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.

PROJECT 1 (PI Megan Shaw, ISMMS):

The underlying hypothesis of this application is that critical molecular features of host-pathogen interactions and responses dictate the pathogenic outcome of viral infection. Thus, a comprehensive understanding of viral-host interactions, innate responses to viral infection, and viral evasion strategies is pivotal for predictive modeling of viral pathogenesis. Here, we will provide a comprehensive overview of the genetic, chemical, and biochemical networks that play a role in controlling influenza virus infection by investigating host-virus interactions in an ex vivo setting using primary human cells. The impact of influenza virus replication on the host will be studied using next generation sequencingtechnologies to interrogate cellular RNA populations to define transcriptome-level changes (RNA-seq),conduct genome-wide survey of promoters engaged by RNA polymerase (GRO-seq), as well as evaluate epigenetic alterations in the chromosomal landscape (CHiP-seq). Furthermore, global alterations in intracellular and extracellular metabolite levels, protein abundance, as well as post-translational modifications induced upon viral infection will be measured. Combining these approaches with genome-wide functional genomic screening and high-throughput protein interactome analysis will enable the generation of high-resolution networks that accurately depict the hierarchies of interactions between influenza virus and the host. By conducting these analyses simultaneously with three viruses that drive varying pathogenic outcomes, computational modeling of these data will enable us to identify critical nodes of the viral-host network that are predictive of viral pathogenesis. The in vivo and clinical impact of these nodes will be evaluated in Projects 2 and 3, respectively.

PROJECT 2 (PI Adolfo Garcia-Sastre, ISMMS):

The underlying hypothesis of our consortium is that host genes and networks involved in viral replication and in early host responses modulate viral pathogenesis and therefore represent targets for therapeutic intervention. In Project 2, we propose an OMICS approach to identify key early genes/networks involved in influenza virus pathogenesis. This will be achieved by modeling global host responses during influenza virus infection in a mouse model in collaboration with the Modeling Core E and the OMICs Cores B, C and D (Genomics, Proteomics and Metabolomics). In Aim 1, we will investigate the early global host response associated with lethal, severe and moderate influenza A virus infection in a mouse model. We will be using three clinically relevant strains of influenza A virus that differ in their virulence, allowing for comparisons of the host responses and interactions associated with different ranges of disease severity. In Aim 2, we will investigate host proteins interacting with influenza virus proteins during viral infection in mouse lungs, in collaboration with the Proteomics Core C, using the same influenza A virus strains as in Aim 1. The models constructed in collaboration with the Modeling Core E by the integration of the data generated in vivo with those ex vivo in Project 1 will predict key genes and networks likely to be involved in virus replication and host responses. In Aim 3, the model-identified networks will be validated by conducting perturbations including: use of specific virus mutants that disrupt key host-virus interactions, use of virus with specific mutations involved in host tropism and pathogenesis, use of pharmacological inhibitors, and use of mouse k.o. or of antisense targeting of key host genes in the mouse model. For antisense targeting in vivo we will be using a validated and innovative technology based on lung delivery of peptide-conjugated morpholino antisense oligomers (PPMO), pioneered by our collaborators Hong Mouton and David Stein. In all these experiments, viruses will be generated in collaboration with the Virus Core (Core G). In Aim 4, the results of these perturbations, approximately 40 per year, on the model networks will be analyzed in a medium throughput or targeted–OMICS approach, and the data will be incorporated into the model in collaboration with the Modeling Core E for model refinement. Targeted host genes in our Project 2 will also be studied for variants and impact in human macrophage function by Project 3. We hypothesize that our integrated approach will result in the identification of novel host targets for therapeutic intervention during influenza virus infection.

PROJECT 3 (PI Steven Wolinsky, Northwestern):

Influenza A virus depends on the host cell machinery for its replication. Recognition of the virus by the host triggers a complex signaling cascade that results in the expression of numerous interferon (IFN)-stimulated genes that respond to the virus and interfere with its life cycle. As an ancient antiviral defense mechanism, the innate immune response is a collection of functionally distinct subsystems that have evolved to counter infection by viruses. The influenza A virus, however, can bring about measures that subvert many components of the host innate immune response to infection. Differences in the genes relevant to the virus life cycle or immunity, from genetic variations or epigenetic factors, can contribute to host resilience. Many aspects of the virus-host interaction have not been described fully. Here, we will conduct a cohort study to determine whether mutation of genes encoding intermediates in signaling pathways and networks host factors identified by our ’omics’ approach for which a plausible biological mechanism of action is found influences the virus life cycle. We will discover rare and disruptive variants in human genes required during virus replication or for host cell modifiers of infection relevant to the virus life cycle or immunity by targeted capture and high-throughput resequencing of the selected genes across individuals. We will map and quantify expression quantitative trait loci by high-throughput sequencing of cDNA libraries and produce genome-wide maps of chromatin accessibility to link genetic variation to changes in gene regulation and molecular phenotype. We will elucidate assess the impact of the predicted function-altering changes in host factors required for virus replication and innate immune defense to understand the mechanisms by which they affect the steps in the influenza A virus life cycle. We will follow the extant men over a subsequent season of influenza prospectively to associate changes in molecular phenotypes with changes in molecular and cellular processes and disease-related phenotype, With this approach, we anticipate finding novel cellular proteins required during virus replication, new host cell modifiers of infection, and their functional importance in restriction of infection that will provide valuable insights into the biological basis of disease.


