COV-IRT is growing dynamically through a structure of research domain subgroups. The on-demand formation of subgroups enables focused research that benefits the broader membership. Subgroups can be formed instantaneously as domain experts determine projects and hypotheses to explore. After formation, a lead (or co-leads) guides discussion and discovery. Subgroups share findings with the entire COV-IRT membership during the weekly meetings, enabling interaction and collaboration across subgroups.

Meet the Subgroups Driving Research in COV-IRT


Lead: Krista Ternus, Ph.D., Signature Science LLC, Austin TX

Learn more about the Microbial subgroup

The COV-IRT microbial subgroup is actively analyzing publicly available bronchoalveolar lavage fluid (BALF) metatranscriptomes to search for correlations between microbial genes, functions, and taxa in COVID-19 patients (e.g. utilizing data from PMIDs: 32020836, 32015507, 32129843, 32228226, 31836714, 30417108, 30389465,, Understanding the potential role the human microbiome plays in the susceptibility to and severity of COVID-19 is an important consideration to improve the precision of COVID-19 diagnosis, outcome prediction, and treatment options. Respiratory tract microbiomes in COVID-19 patients have expressed a reduced diversity of alphaproteobacteria and an increased abundance of respiratory pathogens and oral and upper respiratory commensal bacteria. This is similar to the microbiome of virus-like community-acquired pneumonia patients (PMID: 32463434, 32129843). We hypothesize that specific bacterial species, such as those in the Prevotella genus, are potential pathogens that thrive in COVID-19-induced inflammatory environments at infected mucosal sites (PMID: 28542929). To assess this hypothesis and leverage broader COV-IRT expertise in human disease inflammatory pathways, we are collaborating with the COV-IRT Inflammation subgroup to review supporting data and the presence of Prevotella human-response signatures in COVID-19 patients (e.g., up-regulated human transcripts in Th17-mediated mucosal inflammation). In addition to detecting taxonomic shifts that may be driven by COVID-19 microbiome dysbiosis, we are comparing the functional profile of microbial communities through the analysis of predicted proteins and gene ontology terms assigned to sequence reads. A previous study with a small number of COVID-19 BALF samples (N=8) suggested that the disease causes airway microbiomes to have increased vitamin (e.g., B6, folate, lipoic acid), drug, nucleotide (e.g., purine, pyrimidine), and energy (e.g., methane, nitrogen, sulfur) metabolism, yet decreased amino acid and carbohydrate metabolism ( We are building on the initial findings of this study by analyzing microbial gene ontology in these and additional public BALF metatranscriptomes. In a future collaboration with the COV-IRT Mitochondria and Neurology subgroups, we will evaluate the hypothesis that COVID-19 gut-brain-microbiome functional changes are consistent with a response to host reactive oxygen species production and specific host mitochondrial DNA variants (PMID: 31266851).


Leads: Stephen Baylin, Ph.D., Johns Hopkins Medicine, and Deanne Taylor, Ph.D., University of Pennsylvania/Children’s Hospital of Philadelphia

Learn more about the Inflammation subgroup

The Inflammation subgroup was formed to investigate links between altered immune signaling in COVID-19 patients with known processes of inflammation. We are exploring novel hypotheses derived for the role of the inflammasome, which is linked to both lung epithelial and immune cells in the pathogenesis of pulmonary disease in COVID-19 patients. While it is known that a cytokine storm evolves in many of these patients, the role of the inflammasome in initiating a cytokine storm in coordination with viral entry is not understood.When overactive during infection, this pathway can create severe autoimmune-like inflammation, triggering epithelial cell death even for neighboring cells not harboring the pathogen.

Recent studies quantifying defined interactions through genomics data analyses suggest novel links between induction of key signaling components of the inflammasome and induced defects in DNA repair.  Such defects include Fanconi anemia defects and those that affect homologous recombination (PMID31591209). We are currently evaluating the hypothesis that this phenomenon may help drive the severity of lung pathogenesis in COVID-19 patients. We also aim to understand how age and sex impact the inflammasome to drive the severity of the disease. The ultimate aim of this group is to use a derivation of the above biology to devise a clinically useful serum/plasma protein signature that may be used to monitor disease status, project the course of COVID-19 patients with severe lung disease, predict early infection in COVID-19 patients most likely to progress to severe disease and identify novel therapeutics to treat these patients. Furthermore recent evidence suggests anti-inflammatory strategies may be effective as a potential treatment method for COVID-19. (PMID: 32352871) The therapeutic benefit of pharmacological modulation of a key inflammatory lipid pathway by dexamethasone (PMID: 32610262) provides further rationale for investigating the lipid signaling in COVID-19 patients.

