Dear co-authors, We finally received the reviewers’ comments from Virus Evolution regarding the HCV manuscript. It seems it was difficult to find reviewers for the paper, which may explain the delay. I am attaching the reviewers’ comments here. The deadline to submit our response is July 7. We are already addressing most of the comments, but we will need help with a couple of them. Maggie, podrías ayudarnos con la respuesta a estos dos puntos?: Why were individuals with viral load <1000 copies/mL excluded? Is this a threshold relevant for genomic capture? Also, is this HCV or HIV viral load? While I’m assuming HCV given the next paragraph, clarification should be provided here. It is not clear why the MIDI fragments were even generated if they were just going to be merged with the WG data. Please let me know if you have any questions or comments. Best, Vanessa Dear Dr. Ávila-Ríos, Manuscript ID VEVOLU-2026-018 entitled "HCV Molecular Epidemiology Pipeline: A Tool for Hepatitis C Virus Sequence Clustering" which you submitted to Virus Evolution, has been reviewed. The comments of the reviewer(s) are included at the foot of this letter. The reviewer(s) have recommended publication, but also suggest some major revisions to your manuscript. Therefore, I invite you to respond to the reviewer(s)' comments and revise your manuscript. To revise your manuscript, log into https://mc.manuscriptcentral.com/vevolu and enter your Author Centre, where you will find your manuscript title listed under "Manuscripts with Decisions." Under "Actions," click on "Create a Revision." Your manuscript number has been appended to denote a revision. You may also click the below link to start the revision process (or continue the process if you have already started your revision) for your manuscript. 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Sincerely, Dr. Siobain Duffy Editor in Chief Virus Evolution https://mc.manuscriptcentral.com/vevolu http://ve.oxfordjournals.org/ Reviewer: 1 Comments to the Author This is an important contribution to HCV research globally as well as for Mexico specifically. The method illustrated here will, in my opinion, help move the field in a positive direction. I recommend this for publication, with the exception of these minor changes. 1) This is a novel methodology, and thus, should be compared to the "gold standards". For example, in the manuscript linked below, the authors introduce a new method and start by comparing it to three different datasets in order to illustrate to the readers how it stands against them. A quick google search finds several datasets that are publicly available for HCV and have used alternative methods to identify clusters (one is also included below). While the current manuscript also performs the generation of consensus sequences and alignments, simply comparing the thresholds from other papers to that obtained from AUTO-TUNE would be valuable. I suggest this not as a criticism, but specifically because I believe in the importance of this algorithm, and have little doubt that there will be differences identified upon comparison with different clustering strategies. This would be very important to readers who may be considering whether to use this methodology or not. links: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010745 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0336614 2) In the manuscript you state that the method has an "optional phylogenetic analysis stage". Why wasn't this analysis performed? I personally would enjoy seeing a phylogenetic tree reconstructed for one of the top performing regions identified in this study in order to get a better sense of how the clusters are correlated. Minor changes / suggestions: (i) In the "Sequence Processing and Consensus Generation" section. Was there a depth threshold for base calling? This is a known issue that can lead to reversions to the reference sequence in the generated consensus sequence step. See this paper on SARS-CoV-2 where they see this as a “significant cause of reversions in the tree” for more info: https://www.nature.com/articles/s41592-025-02947-1#Sec2 (ii) Genomic Region Analysis section: Recombination between HCV geno/subtypes can often occur. Was recombination accounted for? Since you are cross-comparing multiple regions of the genome which could conceivably have an alternative evolutionary history, recombination should be accounted for. I am guessing the authors already screened for recombination because they already performed a MEME and FEL analysis, which requires a recombination screen beforehand https://autotune.datamonkey.org/hcv (iii) There should be a reference for bealign or HIV_BETWEEN_F model paper (iv) criticisms of phylogenetic analyses should be scaled down. For example, in the abstract: "Most existing workflows focus on partial regions and require phylogenetic inference, which is computationally intensive and hardly scalable". Hardly scalable is harsh and borderline false when considering advances made by IQTREE and other maximum likelihood methodologies. This is particularly alarming considering this dataset consists only of 286 samples, which even the most computationally intensive phylogenetic approaches could easily handle (ie BEAST). Another