COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes (preprint)

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

RNA-protein interaction analysis of SARS-CoV-2 5'- and 3'-untranslated regions identifies an antiviral role of lysosome-associated membrane protein-2 (preprint)

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is a positive-strand RNA virus. Viral genome is capped at the 5'-end, followed by an untranslated region (UTR). There is poly-A tail at 3'-end, preceded by an UTR. Self-interaction between the RNA regulatory elements present within 5'- and 3'-UTRs as well as their interaction with host/virus-encoded proteins mediate the function of 5'- and 3'-UTRs. Using RNA-protein interaction detection (RaPID) assay coupled to liquid chromatography with tandem mass-spectrometry, we identified host interaction partners of SARS-CoV-2 5'- and 3'-UTRs and generated an RNA-protein interaction network. By combining these data with the previously known protein-protein interaction data proposed to be involved in virus replication, we generated the RNA-protein-protein interaction (RPPI) network, likely to be essential for controlling SARS-CoV-2 replication. Notably, bioinformatics analysis of the RPPI network revealed the enrichment of factors involved in translation initiation and RNA metabolism. Lysosome-associated membrane protein-2a (Lamp2a) was one of the host proteins that interact with the 5'-UTR. Further studies showed that Lamp2 level is upregulated in SARS-CoV-2 infected cells and overexpression of Lamp2a and Lamp2b variants reduced viral RNA level in infected cells and vice versa. In summary, our study provides an useful resource of SARS-CoV-2 5'- and 3'-UTR binding proteins and reveal the antiviral function of host Lamp2 protein.

Rapid community-driven development of a SARS-CoV-2 tissue simulator (preprint)

The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving rapid refinements with a two-to-four week release cycle. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health.