Proteomic Profile Associated With Loss of Spontaneous Human Immunodeficiency Virus Type 1 Elite Control
Bibliographic record
Abstract
BACKGROUND: Elite controllers (ECs) spontaneously control plasma human immunodeficiency virus type 1 (HIV-1) RNA without antiretroviral therapy. However, 25% lose virological control over time. The aim of this work was to study the proteomic profile that preceded this loss of virological control to identify potential biomarkers. METHODS: Plasma samples from ECs who spontaneously lost virological control (transient controllers [TCs]), at 2 years and 1 year before the loss of control, were compared with a control group of ECs who persistently maintained virological control during the same follow-up period (persistent controllers [PCs]). Comparative plasma shotgun proteomics was performed with tandem mass tag (TMT) isobaric tag labeling and nanoflow liquid chromatography coupled to Orbitrap mass spectrometry. RESULTS: Eighteen proteins exhibited differences comparing PC and preloss TC timepoints. These proteins were involved in proinflammatory mechanisms, and some of them play a role in HIV-1 replication and pathogenesis and interact with structural viral proteins. Coagulation factor XI, α-1-antichymotrypsin, ficolin-2, 14-3-3 protein, and galectin-3-binding protein were considered potential biomarkers. CONCLUSIONS: The proteomic signature associated with the spontaneous loss of virological control was characterized by higher levels of inflammation, transendothelial migration, and coagulation. Galectin-3 binding protein could be considered as potential biomarker for the prediction of virological progression and as therapeutic target in ECs.
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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".