The Administrative Core (Core A) will provide a management plan to coordinate the whole FLUOMICS consortium by 1) the establishment of an organizational structure centered around an Executive Committee responsible for monitoring overall program progress, implementing a Pilot Research Program, 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 the infection disease scientist. The Core Director 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. This FLUOMICS proposal complements and does not overlap with 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 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.


Innate signaling pathways can regulate influenza virus replication, and there are viral countermeasures, but there remain critical gaps in our knowledge about how these responses impact viral disease pathogenesis. The overarching goal of this highly integrated program is to systematically address these questions using a systems-based approach to reveal new therapeutic approaches. The goal of the Genomics Core (Core B) is to provide a central resource that will facilitate high throughput sequencing of the transcriptome (mRNA-seq, GRO-Seq, and Nanostring gene expression analysis) and the epigenome (ChIP-Seq) in influenza virus infected cells. The core will also provide data analysis and integration to uncover the virus-host transcriptional response network. This information will be used by other program investigators in the Modeling Core to define host molecular networks and pathways that impact influenza virus infection and to define rare and disruptive gene polymorphisms that influence influenza virus disease pathogenesis (Project 3). Cellular networks, pathways, and genes that are implicated in the virus-host transcriptinal response network will be targeted by RNAi-knockdown to determine how perturbations in the system impact the transcriptional response network to infection by wild-type and specific mutant influenza A viruses. These studies are critical for the overall goal of the program aimed at uncovering the global effects of influenza virus on cellular functions and on the system of key antiviral responses and virus countermeasures.


In this study, we aim to functionally interrogate host-pathogen relationships in human influenza viruses. The Proteomics Core (Core C) will employ a systematic affinity tag/purification-mass spectrometry approach to identify the viral-host protein complexes. The data generated using these initial, unbiased approaches will fuel more targeted, hypothesis-driven research in the subsequent projects. In tandem with this work and with more targeted downstream work, we will be closely monitoring for links to host factors involved in quality control processes, including chaperone function, protein ubiquitination, and protein degradation, which will link this work to the collaborations with Drs. Garcia-Sastre, Chanda and Benner.


The goal of the Metabolomics Core (Core D) is to provide a central resource that will facilitate high throughput characterization of the metabolome in influenza virus infected cells and tissues. We will validate relevant metabolites and localize metabolites within infected tissues samples. The core will also design strategies to uncover host metabolic pathways that are impacted by, and that influence influenza virus infection and disease pathogenesis.


The overall goal of the Modeling Core (Core E) is to drive the integration of global –OMICS data to identify virus-host networks that control the innate immune response and influence pathogenicity. This will be accomplished through two main objectives a) to design and provide tools to analyze -OMICS data and b) to serve as an engine for integrating –OMICS data into network models of pathogenicity that are subject to further refinement in an iterative fashion. This Core will employ existing bioinformatics and systems biology approaches as well as develop novel approaches to identify cellular proteins and networks which influence influenza virus replication and contribute to virulence in vivo. The modeling core will be the engine for translating –OMICS data into biological insight and has a central role in the successful completion of this program. Co-directors Bandyopadhyay and Krogan have a strong history of innovation and collaboration with each other and others on this proposal and are well suited to direct the modeling efforts. Predictions that are based upon our models will be tested in primary cell culture and in animal model systems by employing targeted –OMICS technologies as well as in vivo experimentation and analysis of clinical phenotypes.


Building a predictive model for influenza pathogenesis will require reiterative cycles of data generation and computational analysis. Given the role of large-scale datasets in this effort, effective data management and resource dissemination is critical for not only the success of the program, but for the broader scientific community to fully realize and exploit resources generated by this Center. Towards that end, the Data Management and Resource Dissemination Core (Core E) will act as a central repository for all data and resources generated by the Center, and ensure that these materials are readily accessible by not only other scientists in the program, but also the broader scientific community. The Core will adapt practices, protocols, approaches, and software that have been previously and successfully utilized to integrate and disseminate large-scale datasets within the context of a large program project. Internally, we will focus on project tracking, data consolidation and integration, quality control, and managing a centralized database that is accessible and user-friendly. In addition, the core will work to integrate publically available data, and make these integrated large-scale datasets, and associated resources, available to the scientific community to enable the exploration of novel hypothesis based on the information generated by the program.

VIRUS CORE G (Randy Albrecht, ISSMS):

The Virus Core (Core G) is an essential resource for the needs of projects 1 and 2, which will benefit from a centralized Virus Core by: i) the established expertise with reverse genetics techniques which will facilitate rescue of recombinant influenza viruses that are described below and itemized in Table 1B, ii) the maintenance of influenza virus stocks that have been sequence-confirmed and assessed for quality by hemagglutination and plaque assays, and iii) reduced inter-experimental variation by consistent use of specific virus stock preparations. The Department of Microbiology, Mount Sinai School of Medicine, NY, NY is a pioneer in the application of reverse genetics and the development of recombinant viruses. The well-equipped facilities, established procedures, and properly trained personnel provide a cost-effective Virus Core that will result in efficient production of wild-type and recombinant influenza virus stocks that will be essential for projects 1 and 2. Specifically, Dr. Megan Shaw (Co-PI of project 1) and Dr. Adolfo Garcia-Sastre (Co-PI of project 2) will directly benefit by their close proximity to and direct communication with the Virus Core. Specific functions of the Virus Core are to i) maintain working stocks of wild-type influenza viruses for use by projects 1 and 2, and ii) generate recombinant influenza viruses for use by projects 1 and 2.