Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets

10.1186/1471-2164-12-S3-S2010th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)12SUPPL.

Computational Studies of Host-Pathogen Protein-Protein Interactions - A Case Study of the H.Sapiens-M. Tuberclulosis H37RV System

Ph.DDOCTOR OF PHILOSOPH

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

Background: H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. Results: We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI. Conclusions: The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce \u27annotation principal components\u27, multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol

Haemophilus parasuis Infection Disrupts Adherens Junctions and Initializes EMT Dependent on Canonical Wnt/β-Catenin Signaling Pathway

In this study, animal experimentation verified that the canonical Wnt/β-catenin signaling pathway was activated under a reduced activity of p-β-catenin (Ser33/37/Thr41) and an increased accumulation of β-catenin in the lungs and kidneys of pigs infected with a highly virulent strain of H. parasuis. In PK-15 and NPTr cells, it was also confirmed that infection with a high-virulence strain of H. parasuis induced cytoplasmic accumulation and nuclear translocation of β-catenin. H. parasuis infection caused a sharp degradation of E-cadherin and an increase of the epithelial cell monolayer permeability, as well as a broken interaction between β-catenin and E-cadherin dependent on Wnt/β-catenin signaling pathway. Moreover, Wnt/β-catenin signaling pathway also contributed to the initiation of epithelial-mesenchymal transition (EMT) during high-virulence strain of H. parasuis infection with expression changes of epithelial/mesenchymal markers, increased migratory capabilities as well as the morphologically spindle-like switch in PK-15 and NPTr cells. Therefore, we originally speculated that H. parasuis infection activates the canonical Wnt/β-catenin signaling pathway leading to a disruption of the epithelial barrier, altering cell structure and increasing cell migration, which results in severe acute systemic infection characterized by fibrinous polyserositis during H. parasuis infection

Identification of a nucleoside analog active against adenosine kinase-expressing plasma cell malignancies

Primary effusion lymphoma (PEL) is a largely incurable malignancy of B cell origin with plasmacytic differentiation. Here, we report the identification of a highly effective inhibitor of PEL. This compound, 6-ethylthioinosine (6-ETI), is a nucleoside analog with toxicity to PEL in vitro and in vivo, but not to other lymphoma cell lines tested. We developed and performed resistome analysis, an unbiased approach based on RNA sequencing of resistant subclones, to discover the molecular mechanisms of sensitivity. We found different adenosine kinase–inactivating (ADK-inactivating) alterations in all resistant clones and determined that ADK is required to phosphorylate and activate 6-ETI. Further, we observed that 6-ETI induces ATP depletion and cell death accompanied by S phase arrest and DNA damage only in ADK-expressing cells. Immunohistochemistry for ADK served as a biomarker approach to identify 6-ETI–sensitive tumors, which we documented for other lymphoid malignancies with plasmacytic features. Notably, multiple myeloma (MM) expresses high levels of ADK, and 6-ETI was toxic to MM cell lines and primary specimens and had a robust antitumor effect in a disseminated MM mouse model. Several nucleoside analogs are effective in treating leukemias and T cell lymphomas, and 6-ETI may fill this niche for the treatment of PEL, plasmablastic lymphoma, MM, and other ADK-expressing cancers

Powerful, Scalable and Resource-Efficient Meta-Analysis of Rare Variant Associations in Large Whole Genome Sequencing Studies

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples

A Framework For Detecting Noncoding Rare-Variant associations of Large-Scale Whole-Genome Sequencing Studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 toPMed samples. We also analyze five non-lipid toPMed traits

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this work we jointly model and integrate extensive multi-omics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, re-analysis of the ILCCO data highlights the impact of highly-associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies