BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using existing analytic methodologies. Obstacles to analysis include inference in the face of multiple comparisons, complications arising from correlations among the SNPs (single nucleotide polymorphisms), choice of their genetic parametrization and missing data. In this paper we present an efficient Bayesian model search strategy that searches over the space of genetic markers and their genetic parametrization. The resulting method for Multilevel Inference of SNP Associations, MISA, allows computation of multilevel posterior probabilities and Bayes factors at the global, gene and SNP level, with the prior distribution on SNP inclusion in the model providing an intrinsic multiplicity correction. We use simulated data sets to characterize MISA's statistical power, and show that MISA has higher power to detect association than standard procedures. Using data from the North Carolina Ovarian Cancer Study (NCOCS), MISA identifies variants that were not identified by standard methods and have been externally "validated" in independent studies. We examine sensitivity of the NCOCS results to prior choice and method for imputing missing data. MISA is available in an R package on CRAN.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

Attenuation of inflammatory events in human intervertebral disc cells with a tumor necrosis factor antagonist.

STUDY DESIGN: The inflammatory responses of primary human intervertebral disc (IVD) cells to tumor necrosis factor α (TNF-α) and an antagonist were evaluated in vitro. OBJECTIVE: To investigate an ability for soluble TNF receptor type II (sTNFRII) to antagonize TNF-α-induced inflammatory events in primary human IVD cells in vitro. SUMMARY OF BACKGROUND DATA: TNF-α is a known mediator of inflammation and pain associated with radiculopathy and IVD degeneration. sTNFRs and their analogues are of interest for the clinical treatment of these IVD pathologies, although information on the effects of sTNFR on human IVD cells remains unknown. METHODS: IVD cells were isolated from surgical tissues procured from 15 patients and cultured with or without 1.4 nmol/L TNF-α (25 ng/mL). Treatment groups were coincubated with varying doses of sTNFRII (12.5-100 nmol/L). Nitric oxide (NO), prostaglandin E₂ (PGE₂), and interleukin-6 (IL6) levels in media were quantified to characterize the inflammatory phenotype of the IVD cells. RESULTS: Across all patients, TNF-α induced large, statistically significant increases in NO, PGE₂, and IL6 secretion from IVD cells compared with controls (60-, 112-, and 4-fold increases, respectively; P < 0.0001). Coincubation of TNF-α with nanomolar doses of sTNFRII significantly attenuated the secretion of NO and PGE₂ in a dose-dependent manner, whereas IL6 levels were unchanged. Mean IC₅₀ values for NO and PGE₂ were found to be 35.1 and 20.5 nmol/L, respectively. CONCLUSION: Nanomolar concentrations of sTNFRII were able to significantly attenuate the effects of TNF-α on primary human IVD cells in vitro. These results suggest this sTNFR to be a potent TNF antagonist with potential to attenuate inflammation in IVD pathology.

Growth factor erv1-like modulates Drp1 to preserve mitochondrial dynamics and function in mouse embryonic stem cells.

The relationship of mitochondrial dynamics and function to pluripotency are rather poorly understood aspects of stem cell biology. Here we show that growth factor erv1-like (Gfer) is involved in preserving mouse embryonic stem cell (ESC) mitochondrial morphology and function. Knockdown (KD) of Gfer in ESCs leads to decreased pluripotency marker expression, embryoid body (EB) formation, cell survival, and loss of mitochondrial function. Mitochondria in Gfer-KD ESCs undergo excessive fragmentation and mitophagy, whereas those in ESCs overexpressing Gfer appear elongated. Levels of the mitochondrial fission GTPase dynamin-related protein 1 (Drp1) are highly elevated in Gfer-KD ESCs and decreased in Gfer-overexpressing cells. Treatment with a specific inhibitor of Drp1 rescues mitochondrial function and apoptosis, whereas expression of Drp1-dominant negative resulted in the restoration of pluripotency marker expression in Gfer-KD ESCs. Altogether, our data reveal a novel prosurvival role for Gfer in maintaining mitochondrial fission-fusion dynamics in pluripotent ESCs.

