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Part 2: COVID-19 in Patients: A Transcriptomic Profile
May 6, 2020
Of the few publications with published gene expression data from Covid-19 patients, a recent manuscript in ‘Emerging Microbes and Infections’ investigated the transcriptomic host response to infection of SARS-CoV-2. In order to give more color to the findings, we analyzed the published gene expression data using our AI software.
At PercayAI, we want to share transparent analyses with the scientific community in order to help uncover novel hypotheses to target Covid-19. We are therefore using our AI tools to help colleagues worldwide gain a better collective understanding of the virus and potential treatments. We’re asking other researchers to join us. If you have COVID-19 data you'd like us to analyze using our tool, free of charge, reach out here to get in touch with our team.
In this study, the authors profiled respiratory response through bronchoalveolar lavage fluid (BALF) samples and systemic response through peripheral blood mononuclear cells (PBMC) samples, comparing samples from COVID-19 patients to healthy control samples. The authors observed an increase in cell death and an exaggerated inflammatory cytokine response induced by SARS-CoV-2, which may lead to lymphopenia.
COVID-19 in Patient Blood Samples
We previously analyzed the transcriptome derived from BALF samples and identified key processes in this post. Here, we are focusing on the PBMC samples from the same study and identifying processes that are up- or down-regulated by the infection.
First, we’ll take a look at the upregulated responses found in the PBMC samples of COVID-19 patients.
Upregulated Host Response to COVID-19
General Overview:
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In purple (right), a number of cellular metabolism processes are enhanced, potentially suggesting high energy consumption within the blood cells.
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In teal (bottom), there is an immune cluster that includes a specific macrophage signature, immunoglobulins, and complement signaling.
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In blue (center, right) are cell fate related themes, which focus on replication and cell cycle regulation.
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In green (middle, right), there are a few processes that are associated with cell motility and ECM reorganization including VEGF-A signaling.
Key Takeaways:
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Together, the cellular metabolism and cell fate themes may indicate that the cells are active and proliferative. This may be a negative effect of infection or a compensatory response.
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Interestingly, we found that the macrophages seem to be the only immune cell type upregulated in response to the viral infection.
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Recently, there has been interest in the effect of complement signaling within COVID-19 disease state. Here, we found that complement signaling is upregulated within circulation of these patients.
Next, we’ll take a look at the downregulated processes in the PBMC samples.
Downregulated Host Response to COVID-19
General Overview:
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In purple (bottom), a number of developmental processes are impeded such as Hedgehog and cell migration.
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In teal (middle) are structural features and extracellular proteins that are compromised by COVID-19 infection.
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In blue (top left) are signaling cascades that are mediated through extracellular ligands, with an increase in stress-activated pathways such as MAPK signaling.
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In red (middle, right), there are many immune processes that are mostly associated with innate immunity and natural killer cells.
Key Takeaways:
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Through this analysis, it seems that the infection has led to reduced expression of innate immune cells within peripheral blood. This may be due to relocalization of these cells at the epithelial and mucosal sites where the virus is actively infecting the host.
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We found the cells to exhibit stress signals, possibly due to damage to the host. Markers of structural proteins were also reduced. Both processes may be regulated through their connection to the extracellular environment.
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Additionally, we found the pro-angiogenesis signaling pathway VEGF-A to be upregulated in peripheral blood, and the anti-angiogenic factor thrombospondin to be downregulated. Together, this points to a promotion of angiogenesis during the COVID-19 infection.
In conclusion, our AI tool allowed us to quickly convert available research findings into an easy-to-understand visualization, which helped our computational biologists make connections of their own. The ability for researchers to become well-versed in the current literature and identify scientifically grounded, testable hypotheses in a timely manner will accelerate the timeline to effective drug treatments.
Leave a comment below with your impressions, or send us a message here. If you have COVID-19 data you'd like us to run, free of charge, reach out here, so our team can contact you with potential next steps.
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