Sequencing of TGF-beta pathway genes in familial cases of intracranial aneurysm.

BACKGROUND AND PURPOSE: Familial aggregation of intracranial aneurysms (IA) strongly suggests a genetic contribution to pathogenesis. However, genetic risk factors have yet to be defined. For families affected by aortic aneurysms, specific gene variants have been identified, many affecting the receptors to transforming growth factor-beta (TGF-beta). In recent work, we found that aortic and intracranial aneurysms may share a common genetic basis in some families. We hypothesized, therefore, that mutations in TGF-beta receptors might also play a role in IA pathogenesis. METHODS: To identify genetic variants in TGF-beta and its receptors, TGFB1, TGFBR1, TGFBR2, ACVR1, TGFBR3, and ENG were directly sequenced in 44 unrelated patients with familial IA. Novel variants were confirmed by restriction digestion analyses, and allele frequencies were analyzed in cases versus individuals without known intracranial disease. Similarly, allele frequencies of a subset of known SNPs in each gene were also analyzed for association with IA. RESULTS: No mutations were found in TGFB1, TGFBR1, TGFBR2, or ACVR1. Novel variants identified in ENG (p.A60E) and TGFBR3 (p.W112R) were not detected in at least 892 reference chromosomes. ENG p.A60E showed significant association with familial IA in case-control studies (P=0.0080). No association with IA could be found for any of the known polymorphisms tested. CONCLUSIONS: Mutations in TGF-beta receptor genes are not a major cause of IA. However, we identified rare variants in ENG and TGFBR3 that may be important for IA pathogenesis in a subset of families.

Integrins in human T cell function: Transdominant regulation, conformational constraints, and intracellular effectors

Integrin adhesion molecules have both positive and negative potential in the regulation of peripheral blood T cell (PB T cell) activation, yet their mechanism of action in the mediation of human T lymphocyte function remains largely undefined. The goals of this study then were to elucidate integrin signaling mechanisms in PB T cells.^ By ligating $\beta$1 integrins with mAb 18D3, it was demonstrated that costimulation of PB T cell proliferation induced by coimmobilizing antibodies specific for $\beta$1, $\beta$2, and $\beta$7 integrin subfamilies in conjunction with the anti-CD3 mAb OKT3 was inhibited. Costimulation of T cell proliferation induced by non-integrins CD4, CD26, CD28, CD44, CD45RA, or CD45RO was unaffected. Inhibition of costimulation correlated with diminished IL-2 production. In his manner, $\beta$1 integrins could regulate heterologous integrins of the $\beta$2 and $\beta$7 subfamilies in a transdominant fashion. It was also demonstrated that integrin costimulation of T cell activation was acutely sensitive to the structural conformation of $\beta$1 integrins. Using the cyclic hexapeptide CWLDVC (TBC772, which is based on the $\alpha4\beta1$ integrin binding site in fibronectin) in soluble form, it was shown that integrins locked into a conformation displaying a neo-epitope called the ligand induced binding site (LIBS) recognized by mAb 15/7 were inhibited from sending mitogenic signals to T cells. When BSA-conjugated TBC772 was coimmobilized with anti-CD3 mAb OKT3, costimulation of proliferation occurred. This suggested that temporally uncoupling integrin receptor occupancy from receptor crosslinking inhibited $\beta$1 integrin signaling mechanisms. When subsets of PB T cells were examined to determine those initially activated by integrins within 6 hours of activation, costimulation induced intracellular accumulation of IL-2 predominantly in the CD4$\sp+$ and CD45RO$\sp+$ T cell subsets. This was similar to a number of PB T cell costimulatory molecules including CD26, CD43, CD44. Only CD28 costimulated IL-2 production from both CD45RA$\sp+$ and CD45RO$\sp+$ subpopulations.^ The GTPase Rho has been implicated in regulating integrin mediated stress fiber formation and anchorage dependent growth in fibroblasts, so studies were initiated to determine if Rho played a role in integrin dependent T cell function. In order to perform this, a technique based on scrape-loading was developed to incorporate macromolecules into PB T cells that maintained their functional activity. With this technique, C3 exoenzyme from Clostridium botulinum was incorporated into PB T cells. C3 ADP-ribosylates Rho proteins on Asn$\sp{41},$ which is in close proximity to the Rho effector domain, rendering it inactive. It was demonstrated that functional Rho is not required for basal or upregulated PB T cell adhesion to $\beta$1 integrin substrates, however PB T cell homotypic aggregation induced by PMA, which is an event mediated predominantly by the integrin $\rm\alpha L\beta2,$ was delayed. PB T cells lacking Rho function displayed altered cell morphology on $\beta$1 integrin ligands, producing stellate, dendritic-like pseudopodia. Rho activity was also found to be required for integrin dependent costimulation of proliferation. When intracellular accumulation of IL-2 was measured, inactivation of Rho prevented both integrin and CD28 costimulatory activity. Rho was identified to lie upstream of signals mediating PKC activation and Ca$\sp{++}$ fluxes, as PMA and ionomycin activation of PB T cells was unaffected by the inactivation of Rho. ^

