Antiretroviral resistance remains a major obstacle to the successful and lasting suppression of HIV (Gupta et al., 2012; Günthard et al., 2019). While in resource-rich settings the availability of novel drug classes and personalized HIV treatment have diminished the challenges associated with antiretroviral resistance, resource-limited settings have experienced a continuous increase in antiretroviral resistance, which is now threatening the unprecedented success of the global rollout of antiretroviral treatment (ART) (Fund, 2019; Hauser et al., 2019). In the context of this globalization of antiretroviral resistance, it is becoming increasingly important to understand how human and viral genetic variation are affecting the processes generating or limiting antiretroviral resistance (Aghokeng et al., 2011; Lataillade et al., 2010).

HIV drug-resistant mutations (DRMs) can either be selected in patients on ART experiencing treatment failure (acquired drug resistance, or aDRM) or be transmitted from a patient carrying the resistance mutation to an uninfected individual (transmitted drug resistance, tDRM). As some DRMs have been shown to carry a cost, feeding on the virus fitness and replication capacity, they can revert in the absence of ART. Once the selective pressure favoring those mutations is removed, their frequency within a host continuously decreases at the expense of the wild-type variant, and eventually they become undetectable by standard resistance tests. It has been shown that the time scales on which reversion occurs exhibit a large variation ranging from several months to over 10 years, depending on the fitness cost that in turn is governed by both the type of mutation and the genetic background in which it occurs (Kühnert et al., 2018; Yang et al., 2015). This canonical perspective based on the evolutionary forces of aDRM and tDRM, and their disappearance from the replicating quasi-species, generally disregards the possibility that antiretroviral- resistant mutations are selected in untreated individuals.

One process that may act against the paradigm of DRM emerging only in treated individuals and reverting in untreated individuals is accidental resistance evolution occurring as a collateral effect of viral immune escape. A well-understood instance of this process is evolutionary escape from binding to human leukocyte antigen (HLA), an extremely diverse gene complex encoding for major histocompatibility complex (MHC) proteins. MHC class I proteins (corresponding to HLA class I) are found on the surface of all nucleated cells, and by presenting antigens from the cell interior to the surface, they allow for binding to cytotoxic CD8 T cells (CTL); thus, MHC class I proteins tag the virally infected cell and can subsequently be eliminated by CTL (Markov and Pybus, 2015; Zinkernagel and Doherty, 1979). The high mutation rate associated with replicating HIV predisposes to cellular and humoral immune escape, where the viral epitopes are no longer recognized by the mounted immune effectors. For CTL-mediated immune responses, this process of developing escape mutations remains a critical part of HIV pathogenesis (Leslie et al., 2004). Conversely, the high variability of encoded MHC alleles and their combinations come into play, as the host HLA alleles change as a consequence of transmission (Markov and Pybus, 2015; Zinkernagel and Doherty, 1979; Borghans et al., 2004). If the viral epitope recognized by MHC-I maps to the viral genome at the same region, this could confer an increased viral fitness leading to mutation persistence or even the emergence of a new DRM in an ART-naïve host (Gatanaga et al., 2013). While this phenomenon has been reported for individual HIV mutation:HLA pairs, a systematic assessment of the impact of epitope escape across HIV DRM:HLA pairs in a representative population has not yet been reported.

In this study, we investigated and analyzed the viral and genetic data from ART-naïve patients in the Swiss HIV Cohort Study (SHCS). This is leveraging the unique combination of viral and human genetic data in the SHCS, with over 20,000 genotypic resistance tests and over 5000 patients with information on HLA-I alleles. This allowed us to systematically screen the cohort for associations between DRM:HLA-I pairs and hence for pairs where escape from HLA-I binding might confer the DRM an evolutionary advantage even in the absence of ART.

Our analyses indicate strong evidence for the presence of an evolutionary intrapatient interaction between HIV DRMs and certain HLA-I alleles. Of the three candidate DRM:HLA pairs analyzed by three methods, two were supported by two of the analyses to show this relationship, of which one, RT-E138:HLA-B18, was supported by all three (Table 4). This is notable as this pair has been specifically investigated by Gatanaga et al., 2013, who showed both experimentally and through structural modeling that HLA B18-restricted CTLs select for a mutation in RT138. Our study independently demonstrates that this interaction is relevant at the population level, both in cross-sectional and in longitudinal cohort data. Of note, both DRMs are associated with the nonnucleoside analogue reverse transcriptase inhibitor class of ART drugs, with RT-V179D/F/T being associated with resistance to Etravirine and RT-V179L being associated with Rilpivirine. RT-E138A/G/K/Q is associated with resistance to Etravirine and Rilpivirine (International Antiviral Society, 2019). Estimates of virological failure for these two drugs are upwards of 5% and 11%, for Efavirenz and Rilpivirine, respectively (Sanford, 2012).

