Systems Immunology Analysis Reveals the Contribution of Pulmonary and Extrapulmonary Tissues to the Immunopathogenesis of Severe COVID-19 Patients
Bibliographic record
Abstract
As one of the current global health conundrums, COVID-19 pandemic caused a dramatic increase of cases exceeding 79 million and 1.7 million deaths worldwide. Severe presentation of COVID-19 is characterized by cytokine storm and chronic inflammation resulting in multi-organ dysfunction. Currently, it is unclear whether extrapulmonary tissues contribute to the cytokine storm mediated-disease exacerbation. In this study, we applied systems immunology analysis to investigate the immunomodulatory effects of SARS-CoV-2 infection in lung, liver, kidney, and heart tissues and the potential contribution of these tissues to cytokines production. Notably, genes associated with neutrophil-mediated immune response (e.g. CXCL1) were particularly upregulated in lung, whereas genes associated with eosinophil-mediated immune response (e.g. CCL11) were particularly upregulated in heart tissue. In contrast, immune responses mediated by monocytes, dendritic cells, T-cells and B-cells were almost similarly dysregulated in all tissue types. Focused analysis of 14 cytokines classically upregulated in COVID-19 patients revealed that only some of these cytokines are dysregulated in lung tissue, whereas the other cytokines are upregulated in extrapulmonary tissues (e.g. IL6 and IL2RA). Investigations of potential mechanisms by which SARS-CoV-2 modulates the immune response and cytokine production revealed a marked dysregulation of NF-κB signaling particularly CBM complex and the NF-κB inhibitor BCL3. Moreover, overexpression of mucin family genes (e.g. MUC3A, MUC4, MUC5B, MUC16, and MUC17) and HSP90AB1 suggest that the exacerbated inflammation activated pulmonary and extrapulmonary tissues remodeling. In addition, we identified multiple sets of immune response associated genes upregulated in a tissue-specific manner (DCLRE1C, CHI3L1, and PARP14 in lung; APOA4, NFASC, WIPF3, and CD34 in liver; LILRA5, ISG20, S100A12, and HLX in kidney; and ASS1 and PTPN1 in heart). Altogether, these findings suggest that the cytokines storm triggered by SARS-CoV-2 infection is potentially the result of dysregulated cytokine production by inflamed pulmonary and extrapulmonary (e.g. liver, kidney, and heart) tissues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".