The Role of Coinhibitory Receptors in B Cell Dysregulation in SARS-CoV-2–Infected Individuals with Severe Disease
Bibliographic record
Abstract
Severe SARS-CoV-2 infection is associated with significant immune dysregulation involving different immune cell subsets. In this study, when analyzing critically ill COVID-19 patients versus those with mild disease, we observed a significant reduction in total and memory B cell subsets but an increase in naive B cells. Moreover, B cells from COVID-19 patients displayed impaired effector functions, evidenced by diminished proliferative capacity, reduced cytokine, and Ab production. This functional impairment was accompanied by an increased apoptotic potential upon stimulation in B cells from severely ill COVID-19 patients. Our further studies revealed the expansion of B cells expressing coinhibitory molecules (PD-1, PD-L1, TIM-1, VISTA, CTLA-4, and Gal-9) in intensive care unit (ICU)-admitted patients but not in those with mild disease. The coinhibitory receptor expression was linked to altered IgA and IgG expression and increased the apoptotic capacity of B cells. Also, we found a reduced frequency of CD24hiCD38hi regulatory B cells with impaired IL-10 production. Our mechanistic studies revealed that the upregulation of PD-L1 was linked to elevated plasma IL-6 levels in COVID-19 patients. This implies a connection between the cytokine storm and altered B cell phenotype and function. Finally, our metabolomic analysis showed a significant reduction in tryptophan but elevation of kynurenine in ICU-admitted COVID-19 patients. We found that kynurenine promotes PD-L1 expression in B cells, correlating with increased IL-6R expression and STAT1/STAT3 activation. Our observations provide novel insights into the complex interplay of B cell dysregulation, implicating coinhibitory receptors, IL-6, and kynurenine in impaired B cell effector functions, potentially contributing to the pathogenesis of COVID-19.
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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) | 0.001 | 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".