criticism in the introduction should also be re-worded. "Molecular epidemiology has been successfully applied in HIV to identify key populations and outbreaks for timely intervention (refs). In HCV, its application had been limited (refs), since routine cluster detection relies on phylogenetic workflows that are computationally demanding, and their interpretation requires personnel with advanced training". This statement should be changed because the two sentences can be seen as somewhat contradictory because many HIV cluster identification pipelines also rely upon "... phylogenetic workflows that are computationally demanding, and their interpretation requires personnel with advanced training". (iv) In the Study Population section: "Approximately half of all individuals testing positive during the enrollment period were offered to participate in this study". How and why were they selected? Without illustrating this, it could be interpreted as a source of bias. (v) "following the protocol standardized and validated by the group of Chanson J. Brume et. al." should be changed to: "following the protocol standardized and validated by the BC Centre for Excellence" Reviewer: 2 Comments to the Author Weaver et al. describe an in-depth HCV molecular epidemiology pipeline, presenting comprehensive performance statistics and informative public health guidelines that have the potential to aid future molecular epidemiological studies of HCV outbreaks. However, clarification is needed on numerous points that would help build the case for development of this tool. Importantly, justification is lacking for many methodological choices. Lastly, it is unclear as to why epidemiological characteristics were not incorporated so as to provide meaningful interpretation of the clustering results. Detailed comments are organized below according to each manuscript section and include minor comments. Abstract: • Clustering threshold should include units. Introduction: • Is the statistic for mortality in 2022 in the first paragraph of the introduction specific to Mexico? This is unclear. Also, how could the launch of an elimination program in 2020 be a “response” to an estimated mortality rate in 2022? These last few sentences could use some clarification. • Why would traditional reliance on symptomatic case recognition be problematic for HCV surveillance, considering HCV is being screened leveraging existing HIV surveillance (i.e., regardless of symptom) and since HCV burden is disproportionately concentrated in the HIV population? In other words, among the HIV population, is the screening still only being performed for symptomatic cases? This could be clearer in the introduction. • Since there are no references for “cluster detection relies on phylogenetic workflows that are computationally demanding and their interpretation requires personnel with advanced training,” can you expand? This sentence is an important point for the introduction and is one of the primary arguments for the development of this approach mentioned throughout the text, but without references, a description by the authors of why/how phylogenetic workflows are severely limiting and in need of trained personnel is necessary. I would think this description is especially necessary considering the confusion for most readers as to why you are proposing to stray from phylogenetics and then offer a “phylogenetic analysis stage” in the pipeline. • It is stated in the introduction that HCV’s “genetic heterogeneity and high mutation rate complicate the development of protocols for whole-genome sequence processing, alignment and clustering,” yet we have accomplished this for HIV, so it would be nice to clarify here what makes HCV so difficult to analyze. • Also, why are whole genomes necessary, since single genes have been used for other viral molecular epi analyses and have been sufficiently informative? The answer to this question becomes apparent in the end, but it needs to be addressed up front. Methods: • It is not clear from the description of the study population whether “individuals testing positive” refers to HIV or HCV testing. • “Most participants were HCV treatment-naïve” – can an actual number not be provided here? • Why were individuals with viral load <1000 copies/mL excluded? Is this a threshold relevant for genomic capture? Also, is this HCV or HIV viral load? While I’m assuming HCV given the next paragraph, clarification should be provided here. • “WG and MiDi amplicons were then conducted” is poorly worded because an amplicon is a product and not a method that can be conducted. Also, how can a mid-sized amplicon be amplified to produce a near-whole genome, as is indicated in this section? • Since there is no contextual reference listed, the authors need to expand on how genotype/subtype were determined. • “Sequences were processed at multiple consensus thresholds…to explore the impact of within-host diversity on clustering patterns” – how were sequencing/PCR errors distinguished? But more importantly, I don’t see an assessment of the impact of within-host diversity on clustering, but rather consensus sequence generation success. • Why was the HIV BETWEEN+F substitution model used here? I see not justification or model testing to support