Single genome amplification and molecular cloning of HIV-1 populations in acute HIV-1 infection: implications for studies on HIV-1 diversity and evolutionary rate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Human immunodeficiency virus type 1 (HIV-1) is one of the fastest-evolving human pathogens. Understanding HIV-1 transmission, within-host adaptation, and evolutionary dynamics is pivotal for development of interventions and vaccines. HIV-1 infection is generally caused by a single transmitted founder virus (TFV), and TFV sequences are typically obtained using single genome amplification (SGA). However, suboptimal sample quality can cause sequencing failures, representing considerable losses considering the scarcity of acute HIV-1 infection (AHI) samples. Sequencing failures may be mitigated by molecular cloning (MC), which can be less vulnerable to sample quality but more susceptible to polymerase chain reaction (PCR) errors. Here, we explore the feasibility of supplementing SGA with MC data using samples from clinical and research cohorts to determine whether sequence diversity and evolutionary rate estimates are comparable between the techniques. Methods Plasma samples were selected from participants with documented AHI from an East African research cohort (the International AIDS Vaccine Initiative, 2006–2011) and a clinical cohort from Sweden (1983–2011). SGA and MC sequencing were done on the HIV-1 env V1-V3 region (~940 base pairs). Within-host sequence diversity was determined from maximum likelihood phylogenetic trees, and evolutionary rate by Bayesian phylogenetic analysis. Highlighter plots, Hamming distances, and assessment of star phylogenies were used to quantify TFVs. Results One hundred participants (median age 30.3 years, 15% female), contributing 350 samples from four longitudinal time points, 10–540 days post-infection, met the inclusion criteria. SGA succeeded on 90% of research cohort and 48% of clinical cohort samples. Comparative analysis of linked SGA and MC data from 10 samples indicated that approximately eight sequences were necessary for diversity estimates. Consistently higher sequence diversity was observed among MC relative to SGA sequences (median [IQR]: 0.009 [0.003, 0.015] and 0.004 [0.001, 0.012] substitutions/site, P = .002), whereas evolutionary rates were comparable between the two methods (0.016 [0.012, 0.019] and 0.011 [0.008, 0.020] substitutions/site/year, P = .232). Five participants with samples obtained within 45 days post-infection were eligible for TFV quantification, and all found to have one TFV using both techniques. Conclusion MC data is a suitable supplement for SGA-based HIV-1 studies to preserve the value of precious samples for analysis of evolutionary rate, but not for sequence diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 it