Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load

Migle Gabrielaite; Marc Bennedbæk; Malthe Sebro Rasmussen; Virginia Kan; Hansjakob Furrer; Robert Flisiak; Marcelo Losso; Jens D Lundgren; INSIGHT START Study Group; Rasmus L Marvig

doi:10.1371/journal.pcbi.1010756

. 2023 Jan 3;19(1):e1010756. doi: 10.1371/journal.pcbi.1010756

Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load

Migle Gabrielaite ^1,^*,^#, Marc Bennedbæk ^2,^*,^#, Malthe Sebro Rasmussen ^1,³, Virginia Kan ⁴, Hansjakob Furrer ⁵, Robert Flisiak ⁶, Marcelo Losso ⁷, Jens D Lundgren ²; INSIGHT START Study Group, Rasmus L Marvig ¹

Editor: Nicola Segata⁸

PMCID: PMC9838853 PMID: 36595537

Abstract

Background

Infection with human immunodeficiency virus type 1 (HIV) typically results from transmission of a small and genetically uniform viral population. Following transmission, the virus population becomes more diverse because of recombination and acquired mutations through genetic drift and selection. Viral intrahost genetic diversity remains a major obstacle to the cure of HIV; however, the association between intrahost diversity and disease progression markers has not been investigated in large and diverse cohorts for which the majority of the genome has been deep-sequenced. Viral load (VL) is a key progression marker and understanding of its relationship to viral intrahost genetic diversity could help design future strategies for HIV monitoring and treatment.

Methods

We analysed deep-sequenced viral genomes from 2,650 treatment-naive HIV-infected persons to measure the intrahost genetic diversity of 2,447 genomic codon positions as calculated by Shannon entropy. We tested for associations between VL and amino acid (AA) entropy accounting for sex, age, race, duration of infection, and HIV population structure.

Results

We confirmed that the intrahost genetic diversity is highest in the env gene. Furthermore, we showed that mean Shannon entropy is significantly associated with VL, especially in infections of >24 months duration. We identified 16 significant associations between VL (p-value<2.0x10^-5) and Shannon entropy at AA positions which in our association analysis explained 13% of the variance in VL. Finally, equivalent analysis based on variation in HIV consensus sequences explained only 2% of VL variance.

Conclusions

Our results elucidate that viral intrahost genetic diversity is associated with VL and could be used as a better disease progression marker than HIV consensus sequence variants, especially in infections of longer duration. We emphasize that viral intrahost diversity should be considered when studying viral genomes and infection outcomes.

Trial registration

Samples included in this study were derived from participants who consented in the clinical trial, START (NCT00867048) (23), run by the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT). All the participant sites are listed here: http://www.insight-trials.org/start/my_phpscript/participating.php?by=site

Author summary

Viral intrahost genetic diversity complicates the cure of HIV; nevertheless, there is a lack of large and diverse cohort studies based on near-full and deep-sequenced HIV genomes. Here, we analysed deep-sequenced viral genomes from 2,650 demographically diverse and treatment-naive HIV-infected persons to measure the intrahost genetic diversity of 2,447 genomic codon positions as calculated by Shannon entropy. First, we verified that intrahost genetic diversity is highest in the env gene. Then, we identified that mean Shannon entropy positively associates with viral load, mostly in infections of >24 months duration. Finally, we showed that intrahost diversity in 16 HIV genomic positions significantly associated with viral load and explained 13% of the variance in viral load, whereas equivalent analysis based on variation in HIV consensus sequences explained only 2% of viral load variance. Overall, our study shows that higher viral intrahost genetic diversity positively associates with viral load and is a better disease progression marker than variation of HIV consensus sequences. Our findings further suggest that better understanding of pathogen genomics is required to better address infectious diseases.

Introduction

Human immunodeficiency virus type 1 (HIV) shows high genetic diversity between and within the human hosts [1,2]. While within-host viral diversity is initially limited by the transmission bottleneck, HIV genomes quickly diversify as a result of a high viral replication rate and high error rates during replication [3]. Such high genetic diversity allows the virus to avoid the host’s immune response and can lead to development of drug resistance during treatment [1]. HIV-1 set-point viral load (spVL), the approximately stable viral load (VL) measured by plasma HIV-RNA in the early chronic phase of HIV-infection, is associated with clinical progression of HIV-infection [4]. SpVL has been shown to be associated with human genetics and, even more pronounced, with viral genetics [5].

Intrahost viral genetic diversity balances between adaptation to the host environment and maintenance of replication ability, and it allows to explore intrahost HIV evolution in cross-sectional datasets. Even though longitudinally collected datasets are preferred for intrahost HIV evolution, cross-sectional datasets are easier to collect and scale up [6,7]. Among various methods for intrahost genetic diversity quantification, Shannon entropy is commonly used and easy to interpret [8–10]. Shannon entropy reflects both the number of present variants and their frequencies [9]. Shannon entropy is highest at the genomic position when all possible encoded amino acids (AAs) are present at equal frequencies [11]. Higher HIV intrahost diversity could be a result of the faster evolution of HIV escape mutants or, alternatively, could be a result of a more robust host immune response to HIV [12].

To date, the relationship between intrahost genetic diversity and disease progression markers, e.g. VL, have been investigated in relatively highly selected and small cohorts (up to 187 individuals in Hightower et al., 2012 [10]) and have typically been based on techniques that allows for only low depth sampling for variation in a minor part of the HIV genome [10,12–21]. Investigations based on near-full and deep genome sequencing of HIV genomes from large and diverse cohorts are required to further elucidate the relationship between intrahost genetic diversity and disease progression and generalize previous findings from smaller and more homogenous cohorts.

Participants from across 35 countries in the Strategic Timing of AntiRetroviral Treatment (START) clinical trial represent a demographically diverse cohort of treatment naïve HIV-positive persons infected with a range of different HIV subtypes and for which near-full viral genomes have been deep-sequenced [22–25]. Hence, by using genomic data from the START cohort, we aimed to identify how Shannon entropy at AA positions as a measure of intrahost diversity varies across the HIV genome, and how it relates to the VL. We further aimed to identify the AA positions in which Shannon entropy was most associated with the VL, and finally, we compared whether interhost or intrahost HIV genetic variation better explained variance in VL.

Methods

Ethics statement

Samples included in this study were derived from participants who consented in the clinical trial, START (NCT00867048) [23], run by the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT). The study was approved by the institutional review board or ethics committee at each contributing center, and written informed consent was obtained from all participants. All informed consents were reviewed and approved by participant site ethics review committees. All the participant sites are listed here: http://www.insight-trials.org/start/my_phpscript/participating.php?by=site.

Study population

Samples included in this study were derived from participants from the START trial, which included 4,685 participants fulfilling the following criteria: 1) a CD4⁺ cell count >500 cells/μl at baseline, 2) no history of AIDS, 3) no previous history of antiretroviral treatment and 4) were above 18-years-old at study entry [23,26]. For this study, we included participants which had baseline plasma samples with a VL measurement ≥ 1,000 copies/mL and viral genomes sequenced by next-generation sequencing (N = 3,785).

