Rapid intra-host diversification and evolution of SARS-CoV-2 in advanced HIV infection
Bibliographic record
Abstract
Previous studies have linked the evolution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic variants to persistent infections in people with immunocompromising conditions, but the processes responsible for these observations are incompletely understood. Here we use high-throughput, single-genome amplification and sequencing (HT-SGS) to sequence SARS-CoV-2 spike genes from people with HIV (PWH, n = 22) and people without HIV (PWOH, n = 25). In PWOH and PWH with CD4 T cell counts (i.e., CD4 counts) ≥ 200 cells/μL, we find that most SARS-CoV-2 genomes sampled in each person share one spike sequence. By contrast, in people with advanced HIV infection (i.e., CD4 counts < 200 cells/μL), HT-SGS reveals a median of 46 distinct linked groupings of spike mutations per person. Elevated intra-host spike diversity in people with advanced HIV infection is detected immediately after COVID-19 symptom onset, and early intra-host spike diversity predicts SARS-CoV-2 shedding duration among PWH. Analysis of longitudinal timepoints reveals rapid fluctuations in spike sequence populations, replacement of founder sequences by groups of new haplotypes, and positive selection at functionally important residues. These findings demonstrate remarkable intra-host genetic diversity of SARS-CoV-2 in advanced HIV infection and suggest that adaptive intra-host SARS-CoV-2 evolution in this setting may contribute to the emergence of new variants of concern.
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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 | 0.001 |
| 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".