Projects and Cores

Project 1 - Host-virus networks regulating flu replication and host responses in vivo (Adolfo Garcia-Sastre, ISMMS):

Influenza A virus is a major human respiratory pathogen, and available vaccines and antivirals are of limited efficacy. In order to identify novel targets for therapeutic intervention during influenza virus infection, we have assembled an interdisciplinary team that uses a highly integrated systems level approach to identify and validate key genes/networks involved in virus pathogenesis. The overarching theme of our project “FluOMICS: The NEXT Generation” is to obtain multiple OMICS-based systems level measurements and integrate them using modeling approaches and machine learning algorithms to identify and validate 1) host-virus networks that modulate influenza A virus disease severity, 2) biomarkers in blood that reflect the activation states of these networks and 3) novel host targets for therapeutic interventions. The proposed studies leverage our previous collaborations that generated global datasets and models that predict severity of disease caused by three specific influenza virus strains with different levels of pathogenicity. Our underlying main hypothesis is that host networks involved in viral replication and in early host responses regulate disease outcomes and represent promising targets for therapeutic intervention. We also propose that, in addition to the pathways identified in our previous collaboration, there are additional distinct pathways that result in either resilience or pathogenic outcomes after influenza virus infection, and that specific pathogenic pathways will require tailored therapeutic interventions. To identify networks associated with clinical disease in humans, we propose to integrate into predictive and comprehensive models the OMICS responses during influenza virus infection in three systems 1) human blood from a human cohort of patients with documented influenza virus infection and diverse clinical outcomes (Project 1); 2) mouse blood and tissues from experimentally infected animals under a variety of conditions and perturbations resulting in diverse disease outcomes (Project 1) and 3) relevant primary human cells experimentally infected under controlled conditions and perturbations associated with diverse disease outcomes (Project 2). Samples will be collected, processed and sent to the Technology Core to conduct global transcriptomics, proteomics and metabolomics analysis. OMICS data sets will be integrated and compared by the Modeling Core to generate network models of disease, uncover blood biomarkers and identify key drivers as targets for mechanistic studies and therapeutic intervention (Project 1). In summary, our systems modeling approaches will find correlates and associations between diverse experimental systems that will help us define human blood biomarkers and link them to in vivo and ex vivo signatures for both companion diagnostics and personalized therapies.

Project 2 - Host-virus networks regulating flu replication and host responses ex vivo (Sumit Chanda, SBP):

In this proposal, we hypothesize that multiple, discrete molecular pathways determine influenza disease severity, and that these pathways elicit biomarker signatures that can be identified through network-based modeling of system-level measurements. In Project 2, we propose to utilize leading edge OMICS approaches to identify ex vivo molecular signatures that correlate with clinical disease outcomes. The elucidation and characterization of factors that govern outcome-related biomarker signatures offer the potential for the development of novel antiviral therapies. We will use a systems biology approach to elucidate network signatures associated with clinical influenza severity. Here, we will profile changes to the host transcriptome, proteome, and metabolome, in response to infection using viruses of different pathogenicity, as well as additional host perturbations linked to clinical outcome. Ex vivo molecular signatures correlated with disease severity will be integrated and modeled with in vivo and clinical data generated in Project 1. We also propose to use genetic tools to identify nodes within outcome-related molecular signatures that are critical regulators of host response pathways and viral replication (‘driver genes’). Both of these efforts will rely on a paradigm of reiterative experimentation and modeling to link ex vivo signatures to clinical phenotypes and identify the host proteins and pathways that govern them. Finally, those genes found to regulate pathways and signatures associated with disease outcome (driver genes) will be further characterized. We propose to employ CRISPR-based analysis of transcriptional, epigenetic, proteomic, and metabolic profiles to provide insight into the role of these factors in regulating responses linked to disease outcomes (CRISPR-OMICs). Additional molecular, cellular, biochemical and in vivo studies will be conducted to further characterize those nodes that determine disease outcomes as potential therapeutic targets.


The administrative core, similarly to the administrative core for the original FluOMICS project, will provide a management plan to coordinate the whole “FluOMICS, The NEXT Generation” consortium by 1) the establishment of an organizational structure centered around an Executive Committee responsible for monitoring overall program progress and making decisions on staffing plans, allocation of resources, scientific core usage and other policies; 2) the coordination of conference calls and of annual meetings between the PIs, selected key personnel, the Steering Committee and NIAID; 3) assisting the members of this consortium in reagents and data sharing, manuscript preparations, public release of data and submission of annual progress reports to the NIH; and 4) providing training opportunities in Systems Biology for infection disease scientists. The Core Director, Dr. García-Sastre, has expertise in the coordination of program projects and big consortiums, as he is the Director of one of the NIAID Centers of Excellence of Influenza Research and Surveillance. Moreover, he has been successfully coordinating the previous FluOMICS consortium that generated the preliminary data for this application. The “FluOMICS, The NEXT Generation” proposal complements the Center of Excellence, as the influenza center does not include the use of a systems biology approach in its research agenda. The Core Co-Director, Dr. Chanda, is in charge of the training program to be implemented in years 2 to 5 and he has past experience in organizing training for students in systems biology approaches.