MYC activity mitigates response to rapamycin in prostate cancer through eukaryotic initiation factor 4E-binding protein 1-mediated inhibition of autophagy.

Loss of PTEN and activation of phosphoinositide 3-kinase are commonly observed in advanced prostate cancer. Inhibition of mammalian target of rapamycin (mTOR), a downstream target of phosphoinositide 3-kinase signaling, results in cell cycle arrest and apoptosis in multiple in vitro and in vivo models of prostate cancer. However, single-agent use of mTOR inhibition has limited clinical success, and the identification of molecular events mitigating tumor response to mTOR inhibition remains a critical question. Here, using genetically engineered human prostate epithelial cells (PrEC), we show that MYC, a frequent target of genetic gain in prostate cancers, abrogates sensitivity to rapamycin by decreasing rapamycin-induced cytostasis and autophagy. Analysis of MYC and the mTOR pathway in human prostate tumors and PrEC showed selective increased expression of eukaryotic initiation factor 4E-binding protein 1 (4EBP1) with gain in MYC copy number or forced MYC expression, respectively. We have also found that MYC binds to regulatory regions of the 4EBP1 gene. Suppression of 4EBP1 expression resulted in resensitization of MYC-expressing PrEC to rapamycin and increased autophagy. Taken together, our findings suggest that MYC expression abrogates sensitivity to rapamycin through increased expression of 4EBP1 and reduced autophagy.

Co-regulation of nuclear respiratory factor-1 by NFkappaB and CREB links LPS-induced inflammation to mitochondrial biogenesis.

The nuclear respiratory factor-1 (NRF1) gene is activated by lipopolysaccharide (LPS), which might reflect TLR4-mediated mitigation of cellular inflammatory damage via initiation of mitochondrial biogenesis. To test this hypothesis, we examined NRF1 promoter regulation by NFκB, and identified interspecies-conserved κB-responsive promoter and intronic elements in the NRF1 locus. In mice, activation of Nrf1 and its downstream target, Tfam, by Escherichia coli was contingent on NFκB, and in LPS-treated hepatocytes, NFκB served as an NRF1 enhancer element in conjunction with NFκB promoter binding. Unexpectedly, optimal NRF1 promoter activity after LPS also required binding by the energy-state-dependent transcription factor CREB. EMSA and ChIP assays confirmed p65 and CREB binding to the NRF1 promoter and p65 binding to intron 1. Functionality for both transcription factors was validated by gene-knockdown studies. LPS regulation of NRF1 led to mtDNA-encoded gene expression and expansion of mtDNA copy number. In cells expressing plasmid constructs containing the NRF-1 promoter and GFP, LPS-dependent reporter activity was abolished by cis-acting κB-element mutations, and nuclear accumulation of NFκB and CREB demonstrated dependence on mitochondrial H(2)O(2). These findings indicate that TLR4-dependent NFκB and CREB activation co-regulate the NRF1 promoter with NFκB intronic enhancement and redox-regulated nuclear translocation, leading to downstream target-gene expression, and identify NRF-1 as an early-phase component of the host antibacterial defenses.

Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains. © Institute of Mathematical Statistics, 2010.

Modulation of heat shock transcription factor 1 as a therapeutic target for small molecule intervention in neurodegenerative disease.

