Infer Metagenomic Abundance and Reveal Homologous Genomes Based on the Structure of Taxonomy Tree.

Metagenomic research uses sequencing technologies to investigate the genetic biodiversity of microbiomes presented in various ecosystems or animal tissues. The composition of a microbial community is highly associated with the environment in which the organisms exist. As large amount of sequencing short reads of microorganism genomes obtained, accurately estimating the abundance of microorganisms within a metagenomic sample is becoming an increasing challenge in bioinformatics. In this paper, we describe a hierarchical taxonomy tree-based mixture model (HTTMM) for estimating the abundance of taxon within a microbial community by incorporating the structure of the taxonomy tree. In this model, genome-specific short reads and homologous short reads among genomes can be distinguished and represented by leaf and intermediate nodes in the taxonomy tree, respectively. We adopt an expectation-maximization algorithm to solve this model. Using simulated and real-world data, we demonstrate that the proposed method is superior to both flat mixture model and lowest common ancestry-based methods. Moreover, this model can reveal previously unaddressed homologous genomes.

Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs.

BACKGROUND: Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed. RESULTS: In this paper, we constructed an integrated M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework. CONCLUSIONS: A protein interaction map of M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.

Genetic adaptation of the hypoxia-inducible factor pathway to oxygen pressure among eurasian human populations.

Research into the mechanisms of human adaptation to the hypoxic environment of high altitude is of great interest to the fields of human physiology and clinical medicine. Recently, the gene EGLN1, from the hypoxia-inducible factor (HIF) pathway, was identified as being involved in the hypoxic adaptation of highland Andeans and Tibetans. Both highland Andeans and Tibetans have adapted to an extremely hypoxic habitat and less attention has been paid to populations living in normoxic conditions at sea level and mild-hypoxic environments of moderate altitude, thus, whether a common adaptive mechanism exists in response to quantitative variations of environmental oxygen pressure over a wide range of residing altitudes is unknown. Here, we first performed a genome-wide association study of 35 populations from the Human Genome Diversity-CEPH Panel who dwell at sea level to moderate altitude in Eurasia (N = 691; 0-2,500 m) to identify the genetic adaptation profile of normoxic and mild-hypoxic inhabitants. In addition, we systematically compared the results from the present study to six previously published genome-wide scans of highland Andeans and Tibetans to identify shared adaptive signals in response to quantitative variations of oxygen pressure. For normoxic and mild-hypoxic populations, the strongest adaptive signal came from the mu opioid receptor-encoding gene (OPRM1, 2.54 × 10(-9)), which has been implicated in the stimulation of respiration, while in the systematic survey the EGLN1-DISC1 locus was identified in all studies. A replication study performed with highland Tibetans (N = 733) and sea level Han Chinese (N = 748) confirmed the association between altitude and SNP allele frequencies in OPRM1 (in Tibetans only, P < 0.01) and in EGLN1-DISC1 (in Tibetans and Han Chinese, P < 0.01). Taken together, identification of the OPRM1 gene suggests that cardiopulmonary adaptation mechanisms are important and should be a focus in future studies of hypoxia adaptation. Furthermore, the identification of the EGLN1 gene from the HIF pathway suggests a common adaptive mechanism for Eurasian human populations residing at different altitudes with different oxygen pressures.

An HIV-1 resistance polymorphism in TRIM5α gene among Chinese intravenous drug users.

BACKGROUND: TRIM5α has species-specific restriction activity against replication of many retroviruses, including HIV-1. Though human also express TRIM5α protein, it is less potent in suppressing infection of HIV-1 than most orthologs of other nonhuman primates. Previous association studies suggested that polymorphisms in TRIM5α gene might protect against HIV-1 infection. However, the exact variation accounting for this protective effect was not certain. METHODS: One thousand two hundred ninety-four Chinese intravenous drug users (IDUs), including 1011 Hans and 283 Dai subjects, were investigated for sequence variations in TRIM5α and association with HIV-1 resistance. Resequencing of the putative functional domains in exon2 and exon8 was carried out in 1151 subjects, along with exon2 resequencing in a further 143 HIV-1-infected IDUs. RESULTS: We identified 14 different nucleotide variants, including 4 with minor allele frequency >0.05. We observed that the frequency of 43Y homozygote in seronegative IDUs was significantly higher than that in the HIV-1-infected IDUs, suggesting a protective effect among the homozygote subjects [odds ratio (95% confidence interval) = 0.46 (0.22 to 0.94), P = 0.033, Mantel-Haenszel test]. CONCLUSIONS: we concluded that H43Y might account for the HIV-1 resistance due to TRIM5α gene in Chinese IDUs.

Detecting disease associated modules and prioritizing active genes based on high throughput data.

BACKGROUND: The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology. RESULTS: In this paper, we propose a novel method to detect disease related gene modules or dysfunctional pathways based on global characteristics of interactome coupled with gene expression data. Specifically, we exploit interacting relationships between genes to define a gene's active score function based on the kernel trick, which can represent nonlinear effects of gene cooperativity. Then, modules or pathways are inferred based on the active scores evaluated by the support vector regression in a global and integrative manner. The efficiency and robustness of the proposed method are comprehensively validated by using both simulated and real data with the comparison to existing methods. CONCLUSIONS: By applying the proposed method to two cancer related problems, i.e. breast cancer and prostate cancer, we successfully identified active modules or dysfunctional pathways related to these two types of cancers with literature confirmed evidences. We show that this network-based method is highly efficient and can be applied to a large-scale problem especially for human disease related modules or pathway extraction. Moreover, this method can also be used for prioritizing genes associated with a specific phenotype or disease.