Kinome and phosphoproteome reprogramming underlies the aberrant immune responses in critically ill COVID-19 patients
Bibliographic record
Abstract
Abstract The SARS-CoV-2 infection elicits comprehensive host immune reactions and causes severe diseases in some individuals. However, the molecular basis underlying the excessive, yet non-productive immune responses in severe COVID-19 is not fully understood. To address this, we compared the peripheral blood mononuclear cell (PBMC) proteome and phosphoproteome of sepsis patients positive or negative for SARS-CoV-2 and healthy subjects by quantitative mass spectrometry. We show here that the COVID-19 PBMC proteome and phosphoproteome undergo dynamic changes during disease progression, and the corresponding protein or phosphoprotein signatures can distinguish longitudinal disease states. Furthermore, SARS-CoV-2 infection leads to a global reprogramming of the kinome and the phosphoproteome, resulting in defective adaptive immune response mediated by B and T lymphocytes, compromised innate immune responses involving the SIGLEC and SLAM family of immunoreceptors, and excessive cytokine-JAK-STAT signaling. Besides uncovering the host proteome and phosphoproteome aberrations caused by SARS-CoV-2, our work has recapitulated several reported therapeutic targets for COVID-19 and identified numerous new ones, including the kinases PKG1, CK2, ROCK1/2, GRK2, SYK, JAK2/3, TYK2, DNA-PK and the cytokine IL-12. FUNDING. Ontario Research Fund (ORF)-COVID-19 Rapid Research Fund.
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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 | 0.000 | 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) | 0.001 | 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".