Comparison of longitudinal SARS-CoV-2 nasopharyngeal specimens reveals the transcriptomic COVIDome
Bibliographic record
Abstract
Abstract Introduction/Objective SARS-Cov-2 is well established to introduce a cytokine-like storm among select individuals that results in multisystem failure and death. Comorbidities, age, oxygen status, and real-time appraisal of inflammatory markers in the blood have been used to risk stratify patients, however, these clinical markers do not comprehensively characterize the at-risk population or disease course. To understand the molecular underpinnings of the primary site of SARS-CoV-2 infection, here, we interrogated the transcriptomic profile of the nasopharyngeal tissue among paired SARS-CoV-2 specimens. Methods/Case Report We performed ribosomal depletion RNAseq on 24 primary samples, including 16 paired samples from 8 unique patients who converted between SARS-CoV-2 negative and positive status via clinical diagnostic qRT-PCR. Additional targeted qRT-PCR was performed for ACE2 and TMPRSS2 in an extension sample of 54 paired specimens from 27 unique patients who converted in their SARS-CoV-2 status on the basis of the qRT- PCR test. Differential gene expression, differential correlative expression with ACE2, and correlative expression with viral load was used to identify genes, which were integral to SARS-CoV-2 pathogenesis, so termed the COVIDome. Gene ontologies, pathways, and reactive infiltrate was assessed between specimens and compared with measures of clinical outcome using regression with appropriate correction for multiple hypotheses. Results (if a Case Study enter NA) We observed significant enrichment for ontologies of lymphocyte activation, specifically interferon gamma signaling; (P<1E-20) and platelet activation (P<1E-5). Genes specifically enriched across all three modules included: ADAMDEC1, EPSTI1, GRIP2, IRF7, KLHDC7B, OAS3, OASL, PIK3R4, RSAD2, and XAF1. Using CIBERSORT to approximate immune cell populations from bulk RNA, we observed and enrichment for CD4 immune cells, which was associated with viral status (P<0.01) while high-risk gene signatures were associated with measures of clinical outcome (P<0.05). Conclusion We characterized the pathogenesis of SARS-CoV-2 in longitudinal nasopharyngeal samples of COVID- 19 patients and related these molecular manifestations with measures of clinical outcome. As proof of principal, our findings suggest additional study in a large, longitudinal extension sample is warranted to validate and assess molecular features of clinical outcome associated with SARS-CoV-2 infection.
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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".