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A comprehensive collection of systems biology data characterizing the host response to viral infection.

ABSTRACT: The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

SUBMITTER: Aevermann BD

PROVIDER: S-EPMC4410982 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A comprehensive collection of systems biology data characterizing the host response to viral infection.

Aevermann Brian D BD Pickett Brett E BE Kumar Sanjeev S Klem Edward B EB Agnihothram Sudhakar S Askovich Peter S PS Bankhead Armand A Bolles Meagen M Carter Victoria V Chang Jean J Clauss Therese R W TR Dash Pradyot P Diercks Alan H AH Eisfeld Amie J AJ Ellis Amy A Fan Shufang S Ferris Martin T MT Gralinski Lisa E LE Green Richard R RR Gritsenko Marina A MA Hatta Masato M Heegel Robert A RA Jacobs Jon M JM Jeng Sophia S Josset Laurence L Kaiser Shari M SM Kelly Sara S Law G Lynn GL Li Chengjun C Li Jiangning J Long Casey C Luna Maria L ML Matzke Melissa M McDermott Jason J Menachery Vineet V Metz Thomas O TO Mitchell Hugh H Monroe Matthew E ME Navarro Garnet G Neumann Gabriele G Podyminogin Rebecca L RL Purvine Samuel O SO Rosenberger Carrie M CM Sanders Catherine J CJ Schepmoes Athena A AA Shukla Anil K AK Sims Amy A Sova Pavel P Tam Vincent C VC Tchitchek Nicolas N Thomas Paul G PG Tilton Susan C SC Totura Allison A Wang Jing J Webb-Robertson Bobbie-Jo BJ Wen Ji J Weiss Jeffrey M JM Yang Feng F Yount Boyd B Zhang Qibin Q McWeeney Shannon S Smith Richard D RD Waters Katrina M KM Kawaoka Yoshihiro Y Baric Ralph R Aderem Alan A Katze Michael G MG Scheuermann Richard H RH

Scientific data 20141014

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe ac ...[more]

PMID: 25977790

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Project description:Introduction: Drug-resistant infections are becoming increasingly frequent worldwide, causing hundreds of thousands of deaths annually. This is partly due to the very limited set of protein drug targets known for human-infecting viral genomes. The eleven influenza virus proteins, for instance, exploit host cell factors for replication and suppression of the antiviral immune responses. A systems medicine approach to identify relevant and druggable host factors would dramatically expand therapeutic options. Therapeutic target identification, however, has hitherto relied on static molecular networks, whereas in reality the interactome, in particular during an infection, is subject to constant change. Methods: We developed time-course network enrichment (TiCoNE), an expert-centered approach for discovering temporal response pathways. In the first stage of TiCoNE, time-series expression data is clustered in a human-augmented manner to identify groups of biological entities with coherent temporal responses. Throughout this process, the expert can add, remove, merge, or split temporal patterns. The resulting groups can then be mapped to an interaction network to identify enriched pathways and to analyze cross-talk enrichments and depletions between groups. Finally, temporal response groups of two experiments can be intersected, to identify condition-variant response patterns that represent promising drug-target candidates. Results: We applied TiCoNE to human gene expression data for influenza A virus infection and rhino virus infection, respectively. We then identified coherent temporal response patterns and employed our cross-talk analysis to establish two potential timelines of systems-level host responses for either infection. Next, we compared the two phenotypes and unraveled condition-variant temporal groups interacting on a networks level. The highest-ranking ones we then validated via literature search and wet-lab experiments. This not only confirmed many of our candidates as previously known, but we also identified phospholipid scramblase 1 (encoded by PLSCR1) as a previously not recognized host factor that is essential for influenza A virus infection. Conclusion: With TiCoNE we developed a novel approach for conjointly analyzing molecular networks with time-series expression data and demonstrated its power by identifying temporal drug-targets. We provide proof-of-concept that not only novel targets can be identified using our approach, but also that anti-infective drug target discovery can be enhanced by investigating temporal molecular networks of the host in response to viral infection.

Dataset Information

A comprehensive collection of systems biology data characterizing the host response to viral infection.

Publications

A comprehensive collection of systems biology data characterizing the host response to viral infection.

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