Characterization of Hepatitis C Virus (HCV) Envelope Diversification from Acute to Chronic Infection within a Sexually Transmitted HCV Cluster by Using Single-Molecule, Real-Time Sequencing
Bibliographic record
Abstract
ABSTRACT In contrast to other available next-generation sequencing platforms, PacBio single-molecule, real-time (SMRT) sequencing has the advantage of generating long reads albeit with a relatively higher error rate in unprocessed data. Using this platform, we longitudinally sampled and sequenced the hepatitis C virus (HCV) envelope genome region (1,680 nucleotides [nt]) from individuals belonging to a cluster of sexually transmitted cases. All five subjects were coinfected with HIV-1 and a closely related strain of HCV genotype 4d. In total, 50 samples were analyzed by using SMRT sequencing. By using 7 passes of circular consensus sequencing, the error rate was reduced to 0.37%, and the median number of sequences was 612 per sample. A further reduction of insertions was achieved by alignment against a sample-specific reference sequence. However, in vitro recombination during PCR amplification could not be excluded. Phylogenetic analysis supported close relationships among HCV sequences from the four male subjects and subsequent transmission from one subject to his female partner. Transmission was characterized by a strong genetic bottleneck. Viral genetic diversity was low during acute infection and increased upon progression to chronicity but subsequently fluctuated during chronic infection, caused by the alternate detection of distinct coexisting lineages. SMRT sequencing combines long reads with sufficient depth for many phylogenetic analyses and can therefore provide insights into within-host HCV evolutionary dynamics without the need for haplotype reconstruction using statistical algorithms. IMPORTANCE Next-generation sequencing has revolutionized the study of genetically variable RNA virus populations, but for phylogenetic and evolutionary analyses, longer sequences than those generated by most available platforms, while minimizing the intrinsic error rate, are desired. Here, we demonstrate for the first time that PacBio SMRT sequencing technology can be used to generate full-length HCV envelope sequences at the single-molecule level, providing a data set with large sequencing depth for the characterization of intrahost viral dynamics. The selection of consensus reads derived from at least 7 full circular consensus sequencing rounds significantly reduced the intrinsic high error rate of this method. We used this method to genetically characterize a unique transmission cluster of sexually transmitted HCV infections, providing insight into the distinct evolutionary pathways in each patient over time and identifying the transmission-associated genetic bottleneck as well as fluctuations in viral genetic diversity over time, accompanied by dynamic shifts in viral subpopulations.
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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".