The objective of the Pan-OMICs Technology Core (PTC) is to provide state-of-the-art, innovative -OMICs technologies for the targeted and global characterization of Project samples in a quantitative, reproducible, and efficient manner. The PTC brings together cutting-edge tools and the expertise of leading systems biologists in three critical areas: (1) Genomics led by Dr. Chris Benner at the University of California, San Diego; (2) Proteomics led by Dr. Nevan Krogan at the J. David Gladstone Institutes; and (3) Metabolomics led by Drs. Ed Dennis and Oswald Quehenberger at the University of California, San Diego (lipidomics) as well as by Dr. Leah Shriver at the University of Akron (polar metabolites). The PTC will be responsible for receiving, processing, and analyzing both in vivo samples from infected patients and mice (Project 1) as well as ex vivo infected samples in culture (Project 2). These data will be integrated by the Modeling Core to identify key drivers of influenza virus infection, biomarkers of disease severity, and potential nodes for therapeutic intervention. Upon perturbation of these critical drivers by each respective Project, the PTC will provide these same services in a comparative manner to determine specific, functional consequences of such perturbations for the determination of molecular mechanism. This iterative cycle from systems-to-mechanism is driven by the Pan-OMICs Technology Core integrating multiple labs and Projects to optimally leverage best-in-class -OMICs approaches to map the molecular pathways surrounding influenza virus infection and disease. The PTC has a uniquely challenging and rewarding directive in the generation and integration of coordinated systems data spanning chromatin modifications and architecture, gene expression, protein abundance, posttranslational modifications, protein-protein interactions, lipid profiling, and polar metabolite identification. In the previous iteration of our Fluomics consortium, Drs. Benner, Krogan, Dennis, Quehenberger, and Shriver worked extensively together to tackle these challenges and derive standardized protocols for sample generation, processing, and analysis between different cores and institutes. Formalizing this relationship as a single core under the leadership of Dr. Nevan Krogan, an internationally recognized expert in the design and application of systems biology approaches to interrogate host-pathogen interactions, the PTC is ideally positioned to both effectively and efficiently implement these previously developed pipelines for the accomplishment of our Aims.

Modeling Core C (Rafick-Pierre Sekaly, Case Western Reserve University):

Influenza infection leads to different clinical outcomes that range from benign symptoms to hospitalization and sometimes death (ranging from 12,000 to 56,000 deaths per year in the United States). However, biomarkers predictive of influenza disease outcomes (i.e. symptoms severity, viral replication) which are key for preventive medicine have not yet been identified. Moreover, elucidation of the molecular components that govern predictive signatures will provide important clues that will guide the development of novel therapeutic strategies. “FluOMICS, The Next Generation” will build on a wealth of data gathered already by the previous FluOMICS consortium and pursue two converging hypotheses 1) multiple and discrete host immune response pathways act in concert to determine the pathogenic outcome of influenza infection 2) the crosstalk between influenza and these host pathways results in the establishment of correlated epigenetic, transcriptional, post-translational, metabolic signatures in multiple tissues and cell types. The major objective of the Modeling Core will be to use network-based modeling approaches to integrate the experimental data generated by Projects 1 and 2, identify biological processes that can predict influenza disease outcomes and validate them functionally in collaboration with Projects 1 and 2. The Modeling Core, in collaboration with the Technology Core and the Data Management and Bioinformatics Core, will preprocess, apply the appropriate statistical analysis and interpret all large-scale (OMIC) datasets generated in Projects 1 and 2. We will provide data integration and visual representation for each OMIC dataset and summary tables giving the number and identity of differentially expressed markers (genes/proteins/post-translational modifications/metabolites) associated with disease outcomes. Next, the Modeling Core will provide an integrated view of all these datasets by mapping them to networks of transcriptional nodes and signal transduction pathways which are differentially triggered by different conditions of infection and mimic differences between hosts. Critically, the Modeling Core will apply heuristic approaches and an iterative process with Projects 1 and 2 to unravel and improve on correlations between networks of biological pathways collected in orthogonal sample types (i.e. human/mouse blood, mouse lungs, and human cells) generated by the cores. Finally, the Modeling Core will use a machine learning technique to generate predictive models based on prioritized set of markers able to predict responses to influenza strains linked to various disease states. These markers will include host factors and host-pathogen interactions that predict clinical outcomes and may be mechanistically implicated in symptom severity. The Modeling Core will stand in this U19 as the ultimate infrastructure and resource that will integrate the large body of data generated in this program and provide to the scientific community important deliverables namely validated signatures of disease severity, biomarkers for preventive medicine, and mechanistic cues to the diversity of clinical outcomes.

Data Management and Bioinformatics Core D (Lars Pache, SBP):

The FluOMICS: The Next Generation Data Management and Bioinformatics Core (DMBC) will support the Center's mission at all stages of research and publication, tracking projects and experiments, facilitating reproducible analysis, visualization, and access to center data and results. Research will be accelerated by the centralization of primary and processed data, models, workflows, and pipelines using a comprehensive data management platform. Visualization and analysis of program data will be supported by the development and maintenance of computational tools, including Metascape, a web-tool allowing the annotation, analysis, and prioritization of a wide array of systems-level data. Program data and resources will be disseminated through public repositories and a Center website. The workflow of the DMBC Core has been optimized and improved during the previous years of support of the FluOMICS Consortium, facilitating cooperation and the exchange of data, bioinformatics tools and models between Modeling and Data Management, and resulting in the present integrated DMBC team.