HIV sequencing, alignment, variant calling, and subtyping

The detailed laboratory procedure is described in Bennedbæk et al., 2021 [25]. In short, viral RNA was extracted from plasma and amplified using two amplicons spanning 7,125 nt (HIV-1 HXB2 genome regions 1,485–5,058 and 5,967–9,517) of the 9,719 nt (74%) HIV genome and sequenced by next-generation sequencing (S1 Fig). Reads were first aligned against the GRXh37.p13 human reference genome with Bowtie2 v2.2.8 and only read pairs with neither read aligned to the human genome were retained. Virvarseq [27] was used for alignment of cleaned reads and AA variant calling with the HXB2 HIV reference genome (GenBank accession number K03455.1) with default parameters. Virvarseq alignment, realignment, and AA variant calling were performed per gene basis (rev and tat genes were split into separate analyses for each reading frame). The two amplicons with primer regions excluded did not completely cover the genes gag, pol, rev, and tat, therefore, variant calls for the gene coding for the Gag protein were considered from HXB2 reference genome codon position 240, for Pol until AA position 980, for Rev from position 7, and for Tat from position 52. No sequencing was available for Vpr and Vif (S1 Fig). Minimum Phred quality scores for all called codons were >20. Variant calling was only performed for samples that had at least 10% of the gene covered by aligned reads with a minimum 10-fold sequencing depth. Variants were called if there was a minimum of 500-fold sequencing depth and a minimum of 1% of aligned reads supported the variant. Samples which had ≤500 codon positions with ≥500-fold sequencing depth were excluded from the analysis (N = 1,135). Consensus variants were called if there was a minimum of 10-fold sequencing depth and minimum 50% of aligned reads supported the variant.

Additional analyses were performed to test the robustness of the results with minimum sequencing depth thresholds set to 300-fold and 1,000-fold, respectively (instead of a 500-fold threshold). In these analysis, 1,002 and 1,379 samples with ≤500 codon positions with ≥500-fold sequencing depth were excluded from the analysis, respectively (instead of 1,135 samples).

The detailed subtyping description is available in other publications [22,25]. In short, the consensus sequences were used for subtyping using REGA version 3.0 [28] and the subtypes were confirmed by a manual inspection. Samples with more than three predicted subtypes were labelled as “Mixed”. If less than 100 participants were assigned a particular subtype, the subtype was grouped as “Other” in this study.

Shannon entropy

Shannon entropy was used as a measure of intrahost diversity and was calculated for each AA position in each sample. Shannon entropy H_i at the position i, where

H_{i} = - \sum_{a} f_{a, i} {l o g}_{2} f_{a, i},

and f_a,i is the frequency of the amino acid (AA) a at position i. Mean Shannon entropy at the AA position was used for identification of the most variable HIV-1 genomic positions. The threshold defining the most variable positions was arbitrarily chosen to be 0.3. Sample’s mean Shannon entropy was used for correlation with log₁₀(VL) and regression analyses.

Statistical analysis

The association analysis was performed with R [29]. A multiple linear regression model was fitted to assess associations of VL with sample’s mean Shannon entropy. VL measurements were log₁₀ transformed prior to analysis. Age, sex assigned at birth, self-reported race, duration of infection, the first four principal components of the HIV genetic data, subtype, and the average sequencing depth of positions with ≥500-fold sequencing depth (hereafter referred as average sequencing depth) were considered as covariates to account for population structures. Bonferroni correction was used to account for multiple testing in the association between VL and Shannon entropy at AA positions.

Principal component analysis (PCA) was performed using the consensus AA calls with EIGENSOFT software v6.1.4 [30]. Furthermore, only AA variants which had allele frequency ≥5% and ≤95% were used. PCA analysis was based on 2,180 AA variants and the first four principal components (PCs) were used for regression model construction to account for the population structure as previously described [24]. R software [29] was used for explained variance analysis and visualizations. The explained variance was calculated by comparing the variable’s sum of squares with the total sum of squares from analysis of variance (ANOVA) analysis. The final model during the model selection was chosen based on the ANOVA analysis when comparing to previous models. The significant associations from the multiple linear association analysis for the Shannon entropy of the 2,447 HIV AA positions using the same covariates as in the previous regression analysis were referenced against the best-defined cytotoxic T lymphocyte (CTL) epitopes from the HIV molecular immunology database [31]. Additional robustness re-analyses were performed using 300-fold and 1000-fold sequencing depth thresholds, resulting in 2,542 and 2,085 HIV AA positions being considered, respectively.

A 5-fold cross validation was performed by randomly splitting the dataset into 5 equal parts, using one part as a test dataset and the rest as training data to calculate parameters for the regression model. The model was evaluated by calculating root-mean-square deviation (RMSE) and R2 parameters after fitting the model on the test and train datasets.

The duration of infection variable was based on a study by Sharma et al. 2019 [32] where the duration of infection was determined to be ≤6 months based on multi assay algorithm (MAA) serological and non-serological markers of recent infection or the self-reported date of infection. 6–24 month duration of infection was determined 1) if MAA failed to confirm a recent infection, infection date was unknown and the diagnosis date was <6 months before randomization or 2) if HIV diagnosis was 6–24 months before randomization. Participants were considered to be infected for >24 months if their HIV diagnosis was >24 months before randomization.

Dryad DOI

https://doi.org/10.5061/dryad.6djh9w13s [26]

Results

Study participants and genome sequencing

A total of 3,785 START clinical trial participants had HIV genomes sequenced [22,24,25]. After read alignment to the HXB2 reference genome, 1,135 genomes had 500 or less (out of 2,549) codon positions covered by ≥500 reads. These genomes were removed from further analysis leaving 2,650 HIV genomes in the study (S1 Table). It should be noted that the samples of the excluded genomes had a significantly lower VL than the included HIV genomes (Wilcoxon signed-rank test, p-value<2.2x10^-16). The included genomes on average had a 5,521-fold (range 214–40,501-fold) sequencing depth, and an average sequencing depth of 11,638-fold (range 667–163,667-fold) for the positions included in this analysis (i.e., the positions with ≥500-fold sequencing depth). VL of the included samples correlated weakly with sequencing depth (Pearson’s correlation coefficient– 0.12).

Shannon entropy calculation

While we calculated Shannon entropy for all 2,549 HIV genomic codon positions within the targeted genomic regions (Fig 1), 102 positions, which had sufficient sequencing depth in ≤800 samples, were excluded from further analysis as these positions showed an aberrant number of samples with sufficient sequencing depth (may be a result of unsuccessful mapping of reads to highly variable genomic regions) and coincided with high entropy. The excluded positions were all either within Env or near the start or end of amplicons (Fig 1 and S2 Table). Accordingly, a total of 2,447 positions were included with a distribution over the HIV genome as follows: Gag—262 positions, Pol—979 positions, Env—799 positions, Vpu—78 positions, Tat—47 positions, Rev—107 positions, Nef—207 positions.