the use of this model. • Why was a maximum distance threshold of 0.3 subs/site chosen? • Snakemake, Python, and SLURM should all be cited. • It is not clear why the MIDI fragments were even generated if they were just going to be merged with the WG data. Results: • What is the significance of the merge quality and differences in merge quality between genotypes? • Again, the thresholds need units. • What is the significance of different thresholds across subtypes? While it is an important consideration for future analysis, what is the hypothesized explanation and can it be explained by cluster-specific thresholds for other organisms, for example? • Gene names should be italicized. • I am unclear as to the following statement: “with 221 sequences generating 2,380 region-specific sequences.” Can the authors please calrify? • Why weren’t whole genomes also analyzed with clustering? In fact, the methods section on Genomic Region Analysis states that whole genomes were considered in the pipeline. I understand that different regions will exhibit different levels of diversity, but could this be overcome by utilizing the whole genome, especially since genotype 4d was so inconsistent across regions? • Why was NS5B chosen for network visualization and interpretation? • It is not clear what the difference is between the comparison of regions using the Jaccard similarity index and the next section on comparison of regions using ARI or why both were performed. • What is the hypothesis as to the reason for lack of clustering consistency across regions for genotype 4d? Discussion: • Some attempts at generalizing in the discussion should be toned down, given the sub-optimal performance for genotype 4d. While sample size may play a role, this hypothesis is just that – an hypothesis. And so cluster-based epi inference may not be applicable to non-1a genotypes. • DAA needs to be defined. • Was anything gained from this clustering approach regarding HCV evolution or molecular epidemiology? Especially since it appears another clustering study was done for HCV globally. Associate Editor: Poon, Art Comments to the Author: Thank you for submitting your manuscript to Virus Evolution for consideration. I apologize for the lengthy review process - it was very difficult to obtain reviewers for this manuscript, and I extend my gratitude to the two reviewers who finally accepted this assignment. Your manuscript has been evaluated by two peer reviewers with expertise in the molecular epidemiology of hepatitis C virus. Both reviewers have raised substantial points about the justification for the methodology and the interpretation of results. Consequently, my recommendation to the EIC is to request a major revision of the manuscript. --- # DRAFT POINT-BY-POINT RESPONSES > **Status note for co-authors.** The responses below are drafted from > information currently available in the analysis repository (pipeline code, > configuration files, and results documents: `PAPER.md`, `RESULTS_*.md`, > `FINAL_CLUSTERING_ANSWERS.md`, `Snakefile`, `configs/config.yaml`, > `scripts/`). Each point is tagged: > > - **[REPO]** — answerable now from repository information; draft text provided. > - **[CO-AUTHOR]** — requires clinical/cohort information not in the repo > (Maggie / Vanessa / clinical team), e.g. viral-load rationale, patient > selection, treatment-naïve counts, genotyping reference. Flagged with what > we need. > - **[EDITORIAL]** — wording/citation change; no new analysis required. > > Numbers cited below are pulled from the results documents; please verify > against the final manuscript version before submission, since some results > files contain updated figures relative to earlier drafts (e.g. person counts > n=221 for 1a / n=52 for 4d at the 0.2 consensus threshold; singletons = 0 / > 100% networked in the corrected tables). --- ## Reviewer 1 **1) Comparison against gold-standard datasets / published thresholds. [CO-AUTHOR + REPO]** We agree this comparison strengthens the paper and we can perform part of it now. AUTO-TUNE produces per-region, per-genotype thresholds (Table 1 in Results); for the regions most commonly used in published HCV clustering work we can tabulate our thresholds against literature values. Our NS5B threshold for genotype 1a is 0.01386 subs/site and NS3 is 0.02942, which we can place beside fixed thresholds used in the two suggested papers (PLoS Comput Biol 2021;17:e1010745 and PLoS One 2024;e0336614) and other HCV clustering studies. **Action:** add a new sub-section/supplementary table "Comparison of AUTO-TUNE thresholds with published HCV clustering thresholds." The reviewer's expectation that differences will emerge is consistent with our central finding that optimal thresholds are strongly region- and genotype-dependent (>10-fold variation, 0.00354–0.04571). *Co-author input needed to finalize the literature threshold values and ensure the comparison datasets are described accurately.