent deep phylogenetic studies show that fungal and animal lineages are relatively closely related in the opisthokont branch of eukaryotes. The presence in plants of cell cycle regulators such as Rb, E2F and cyclins A and D, that appear lost in yeast, suggests cell cycle control in the last common ancestor of the eukaryotes was implemented with this set of regulatory proteins. Forward genetics in non-opisthokonts, such as plants or their green algal relatives, will provide direct information on cell cycle control in these organisms, and may elucidate the potentially more complex cell cycle control network of the last common eukaryotic ancestor.