High-throughput genomics projects have resulted in a rapid accumulation of protein sequences. Therefore, computational methods that can predict protein functions and functional sites efficiently and accurately are in high demand. In addition, prediction methods utilizing only sequence information are of particular interest because for most proteins, 3-dimensional structures are not available. However, there are several key challenges in developing methods for predicting protein function and functional sites. These challenges include the following: the construction of representative datasets to train and evaluate the method, the collection of features related to the protein functions, the selection of the most useful features, and the integration of selected features into suitable computational models. In this proposed study, we tackle these challenges by developing procedures for benchmark dataset construction and protein feature extraction, implementing efficient feature selection strategies, and developing effective machine learning algorithms for protein function and functional site predictions. We investigate these challenges in three bioinformatics tasks: the discovery of transmembrane beta-barrel (TMB) proteins in gram-negative bacterial proteomes, the identification of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs), and the identification of helix-turn-helix (HTH) motifs from protein sequence.

Background: The high incidence of mutations and cytogenetic abnormalities in patients with myelodysplastic syndrome (MDS) suggests the involvement of DNA repair mechanism defects in the pathogenesis of this disorder. The first part of this work was focused on monitoring of gene expression of DNA repair genes in MDS patients and on their alterations during disease progression. In the second part, next generation sequencing was used to detect single nucleotide polymorphisms (SNPs) and mutations in DNA repair genes and their possible association with MDS development was evaluated. Methods: Expression profiling of 84 DNA repair genes was performed on bone marrow CD34+ cells of patients with MDS. Screening cohort consisted of 28 patients and expression of selected genes was further validated on larger cohort of 122 patients with all subtypes of MDS. Paired samples were used for monitoring of RAD51 and XRCC2 gene expression during disease progression. Immunohistochemical staining for RAD51 recombinase protein was done on samples acquired by trephine-biopsy. Targeted enrichment resequencing of exonic parts of 84 DNA repair genes was performed on the screening cohort of MDS patients. Real-time PCR was used for genotyping of selected SNPs in the population study. Results: RAD51 and XRCC2 genes showed...

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Conduct disorder (CD) is a psychiatric syndrome in childhood and adolescence that is one of the most common childhood disorders with continuously increasing prevalence but uncertain pathogenesis. We performed a genome-wide, family-based association study of CD using P2BAT/FBAT software. The data is gathered from Collaborative Study on the Genetics of Alcoholism (COGA) and International Multi-Center ADHD Genetics Project (IMAGE). Using COGA data, we identified 20 markers which showed suggestive associations (p<10-3) with CD. Nine of them are located in known genes. Two genes, ADAM10 and CAMK2A, which had been reported associated with Alzheimer's disease (AD), bipolar disorder, and depression, were of more concern. Using IMAGE sample, our results were well replicated. This study identified several CD associated genetic variants, especially two novel candidate genes. These findings may serve as a resource for replication in other populations to elucidate the potential role of these genetic variants in CD.

Alzheimer's disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and age at onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 single nucleotide polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8×10) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The hazards ratio (HR) with its 95% confidence interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR=1.33, 95%CI=1.13-1.57, p=5.0×10). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR=1.34, 95%CI=1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p=0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p=2.27×10 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD.