miRNA/non-coding RNA

Lead: Afshin Beheshti, Ph.D., KBR, NASA Ames Research Center

Learn more about the miRNA/non-coding RNA subgroup

The overall function of the miRNA/non-coding RNA subgroup is to focus on novel biology related to both SARS-COV-2 and the host. Currently, there is limited knowledge on the impact of non-coding RNAs on SARS-COV-2 and the host after infection. Previous studies with other viruses, including coronaviruses, have shown that viruses can co-opt host miRNAs to enhance immune system evasion and viral replication (PMID: 28555130, PMID: 27373545). In addition, it is becoming apparent that the resulting infection due to COVID-19 in patients causes a systemic impact on the entire body as is evident by the growing list of symptoms during COVID-19 infection. A single miRNA can regulate hundreds of mRNAs and circulate throughout the body to contribute to the systemic impact of specific diseases (PMID: 30123182). We hypothesize that one or more miRNAs contribute to the downstream effects in the patient caused by SARS-COV-2 infection. The goals of the group are to identify the specific miRNAs associated with SARS-COV-2, determine if these miRNAs can be utilized as a novel biomarker/detection method for COVID-19, design a potential miRNA-based therapeutic/vaccine, and implement a miRNA-based COVID-19 therapy. This subgroup is currently pursuing its goals by utilizing predictive miRNA analysis on RNA-seq data and analysis on miRNA-seq data on COVID-19 from publicly available databases. The data that has been generated to date has compared predicted miRNAs from existing RNA-seq and related to specific regions of SARS-CoV-2. In addition, predicted miRNAs are being compared to viral load in patients to determine potential ideal miRNA candidate to target for therapeutics. We have also started to test potential miRNAs based targets in both in vitro and in vivo COVID-19 models. Lastly, this group is exploring the regulatory roles of other non-coding RNAs in COVID-19 infection, such as long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), with similar techniques.

Radiology and Imaging

Lead: Elena Casiraghi, Ph.D., Università degli Studi di Milano;

Co-Lead: Giorgio Valentini, PhD., Università degli Studi di Milano

Co-Lead: Andrea Esposito, M.D., Ospedale Policlinico di Milano

Learn more about the Radiology and Imaging subgroup

The radiology and imaging subgroup is primarily interested in answering the research question: Can we predict clinical outcomes and patient risk of COVID-19 emergency room patients using ordinary and inexpensive clinical data and X-Ray examination? To answer this question we are collecting clinical data and chest X-rays from patients admitted to the ER in Milan hospitals for any lung illness. We will start by separately processing clinical and imaging data. Clinical data will be analyzed through statistical (PMID: 30034025) and machine learning (ML) methods such as Random Forest, logistic regression, and semi-supervised graph-based algorithms (PMID: 29045534, PMID: 28376093, PMID: 32107391,); X-ray images through deep neural networks (PMID: 30134902, PMID: 31000728), trained by transfer learning (PMID: 29474911) in case of image datasets of limited cardinality, to identify their separate potential in determining COVID-19 risk assessment. The models developed with the two types of data (clinical and X-ray) will then be merged into a multi-modal classifier for patients’ risk stratification. We plan to extend the bi-modal imaging/clinical prediction system to include other sources of data (e.g. omics and EHR data) using integrated ML approaches, in collaboration with other COV-IRT subgroups, to further improve the performance of the ML-based prediction system. To gain trust and increase applicability, all the classification results will be supported by explanations (PMID: 30023379, PMID: 31322793), which will report the reasons behind the predicted patient risk and will highlight the clinical variables or lung areas identified as being the main factors causing the specific prediction. To this aim, we will develop and apply XAI (Explainable Artificial Intelligence) methods to provide human-understandable motivations underlying machine learning predictions, thus “opening the black-box” prediction models (L. H. Gilpin et al IEEE 5th International Conference on Data Science and Advanced Analytics, 2018, doi: 10.1109/DSAA.2018.00018)


