Transcriptional profiling of leukocytes in critically ill COVID19 patients: implications for interferon response and coagulation
Bibliographic record
Abstract
BACKGROUND: COVID19 is caused by the SARS-CoV-2 virus and has been associated with severe inflammation leading to organ dysfunction and mortality. Our aim was to profile the transcriptome in leukocytes from critically ill patients positive for COVID19 compared to those negative for COVID19 to better understand the COVID19-associated host response. For these studies, all patients admitted to our tertiary care intensive care unit (ICU) suspected of being infected with SARS-CoV-2, using standardized hospital screening methodologies, had blood samples collected at the time of admission to the ICU. Transcriptome profiling of leukocytes via ribonucleic acid sequencing (RNAseq) was then performed and differentially expressed genes as well as significantly enriched gene sets were identified. RESULTS: We enrolled seven COVID19 + (PCR positive, 2 SARS-CoV-2 genes) and seven age- and sex-matched COVID19- (PCR negative) control ICU patients. Cohorts were well-balanced with the exception that COVID19- patients had significantly higher total white blood cell counts and circulating neutrophils and COVID19 + patients were more likely to suffer bilateral pneumonia. The mortality rate for this cohort of COVID19 + ICU patients was 29%. As indicated by both single-gene based and gene set (GSEA) approaches, the major disease-specific transcriptional responses of leukocytes in critically ill COVID19 + ICU patients were: (i) a robust overrepresentation of interferon-related gene expression; (ii) a marked decrease in the transcriptional level of genes contributing to general protein synthesis and bioenergy metabolism; and (iii) the dysregulated expression of genes associated with coagulation, platelet function, complement activation, and tumour necrosis factor/interleukin 6 signalling. CONCLUSIONS: Our findings demonstrate that critically ill COVID19 + patients on day 1 of admission to the ICU display a unique leukocyte transcriptional profile that distinguishes them from COVID19- patients, providing guidance for future targeted studies exploring novel prognostic and therapeutic aspects of COVID19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.077 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".