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.

ABERRATIONS OF A PUTATIVE TUMOR SUPPRESSOR GENE SEL1L IN PANCREATIC DUCTAL ADENOCARCINOMA

Introduction: Pancreatic cancer is the fourth leading cause of cancer-related death among males and females in the United States. Sel-1-like (SEL1L) is a putative tumor suppressor gene that is downregulated in a significant proportion of human pancreatic ductal adenocarcinoma (PDAC). It was hypothesized that SEL1L expression could be down-modulated by somatic mutation, loss of heterozygosity (LOH), CpG island hypermethylation and/or aberrantly expressed microRNAs (miRNAs). Material and methods: In 42 PDAC tumors, the SEL1L coding region was amplified using reverse transcription polymerase chain reaction (RT-PCR), and analyzed by agarose gel electrophoresis and sequenced to search for mutations. Using fluorescent fragment analysis, two intragenic microsatellites in the SEL1L gene region were examined to detect LOH in a total of 73 pairs of PDAC tumors and normal-appearing adjacent tissues. Bisulfite DNA sequencing was performed to determine the methylation status of the SEL1L promoter in 41 PDAC tumors and 6 PDAC cell lines. Using real-time quantitative PCR, the expression levels of SEL1L mRNA and 7 aberrantly upregulated miRNAs that potentially target SEL1L were assessed in 42 PDAC tumor and normal pairs. Statistical methods were applied to evaluate the correlation between SEL1L mRNA and the miRNAs. Further the interaction was determined by functional analysis using a molecular biological approach. Results: No mutations were detected in the SEL1L coding region. More than 50% of the samples displayed abnormally alternate or aberrant spliced transcripts of SEL1L. About 14.5% of the tumors displayed LOH at the CAR/CAL microsatellite locus and 10.7% at the RepIN20 microsatellite locus. However, the presence of LOH did not show significant association with SEL1L downregulation. No methylation was observed in the SEL1L promoter. Statistical analysis showed that SEL1L mRNA expression levels significantly and inversely correlated with the expression of hsa-mir-143, hsa-mir-155, and hsa-mir-223. Functional analysis indicated that hsa-mir-155 acted as a suppressor of SEL1L in PL18 and MDAPanc3 PDAC cell lines. Discussion: Evidence from these studies suggested that SEL1L was possibly downregulated by aberrantly upregulated miRNAs in PDAC. Future studies should be directed towards developing a better understanding of the mechanisms for generation of aberrant SEL1L transcripts, and further analysis of miRNAs that may downregulate SEL1L.

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. ^

Using spatial linear models with SAR and CAR structure to examine Texas lung cancer incidence rates

Scholars have found that socioeconomic status was one of the key factors that influenced early-stage lung cancer incidence rates in a variety of regions. This thesis examined the association between median household income and lung cancer incidence rates in Texas counties. A total of 254 individual counties in Texas with corresponding lung cancer incidence rates from 2004 to 2008 and median household incomes in 2006 were collected from the National Cancer Institute Surveillance System. A simple linear model and spatial linear models with two structures, Simultaneous Autoregressive Structure (SAR) and Conditional Autoregressive Structure (CAR), were used to link median household income and lung cancer incidence rates in Texas. The residuals of the spatial linear models were analyzed with Moran's I and Geary's C statistics, and the statistical results were used to detect similar lung cancer incidence rate clusters and disease patterns in Texas.^

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.

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. ^