* (TODO: SWEAVER) **2) Why wasn't the optional phylogenetic analysis performed? Show a tree for a top region. [REPO — already done]** The phylogenetic analysis **was** performed and the results are available; they were not fully integrated into the submitted draft. We generated 90 maximum-likelihood trees (FastTree, GTR with JC fallback) at the 0.2 consensus threshold across all genotypes and regions, with SH-like support values, Robinson-Foulds topology comparisons, and HyPhy SLAC/FEL/MEME selection analyses (see `RESULTS_PHYLOGENETIC.md`). **Action:** we will (a) add the phylogenetic results section to the manuscript and (b) include a figure showing the reconstructed tree for a top-performing genotype 1a region (e.g. NS5B or NS3, which produced 6–7 balanced clusters) so the relationship between the phylogeny and the network clusters can be visualized directly. Summary support statistics are available now: genotype 1a whole-genome mean SH-like support 0.836 (45.7% of nodes >0.95); E1 lowest (mean 0.609); genotype 4d whole-genome mean 0.922. (TODO: SWEAVER) **Minor (i) Depth threshold for base calling / reversion artifacts. [REPO + CO-AUTHOR]** Important clarification: the consensus step in *this* pipeline operates on two already-assembled consensus sequences per patient — the whole-genome (WG) and the mid-sized (MIDI) amplicon products — and **merges** them (`scripts/merge_overlap.py`). At each overlapping position the merge takes the majority base when fewer than three distinct bases are present and assigns 'N' when three or more distinct bases occur (ambiguity). The "consensus thresholds" (0.01–0.25) refer to the minor-variant frequency cutoff used when the *original* per-sample consensus sequences were called upstream of this pipeline. **Action:** (a) clarify in Methods that base calling/consensus from reads occurred upstream and state the depth/frequency parameters used there (*co-author/wet-lab input needed for the exact read-depth threshold*); (b) note that the SARS-CoV-2 reversion concern (Nature Methods 2025) is mitigated here because we did not impose reference-filling at low depth — ambiguous positions were called 'N' rather than reverted to reference, and our merge quality metrics show very low fragment disagreement (overall mean mismatch rate 1.9%, median 1.0%; mean N introduced 7.6, median 0.0 at the 0.2 threshold). (TODO: SWEAVER/MAGGIE) **Minor (ii) Recombination screening before MEME/FEL and across regions. [REPO — needs new analysis]** Honest status: the current pipeline does **not** include an explicit recombination/GARD screen. The reviewer is correct that MEME/FEL assume no recombination and that cross-region comparisons could be confounded by recombination. Note that our analyses are run **within** single genotype/subtype groups and largely **within** single contiguous genomic regions, which limits (but does not eliminate) inter-genotype recombination concerns. **Action:** we propose to add a GARD (Genetic Algorithm for Recombination Detection, HyPhy) screen on the per-region, per-genotype alignments and report the results; the selection analyses can then be re-run on recombination-free partitions where breakpoints are detected. We will state this as a methodological addition in the revision. *This requires a new analysis run; flagging for planning.* (TODO: SWEAVER) **Minor (iii) Reference for bealign / HIV_BETWEEN_F. [EDITORIAL — REPO confirms usage]** Confirmed: `configs/config.yaml` and the `Snakefile` use `bealign -q -m HIV_BETWEEN_F -K`. **Action:** add citation for `bealign` (part of the BioExt / HyPhy ecosystem; cite the BioExt/`bealign` reference and the HIV-TRACE/`hivtrace` paper of Kosakovsky Pond et al., Mol Biol Evol 2018, which distributes the alignment scoring matrices). The `HIV_BETWEEN_F` scoring matrix derives from the HyPhy/HIV-TRACE tooling — we will cite the appropriate methods paper. (See also Reviewer 2's model-justification point below.) (TODO: VANESSA) **Minor (iv) Tone down "hardly scalable" / phylogenetics criticisms. [EDITORIAL]** Agreed. **Action:** revise the abstract sentence "...require phylogenetic inference, which is computationally intensive and hardly scalable" to a measured statement, e.g. that phylogenetic workflows can require specialized expertise and that an alignment-and-distance approach offers an accessible, automatable alternative — explicitly acknowledging that modern ML tools (IQ-TREE, FastTree) and Bayesian methods (BEAST) readily handle datasets of this size (n=286). We will remove the implication that phylogenetics does not scale. (TODO: VANESSA) **Minor (iv-bis) Contradictory HIV vs HCV statement in Introduction. [EDITORIAL]** Agreed — the two sentences are in tension because HIV cluster-detection pipelines also rely on computationally demanding, expertise-intensive phylogenetics. **Action:** reword so the distinction is that HCV has lacked *standardized, validated* surveillance workflows rather than implying HIV methods are not also demanding; emphasize the gap in HCV-specific standardized protocols (multi-genotype handling, fragment merging, threshold selection). (TODO: VANESSA) **Minor (iv) Study Population — selection of ~half of positives. [CO-AUTHOR]** Cannot be answered from the repository. **Need from clinical team (Maggie/Vanessa):** the basis for offering enrollment to approximately half of positive individuals (consecutive sampling, capacity constraints, randomization, clinic schedule, consent, etc.), so we can state the selection mechanism and address potential selection bias explicitly. (TODO: VANESSA) **Minor (v) "Chanson J. Brume et al." → "BC Centre for Excellence". [EDITORIAL]** Agreed. **Action:** change the attribution to "following the protocol standardized and validated by the BC Centre for Excellence in HIV/AIDS." *Please confirm exact preferred wording / citation with the protocol source.