The most variable positions in the sampled HIV genomes

A total of 2,447 AA positions were considered for the most variable position identification. Overall, 36 positions had mean Shannon entropy ≥0.3 (S3 Table and Fig 1): five high entropy positions were in Gag, two in Pol, and 29 in Env. Mean Shannon entropy was significantly higher (Wilcoxon signed-rank test, p-value<2.2x10^-16) within Env variable loop regions than in other Env positions. The entropy distributions in the most variable positions were similar between different races and subtypes (S1 Table and S2 and S3 Figs).

If we performed the analysis using a more stringent 1,000-fold requirement for sequencing depth of included positions (instead of the 500-fold requirement in main analysis), we identified the same positions to show high entropy; except if the positions were excluded from the analysis due to the increased requirement for sequencing depth (S4 Table). If we used a less stringent 300-fold requirement for sequencing depth, we also identified the same positions to show high entropy; yet we then also identified additional 22 high entropy positions that did not have enough sequencing depths to be included in the more stringent analyses (S4 Table).

Intrahost diversity correlation with viral load

To investigate the linear dependence between sample’s mean Shannon entropy and log₁₀ VL, and the duration of infection, respectively, we performed a univariate linear regression of log₁₀ VL and the sample’s mean Shannon entropy, and explored their differences based on the duration of infection (Fig 2). We observed that entropy increased with VL regardless of the duration of infection. Sample’s mean Shannon entropy alone, as deduced by ANOVA analysis, explained 4.14% of the observed VL variance. Contrarily, a univariate model of log₁₀ VL association with the average sequencing depth explained 0.68% of the variance in VL (S5 Table).

The median VL was lowest in the infections with >24 months duration while the median of the sample’s mean Shannon entropy was highest in this group. Pearson’s correlation coefficient between VL and the sample’s mean Shannon entropy was 0.20, 0.19 and 0.33 for infection duration of <6 months, 6–24 months and >24 months, respectively. Sample’s mean Shannon entropy, as deduced by ANOVA analysis, explained 3.91%, 3.56%, and 10.70% of the variance in VL for infection duration of <6 months, 6–24 months and >24 months, respectively. The explained variance using a 300-fold and 1000-fold sequencing depth thresholds, respectively, was comparable to the main analysis (S4 Table). ANOVA analysis revealed that all three variables: sample’s mean Shannon entropy (p-value<2.2x10^-16), duration of infection (p-value = 3.5x10^-9) and their interaction (p-value = 2.0x10^-2) have a significant effect on VL.

Mean intrahost diversity association with viral load

Next, we built a multiple linear regression model to further explore the relationship between log₁₀ VL and Shannon entropy (S5 Table). The final model included participant’s age, sex assigned at birth, race, duration of infection and the first four PCs (which are used to account for the viral genetic population structure) as covariates (S5 Table). In total, the final regression model (i.e., sample’s mean Shannon entropy together with all the covariates) explained 10.3% of the variance in VL. The explained variance in VL for the re-analysis using a 300-fold and 1000-fold sequencing depth thresholds, respectively, was comparable to the 500-fold threshold used in the main analysis (S4 Table). The final model including only the participants infected for >24 months explained 13.0% of the variance in VL. If we added the average sequencing depth as a covariate to the multiple linear regression model together with other covariates, the model explained 10.7% rather than 10.3% of the variance (S5 Table). To confirm that our findings are not affected by overfitting, we performed a 5-fold cross validation analysis. The average RMSE of the training and testing dataset was 0.574 (0.571–0.577) and 0.578 (0.565–0.587), respectively. The explained variance was 9.3% for the test dataset and 10.4% for the train dataset. Shannon entropy averaged per gene for each sample in the final multiple linear regression model (Model 6 in S5 Table) did not associate significantly with VL (p-value = 0.31 for Gag, p-value = 0.24 for Pol, p-value = 0.48 for Env, p-value = 0.48 for Vpu, p-value = 0.33 for Rev, p-value = 0.34 for Tat, p-value = 0.21 for Nef).

HIV intrahost position entropy association with viral load

To disentangle the effect intrahost diversity at the specific HIV genome positions has on log₁₀ VL, we performed the multiple linear association analysis for the Shannon entropy of 2,447 HIV AA positions using the same covariates as in the previous regression analysis (i.e., participant’s age, sex assigned at birth, race, duration of infection and the first four PCs). In 16 cases (six positions in Pol, nine positions in Env and one position in Nef; Fig 3) the VL association with the Shannon entropy at the specific HIV AA position was below Bonferroni-corrected p-value threshold (p-value<2.0x10^-5). In all 16 positions the association was driven by a general trend and not a few outliers (Table 1 and S4 Fig). Together Shannon entropy values from the 16 associated AA positions explained 12.6% of the variance in VL. The full model explained 21.9% of the variance in the VL (S6 Table). Average RMSE values from train and test data in the 5-fold cross validation analysis were similar (S7 Table). The joint model including all 16 positions revealed that only half of the variants were significantly associated with VL when considered together (S5 Table) which is likely a result of multicollinearity between the Shannon entropy in different HIV genomic positions (S5 Fig). The regression model with the consensus AA variants using the same covariates showed that 13 significantly associated consensus AA variants explain 0.01−0.65% of the variance in VL and the full model with the covariates explained 6.2% of the variance in VL (S8 Table).

Fig 3 — Red horizontal line marks the Bonferroni p-value threshold (p = 2.0×10⁻⁵). The x-axis denotes gene AA positions. A total of 2,447 HIV AA positions are shown on the x-axis. Gag positions 1–239, Pol positions 981–1004, Rev positions 1–6 and Tat positions 1–51 are not shown, as they were not covered by the amplicons.

Table 1. Sixteen HIV amino acid (AA) positions where Shannon entropy was significantly associated with viral load (VL).