From gene to protein: Investigating the disease etiology of retinitis pigmentosa

Retinitis pigmentosa (RP) is a name given to a group of inherited retinal dystrophies that lead to progressive photoreceptor degeneration, and thus, visual impairment. It is evident at both the clinical and the molecular level that these are heterogeneous disorders, with wide variation in severity, mode of inheritance, and phenotype. The genetics of RP are not simple; the disease can be inherited in dominant, recessive, X-linked, and digenic modes. Autosomal dominant RP (adRP) results from mutations in at least ten mapped loci, but there may be dozens of genetic loci where mutations can cause RP. To date, there are over a hundred genes known to cause retinal degenerative diseases, and less than half of these have been cloned (RetNet). Among the dozens of retinitis pigmentosa loci known to exist, only a few have been identified and the remainders are inferred from linkage studies. Today, the genes for seven of the twelve-adRP loci have been identified, and these are rhodopsin, peripherin/RDS, NRL, ROM1, CRX, RP13 and RP1. My research projects involved a combination of the continued search for genes involved in retinal dystrophies, as well the investigation into the role of peripherin/RDS and RP1 in the disease etiology of autosomal dominant RP. ^ Most of the mutations leading to inherited retinal disorders have been identified in predominately retina expressed genes like rhodopsin, peripherin/RDS, and RP1. Expressed sequence tags (ESTs) that were retina-specific were culled from sequence databases and, together with laboratory analysis, were analyzed as potential candidate genes for retinal dystrophies. Thirteen of the fifty-five identified retina-specific ESTs mapped to within candidate regions for inherited retinopathies. One of these is RP1L1, a homologue of RP1 and a potential cause of adRP. ^ Once a disease-associated gene has been identified, elucidating the role of that gene in the visual process is essential for understanding what happens when the process is defective as it is in adRP. My next projects involved investigating the role of a novel 5′ donor +3 splice site mutation on the mRNA of peripherin/RDS in adRP affected individuals, and comparative sequencing in RP1 to define conserved regions of the protein. Comparative sequencing is a powerful way to delineate critical regions of a sequence because different regions of a gene have different functions, and each region is subject to different levels of functional or structural constraints. Establishing a framework of conserved domains is beneficial not only for structural or functional studies, but can also aid in determining the potential effects of mutations. With the completion of sequencing of human genome, and other organisms such as Saccharomyces cerevisiae, Caenorhabditis elegans , and Drosophila, the facility of comparative sequencing will only increase in the future. Comparative sequencing has already become an established procedure for pinpointing conserved regions of a protein, and is an efficient way to target regions of a protein for experimental and/or evolutionary analysis. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

A MOLECULAR APPROACH FOR DETECTING GENETIC DAMAGE USING THE ECO R1 FAMILY OF HUMAN DNA (SEQUENCING)