* (TODO: VANESSA) --- ## Reviewer 2 ### Abstract **• Clustering threshold should include units. [EDITORIAL]** Agreed. **Action:** report thresholds as substitutions/site throughout (genetic distance thresholds are in subs/site; TN93 distances and the 0.3 maximum distance are in subs/site). (TODO: STEVEN) ### Introduction **• Is the 2022 mortality statistic Mexico-specific, and how can a 2020 program be a "response" to 2022 mortality? [EDITORIAL + CO-AUTHOR]** The repository draft (Introduction) gives the 2022 figure as Mexico-specific (11,851 HCV-related deaths, 9.2/100,000, per Mexico's 2022 Annual Epidemiological Surveillance Report). The chronology is indeed worded confusingly. **Action:** clarify that the National HCV Elimination Program (launched 2020) preceded these surveillance figures and was a response to the broader WHO 2030 elimination targets / earlier burden estimates, not to the 2022 mortality number. *Confirm framing with co-authors.* (TODO: VANESSA) **• Why is symptomatic-case recognition problematic given HCV is screened via existing HIV surveillance? [EDITORIAL/CO-AUTHOR]** Reasonable point. **Action:** clarify in the Introduction that within the HIV clinic population screening is *not* symptom-driven (it leverages routine HIV care contact, regardless of symptoms), and that the symptomatic-recognition limitation applies to general-population HCV surveillance outside such integrated programs — which is precisely why an HIV-clinic-embedded molecular approach is advantageous. *Co-author confirmation of screening practice.* (TODO: VANESSA) **• Expand, with references, on why phylogenetic workflows are "computationally demanding and require advanced training." [EDITORIAL]** Tied to Reviewer 1's tone comment. **Action:** add references and a concrete description (model selection, tree search/bootstrap, convergence diagnostics for Bayesian methods, interpretation expertise), and reconcile with the fact that we *do* offer an optional phylogenetic stage — framing it as optional/confirmatory rather than required for routine clustering. (TODO: VANESSA) **• What makes HCV harder to analyze than HIV (since we manage HIV)? [EDITORIAL/REPO]** Partly addressable from the draft (8 genotypes / >90 subtypes with distinct geography and transmission; high in-vivo mutation rate; fragment merging across WG+MIDI; absence of standardized multi-genotype thresholds). **Action:** make this explicit in the Introduction. *Co-authors may wish to add clinical framing.* (TODO: VANESSA) **• Why are whole genomes necessary, since single genes suffice elsewhere? [REPO]** Our own results answer this and we will move the answer "up front": no single region is universally optimal — thresholds and clustering behavior vary >10-fold across regions, conserved regions (core, NS4A/B, NS5A) are uninformative (single dominant cluster, R1/R2 13.7–19.8), and regions disagree substantially (ARI mean 0.312 for 1a, 0.089 for 4d). WG sequencing is what lets the pipeline *choose* and *cross-validate* informative regions per genotype. **Action:** add a sentence in the Introduction previewing this rationale. (TODO: VANESSA) ### Methods **• Does "individuals testing positive" refer to HIV or HCV? [CO-AUTHOR]** **Need clarification from clinical team.** (Same ambiguity as Reviewer 1.) (TODO: VANESSA) **• "Most participants were HCV treatment-naïve" — give a number. [CO-AUTHOR]** Not in the repository. **Need the actual count/percentage of treatment-naïve participants from the cohort database.** (TODO: VANESSA) **• Why exclude viral load <1000 copies/mL? Is this relevant to genomic capture? HCV or HIV VL? [CO-AUTHOR]** This is one of the two points Vanessa flagged for Maggie. Not derivable from repo. **Need:** (a) confirmation that this is **HCV RNA** viral load; (b) the rationale — almost certainly a genomic-capture/sequencing-success threshold (below ~1000 IU/mL whole-genome amplification typically fails), which we can state once confirmed. **Action:** add explicit clarification + justification in Methods. (TODO: VANESSA) **• "WG and MiDi amplicons were then conducted" wording; how can a mid-sized amplicon yield a near-whole genome? [EDITORIAL/CO-AUTHOR]** Agreed the wording is wrong (an amplicon is a product, not a method). **Action:** reword to describe WG and MIDI *amplification/sequencing* strategies. The MIDI fragment does **not** by itself yield a near-whole genome — it is **merged** with the WG product (`merge_overlap.py`) to improve coverage in the overlap region. **Action:** clarify the merge relationship in Methods. *Wet-lab co-author to confirm amplicon design wording.