Gene	AA position	Effect size (95% CI)	p-value	Mean Shannon entropy	CTL epitope coordinates (HLA alleles) ^a
Pol	34	0.229 (0.129–0.330)	7.53×10⁻⁶	0.10
Pol	75	0.262 (0.156–0.369)	1.32×10⁻⁶	0.07
Pol	97	0.221 (0.143–0.298)	3.15×10⁻⁸	0.19	90–98 (B*44)
Pol	195	0.161 (0.089–0.233)	1.09×10⁻⁵	0.28	188–198 (C*01:02)
Pol	329	0.263 (0.146–0.380)	1.02×10⁻⁵	0.07	328–336 (A30:02, C12:02)
Pol	441	0.338 (0.213–0.463)	1.33×10⁻⁷	0.09
Env	32	0.168 (0.095–0.240)	5.96×10⁻⁶	0.22	31–39 (B18:01,B44); 31–40 (B*44:02)
Env	87	0.136 (0.076–0.196)	8.71×10⁻⁶	0.34
Env	102	0.340 (0.193–0.487)	6.03×10⁻⁶	0.06
Env	240	0.162 (0.090–0.234)	1.03×10⁻⁵	0.23
Env	336	0.168 (0.098–0.237)	2.41×10⁻⁶	0.34
Env	453	0.400 (0.246–0.555)	4.17×10⁻⁷	0.07
Env	674	0.246 (0.147–0.345)	1.36×10⁻⁶	0.18
Env	775	0.353 (0.192–0.513)	1.70×10⁻⁵	0.07	770–780 (A03:01, A30:01)
Env	812	0.274 (0.149–0.399)	1.78×10⁻⁵	0.10	805–814 (B*40:01)
Nef	15	0.210 (0.119–0.301)	6.50×10⁻⁶	0.22	13–20 (B*08:01)

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^a CTL epitope reference: https://www.hiv.lanl.gov/content/immunology/tables/optimal_ctl_summary.html

Only three associated AA positions (Pol34, Env453 and Env812) had no mutation entries based on the data from HIV mutation browser [33], i.e., were conserved positions. Seven out of 16 associated AA positions overlapped with one or more best-defined CTL epitopes (Table 1). We did not find that the associated AA positions overlapped significantly differently with the CTL epitope positions than expected by chance (Fisher’s exact test; p-value = 1) [31].

Discussion

While we and others have associated variation in consensus sequences of the HIV-1 quasispecies with disease progression markers in studies with >1,000 individuals [5,24,34], equivalent analyses of intrahost viral diversity have, despite showing strong associations, been conducted on cohorts with only up to 187 individuals [10] and have typically been based on low depth sampling of genetic variation in only a minor part of the HIV genome [10,12–21]. Consequently, our study addresses the need for associations between intrahost diversity and disease progression markers in a large and demographically diverse cohort with near-full deep-sequenced HIV genomes. Moreover, we compare the strength of the association between VL and intrahost diversity with VL and consensus HIV AA variants (Table 2).

Table 2. Variance in viral load (VL) explained by linear regression analysis with different models.

PC1-PC4 denote first four principal components of principal component analysis performed on consensus AA variants to account for the viral population structure. The 16 and 13 HIV AA positions are all positions significantly associated with VL when position-wise Shannon entropy or consensus variants, respectively, are used to explain VL.

Model	% of VL variance explained
VL ~ Sample’s mean Shannon entropy (all samples)	4.14
VL ~ Sample’s mean Shannon entropy (samples from <6 months infection duration)	3.91
VL ~ Sample’s mean Shannon entropy (samples from 6–24 months infection duration)	3.56
VL ~ Sample’s mean Shannon entropy (samples from >24 months infection duration)	10.7
VL ~ Average sequencing depth	0.68
VL ~ Sample’s mean Shannon entropy + age + sex + race + duration of infection + [PC1-PC4]	10.3
VL ~ Sample’s mean Shannon entropy + age + sex + race + duration of infection + [PC1-PC4] + average sequencing depth	10.7
VL ~ Shannon entropy at 16 HIV AA positions + age + sex + race + duration of infection + [PC1-PC4]	21.9
VL ~ Consensus variants at 13 HIV AA positions + age + sex + race + duration of infection + [PC1-PC4]	6.2

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We first identified the most variable positions of the HIV genomes. Then, we built a multiple regression model to associate the differences in VL with the sample’s mean intrahost diversity, and finally, we identified 16 HIV genome positions where intrahost diversity of the position was associated with VL, which is an established marker of HIV disease progression [4]. Note, while VL is a useful measure, VL tends to increase over time and to different degrees in different individuals, and this increase may be an important component of progression [35].

Our most variable HIV genome position analysis confirmed that intrahost diversity is not equally distributed across the HIV genome and is higher in Env with several higher entropy positions in Gag and Pol [36,37]. Additionally, we could not find an association of HIV intrahost diversity and the HIV subtype or the race of the participant. It is important to emphasize that we identified the most variable HIV genome positions as an absolute measure across the genome and not relative to the background variability of a given genomic region, i.e., we used the same threshold for genome variability throughout the genome without considering the overall higher or lower variability of the specific gene. Furthermore, our analysis of Shannon entropy averaged per gene for each sample showed that that the relationship between Shannon entropy and VL is not driven by intrahost diversity in one particular gene.

The relationship between VL and sample’s mean Shannon entropy revealed a positive correlation between these parameters where the infections of short duration showed a weak correlation and infections of longer duration were moderately correlated with VL which could be explained by the initial transmission bottleneck followed by viral diversification [38–40]. It is in agreement with the established knowledge that spVL as a prognostic marker can only be employed in chronic infection [41]. These findings concur with a study by Kafandro et al. 2017, where the authors showed that Shannon entropy can confidently discriminate between recent and chronic infections [42]. Moreover, the lower VL in chronic infections than in recent infections might be due to the nature of the cohort where participants with a longer infection duration who still had a high CD4⁺ cell count are likely to better control the infection innately [7]. Finally, our results indicate that the sample’s mean Shannon entropy, especially in infections of longer duration, is informative when explaining VL variation among patients since it together with other covariates in our model explains >10% of the overall variance in VL. Our findings are in line with a smaller study by Bello et al. 2007 which found a significant positive correlation between viral genetic changes and VL in long-term nonprogressors (LTNP) [15] even though our study was not limited to LTNP. Note, that the observed results could not be explained by the fact that more variability is observed in higher sequencing depth. Additionally, while the directionality of the relationship between VL and intrahost diversity cannot be determined from our study, the work by Bello et al. 2007 showed that increase in intrahost diversity directly leads to the increase of VL [15].

The regression model in our main analysis did not include HIV subtypes as subtype assignment did not improve the model significantly; instead, the first four principal components of genome consensus sequences were sufficient to account for the genomic population structure. We also tested how robust our results were to changes in the sequencing depth required to include positions (and thus samples) and to the addition of the average sequencing depth as a covariate to the model. Results were robust to both tests, and, while VL of the included samples correlated weakly with sequencing depth, we found average sequencing depth alone to explain little of the variance in VL. Finally, we obtained similar results in our analysis when we applied 5-fold cross validation, suggesting that the model was not overfitted.

Our analysis showed that VL was significantly associated with the intrahost diversity of 16 HIV AA positions and as shown by the joint multiple regression model, the intrahost diversity in these HIV AA positions is not independent of each other and has a collinear relationship. One explanation for VL association with Shannon entropy at these HIV AA positions could be that these positions are less conserved and acquire mutations faster. We hypothesize that these processes correlate with the acquisition of other adaptive mutations, and, therefore, higher VL [6]. On the other hand, higher Shannon entropy in the associated positions could provide an advantage to the virus when avoiding the immune response, so the higher VL would be a direct outcome of the increased entropy as some of the associated positions are a part of best-defined CTL epitopes. [31,36,37]. While both explanations are plausible, we did not find support that increased Shannon entropy at these positions could give rise to a direct increase in VL. Furthermore, by using a multiple regression model with HIV consensus sequences and the same covariates as in this work, we confirmed that VL associates poorly with HIV consensus sequences [21]. Therefore, we conclude that the HIV genome position entropy is a substantially better marker of VL than the HIV mutations identified from consensus sequences [24,34]. However, the association of VL with Shannon entropy in the 16 identified HIV AA positions should be further tested in other studies, and more work is needed to define how intrahost genome diversity at an early point in time could be a predictor of later changes in CD4+ cell count, VL or other markers.