Lead: Kyle Hernandez, Ph.D., University of Chicago

Learn more about the Modeling subgroup

This subgroup takes an interdisciplinary analysis and modeling approach to understand host-viral dynamics and interactions with environmental factors. Our goal is to bring together data across all the COV-IRT subgroups to use statistical analyses, mechanistic models, agent-based models, and machine learning methods to address the research question posed by the subgroup. Currently, this subgroup is: 1) developing methods to evaluate anomalies in reporting of COVID-19 deaths, and how this could impact predictive models; 2) evaluating the influence of human behavior (e.g., activity, interpersonal contacts) on virus spread; 3) evaluating the impact of environmental factors on the transmission of the disease, such as population density and weather conditions; 4) establishing corrected and accurate transmission and mortality rates to relate to virus strains geographically 5) using deep learning to detect COVID-19 from medical imaging data; and 6) conducting a large-scale analysis of COVID-19 resources on Github to identify patterns in collaboration and types of resources available to assess the form and state of “open science”. We will collaborate with other subgroups to integrate EHR, medical imaging, genomics, transcriptomics, and epidemiological data to explore host-viral interactions, predict future outbreaks, detect biomarkers, and design therapeutics.

Population and ethnicity

Lead: Eve Syrkin Wurtele, Ph.D., Iowa State University

Learn more about the Population and ethnicity subgroup

Our goal is to develop new avenues for precision diagnostics and therapeutics to mitigate COVID-19. The COVID-19 pandemic affects populations differently. Notably, in the US and Britain, ethnic minorities including people of African descent have disproportionately high morbidity and mortality (PMID: 32543702, PMID: 32557713). Many factors are likely responsible (PMID: 32664879, PMID: 32543702, PMID: 32557713, Our team is focused specifically on molecular factors that may be related to this disparity in COVID-19 severity.

Gene expression is the interaction of environment by genetics, and as such can reveal clues about the molecular milieu that is caused by either. One approach we are taking is to computationally mine raw data from aggregated ‘omics studies on COVID-19 disease as these become publicly available. As of July 31, 2020, data from 11 single cell and bulk RNA studies are available ( We will integrate these data with the metadata (information about the technical samples and the individuals sampled, such as race/ethnicity, age, gender, disease severity, time since infection, and obesity) to identify prognostic markers of COVID-19 and to develop a new understanding of how genetics may affect disease progression and how these may vary across populations.

Our analysis of RNA-Seq data is identifying transcripts of known protein-coding genes as well as transcripts representing novel species-specific protein-coding genes (often called “orphan” or “de novo” genes”. Some orphan genes, if of very recent origin, may be population-specific. Orphan genes are thought to be a key enabler of speciation (PMID: 23949544, PMID: 25151064).  Of those functionally characterized to date, many are implicated in pathogenesis (PMID: 29878511, PMID: 10706094) or immune function (PMID: 30075701, PMID: 30635418). The known genes and novel orphan genes that we identify as being associated with race will be functionally assessed in silico and selected genes will be subject to wet-lab experimentation. Key in silico tools that will facilitate our analysis include MetaOmGraph, a Java software that empowers flexible, interactive statistical analysis and exploration of aggregated data sets by domain experts (PMID: 31956905), pyrpipe, a Python package for reproducibility and flexibility in RNA-Seq analysis, fagin, an R tool (PMID: 31455236), phylostratr, for automated inference of gene age (PMID: 30873536), and other packages.


Lead: Amanda Saravia-Butler, Ph.D. Logyx, LLC, NASA Ames Research Center

Learn more about the Pipeline subgroup

The pipeline subgroup develops consensus pipelines for processing raw RNA sequence data to generate alignment, quantification, and variant call data, which will subsequently be used by other COV-IRT subgroups for downstream analyses. This ensures that all COV-IRT subgroups performing downstream analyses start with data that was processed the same way, thereby eliminating biases that may arise from using different tools to generate processed data prior to downstream analyses.


