Yellow fever disease severity is driven by an acute cytokine storm modulated by an interplay between the human gut microbiome and the metabolome
Bibliographic record
Abstract
Abstract Infection with Yellow Fever (YF) can lead to multiple outcomes ranging from death from total organ failure to clearance of viremia and survival. The mechanisms underlying these differences in clinical outcomes have yet to be defined. We had access to a cohort of YF infected subjects that showed these range of outcomes. An unbiased integrated OMICs approach was used to identify pathways and effector molecules that drive the severe disease and death as compared to resolution of infection. We used the MELD and SIC score as objective markers of disease severity. We show that a specific signature of upregulated innate pro-inflammatory cytokines significantly demarcates subjects with severe disease leading to death from subjects who clear virus. Pathogen sequencing showed heightened levels of proteolytic bacteria at the i.e Actinobacteria and these were correlated to lower levels of tryptophan and tyrosine amino acids measured by untargeted metabolomics. These two features were significantly associated to MELD scores synonymous of milder disease. Propionate a bacterial metabolite that triggers Treg differentiation that can as well limit the hyperimmune activation associated to severe outcome was also associated to improved outcome. Our results suggest a model whereby proteolytic bacteria limit the availability of the aromatic amino acid pool available for cytokine production thereby preventing the induction of the cytokine storm that is associated to severe disease and death.
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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".