Our study has several limitations. We based our analysis on the full dataset to have maximum statistical power instead of leaving a part of the dataset out for validation. While our analysis only included a single isolate per participant (i.e., a single time-point), samples from multiple time-points could have provided further information about time-dependent relationships between identified associations, viral genome diversification, and markers of disease progression. Additionally, the inclusion of host genetics in the association analysis between Shannon entropy and VL could have furthered our understanding of the relationship between the intrahost diversity at the specific positions and host immunotypes. Furthermore, success of sequencing varied across both samples and genomic positions which limited the number of samples and genomic positions that could be analysed, and we only included variants presented by at least 1% of covering sequencing reads which may make us disregard variation. Moreover, sequencing was based on two amplicons covering the majority but not the full length of the HIV genome which prevented us from identifying associations outside of the sequenced amplicons. Sequence reads were aligned against the HXB2 reference genome which belongs to subtype B and, therefore, some reads from other subtypes might not have been aligned at the most variable genomic regions. Finally, no participants with low VL were included in the analysis because of the nature of the cohort and, therefore, our analysis fails to identify associations dependent on inclusion of such participants.

In conclusion, by using a large and demographically diverse cohort of HIV-infected treatment-naive participants, we confirmed that the highest intrahost diversity is accumulated in the Env region. Furthermore, we showed that both sample’s mean intrahost genetic diversity and the intrahost genetic diversity at the 16 HIV genomic positions have a positive relationship with VL, and that intrahost genetic diversity is a better marker of disease progression than HIV variants identified from consensus sequences. Accordingly, HIV intrahost genetic diversity should be taken into account when analysing HIV genomes and the clinical outcomes.

Supporting information

S1 Table. Characteristics of 2,650 participants from the START trial included in this analysis.

IQR (interquartile range).

(DOCX)

Click here for additional data file.^{(15.1KB, docx)}

S2 Table. HIV genomic positions for which ≤800 samples had a minimum of 500-fold genome sequencing depth at position.

(DOCX)

Click here for additional data file.^{(14KB, docx)}

S3 Table. HIV genome’s mean Shannon entropy at the most variable positions.

(DOCX)

Click here for additional data file.^{(16.1KB, docx)}

S4 Table. Summary of the total considered positions, explained variance and Pearson’s correlation coefficient based on the infection duration in the robustness analysis of the results when changing the sequencing depth requirement for positions to be included from 500-fold to 300-fold and 1,000-fold, respectively.

(DOCX)

Click here for additional data file.^{(15.3KB, docx)}

S5 Table. Linear regression analysis of predictors of the viral load (VL).

(DOCX)

Click here for additional data file.^{(19.4KB, docx)}

S6 Table. Joint multiple linear regression model with the Shannon entropy values from significantly associated AA positions.

P-values below the 0.05 threshold are marked in bold.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

S7 Table. Average RMSE values for the train and test datasets from the 5-fold cross validation analysis for the 16 Shannon entropy HIV AA positions associated with VL.

(DOCX)

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S8 Table. Joint model of multiple linear regression with the consensus AA variants which were significantly associated with viral load. P-values below the 0.05 threshold are marked in bold.

(DOCX)

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S1 Fig. An illustration of the HIV genome and the positions covered by the two amplicons sequenced in this study.

(DOCX)

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S2 Fig. The density plots of the mean Shannon entropy in different races of the 36 most variable positions.

(DOCX)

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S3 Fig. The density plots of the mean Shannon AA entropy in different subtypes of the 36 most variable positions.

(DOCX)

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S4 Fig. Relationship between log10 viral load and Shannon entropy in the 16 most significantly associated HIV amino acid positions. Blue line marks smoothed conditional means.

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S5 Fig. Pairwise correlation matrix between covariates (bottom triangle).

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Acknowledgments

We would like to thank all participants in the START trial and all trial investigators (see N Engl J Med 2015; 373:795–807 for the complete list of START investigators). The START trial was supported by the National Institute of Allergy and Infectious Diseases, Division of AIDS (United States) (NIH Grants UM1-AI068641, UM1-AI120197 and 1U01-AI36780), National Institutes of Health Clinical Center, National Cancer Institute, National Heart, Lung, and Blood Institute, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Agence Nationale de Recherches sur le SIDA et les Hépatites Virales (France), National Health and Medical Research Council (Australia), National Research Foundation (Denmark), Bundes ministerium für Bildung und Forschung (Germany), European AIDS Treatment Network, Medical Research Council (United Kingdom), National Institute for Health Research, National Health Service (United Kingdom), and University of Minnesota. Antiretroviral drugs were donated to the central drug repository by AbbVie, Bristol-Myers Squibb, Gilead Sciences, GlaxoSmithKline/ViiV Healthcare, Janssen Scientific Affairs, and Merck.

Data Availability

All non-personally identifiable data, e.g. all output from Virvarseq, and relevant code is deposited at Dryad repository: https://doi.org/10.5061/dryad.6djh9w13s. Personal identifiable data can be accessed through agreement with the INSIGHT START Study Group (http://www.insight-trials.org/research_proposal) to respect the donor consent of the participants in the START study. Personal identifiable data include individual-level metadata.

Funding Statement

The work was supported by the Danish National Research Foundation (grant 126). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r001

Decision Letter 0

Rob J De Boer, Nicola Segata

13 Jan 2022

Dear Ms. Gabrielaite,

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Associate Editor

PLOS Computational Biology

Rob De Boer

Deputy Editor

PLOS Computational Biology

***********************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Gabirelaite et al. report the results from a large cross-sectional study of a HAART-naïve HIV-1 infected cohort aimed at exploring the correlation between viral intra-host genetic diversity, analyzed by deep sequencing, and viral load, which can be considered a marker of disease progression.

Given the remarkable size and the demographic heterogeneity of the sample analyzed, this study is quite an unprecedented effort, which includes more than 2000 subjects. The scientific research question, i.e. whether the intra-host sequence diversity found in a chronically infected individual correlates with set point viral load, is very interesting and relevant both for the potential implication for predicting clinical outcome and for understanding the dynamics and the mechanism of virus evolution in vivo. Results presented show a weak, though significant, correlation with diversity, assessed by calculating Shannon Entropy. According to the study, HIV-1 env is the genetic region with the highest concentration of highly diverse position, in agreement with the high selective pressure to which Env is subjected.