These results have major implications for our understanding of the evolutionary epidemiology in viral infections as they demonstrate a considerable interaction between the processes of drug resistance evolution and immune escape observed for several drug classes and HLA alleles in a representative patient population. This extends the standard paradigm that resistance mutations are acquired in treated individuals, may become transmitted, but eventually disappear in treated individuals with the possibility that resistance mutations newly emerge in untreated individuals due to immune escape. While this mechanism does obviously not account for the majority of DRMs in patients with untreated HIV, it may not be a negligible phenomenon.

In fact, HLA type-driven viral evolution in DRM-relevant CTL epitopes may be particularly relevant in light of the estimated 10% with a DRM in ART-naïve European HIV-positive patients, and even higher figures in low-resource settings, where continuing issues with access to treatment and adherence exacerbate the risk of treatment failure (Günthard et al., 2019; Hofstra et al., 2016; Wittkop et al., 2011; Chimukangara et al., 2019; Pessôa and Sanabani, 2017). As HLA is extremely diverse in the human population, and exhibits high variation in allelic frequency in different geographic regions (Piazza et al., 1980), this DRM:HLA link may partially explain regional variations in pre-treatment drug resistance. Accordingly, we would expect the emergence of certain DRMs in the population that is ART-naïve, or specifically, naïve to Etravirine and Rilpivirine, if the local population has a higher prevalence of the HLA types indicated in our analyses.

As the SHCS primarily consists of individuals of white ethnicity from Switzerland and surrounding countries, our study is statistically best powered to detect DRM:HLA pairs amongst white patients, and may be too underpowered to detect DRM:HLA pairs involving HLA-I alleles more prevalent in non-white, low-resource settings – precisely where DRMs are a more urgent issue. This is even concerning considering the high number of pairs eliminated after filtering out those with insufficient numbers to power an analysis (Figure 1). This lack of power may explain, for example, RT-V179:HLA-B35 indicates a DRM:HLA association in the longitudinal analysis, but not in the cross-sectional analysis with the interaction term (Table 4). It is conceivable that with greater numbers of patients and more years of follow-up that more DRM:HLA pairs would be detected and that these inter-analyses inconsistencies would be resolved, though we should not exclude the possibility of other sources for such discrepancies, for example, imprecise estimates of HIV infection time. The limitation of most sequences to the pol gene also made the analyses underpowered to find DRM:HLA relationships in other genes.

Despite these limitations, our study is strengthened by its methodological breadth and thoroughness. While other studies have examined the link between HLA-I and DRMs (Ahlenstiel et al., 2007; Bailey et al., 2007), this study is on a numerically larger scale, and is unique to systematically examine an entire HIV cohort population’s DRM profiles and HLA-I types to screen for potential DRM:HLA pairs. As the cross-sectional analysis took into account duration of infection, it thus effectively excluded from consideration tDRMs that were disadvantageous to viral fitness in ART-naïve patients, identifying any DRMs that remained over time despite the lack of selection pressure from ART, thus mitigating the possibility that these DRMs are merely tDRMs with no relevance to viral pathogenesis in the patient. Additionally, as it is now clinical practice to immediately initiate ART in newly diagnosed patients since several years, there is now hardly ever more than one ART-naïve sequence per patient, thus making our longitudinal analysis very unique and difficult to replicate in the future (World Health Organization, 2016; Ryom et al., 2016).

By utilizing three different analytical approaches, especially by combining the longitudinal and cross-sectional approaches, we are able to identity and validate DRM:HLA pairs where there is this epitope–mutation interaction. The NetMHCPan analyses allowed us to connect the associations we statistically detected at a population level with predicted MHC binding, which was additionally supported by prior experimental findings. This screening process is also strengthened by the restriction to pairs where the HLA-I and DRM frequencies have sufficient power, thus reducing the number of performed tests and the magnitude of the Benjamini–Hochberg multiple testing adjustment.

Our findings not only have an impact on our understanding of why DRMs tend to be transmitted and maintained in certain individuals, but may also help inform ART in the future. While it would not be feasible to tailor ART treatment based on personal HLA genotyping in resource-limited settings, this information could be used to help anticipate a higher frequency of certain DRMs where a corresponding HLA-I type is more prevalent. As HIV sequencing progresses, more complete DRM:HLA data on other genes, particularly integrase, will become available at sufficiently powered frequencies, enabling us to detect potential DRM:HLA pairs that may affect the efficacy of integrase inhibitors, a newer and increasingly used ART drug class.

