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Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions.

ABSTRACT: Identifying the shared and pathogen-specific components of host transcriptional regulatory programs is important for understanding the principles of regulation of immune response. Recent efforts in systems biology studies of infectious diseases have resulted in a large collection of datasets measuring host transcriptional response to various pathogens. Computational methods to identify and compare gene expression modules across different infections offer a powerful way to identify strain-specific and shared components of the regulatory program. An important challenge is to identify statistically robust gene expression modules as well as to reliably detect genes that change their module memberships between infections.We present MULCCH (MULti-task spectral Consensus Clustering for Hierarchically related tasks), a consensus extension of a multi-task clustering algorithm to infer high-confidence strain-specific host response modules under infections from multiple virus strains. On simulated data, MULCCH more accurately identifies genes exhibiting pathogen-specific patterns compared to non-consensus and nonmulti-task clustering approaches. Application of MULCCH to mammalian transcriptional response to a panel of influenza viruses showed that our method identifies clusters with greater coherence compared to non-consensus methods. Further, MULCCH derived clusters are enriched for several immune system-related processes and regulators. In summary, MULCCH provides a reliable module-based approach to identify molecular pathways and gene sets characterizing commonality and specificity of host response to viruses of different pathogenicities.The source code is available at https://bitbucket.org/roygroup/mulcchsroy@biostat.wisc.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Niu Z

PROVIDER: S-EPMC5860402 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions.

Niu Zhen Z Chasman Deborah D Eisfeld Amie J AJ Kawaoka Yoshihiro Y Roy Sushmita S

Bioinformatics (Oxford, England) 20160121 10

<h4>Motivation</h4>Identifying the shared and pathogen-specific components of host transcriptional regulatory programs is important for understanding the principles of regulation of immune response. Recent efforts in systems biology studies of infectious diseases have resulted in a large collection of datasets measuring host transcriptional response to various pathogens. Computational methods to identify and compare gene expression modules across different infections offer a powerful way to iden ...[more]

PMID: 26801959

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Project description:BackgroundIn the rodent incisor during amelogenesis, as ameloblast cells transition from secretory stage to maturation stage, their morphology and transcriptome profiles change dramatically. Prior whole genome transcriptome analysis has given a broad picture of the molecular activities dominating both stages of amelogenesis, but this type of analysis has not included miRNA transcript profiling. In this study, we set out to document which miRNAs and corresponding target genes change significantly as ameloblasts transition from secretory- to maturation-stage amelogenesis.ResultsTotal RNA samples from both secretory- and maturation-stage rat enamel organs were subjected to genome-wide miRNA and mRNA transcript profiling. We identified 59 miRNAs that were differentially expressed at the maturation stage relative to the secretory stage of enamel development (False Discovery Rate (FDR)<0.05, fold change (FC)≥1.8). In parallel, transcriptome profiling experiments identified 1,729 mRNA transcripts that were differentially expressed in the maturation stage compared to the secretory stage (FDR<0.05, FC≥1.8). Based on bioinformatics analyses, 5.8% (629 total) of these differentially expressed genes (DEGS) were highlighted as being the potential targets of 59 miRNAs that were differentially expressed in the opposite direction, in the same tissue samples. Although the number of predicted target DEGs was not higher than baseline expectations generated by examination of stably expressed miRNAs, Gene Ontology (GO) analysis showed that these 629 DEGS were enriched for ion transport, pH regulation, calcium handling, endocytotic, and apoptotic activities. Seven differentially expressed miRNAs (miR-21, miR-31, miR-488, miR-153, miR-135b, miR-135a and miR298) in secretory- and/or maturation-stage enamel organs were confirmed by in situ hybridization. Further, we used luciferase reporter assays to provide evidence that two of these differentially expressed miRNAs, miR-153 and miR-31, are potential regulators for their predicated target mRNAs, Lamp1 (miR-153) and Tfrc (miR-31).ConclusionsIn conclusion, these data indicate that miRNAs exhibit a dynamic expression pattern during the transition from secretory-stage to maturation-stage tooth enamel formation. Although they represent only one of numerous mechanisms influencing gene activities, miRNAs specific to the maturation stage could be involved in regulating several key processes of enamel maturation by influencing mRNA stability and translation.

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Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions.

Publications

Multi-task consensus clustering of genome-wide transcriptomes from related biological conditions.

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