Overall, I think the outcome improves our knowledge of intra-host viral evolution, contributing to the notion that intra-host diversity somehow benefits the persistence of virus replication.

I wouls like to suggest a few clarifications, for the benefit of the reader:

1) What is the variability of sequencing depth of the samples that remained after excluding from analyses those with 500 or less codon positions covered by ≥500 reads? Was there an objective criterion for choosing a 500 reads threshold?

2) On a similar topic, what were the criteria for excluding positions for which ≤800 samples had mean Shannon entropy calculations? How was the 800 samples threshold decided?

3) given the highest concentration of positions featuring high level of diversity within Env, it would be useful to include a short comment on the location of those positions relativel to the most prominent features of Env, such as the variable loops, which could be relevant to immune escape from humoral immunity.

Reviewer #2: This paper describes the relationship between nucelotide diversity of HIV, as measured by Shannon entropy, and viral load in subjects who are treatment naiive to HIV. No major justification is given as to why this is an interesting association to study. The manuscript also discusses but does not focus on the relationship between Shannon entropy and disease progression, but perhaps underplays this positive result as it has already been described.

Major concerns:

-The implications of finding a correlation of entropy with viral load are not clear. Perhaps if this association could explain disease progression by combination with some other data from the patients, that would be a major advance. Alternatively, the authors might focus on the uniqueness of this cohort, and their lack of treatment. Are certain loci more or less likely to be diverse than when compared to published data sets from treated individuals?-

-The authors show that diversity 'explains' VL, but the directionality of this association is not clear-- could higher VL enable more diversity?

-A separate test set is needed to provide confidence in model predictions (association of single AA positions and predictive ability of entropy on VL).

-It would be nice to see graphs of VL vs entropy for at least of few of the associated positions. Are a few extreme points driving these associations?

-Is VL associated with entropy at the gene level?

**********

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Reviewer #1: Yes

Reviewer #2: None

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PLoS Comput Biol. 2023 Jan 3;19(1):e1010756. doi: 10.1371/journal.pcbi.1010756.r002

Author response to Decision Letter 0

22 Mar 2022

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Submitted filename: plos_comp_bio_rebuttal_20210304_final.docx

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r003

Decision Letter 1

Rob J De Boer, Nicola Segata

4 May 2022

Dear Ms. Gabrielaite,

Thank you very much for submitting your manuscript "Deep-sequencing of viral genomes from treatment-naive HIV-infected persons shows positive association between intrahost genetic diversity and viral load" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, provided that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Nicola Segata

Associate Editor

PLOS Computational Biology

Rob De Boer

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I am satisfied with authors, answers and modifications in the revised manuscript.

Reviewer #2: I found some responses to my previous concerns to be unsatisfactory:

1) I understand that the authors do not want to change the scope of their manuscript, and that is certainly a reasonable choice. However, given the broad audience of this journal, it would be helpful to better explain to the reader why a relationship between VL and diversity are interesting in the abstract. It seems to me from the discussion that the authors do not believe that diversity might drive VL. Might the identified relationship allow a future researcher to model other predictors of VL by using diversity as a proxy? Is there some other utility? If not, the authors should emphasize in the abstract what they mention in the introduction – previous studies have found conflicting relationships between VL and intrahost diversity. [Minor concern]

2) The authors have responded to my question about specific genes being associated with VL by presenting what appears to be a fundamentally different analysis than performed for the genome as a whole (simple correlation vs multivariate regression). While a single simple correlation was done for sample mean entropy vs VL, what is reported is a correlation coefficient (and no p value), whereas only p-values (and not correlation coefficients) are reported for the gene-level analyses. [Minor concern]

3) Given the centrality of the machine learning model to the data set, and the large number of positions tested, overfitting is a major possibility, particularly for the report of specific loci. I think it is appropriate and necessary for the authors to repeat their model using 800 samples as a training set and reserving 200 as a test set. The authors can compare RMSE in training and test sets and see if the same 16 positions are most correlated with VL in the test set. [Major criticism]

I also have some other concerns not mentioned in the first review:

1) The abstracts says that “genetic diversity … better disease progression marker than variation of HIV consensus sequences.” A similar claim is repeated twice in the discussion. However, I was unable to locate where this comparison emerges from, as I do not see a model presented that attempts to predict VL from consensus sequence. [Major criticism]

2) On closer reading, I now see that the manuscript says “ In total, the final regression model (i.e., sample’s mean Shannon entropy *together with all the covariates*) explained 10.3% of the variance in VL.” How much variance in VL does a model that includes the covariates (including duration of infection!) but not mean Shannon entropy explain? In other words, how much of the performance is due to the Shannon entropy parameter? [Major criticism]

3) On page 8—“It should be noted that the samples of the excluded genomes had a significantly lower VL than the included HIV genomes”. Did coverage within the included samples also correlate with VL? This would suggest residual confounding.

I also found the answer to reviewer #1s question unsatisfactory:

1) Given the per-base coverage threshold of 500 reads, it is more appropriate to exclude any samples with less than this coverage averaged across their genome.

Reviewer #3: In this work, the authors study the correlation between intra-host genetic diversity during HIV-1 infections and viral load which, in turn, is a marker of disease progression. The issue is interesting from several standpoints going from a better understanding of the rules governing virus-host interactions to improving the precision of predicting clinical outcomes of the infection. The take home message of the work improves knowledge in these fields.

I have a few minor issues that the authors could address, in my opinion, to improve the manuscript.

1. In the background section of the abstract they state that "viral intrahost genetic diversity remains a major obstacle to the eradication of HIV". I think that the word "eradication" is evocative of making the virus disappear from the human population. If so, I do not see a direct relationship between intra-host genetic diversity and eradication. Inter-host diversity is rather the important issue. Intra-host diversity is rather correlated to "eradication" of the virus from the patient, case for which I rather find more appropriate to use the term "cure". The same comment applies to the first line of the author summary.

2. Page 9, line 6 from bottom. How the conclusion that Shannon entropy accounts for 4.14% of the VL variance was reached is not straightforward (at least for me). For the sake of clarity, it should be indicated how this value (and, similarly, those indicated at lines 3 and 4 of page 10) was deduced.

3. Also, at line 4 from the top of page 10, it is referred the effect of the interaction between Shannon entropy and duration of infection. Where is this parameter measured?

4. Last sentence of the Results section. It is an interesting observation. Wouldn't it be worth to make a short comment on this in the discussion section?

5. Lines 5 to 4 from bottom of page 12. It could be interesting to add a comment for the fact that the study by Bello and coll. concerned long-term non-progressors while the present one did not (even if in that case the directionality of the correlation could be determined while in this work it could not).

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No: I do not see a code availability statement. A supplementary table of entropy x sample would also be helpful.

