Binhua Liang

Nationwide Molecular Surveillance of Pandemic H1N1 Influenza A Virus Genomes: Canada, 2009

Background In April 2009, a novel triple-reassortant swine influenza A H1N1 virus (“A/H1N1pdm”; also known as SOIV) was detected and spread globally as the first influenza pandemic of the 21st century. Sequencing has since been conducted at an unprecedented rate globally in order to monitor the diversification of this emergent virus and to track mutations that may affect virus behavior. Methodology/Principal Findings By Sanger sequencing, we determined consensus whole-genome sequences for A/H1N1pdm viruses sampled nationwide in Canada over 33 weeks during the 2009 first and second pandemic waves. A total of 235 virus genomes sampled from unique subjects were analyzed, providing insight into the temporal and spatial trajectory of A/H1N1pdm lineages within Canada. Three clades (2, 3, and 7) were identifiable within the first two weeks of A/H1N1pdm appearance, with clades 5 and 6 appearing thereafter; further diversification was not apparent. Only two viral sites displayed evidence of adaptive evolution, located in hemagglutinin (HA) corresponding to D222 in the HA receptor-binding site, and to E374 at HA2-subunit position 47. Among the Canadian sampled viruses, we observed notable genetic diversity (1.47×10−3 amino acid substitutions per site) in the gene encoding PB1, particularly within the viral genomic RNA (vRNA)-binding domain (residues 493–757). This genome data set supports the conclusion that A/H1N1pdm is evolving but not excessively relative to other H1N1 influenza A viruses. Entropy analysis was used to investigate whether any mutated A/H1N1pdm protein residues were associated with infection severity; however no virus genotypes were observed to trend with infection severity. One virus that harboured heterozygote coding mutations, including PB2 D567D/G, was attributed to a severe and potentially mixed infection; yet the functional significance of this PB2 mutation remains unknown. Conclusions/Significance These findings contribute to enhanced understanding of Influenza A/H1N1pdm viral dynamics.

A Comparison Of Parallel Pyrosequencing And Sanger Clone-based Sequencing And Its Impact On The Characterization Of The Genetic Diversity Of Hiv-1.

Background Pyrosequencing technology has the potential to rapidly sequence HIV-1 viral quasispecies without requiring the traditional approach of cloning. In this study, we investigated the utility of ultra-deep pyrosequencing to characterize genetic diversity of the HIV-1 gag quasispecies and assessed the possible contribution of pyrosequencing technology in studying HIV-1 biology and evolution. Methodology/Principal Findings HIV-1 gag gene was amplified from 96 patients using nested PCR. The PCR products were cloned and sequenced using capillary based Sanger fluorescent dideoxy termination sequencing. The same PCR products were also directly sequenced using the 454 pyrosequencing technology. The two sequencing methods were evaluated for their ability to characterize quasispecies variation, and to reveal sites under host immune pressure for their putative functional significance. A total of 14,034 variations were identified by 454 pyrosequencing versus 3,632 variations by Sanger clone-based (SCB) sequencing. 11,050 of these variations were detected only by pyrosequencing. These undetected variations were located in the HIV-1 Gag region which is known to contain putative cytotoxic T lymphocyte (CTL) and neutralizing antibody epitopes, and sites related to virus assembly and packaging. Analysis of the positively selected sites derived by the two sequencing methods identified several differences. All of them were located within the CTL epitope regions. Conclusions/Significance Ultra-deep pyrosequencing has proven to be a powerful tool for characterization of HIV-1 genetic diversity with enhanced sensitivity, efficiency, and accuracy. It also improved reliability of downstream evolutionary and functional analysis of HIV-1 quasispecies.

Identification of breast cancer patients based on human signaling network motifs

Identifying breast cancer patients is crucial to the clinical diagnosis and therapy for this disease. Conventional gene-based methods for breast cancer diagnosis ignore gene-gene interactions and thus may lead to loss of power. In this study, we proposed a novel method to select classification features, called “Selection of Significant Expression-Correlation Differential Motifs” (SSECDM). This method applied a network motif-based approach, combining a human signaling network and high-throughput gene expression data to distinguish breast cancer samples from normal samples. Our method has higher classification performance and better classification accuracy stability than the mutual information (MI) method or the individual gene sets method. It may become a useful tool for identifying and treating patients with breast cancer and other cancers, thus contributing to clinical diagnosis and therapy for these diseases.

The frequencies of naturally occurring protease inhibitor resistance mutations in HIV proviral sequences of drug naïve sex workers in Nairobi, Kenya and their correlation with host immune response driven positively selected mutations in HIV-1

Background Sub Saharan Africa accounts for 69% of the people living with HIV globally. Antiretroviral therapy (ART) has saved 9 million life years in Sub Saharan Africa. However, drug resistance mutations reduce the effectiveness of ART, and need to be monitored for effective ART. Naturally occurring primary antiretroviral drug resistance mutations have not been well analyzed in ART naive HIV+ patients from Kenya.

Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on “guilt by association” analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on “guilt by association” analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.

Pyrosequencing dried blood spots reveals differences in HIV drug resistance between treatment naïve and experienced patients.

Dried blood spots (DBS) are an alternative specimen collection format for HIV-1 genotyping. DBS produce HIV genotyping results that are robust and equivalent to plasma when using conventional sequencing methods. However, using tagged, pooled pyrosequencing, we demonstrate that concordance between plasma and DBS is not absolute and varies according to viral load (VL), duration of HIV infection and antiretroviral therapy (ART) status. The plasma/DBS concordance is the highest when VL is ≥5,000 copies/ml and/or the patient has no ART exposure and/or when the duration of HIV infection is ≤2 years. Stepwise regression analysis revealed that VL is most important independent predictor for concordance of DBS with plasma genotypes. This is the first study to use next generation sequencing to identify discordance between DBS and plasma genotypes. Consideration should be given to VL, duration of infection, and ART exposure when interpreting DBS genotypes produced using next generation sequencing. These findings are of particular significance when DBS are to be used for clinical monitoring purposes.

Systematic analysis of host immunological pressure on the envelope gene of human immunodeficiency virus type 1 by an immunobioinformatics approach.

As the number of HIV-1 sequences has increased in the public database and new tools of immunological bioinformatics have become available, making it possible to better understand at a population level how host immune response drives the evolution of HIV-1 envelope (Env). We analyzed 1100 unique full-length envelope sequences and systematically determined positive selection (PS) sites by QUASI analysis and found that PS sites were widely dispersed across Env. The frequency of Env PS sites appears to be relatively stable over time. Moreover, between 25% and 61% of PS sites are shared between subtypes A, B, C, and D, suggesting that host immune responses target the same regions of Env gene across different clades at the population level. Significant correlations were observed between PS sites and Neutralizing antibody (NAb) response, as well as PS sites and Th epitopes. Furthermore, NAb sites in combination with cytotoxic-T lymphocyte (CTL) epitopes and proteasome cleavage sites were also significantly associated with PS sites, suggesting NAb may be the major force driving the evolution of HIV-1 Env. We also identified regions that are free from PS, but heavily targeted by CTL or NAb, implying that functional constraints may be responsible for the lack of positive selection in these regions. These findings should help researchers to identify epitopes or regions of HIV-1 that may aid in designing vaccines.

Unraveling the characteristics of microRNA regulation in the developmental and aging process of the human brain

BackgroundStructure and function of the human brain are subjected to dramatic changes during its development and aging. Studies have demonstrated that microRNAs (miRNAs) play an important role in the regulation of brain development and have a significant impact on brain aging and neurodegeneration. However, the underling molecular mechanisms are not well understood. In general, development and aging are conventionally studied separately, which may not completely address the physiological mechanism over the entire lifespan. Thus, we study the regulatory effect between miRNAs and mRNAs in the developmental and aging process of the human brain by integrating miRNA and mRNA expression profiles throughout the lifetime.MethodsIn this study, we integrated miRNA and mRNA expression profiles in the human brain across lifespan from the network perspective. First, we chose the age-related miRNAs by polynomial regression models. Second, we constructed the bipartite miRNA-mRNA regulatory network by pair-wise correlation coefficient analysis between miRNA and mRNA expression profiles. At last, we constructed the miRNA-miRNA synergistic network from the miRNA-mRNA network, considering not only the enrichment of target genes but also GO function enrichment of co-regulated target genes.ResultsWe found that the average degree of age-related miRNAs was significantly higher than that of non age-related miRNAs in the miRNA-mRNA regulatory network. The topological features between age-related and non age-related miRNAs were significantly different, and 34 reliable age-related miRNA synergistic modules were identified using Cfinder in the miRNA-miRNA synergistic network. The synergistic regulations of module genes were verified by reviewing miRNA target databases and previous studies.ConclusionsAge-related miRNAs play a more important role than non age-related mrRNAs in the developmental and aging process of the human brain. The age-related miRNAs have synergism, which tend to work together as small modules. These results may provide a new insight into the regulation of miRNAs in the developmental and aging process of the human brain.

Identification of cancer risk lncRNAs and cancer risk pathways regulated by cancer risk lncRNAs based on genome sequencing data in human cancers

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. The complexity of cancer can be reduced to a small number of underlying principles like cancer hallmarks which could govern the transformation of normal cells to cancer. Besides, the growth and metastasis of cancer often relate to combined effects of long non-coding RNAs (lncRNAs). Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of six types of human cancer patients from The Cancer Genome Atlas (TCGA), and identified six risk pathways and twenty three lncRNAs. In addition, twenty three cancer risk lncRNAs which were closely related to the occurrence or development of cancer had a good classification performance for samples of testing datasets of six cancer datasets. More important, these lncRNAs were able to separate samples in the entire cancer dataset into high-risk group and low-risk group with significantly different overall survival (OS), which was further validated in ten validation datasets. In our study, the robust and effective cancer biomarkers were obtained from cancer datasets which had information of normal-tumor samples. Overall, our research can provide a new perspective for the further study of clinical diagnosis and treatment of cancer.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes.

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.