Mass spectrometry analysis of gut tissue in acute SIV-infection in rhesus macaques identifies early proteome alterations preceding the interferon inflammatory response
Bibliographic record
Abstract
HIV infection damages the gut mucosa leading to chronic immune activation, increased morbidities and mortality, and antiretroviral therapies, do not completely ameliorate mucosal dysfunction. Understanding early molecular changes in acute infection may identify new biomarkers underlying gut dysfunction. Here we utilized a proteomics approach, coupled with flow cytometry, to characterize early molecular and immunological alterations during acute SIV infection in gut tissue of rhesus macaques. Gut tissue biopsies were obtained at 2 times pre-infection and 4 times post-infection from 6 macaques. The tissue proteome was analyzed by mass spectrometry, and immune cell populations in tissue and blood by flow cytometry. Significant proteome changes (p < 0.05) occurred at 3 days post-infection (dpi) (13.0%), 14 dpi (13.7%), 28 dpi (16.9%) and 63 dpi (14.8%). At 3 dpi, proteome changes included cellular structural activity, barrier integrity, and activation of epithelial to mesenchymal transition (EMT) (FDR < 0.0001) prior to the antiviral response at 14 dpi (IFNa/g pathways, p < 0.001). Novel EMT proteomic biomarkers (keratins 2, 6A and 20, collagen 12A1, desmoplakin) and inflammatory biomarkers (PSMB9, FGL2) were associated with early infection and barrier dysfunction. These findings identify new biomarkers preceding inflammation in SIV infection involved with EMT activation. This warrants further investigation of the role of these biomarkers in chronic infection, mucosal inflammation, and disease pathogenesis of HIV.
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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".