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Matteo Negroni

Figure Files:

Data Requirements:

Reproducibility:

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

PLoS Comput Biol. 2023 Jan 3;19(1):e1010756. doi: 10.1371/journal.pcbi.1010756.r004

Author response to Decision Letter 1

23 May 2022

Attachment

Submitted filename: plos_comp_bio_rebuttal2_20220520.docx

Click here for additional data file.^{(26.3KB, docx)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r005

Decision Letter 2

Rob J De Boer, Nicola Segata

18 Jul 2022

Dear Ms. Gabrielaite,

When you are ready to resubmit, please upload the following:

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Sincerely,

Nicola Segata

Associate Editor

PLOS Computational Biology

Rob De Boer

Deputy Editor

PLOS Computational Biology

***********************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: The revised manuscript reports an association between viral load and entropy in cross-sectional HIV samples. Previous papers have found conflicting results between viral load and entropy, and this paper attempts to resolve this issue.

However, I have not been convinced that coverage and other artifacts have been appropriately accounted for. The authors’ apparent unwillingness to rerun the analysis with a held-out test set, or with any change of parameters (e.g. coverage threshold for sample inclusion), does not invoke confidence. If the authors are not willing to demonstrate rigor in this analysis, the impact of this finding seems low in the context of previous studies showing conflicting results.

In addition, the abstract concludes that entropy “could be used as a better disease progression marker than HIV consensus sequence variants, especially in infections of longer duration” by pointing to a previous publication. Unless the analysis is performed on the same exact sample set (see below for one possible suggestion), this comparison should not be included prominently in the abstract.

Specific points:

-The authors have demonstrated that coverage correlates with VL in the data set. Yet, to my knowledge, no attempt has been made to demonstrate that coverage doesn’t impact entropy and/or correlations of entropy and VL. One way to show this would be to repeat the ANOVA analysis or linear regression in Supplementary Table 4 with coverage as a variable (and also include a model that just includes coverage for comparison with the model that just includes entropy). Another would be to remove samples with low coverage (e.g. < 500 or < 750 fold coverage) and repeat the analysis; as it stands, samples with average coverage below the per-position threshold for calculation of entropy are included, which is not best practice.

-If the authors want to include a comparison of entropy vs consensus sequence prediction of viral load, an analysis of just the PCs needs to be included in Supplementary Table 4. Reference to a different but related analysis is not a sufficient replacement.

-Page 11: It is not exactly clear what is meant by “the joint model including all 16 positions”. Does this also include PCs? One possible explanation for some of the variants being unpredictive in this context is that some positions are more likely to have diversity due to alignment errors, which are more likely to occur on particular genomic backgrounds.

Writing issues and minor suggestions:

-Page 9: What is the nature of positions in the env gene that do not have high coverage in a lot of samples? Are these impact by indels or similar?

-Page 9: Why is the % of positions in each gene an important number to discuss in the results?

-Page 10: In the results, PCs come out of seemingly nowhere. While this is mentioned in the results, the authors should walk the reader through what these are used for (genetic background of virus).

-Page 10: “Bonferroni corrected p-value threshold” would be more straightforward than “our p-value threshold”

-Page 11: The authors should speculate on why the by-gene entropy analysis fails to associate with VL when the average of these values does associate with VL (unless I am misunderstanding what was done).

-Page 11: It is not exactly clear what is meant by “the joint model including all 16 positions”. Does this also include PCs?

-Page 12: Final sentence. What evidence is being used to support this important claim?

Reviewer #3: The authors have nicely followed the suggestions made. I am happy with this revised version.

Reviewer #4: This paper examines the relationship between intrahost viral diversity and viral load in patients enrolled in a clinical trial of early ART initiation. The study design of the parent trial means that participants tended to be either relatively early in their infection, or more likely to be relatively slow progressors, having maintained a CD4 count in the normal range (>500) without treatment. On the other hand, viral controllers are excluded from this analysis by virtue of not having sufficiently high viral load for amplification and sequencing (cut-off 1000 copies/ml).

Although previous studies have suggested that the extent of intrahost viral diversity is likely to vary over the course of HIV infection (for example, being lower in late disease when host immune responses are less effective), this is a cross-sectional rather than a longitudinal study, with only a single sample being studied in each patient. I think there are some concerns about trying to relate this to viral load in patients early in infection, during the very dynamic stages of primary HIV infection - in many ways I would be more comfortable if the analysis focused on patients who had already reached set-point viral load (usually some time between 6 and 24 months), although I appreciate that the authors have calculated the mean Shannon entropy separately for patients infected less than 6 months, 6-24 months and >24 months. I think it is also important to note that the amplicons do not cover the full length of the HIV genome, missing out p17 as well as vif and vpu.

I am not convinced that the debate about the relationship between viral diversity and viral load in HIV infection is as intense as the authors suggest. If this is a key argument about the novelty of this paper, then I think the authors should have made more effort to review the literature about HIV diversity and viral load/clinical outcome, focusing on the major studies on this topic. A number of key papers, which were not cited, have come to the same conclusions as the authors, for example: Esbjornsson et al, NEJM, 2012 (which is a longitudinal study that demonstrates increasing diversity over time up to a “diversity threshold” heralding the onset of AIDS), and a very similar study to this one, by Blanquart et al in PLOS Biology 2017, in which the authors focus specifically on the early stages of HIV infection and conclude that viral diversity contributes around one third of set-point viral load variability.

In contrast, the main argument to the contrary was reported by Wolinsky et al (Science 1996) in a select group of patients with very rapid disease progression, who were assumed to have mounted very limited immune responses to the virus.

I don't think anyone in the HIV field will be surprised to hear that the greatest diversity is found in the env gene, so the main novelty in the paper is the identification of amino acids where variation correlates significantly with viral load: several of these are found in the vicinity of T-cell epitopes but the relationship between env amino acids and antibody-binding epitopes has not been considered - I think this would add considerably to the paper (see, for example, Andrews et al, Scientific Reports, 2018).

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #2: No: A promise is made to upload these items, but this has not yet been delivered on.

Reviewer #3: None

Reviewer #4: Yes

**********

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Reviewer #2: No

Reviewer #3: Yes: Matteo Negroni

Reviewer #4: No

Figure Files:

Data Requirements:

Reproducibility:

PLoS Comput Biol. 2023 Jan 3;19(1):e1010756. doi: 10.1371/journal.pcbi.1010756.r006

Author response to Decision Letter 2

31 Aug 2022

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Submitted filename: manuscript_plos_comp_bio_rebuttal3_20220825.docx

Click here for additional data file.^{(23.9KB, docx)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r007

Decision Letter 3

Rob J De Boer, Nicola Segata

10 Oct 2022

Dear Ms. Gabrielaite,

Thank you very much for submitting your manuscript "Deep-sequencing of viral genomes from treatment-naive HIV-infected persons shows positive association between intrahost genetic diversity and viral load" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Sincerely,

Nicola Segata

Academic Editor

PLOS Computational Biology

Rob De Boer

Section Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: The manuscript has been much improved in terms of rigor by the addition of new analyses. While cross-validation is not the same as an independent test set, the direct analyses of the effects of coverage and sequence validation has satisfied my concerns.