Background: The knowledge of factors influencing disease progression in patients with established coronary heart disease (CHD) is still relatively limited. One potential pathway is related to peroxisome proliferator–activated receptor gamma coactivator-1 alpha (PPARGC1A), a transcription factor linked to energy metabolism which may play a role in the heart function. Thus, its associations with subsequent CHD events remain unclear. We aimed to investigate the effect of three different SNPs in the PPARGC1A gene on the risk of subsequent CHD in a population with established CHD. Methods: We employed an individual-level meta-analysis using 23 studies from the GENetIcs of sUbSequent Coronary Heart Disease (GENIUS-CHD) consortium, which included participants (n = 80,900) with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. Three variants in the PPARGC1A gene (rs8192678, G482S; rs7672915, intron 2; and rs3755863, T528T) were tested for their associations with subsequent events during the follow-up using a Cox proportional hazards model adjusted for age and sex. The primary outcome was subsequent CHD death or myocardial infarction (CHD death/myocardial infarction). Stratified analyses of the participant or study characteristics as well as additional analyses for secondary outcomes of specific cardiovascular disease diagnoses and all-cause death were also performed. Results: Meta-analysis revealed no significant association between any of the three variants in the PPARGC1A gene and the primary outcome of CHD death/myocardial infarction among those with established CHD at baseline: rs8192678, hazard ratio (HR): 1.01, 95% confidence interval (CI) 0.98–1.05 and rs7672915, HR: 0.97, 95% CI 0.94–1.00; rs3755863, HR: 1.02, 95% CI 0.99–1.06. Similarly, no significant associations were observed for any of the secondary outcomes. The results from stratified analyses showed null results, except for significant inverse associations between rs7672915 (intron 2) and the primary outcome among 1) individuals aged ≥65, 2) individuals with renal impairment, and 3) antiplatelet users. Conclusion: We found no clear associations between polymorphisms in the PPARGC1A gene and subsequent CHD events in patients with established CHD at baseline.

Two polymorphisms in the promoter region of macrophage migration inhibitory factor (MIF)—rs755622 and rs5844572—exhibit prognostic relevance in inflammatory diseases. The aim of this study was to investigate a correlation between these MIF promoter polymorphisms and the severity of hepatitis C virus (HCV)-induced liver fibrosis. Our analysis included two independent patient cohorts with HCV-induced liver fibrosis (504 and 443 patients, respectively). The genotype of the single nucleotide polymorphism (SNP) -173 G/C and the repeat number of the microsatellite polymorphism -794 CATT5–8 were determined in DNA samples and correlated with fibrosis severity. In the first cohort, homozygous carriers of the C allele in the rs755622 had lower fibrosis stages compared to heterozygous carriers or wild types (1.25 vs. 2.0 vs. 2.0; p = 0.03). Additionally, 7 microsatellite repeats were associated with lower fibrosis stages (<F2) (p = 0.04). Comparable tendencies were observed in the second independent cohort, where fibrosis was assessed using transient elastography. However, once cirrhosis had been established, the C/C genotype and higher microsatellite repeats correlated with impaired liver function and a higher prevalence of hepatocellular carcinoma. Our study demonstrates that specific MIF polymorphisms are associated with disease severity and complications of HCV-induced fibrosis in a stage- and context-dependent manner.

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The utility of hypervariable DNA polymorphisms as a general population genetics method was studied in three fish species by the use of multilocus DNA fingerprinting. Laboratory lines and field caught specimens from Belize and Florida of the clonal species <i>Rivulus marmoratus</i> were examined to determine the relative contributions of mutation and migration to genetic variation in the species. Specimens of <i>Poecilia latipinna</i>, the sailfin molly, from Florida and Georgia were used to explore the properties of hypervariable markers in the context of an outbred and abundant species that exhibits typical levels of genetic variability at nuclear loci. The results were compared to those of a previous allozyme survey of the same populations. Samples of <i>Morone saxatilis</i> from the Chesapeake Bay system were used to investigate the utility of hypervariable markers in the description of genetic variation of an outbred species depauperate in other measures of genetic variation. The results of this study indicate that variation observed among among <i>R. marmoratus</i> clones characterized by hypervariable loci may be the result of natural selection; based on the analyses of mutation rates and population structure. Results from the work with <i>P.latipinna</i> showed that hypervariable loci could have general utility as a method for studying population structure. This utility was demonstrated in the examination of Chesapeake Bay populations of <i>M. saxatilis</i>. Large degrees of interindividual variation at hypervariable loci permitted the characterization of population structure within Chesapeake Bay populations of this species. / Ph. D.