Lead: Afshin Beheshti, Ph.D., KBR, NASA Ames Research Center

The goal of the mitochondrial subgroup is to analyze and explore the role of mitochondria in the host response to SARS-CoV-2 infection. The mitochondria has been previously shown to play an important role in many types of diseases including viral and bacterial infections (PMID: 32200805, PMID: 31744765, PMID: 28346446, PMID: 31715109, 32470613). Specifically, it has been shown that mitochondrial dysfunction will greatly impact immune functions and increase inflammatory factors due to disease onset (PMID: 27423788). Currently, there are limited reports in the scientific literature addressing mitochondrial dysregulation in the host in response to COVID-19 infection. Recent perspectives and data are starting to show that COVID-19 infection causes mitochondrial suppression in the body which has a downstream impact on which populations are affected the most (PMID: 32564017, PMID: 32708430, PMID: 32589264, PMID: 32574708). Studies using quantitative translatome and proteome mass spectrometry have also revealed the upregulation of glycolysis and glucose metabolism as a function/result of mitochondrial dysregulation in response to SARS-CoV-2 infection. [PMID: 32408336]. The mitochondrial subgroup is currently analyzing RNA-sequencing and proteomics data from COVID-19 infected and uninfected subjects, and has discovered a difference associated with SARS-COV-2 infection status, which may indicate greatly suppressed mitochondrial functions in the host. Specifically, we have observed that nuclear mitochondrial genes are heavily suppressed as a function of viral load in COVID-19 positive patients and additional related mitochondrial genes involved with compensation of suppression of mitochondrial functions are being upregulated as expected. This effect is heavily tied to the upregulation of glycolysis related pathways with the data being analyzed. Through this discovery, this group is further exploring the specific mitochondrial functions impacted and the downstream effects that occur in the host. We hypothesize that this suppression contributes to the downstream effects responsible for the systemic nature of COVID-19 and that mitochondrial dysregulation is a factor in the variation in susceptibility of individuals to COVID-19. Further investigation of mitochondrial dysregulation in COVID-19 patients is of great importance, since there is significant potential in both discovery of mitochondrial function related therapeutics and of the mechanisms behind the systemic disease resulting from SARS-CoV-2 infection.


Lead: Sonia Villapol, Ph.D., Houston Methodist Research Institute, TX

It is now well known that COVID-19 affects the central nervous system. (PMID: 32528783). Damage to the olfactory nerve terminals in the nasal cavity has been shown in COVID-19 patients, affecting the sense of smell (PMID: 32253535). Additionally, other studies have demonstrated that SARS-CoV-2 crosses the blood-brain barrier, reaching the cerebral vasculature through general circulation (PMID: 32367431). Ischemic stroke, cerebral venous thrombosis, and cerebral hemorrhage have also been associated with COVID-19 (PMID: 32568626, PMID: 32563566, PMID: 32554423, PMID: 32542103, PMID: 32530738).

COVID-19 patients that present with neurological manifestations such as headaches and confusion (PMID: 32565914) may be at risk of a higher incidence of neurodegenerative disorders in the future (PMID: 32364119, PMID: 32373651). SARS-CoV-2 also affects the brain-gut-microbiome axis by binding to the intestinal ACE2 receptors, substantially altering the intestinal flora and aggravating systemic inflammation or the cytokine storm in the most seriously ill patients (PMID: 32497191, PMID: 32495940, PMID: 32430279, PMID: 32396996). Uncovering the composition of the microbiota and its metabolic products in the context of COVID-19 can help determine novel biomarkers of the disease and help identify new therapeutic targets.(PMID: 328277059).

The neurology subgroup is investigating the changes in the gut microbiomes to viral load and inflammatory responses, in addition to links to COVID-19 associated neurological problems, such as stroke or multi-thrombosis. This subgroup will also analyze neuroimaging scans and ultrasounds in COVID-19 patients to characterize cardiovascular disturbances caused by SARS-CoV-2 and explore various mechanisms that induce brain recovery using animal models. Our group establishes collaborations with the “Microbial,” “Radiology and Imaging,” “Inflammation,” and other subgroups to determine the bacterial composition and the inflammatory states during different phases of COVID-19. Specifically, how these multisystemic and peripheral changes post-infection can affect neurological functions in the short and long term.

Knowledge Graphs

Lead: Justin Reese, Ph.D., Lawrence Berkeley National Lab

Co-Lead: Deepak Unni, Lawrence Berkeley National Lab

This subgroup is interested in connecting biological and biomedical datasets relevant to COVID-19 to create COVID-19 specific knowledge graphs (KGs). The subgroup includes members from KG-COVID-19, KnetMiner, and Palantir N3C knowledge graph teams. The KGs are then visualized in an easy and effective way for scientists and clinicians to use via Rothamsted Research’s KnetMiner team. The KGs are also provided as machine-readable data, in the multiple forms of downloadable dumps, an RDF/SPARQL endpoint [] and Neo4j/Cypher endpoint []. Thanks to previous work by the Knetminer team [PMID: 30085931], data are unified into a common model, aligned to a standard schema, ontologies and other datasets (including KG-COVID-19, mentioned below), and, in general, compliant with FAIR data principles. This facilitates the exploration and analysis of knowledge graphs that integrate heterogeneous data sources and a variety of different kinds of biological and medical knowledge.