* (TODO: STEVEN/MAGGIE) **• Expand on how genotype/subtype was determined (no reference). [CO-AUTHOR]** The CSV→FASTA step (`scripts/csv2fasta.py`) consumes pre-assigned genotype labels; genotyping itself was performed upstream. **Need from co-authors:** the genotyping method/tool and reference (e.g. Geno2pheno, the LANL HCV tool, phylogenetic assignment, or a commercial assay) so it can be cited. (TODO: MAGGIE) **• "explore the impact of within-host diversity on clustering" — how were sequencing/PCR errors distinguished, and the analysis shown is consensus-generation success, not clustering impact. [REPO]** Valid. **Action:** (a) soften the claim — the consensus-threshold sweep (0.01–0.25) primarily affected *sequence generation success and inclusion*, not clustering topology; our own Discussion already notes RF distance across thresholds was low (mean 0.217 within regions; 695 tree pairs identical), showing threshold choice mainly affects inclusion rather than evolutionary signal. (b) Regarding error vs. true diversity: the merge step flags disagreement via mismatch rate and assigns 'N' at ambiguous positions rather than calling a variant, but we do not formally separate PCR/sequencing error from genuine within-host diversity — we will state this limitation explicitly rather than over-claim. (TODO: VANESSA) **• Why HIV BETWEEN+F substitution model? No justification/model testing. [REPO — partial + EDITORIAL]** Confirmed in config (`-m HIV_BETWEEN_F`). This is the scoring matrix used for codon-aware alignment by `bealign`, chosen because it is the validated default in the HIV-TRACE/BioExt alignment toolchain for handling diverged viral sequences; it is used here for **alignment scoring**, not as the evolutionary model for distance estimation (distances use TN93). **Action:** clarify this distinction in Methods (alignment scoring matrix vs. TN93 distance model) and cite the BioExt/HIV-TRACE references; acknowledge that the matrix is HIV-derived and note that codon-aware alignment is robust to this choice for our purpose. *We did not perform formal substitution-model selection; we will state this.* (TODO: STEVEN) **• Why a maximum distance threshold of 0.3 subs/site? [REPO]** Confirmed (`tn93 -t 0.3`, `configs/config.yaml: tn93.threshold: 0.3`). **Action:** clarify that 0.3 is the **maximum pairwise distance retained/computed** by TN93 (a computational cap to exclude clearly unrelated/cross-genotype pairs and bound output size), **not** the clustering threshold. Clustering thresholds are the much smaller AUTO-TUNE values (≤0.046). We will state the rationale: 0.3 subs/site comfortably exceeds any plausible within-subtype transmission distance while trimming the distance file. (TODO: STEVEN) **• Cite Snakemake, Python, and SLURM. [EDITORIAL — REPO confirms usage]** Confirmed (Snakemake workflow, Python 3.11+, SLURM integration). **Action:** add citations for Snakemake (Mölder et al. 2021), Python, and SLURM (Yoo et al. 2003). (TODO: VANESSA) **• Why generate MIDI fragments if they're just merged with WG? [CO-AUTHOR + REPO]** Second of the two points Vanessa flagged. From the code, the MIDI fragment is merged with the WG product to **improve/complete coverage** — the two amplification strategies cover different parts of the genome with different success, and merging fills gaps and increases the proportion of full-length, high-quality consensus sequences. Our merge metrics quantify the benefit (mean overlap 396 bp; low mismatch). **Action:** state explicitly that MIDI provides complementary coverage and rescues samples/regions where WG amplification was incomplete, increasing the number of analyzable near-full-genome sequences. *Wet-lab co-author should confirm the amplification-design rationale (e.g. MIDI's higher success at lower viral load / specific genome region).* (TODO: VANESSA) ### Results **• Significance of merge quality and inter-genotype differences. [REPO]** **Action:** add interpretation — low mismatch rates (overall 1.9%) and low N introduction (mean 7.6, median 0.0) indicate WG and MIDI products are concordant, so merging does not introduce chimeric/artifactual sequence; genotype differences in overlap length (1a 417.6 bp vs 4d 261.6 bp) reflect genotype-specific amplification efficiency, not quality problems. (TODO: STEVEN) **• Thresholds need units. [EDITORIAL]** Agreed — subs/site throughout. **• Significance of different thresholds across subtypes; hypothesized explanation; precedent in other organisms? [REPO + EDITORIAL]** **Action:** state that region/subtype-specific optimal thresholds reflect differing genetic diversity and selective constraint per region (envelope/NS regions diversify under immune pressure; structural/conserved regions are constrained), directly motivating AUTO-TUNE over fixed thresholds. We will note the parallel with HIV, where cluster-specific/region-specific thresholds are also used, as supporting precedent. (TODO: VANESSA) **• Gene names should be italicized. [EDITORIAL]** Agreed — italicize gene/region names per journal style. **• Clarify "221 sequences generating 2,380 region-specific sequences." [EDITORIAL/REPO]** **Action:** clarify wording. 