I do not want to unnecessarily burden the authors, but one thing the authors and editor might consider together is improving the readability of the manuscript by including a figure in the main text that summarizes the models tried and the relative predictive power of these models. Having to go to the supplement to understand the evidence for key claims (in particular the relative predictive power of sequence/PC and entropy, mentioned in abstract) is not ideal.

Reviewer #4: The authors have addressed only one of my comments, about the influence of time since infection on the analysis, and have either ignored or failed to address the other comments. I think that the question of the novelty of their findings remains to be answered.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #4: None

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #2: No

Reviewer #4: No

Figure Files:

Data Requirements:

Reproducibility:

References:

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

PLoS Comput Biol. 2023 Jan 3;19(1):e1010756. doi: 10.1371/journal.pcbi.1010756.r008

Author response to Decision Letter 3

31 Oct 2022

Attachment

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r009

Decision Letter 4

Rob J De Boer, Nicola Segata

17 Nov 2022

Dear Ms. Gabrielaite,

Thank you very much for submitting your manuscript "Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Sincerely,

Nicola Segata

Academic Editor

PLOS Computational Biology

Rob De Boer

Section Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: I appreciate that the authors attempted to meet my request for a Table 2 for a direct comparison of models. However, this table is confusing and not helpful in its current form-- as it is hard to compare prose across the various models. Information on entropy should be removed (this doesn't need to summarize the entire paper!) and versions of the same exact model should be compared in a simple table, where each row corresponds to the data included in that model and the value presented in each cell shows the total performance of the model (e.g. % of VL variance explained). Values of table should only include numbers, and explanations of what these numbers mean should be included in the headers of the column(s) (indicating that the equivalent metrics are being compared across models).

Alternatively, the authors could remove this table.

Reviewer #4: I am glad that the authors have now addressed my comments.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

Reviewer #2: None

Reviewer #4: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #2: No

Reviewer #4: No

Figure Files:

Data Requirements:

Reproducibility:

References:

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

PLoS Comput Biol. 2023 Jan 3;19(1):e1010756. doi: 10.1371/journal.pcbi.1010756.r010

Author response to Decision Letter 4

22 Nov 2022

Attachment

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r011

Decision Letter 5

Rob J De Boer, Nicola Segata

23 Nov 2022

Dear Ms. Gabrielaite,

We are pleased to inform you that your manuscript 'Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Nicola Segata

Academic Editor

PLOS Computational Biology

Rob De Boer

Section Editor

PLOS Computational Biology

***********************************************************

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1010756.r012

Acceptance letter

Rob J De Boer, Nicola Segata

23 Dec 2022

PCOMPBIOL-D-21-02241R5

Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load

Dear Dr Gabrielaite,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Zsofi Zombor

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Characteristics of 2,650 participants from the START trial included in this analysis.

IQR (interquartile range).

(DOCX)

Click here for additional data file.^{(15.1KB, docx)}

S2 Table. HIV genomic positions for which ≤800 samples had a minimum of 500-fold genome sequencing depth at position.

(DOCX)

Click here for additional data file.^{(14KB, docx)}

S3 Table. HIV genome’s mean Shannon entropy at the most variable positions.

(DOCX)

Click here for additional data file.^{(16.1KB, docx)}

(DOCX)

Click here for additional data file.^{(15.3KB, docx)}

S5 Table. Linear regression analysis of predictors of the viral load (VL).

(DOCX)

Click here for additional data file.^{(19.4KB, docx)}

S6 Table. Joint multiple linear regression model with the Shannon entropy values from significantly associated AA positions.

P-values below the 0.05 threshold are marked in bold.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

S7 Table. Average RMSE values for the train and test datasets from the 5-fold cross validation analysis for the 16 Shannon entropy HIV AA positions associated with VL.

(DOCX)

Click here for additional data file.^{(14.1KB, docx)}

S8 Table. Joint model of multiple linear regression with the consensus AA variants which were significantly associated with viral load. P-values below the 0.05 threshold are marked in bold.

(DOCX)

Click here for additional data file.^{(16.9KB, docx)}

S1 Fig. An illustration of the HIV genome and the positions covered by the two amplicons sequenced in this study.

(DOCX)

Click here for additional data file.^{(340.7KB, docx)}

S2 Fig. The density plots of the mean Shannon entropy in different races of the 36 most variable positions.

(DOCX)

Click here for additional data file.^{(205KB, docx)}

S3 Fig. The density plots of the mean Shannon AA entropy in different subtypes of the 36 most variable positions.

(DOCX)

Click here for additional data file.^{(260.3KB, docx)}

S4 Fig. Relationship between log10 viral load and Shannon entropy in the 16 most significantly associated HIV amino acid positions. Blue line marks smoothed conditional means.

(DOCX)

Click here for additional data file.^{(449.4KB, docx)}

S5 Fig. Pairwise correlation matrix between covariates (bottom triangle).

(DOCX)

Click here for additional data file.^{(342.5KB, docx)}

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Data Availability Statement

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PERMALINK

Deep-sequencing of viral genomes from a large and diverse cohort of treatment-naive HIV-infected persons shows associations between intrahost genetic diversity and viral load

Migle Gabrielaite

Marc Bennedbæk

Malthe Sebro Rasmussen

Virginia Kan

Hansjakob Furrer

Robert Flisiak

Marcelo Losso

Jens D Lundgren

Rasmus L Marvig

Roles

Abstract

Background

Methods

Results

Conclusions

Trial registration

Author summary

Introduction

Methods

Ethics statement

Study population

HIV sequencing, alignment, variant calling, and subtyping

Shannon entropy

Statistical analysis

Dryad DOI

Results

Study participants and genome sequencing

Shannon entropy calculation

Fig 1.

The most variable positions in the sampled HIV genomes

Intrahost diversity correlation with viral load

Fig 2. The relationship between sample’s mean Shannon entropy, viral load (VL) and infection duration.

Mean intrahost diversity association with viral load

HIV intrahost position entropy association with viral load

Fig 3. Manhattan plot of the associations between viral load (VL) and Shannon entropy at AA positions projected on the HIV genome.

Table 1. Sixteen HIV amino acid (AA) positions where Shannon entropy was significantly associated with viral load (VL).

Discussion

Table 2. Variance in viral load (VL) explained by linear regression analysis with different models.

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Rob J De Boer

Nicola Segata

Roles

Author response to Decision Letter 0

Decision Letter 1

Rob J De Boer

Nicola Segata

Roles

Author response to Decision Letter 1

Decision Letter 2

Rob J De Boer

Nicola Segata

Roles

Author response to Decision Letter 2

Decision Letter 3

Rob J De Boer

Nicola Segata

Roles

Author response to Decision Letter 3

Decision Letter 4

Rob J De Boer

Nicola Segata

Roles

Author response to Decision Letter 4

Decision Letter 5

Rob J De Boer

Nicola Segata

Roles

Acceptance letter

Rob J De Boer

Nicola Segata

Roles

Associated Data

Supplementary Materials