Neurodegenerative diseases such as Huntington disease are devastating disorders with no therapeutic approaches to ameliorate the underlying protein misfolding defect inherent to poly-glutamine (polyQ) proteins. Given the mounting evidence that elevated levels of protein chaperones suppress polyQ protein misfolding, the master regulator of protein chaperone gene transcription, HSF1, is an attractive target for small molecule intervention. We describe a humanized yeast-based high-throughput screen to identify small molecule activators of human HSF1. This screen is insensitive to previously characterized activators of the heat shock response that have undesirable proteotoxic activity or that inhibit Hsp90, the central chaperone for cellular signaling and proliferation. A molecule identified in this screen, HSF1A, is structurally distinct from other characterized small molecule human HSF1 activators, activates HSF1 in mammalian and fly cells, elevates protein chaperone expression, ameliorates protein misfolding and cell death in polyQ-expressing neuronal precursor cells and protects against cytotoxicity in a fly model of polyQ-mediated neurodegeneration. In addition, we show that HSF1A interacts with components of the TRiC/CCT complex, suggesting a potentially novel regulatory role for this complex in modulating HSF1 activity. These studies describe a novel approach for the identification of new classes of pharmacological interventions for protein misfolding that underlies devastating neurodegenerative disease.

Latent factor analysis to discover pathway-associated putative segmental aneuploidies in human cancers.

Tumor microenvironmental stresses, such as hypoxia and lactic acidosis, play important roles in tumor progression. Although gene signatures reflecting the influence of these stresses are powerful approaches to link expression with phenotypes, they do not fully reflect the complexity of human cancers. Here, we describe the use of latent factor models to further dissect the stress gene signatures in a breast cancer expression dataset. The genes in these latent factors are coordinately expressed in tumors and depict distinct, interacting components of the biological processes. The genes in several latent factors are highly enriched in chromosomal locations. When these factors are analyzed in independent datasets with gene expression and array CGH data, the expression values of these factors are highly correlated with copy number alterations (CNAs) of the corresponding BAC clones in both the cell lines and tumors. Therefore, variation in the expression of these pathway-associated factors is at least partially caused by variation in gene dosage and CNAs among breast cancers. We have also found the expression of two latent factors without any chromosomal enrichment is highly associated with 12q CNA, likely an instance of "trans"-variations in which CNA leads to the variations in gene expression outside of the CNA region. In addition, we have found that factor 26 (1q CNA) is negatively correlated with HIF-1alpha protein and hypoxia pathways in breast tumors and cell lines. This agrees with, and for the first time links, known good prognosis associated with both a low hypoxia signature and the presence of CNA in this region. Taken together, these results suggest the possibility that tumor segmental aneuploidy makes significant contributions to variation in the lactic acidosis/hypoxia gene signatures in human cancers and demonstrate that latent factor analysis is a powerful means to uncover such a linkage.

Prolactin receptor signaling is essential for perinatal brown adipocyte function: a role for insulin-like growth factor-2.

BACKGROUND: The lactogenic hormones prolactin (PRL) and placental lactogens (PL) play central roles in reproduction and mammary development. Their actions are mediated via binding to PRL receptor (PRLR), highly expressed in brown adipose tissue (BAT), yet their impact on adipocyte function and metabolism remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: PRLR knockout (KO) newborn mice were phenotypically characterized in terms of thermoregulation and their BAT differentiation assayed for gene expression studies. Derived brown preadipocyte cell lines were established to evaluate the molecular mechanisms involved in PRL signaling on BAT function. Here, we report that newborn mice lacking PRLR have hypotrophic BAT depots that express low levels of adipocyte nuclear receptor PPARgamma2, its coactivator PGC-1alpha, uncoupling protein 1 (UCP1) and the beta3 adrenoceptor, reducing mouse viability during cold challenge. Immortalized PRLR KO preadipocytes fail to undergo differentiation into mature adipocytes, a defect reversed by reintroduction of PRLR. That the effects of the lactogens in BAT are at least partly mediated by Insulin-like Growth Factor-2 (IGF-2) is supported by: i) a striking reduction in BAT IGF-2 expression in PRLR KO mice and in PRLR-deficient preadipocytes; ii) induction of cellular IGF-2 expression by PRL through JAK2/STAT5 pathway activation; and iii) reversal of defective differentiation in PRLR KO cells by exogenous IGF-2. CONCLUSIONS: Our findings demonstrate that the lactogens act in concert with IGF-2 to control brown adipocyte differentiation and growth. Given the prominent role of brown adipose tissue during the perinatal period, our results identified prolactin receptor signaling as a major player and a potential therapeutic target in protecting newborn mammals against hypothermia.