221 unique genotype-1a persons were each analyzed across 12 genomic regions, yielding up to 12 region-specific sequences per person; summed across regions (with some regions missing for some persons) this totals ~2,380 region-specific sequences. We will reword to avoid implying 221 persons became 2,380 distinct samples, and present per-region node counts (Table 3) instead of (or alongside) the sum. (TODO: STEVEN) **• Why weren't whole genomes also clustered, since Methods says they were considered? Could WG overcome 4d inconsistency? [REPO]** WG **was** run through clustering. The result: WG behaves like the conserved regions — a single dominant cluster (1a: 2 clusters, largest 199/210; ARI vs core = 1.000), i.e. WG clustering is dominated by conserved sites and is *uninformative*, so it was de-emphasized rather than omitted. **Action:** report the WG clustering result explicitly and explain that WG does **not** overcome 4d inconsistency because the conserved bulk of the genome swamps the variable, epidemiologically informative regions. This is itself a key finding (per Discussion). (TODO: STEVEN) **• Why was NS5B chosen for network visualization/interpretation? [REPO]** **Action:** justify — NS5B is among the optimal regions (6 balanced clusters, R1/R2 2.30, 100% networked for 1a), is the conventional HCV genotyping/clustering target with the most published precedent (facilitating cross-study comparison), and is reliably amplified. (If a different region was actually used for the figure, we will state and justify that instead.) (TODO: STEVEN) **• Difference between the Jaccard-index comparison and the ARI comparison; why both? [REPO]** **Action:** clarify the complementary roles — Jaccard measures pairwise overlap of specific cluster memberships (set overlap), while ARI measures overall partition agreement corrected for chance. Both are reported because high set-overlap can coexist with low chance-corrected partition agreement (and vice versa); together they distinguish "same sequences grouped" from "same global clustering structure." We additionally use Krippendorff's α (`network_congruence_analysis.py`) for networked/singleton reliability. We will state explicitly what each adds. (TODO: STEVEN) **• Hypothesis for lack of clustering consistency across regions in 4d. [REPO]** **Action:** present the hypotheses already in the analysis: (a) smaller sample size (n=52 vs 221) reduces power and stability; (b) higher inter-region RF distance (mean 0.851) and low ARI (0.089) / Krippendorff α (0.361) indicate genuinely region-dependent signal; (c) possible greater transmission-network fragmentation. We will frame these as hypotheses, not conclusions (see Discussion comment below). (TODO: STEVEN) ### Discussion **• Tone down generalizations given 4d's poor performance; cluster-based inference may not apply to non-1a genotypes. [EDITORIAL]** Agreed. **Action:** explicitly state that robust performance was demonstrated chiefly for genotype 1a; that 4d performed sub-optimally and minor genotypes were underpowered; and that applicability to non-1a genotypes remains a hypothesis requiring larger samples. Sample size is offered as one possible explanation among several, not as established. (TODO: VANESSA ) **• Define DAA. [EDITORIAL]** **Action:** define "direct-acting antiviral (DAA)" at first use. **• Was anything gained re: HCV evolution / molecular epidemiology, especially vs. an existing global HCV clustering study? [REPO + EDITORIAL]** **Action:** strengthen the "what we learned" message: (1) optimal clustering regions for HCV are region- and genotype-specific and conserved regions / whole genome are *uninformative* for clustering — a concrete, transferable guideline; (2) AUTO-TUNE yields reproducible, dataset-specific thresholds, removing reliance on arbitrary fixed cutoffs; (3) FEL/SLAC selection signatures recapitulate known HCV biology (immune-driven diversification in envelope/NS regions, purifying selection in structural regions); (4) we provide an automated, Snakemake-based pipeline as a reusable tool. We will contrast our region/threshold-optimization contribution with the global-clustering study suggested by Reviewer 1 (complementary scope: methodology/region selection vs. global epidemiology). *Co-authors to confirm which global HCV clustering study to cite and contrast.* (TODO: VANESSA ) --- ## Summary of items requiring co-author / clinical input (cannot be answered from the repository) 1. **Viral-load <1000 exclusion** — confirm it is HCV RNA and provide the genomic-capture rationale. *(Maggie/Vanessa)* 2. **HIV vs HCV "testing positive"** — disambiguate in Study Population/Methods. 3. **Treatment-naïve count** — exact number/percentage. 4. **Patient selection (~half offered enrollment)** — selection mechanism and bias statement. 5. **Genotyping/subtyping method and reference** — tool/assay used upstream. 6. **Amplicon-design wording** — WG vs MIDI amplification strategy and why MIDI is generated (coverage rescue / low-VL performance). 7. **Upstream base-calling depth/frequency threshold** — for the reversion concern (Reviewer 1 minor i). 8. **Literature thresholds and the global HCV clustering study** to use in the gold-standard comparison (Reviewer 1 #1; Reviewer 2 final Discussion point). ## Summary of new analyses proposed 1. **GARD recombination screen** on per-region/per-genotype alignments, with selection analyses re-run on recombination-free partitions (Reviewer 1 ii). 2. **Threshold comparison table** vs. published HCV clustering thresholds (Reviewer 1 #1). 