A clone of the primary Eco R1 family of human DNA sequences has been used as an indicator sequence for detecting alterations induced by a toxic agent. Specific clones of this family have been examined and compared to the consensus sequence to determine the normal variability of this family. Though variations were observed, data indicated that such clones can be used to study induced DNA modifications. This DNA was exposed to the toxic agent dimethyl sulfate under various conditions and a distinct pattern of aberrations was shown to occur. It is suggested that this approach be used to characterize patterns of damage induced by various agents in the ultimate development of a system capable of monitoring human genotoxic exposure. ^

DEVELOPMENT OF NOVEL METHODS TO MINIMIZE THE IMPACT OF SEQUENCING ERRORS IN THE NEXT-GENERATION SEQUENCING DATA ANALYSIS

Next-generation sequencing (NGS) technology has become a prominent tool in biological and biomedical research. However, NGS data analysis, such as de novo assembly, mapping and variants detection is far from maturity, and the high sequencing error-rate is one of the major problems. . To minimize the impact of sequencing errors, we developed a highly robust and efficient method, MTM, to correct the errors in NGS reads. We demonstrated the effectiveness of MTM on both single-cell data with highly non-uniform coverage and normal data with uniformly high coverage, reflecting that MTM’s performance does not rely on the coverage of the sequencing reads. MTM was also compared with Hammer and Quake, the best methods for correcting non-uniform and uniform data respectively. For non-uniform data, MTM outperformed both Hammer and Quake. For uniform data, MTM showed better performance than Quake and comparable results to Hammer. By making better error correction with MTM, the quality of downstream analysis, such as mapping and SNP detection, was improved. SNP calling is a major application of NGS technologies. However, the existence of sequencing errors complicates this process, especially for the low coverage (

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Identification, purification, sequencing and characterization of human lung fibroblast motility -stimulating factor for human soft tissue sarcoma cells

Paracrine motogenic factors, including motility cytokines and extracellular matrix molecules secreted by normal cells, can stimulate metastatic cell invasion. For extracellular matrix molecules, both the intact molecules and the degradative products may exhibit these activities, which in some cases are not shared by the intact molecules. We found that human peritumoral and lung fibroblasts secrete motility-stimulating activity for several recently established human sarcoma cell strains. The motility of lung metastasis-derived human SYN-1 sarcoma cells was preferentially stimulated by human lung and peritumoral fibroblast motility-stimulating factors (FMSFs). FMSFs were nondialyzable, susceptible to trypsin, and sensitive to dithiothreitol. Cycloheximide inhibited accumulation of FMSF activity in conditioned medium; however, addition of cycloheximide to the migration assay did not significantly affect motility-stimulating activity. Purified hepatocyte growth factor/scatter factor (HGF/SF), rabbit anti-hHGF, and RT-PCR analysis of peritumoral and lung fibroblast HGF/SF mRNA expression indicated that FMSF activity was unrelated to HGF/SF. Partial purification of FMSF by gel exclusion chromatography revealed several peaks of activity, suggesting multiple FMSF molecules or complexes.^ We purified the fibroblast motility-stimulating factor from human lung fibroblast-conditioned medium to apparent homogeneity by sequential heparin affinity chromatography and DEAE anion exchange chromatography. Lysylendopeptidase C digestion of FMSF and sequencing of peptides purified by reverse phase HPLC after digestion identified it as an N-terminal fragment of human fibronectin. Purified FMSF stimulated predominantly chemotaxis but chemokinesis as well of SYN-1 sarcoma cells and was chemotactic for a variety of human sarcoma cells, including fibrosarcoma, leiomyosarcoma, liposarcoma, synovial sarcoma and neurofibrosarcoma cells. The motility-stimulating activity present in HLF-CM was completely eliminated by either neutralization or immunodepletion with a rabbit anti-human-fibronectin antibody, thus further confirming that the fibronectin fragment was the FMSF responsible for the motility stimulation of human soft tissue sarcoma cells. Since human soft tissue sarcomas have a distinctive hematogenous metastatic pattern (predominantly lung), FMSF may play a role in this process. ^