Bacterial vaginosis as a risk factor for high-grade cervical lesions and cancer in HIV-seropositive women.

OBJECTIVE: To assess the effect of bacterial vaginosis (BV) on the risk of high-grade squamous intraepithelial lesions (HSIL) among HIV-seropositive women. METHODS: A hospital-based prospective cohort study of HIV-seropositive women was conducted in Johannesburg, South Africa from January 2005 to September 2009. Multivariate log-binomial and Poisson regressions were used to estimate prevalence and rate ratios, respectively. RESULTS: Among 1954 HIV-seropositive women, the baseline prevalence of HSIL was 17%. BV prevalence was high (54%) and showed no association with prevalence of HSIL (adjusted prevalence ratio, 1.12; 95% confidence intervals (CI), 0.92-1.35) nor with cervical lesion progression at follow-up visit (n=503) (adjusted rate ratio: 1.00; 95% CI, 0.65-1.53). CONCLUSION: Among HIV-seropositive women, BV was not associated with an increased risk of HSIL or cervical lesion progression.

A C-terminal motif found in the beta2-adrenergic receptor, P2Y1 receptor and cystic fibrosis transmembrane conductance regulator determines binding to the Na+/H+ exchanger regulatory factor family of PDZ proteins.

The Na+/H+ exchanger regulatory factor (NHERF) binds to the tail of the beta2-adrenergic receptor and plays a role in adrenergic regulation of Na+/H+ exchange. NHERF contains two PDZ domains, the first of which is required for its interaction with the beta2 receptor. Mutagenesis studies of the beta2 receptor tail revealed that the optimal C-terminal motif for binding to the first PDZ domain of NHERF is D-S/T-x-L, a motif distinct from those recognized by other PDZ domains. The first PDZ domain of NHERF-2, a protein that is 52% identical to NHERF and also known as E3KARP, SIP-1, and TKA-1, exhibits binding preferences very similar to those of the first PDZ domain of NHERF. The delineation of the preferred binding motif for the first PDZ domain of the NHERF family of proteins allows for predictions for other proteins that may interact with NHERF or NHERF-2. For example, as would be predicted from the beta2 receptor tail mutagenesis studies, NHERF binds to the tail of the purinergic P2Y1 receptor, a seven-transmembrane receptor with an intracellular C-terminal tail ending in D-T-S-L. NHERF also binds to the tail of the cystic fibrosis transmembrane conductance regulator, which ends in D-T-R-L. Because the preferred binding motif of the first PDZ domain of the NHERF family of proteins is found at the C termini of a variety of intracellular proteins, NHERF and NHERF-2 may be multifunctional adaptor proteins involved in many previously unsuspected aspects of intracellular signaling.

beta2-Adrenergic receptor regulation by GIT1, a G protein-coupled receptor kinase-associated ADP ribosylation factor GTPase-activating protein.

G protein-coupled receptor activation leads to the membrane recruitment and activation of G protein-coupled receptor kinases, which phosphorylate receptors and lead to their inactivation. We have identified a novel G protein-coupled receptor kinase-interacting protein, GIT1, that is a GTPase-activating protein (GAP) for the ADP ribosylation factor (ARF) family of small GTP-binding proteins. Overexpression of GIT1 leads to reduced beta2-adrenergic receptor signaling and increased receptor phosphorylation, which result from reduced receptor internalization and resensitization. These cellular effects of GIT1 require its intact ARF GAP activity and do not reflect regulation of GRK kinase activity. These results suggest an essential role for ARF proteins in regulating beta2-adrenergic receptor endocytosis. Moreover, they provide a mechanism for integration of receptor activation and endocytosis through regulation of ARF protein activation by GRK-mediated recruitment of the GIT1 ARF GAP to the plasma membrane.