3. **Phylogenetic figure** for a top genotype-1a region (e.g. NS5B/NS3) plus integration of the already-completed phylogenetic results section (Reviewer 1 #2). --- ## GARD recombination screen — results and impact on existing analyses (Reviewer 1 ii) We ran a GARD (Genetic Algorithm for Recombination Detection, HyPhy) screen on the 0.2-consensus, per-region alignments (42 datasets: genotypes 1a/1b/4d × 14 regions with ≥10 sequences; genotypes 2a/2b/3a excluded for insufficient sample size, whole-genome alignments excluded). The screen runs in an isolated `gard_results/` directory and does not modify any existing pipeline output. **Headline result for genotype 1a:** the GARD breakpoints detected in genotype 1a are statistical detections without an underlying biological recombination signal, and **they do not alter either the selection or the clustering conclusions.** This was established with two direct, quantitative tests (below), not by inspection of the breakpoint screen alone. ### Breakpoint screen GARD's step-up procedure accepts a breakpoint when it improves the corrected AIC (c-AIC) over a single-partition model. Across the completed datasets this flagged breakpoints in most regions of all three genotypes. However, the c-AIC criterion is sensitive to rate variation and to the large, low-diversity within-subtype alignments used here (genotype 1a, n = 213), so a raw breakpoint count overstates the biological signal. The post-hoc topological assessment is decisive: for the genotype-1a regions, the two partition trees flanking each breakpoint are highly concordant once unsupported (near-zero-length) branches are collapsed (supported-branch Robinson-Foulds ≈ 0.08–0.20 for the single-gene NS3/NS5B breakpoints), indicating shallow, near-tip incongruence consistent with homoplasy among near-identical sequences rather than the deep, well-supported topological conflict that recombination produces. ### Impact on selection analyses (FEL) For the two genotype-1a single genes with the strongest breakpoint signal (NS3, breakpoint at codon 342; NS5B, breakpoint at codon 345) we split each alignment at the codon-snapped breakpoint, built an independent tree per partition (FastTree), and re-ran FEL on each recombination-free partition. Comparing the resulting site-wise inferences to the original whole-gene FEL: | Gene | Whole-gene positively selected sites | Recovered in partitioned analysis | All-significant-site concordance | |------|--------------------------------------|-----------------------------------|----------------------------------| | NS3 | 5 (174, 382, 557, 586, 609) | 3/5 exactly (174, 557, 609) | 421/501 (84%) | | NS5B | 9 (47, 81, 90, 117, 184, 213, 309, 421, 544) | 7/9 exactly | 248/341 (73%) | No site changed the sign of selection (positive ↔ negative) between the whole-gene and partitioned analyses. The sites that differ sit at the p ≈ 0.1 significance margin, where calls fluctuate from sampling noise and from the reduced per-partition power, not from recombination. **The selection inferences for genotype 1a are therefore robust to the detected breakpoints.** ### Impact on TN93 distances and clustering Genetic-distance clustering (TN93 → HIV-TRACE/`hivnetworkcsv`) is computed per-region, so a within-region breakpoint can in principle bias clustering only if a recombinant sequence's whole-region distance averages two different ancestries and thereby crosses the clustering threshold. We tested this directly for NS3 and NS5B by computing TN93 on each partition and comparing to the whole-region distances at the auto-tuned 1a thresholds (NS3 0.00745; NS5B 0.00099): - Per-partition distances are near-perfectly correlated with the whole-region distances (Pearson r = 0.93–0.99), and the two halves are mutually correlated (r = 0.94 for NS3, 0.82 for NS5B). Splitting at the breakpoint does not meaningfully change any pairwise distance. - **Zero sequence pairs showed the recombinant signature** (closely related in one half, distantly related in the other: one half ≤ threshold, the other > 3× threshold). The small number of pairs whose threshold-link status is partition- sensitive (206/21,736 for NS3; 4/21,736 for NS5B) all sit at the cutoff with both halves near threshold — i.e. short-alignment sampling variance, not recombination. **Genotype-1a clustering is therefore robust to the detected breakpoints.** ### Proposed manuscript statement We will add to Methods that recombination was screened per-region/per-genotype with GARD, and to Results/Discussion that GARD-detected breakpoints in genotype 1a reflect shallow within-subtype topological incongruence (homoplasy) rather than recombination, and — confirmed by re-running FEL on recombination-free partitions (>80% concordance of significant sites, no sign changes) and by partition-wise TN93 distance comparison (r = 0.93–0.99, no recombinant-signature pairs) — do not affect the selection or clustering conclusions. Where genuine deep incongruence is detected (several genotype-1b/4d regions), we will note it and, for any affected downstream analysis, report results on the recombination-free partitions. *Status: the full 42-dataset screen is being finalized (the heaviest genotype-1a and 4d concatenated-region runs are still completing on SLURM); the genotype-1a robustness analyses summarized above are complete. Supporting outputs and the regeneration scripts are in `gard_results/` (`summarize_gard.py`, `partition_fel.sh`).*