The COVID-19 knowledge graphs will include Omics’, publications, drugs-target interactions, protein-protein interactions, reaction/pathway, Electronic Health Records (EHR), and clinical data. Machine learning will be used to predict novel relationships between drugs, diseases, and other biological data. This is primarily performed by the COVID19 LB KG team. This group will collaborate with the Clinical Scenarios Data Analytics subgroup to integrate N3C data where possible. In the future, the group will also collaborate with other COV-IRT subgroups to integrate additional biological data.

Machine Learning and Network Medicine for drug repurposing

Lead: Alberto Paccanaro, Ph.D., Fundação Getúlio Vargas and Royal Holloway, University of London

Drug repurposing, the process of finding new therapeutic indications for already marketed drugs, has emerged as a promising alternative to new drug development, (PMID: 30310233) since the use of de-risked compounds translates into lower costs and shorter development times (PMID: 32205870). This subgroup focuses on the development of machine learning and network medicine approaches for drug repurposing for COVID-19.  We are currently working on a matrix decomposition model to predict Broad Spectrum Antiviral Agents (BSAAs) on the basis of a recently published dataset (PMID: 32081774) of known associations between viruses and BSAAs. We are also developing an approach based on ideas from Network Medicine that is aimed at  predicting drugs that will perturb the COVID-19 “disease module” on the human interactome. This module is constituted by the set of proteins that are central to the host-pathogen interaction (host proteins) and it has been recently experimentally identified (PMID: 32353859). Currently, we are developing methods that prioritize drugs by calculating network distances on the interactome between the set of host proteins and the set of drug targets using diffusion kernels (DOI: We are also expanding the set of known drug targets using a drug target prediction algorithm that we have recently developed. An important question to be addressed in this approach, is how to prioritize the host proteins within the disease module. In collaboration with the miRNA/non-coding RNA and mitochondrial subgroups, we are attempting to address this using RNA-Seq data from SARS-CoV-2 infected cell lines.

Host/Virus Interactions

Leads: Joe Guarnieri and Joseph M Dybas, University of Pennsylvania/Children’s Hospital of Philadelphia

The host/virus interactions subgroup is exploring the interactions between SARS-COV-2 and the mammalian/human host. These interactions occur at two levels: intra-cellular (cell-autonomous) and inter-cellular (cell non-autonomous). In cell-autonomous interactions the virus exploits the biochemistry and molecular biology of the host cell to advance its propagation. This may involve activating cellular biosynthetic and apoptotic/necrotic functions while inhibiting cellular anti-viral processes that suppress viral replication and propagation. Special interests include viral manipulation of the ubiquitin and mitochondrial systems and inhibition of interferon signaling. The cell non-autonomous interactions include the viral exploitation of the inflammatory system to facilitate viral distribution, inhibition of immune cell activation, and blocking inter-cellular interferon signaling to impair target cell activation of antiviral processes. This group also focuses on better understanding how the genetic makeup of the host and the variance of the strain can impact such interaction. By understanding and developing systems to thwart the viral manipulation of the host, we hope to develop approaches to mitigate the most devastating effects of COVID-19.


Lead: Bianca Cerqueira, KBR

The goal of this subgroup is to provide funding to support the research objectives of all subgroups and collaborators within COV-IRT. The funding subgroup informs COV-IRT members of upcoming funding opportunities for topics related to the COVID-19 pandemic. Our subgroup helps coordinate research objectives, data availability, narratives, budgets, research agreements, and logistics surrounding multi-institutional grant applications.


Lead: Sigrid Reinsch, NASA Ames Research Cente

In order to embody open science, COV-IRT must reach an audience not only across the scientific community but beyond academia as well. The purpose of the outreach subgroup is to share the scientific advancements and progress made by COV-IRT with these communities in order to facilitate collaboration across the scientific community and to increase openness between scientists and the public. The outreach subgroup focuses on growing COV-IRT’s social media and online presence and widely promoting the well-attended COV-IRT symposia in order to communicate with a large and diverse audience. The outreach subgroup is also focusing on establishing and disseminating online protocols and guidelines to help the local communities better respond to the pandemic by putting in place simple actions, based on science, to prevent the spread of the disease. This can be as simple as social distancing rules and as involved as providing “do-it-yourself” guidelines for ad-hoc on-site testing by students and staff to make our schools and local communities more resilient. In addition, the capacity of COV-IRT to accomplish its concrete scientific goals comes from the diversity of the abilities, expertise, perspectives, and resources of its members. Therefore, the secondary goal of the outreach subgroup is to attract new members to COV-IRT. Finally, the outreach subgroup serves as a conduit for inquiries about membership and the group in general.