The individual-level datasets generated and analyzed for the current study do not fullfill the requirements for open data access: (1) The SHCS informed consent states that sharing data outside the SHCS network is only permitted for specific studies on HIV infection and its related complications, and to researchers who have signed an agreement detailing the use of the data and biological samples; and (2) the data is too dense and comprehensive to preserve patient privacy in persons living with HIV. Per Swiss law, data cannot be shared if data subjects have not agreed or if data is too sensitive to share. Investigators with a request for the data that support the findings of this study should contact the corresponding author Roger Kouyos and the Scientific Board of the SHCS. The provision of data will be considered by the Scientific Board of the SHCS and the study team and is subject to Swiss legal and ethical regulations, and is outlined in a material and data transfer agreement. We have however, provided the data files (with the rows anonymized and randomly re-assorted) with the bare minimum number of variables necessary to do the core analyses and to assemble the figures as shown in the manuscript. The code for the analysis can be found on Github repository: https://github.com/hnyhnyhny/HNGUYEN_HLA_DRM (copy archived at https://archive.softwareheritage.org/swh:1:rev:4a03919f07748ff22c4bf529100505ecc78b57cd).

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Acceptance summary:

This paper uses a rigorous methodological approach to identify an interesting and novel mechanism of HIV drug resistance in addition to acquired and transmitted drug resistance: accidental resistance arising coincidentally due to immune escape. This finding is of potential clinical importance as such mutations can arise spontaneously during untreated HIV infection in the context of a specific HLA-type.

Decision letter after peer review:

Thank you for submitting your article "Systematic screening of viral and human genetic variation identifies antiretroviral resistance and immune escape link" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, including Joshua T Schiffer as the Reviewing Editor and Reviewr #1, and the evaluation has been overseen by Jos van der Meer as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Please expand and clarify the description of the DRM and genotyping data. Please specify the number of different HLA-types, the relative frequencies of the predominant HLA-types, the number of different drug resistance variants found in ART-naïve patients and their relative frequency in the population as well as frequencies of multiple DRMs.

2. In the results, please include a first paragraph that puts the subsequent analysis into context and describe the purpose of the cross-sectional and survival analyses before presenting the results.

3. Either eliminate the term "literature search" or specifically describe the search methodology that was used to perform this search.

4. Include viral load data if available as described by Reviewer 1.

Reviewer #1:

This paper by Nguyen and colleagues identifies 3 possible HIV mutations which are linked by HLA-association and drug resistance. The conclusions are justified based on the analysis. The results are unlikely to influence clinical practice as the identified mutations are of only occasional importance in the clinic. However, the general concept is of great relevance to HIV and other viral infections.

Strengths of the study are:

1. Novel conclusions: the paper is important because it identifies a new source of possible drug resistance in addition to selective pressure during failed therapy and transmitted drug resistance.

2. Methodology strength: the authors do a nice job of attempting to move towards causality rather than correlation. Specifically, they establish strength of correlation, temporality (the survival analysis showing accrual of the new mutation over time), assessment of possible confounding variables such as ruling out transmitted mutations with the survival analysis and looking at an interaction term with infection time, assessing mechanistic plausibility with literature review and utilizing a very large cohort.

3. Thoughtful and clearly written intro and discussion.

There were no major weaknesses with the study from my perspective.

This study was extremely clearly written and I believe the scientific conclusions are justified by the analysis. One area of interest would be viral loads among study participants with specific HLA/drug resistance pairs. If possible and if data is available, then it would be interesting to see whether onset of a new mutation as seen in the survival analysis is associated with a decrease in viral load due to a fitness cost. There would be a natural comparison to make versus viral load changes in all other participants, as well as those with relevant HLA types who do not develop new drug resistance mutations. A similar approach with CD4 T cell count trajectories would also be interesting and in theory easy to perform.

Reviewer #2:

Drug resistance mutations (DRMs) are known to emerge and be selected for in patients on ART experiencing treatment failure. Transmission of DRMs can lead to high levels of drug resistance in treatment-naïve individuals in resource-limited settings, which may compromise first-line treatment regimens. Here the authors investigated whether the emergence and persistence of DRMs in untreated individuals could also occur as a side effect of immune selection. To this end, they screened for associations between drug resistance mutations and different HLA-1 alleles in a large cohort of nearly 4,000 treatment-naïve patients.

The authors identified three DRM-HLA-1 pairs where the presence of the HLA-1 allele was predictive of the patient having acquired the DRM prior to starting ART. Similar analyses of other patients cohorts have also identified potential associations between DRMs and specific HLA-1 alleles, so in that sense, the novelty of these findings is limited. However, this study substantially extends previous work by incorporating additional analyses that account for the duration of infection before treatment and the time to emergence of drug resistance, leveraging their exceptionally detailed dataset. These analyses revealed that patients without the HLA-B18 allele who had acquired the DRM at transmission were likely to revert to wildtype over time due to the fitness cost associated with the mutation, whereas patients with the HLA-B18 allele either retained the transmitted DRM or generated it de novo to escape immune pressure. Longitudinal survival analysis further indicated that patients with the HLA-B18 and HLA-B35 alleles who did not initially have the associated DRMs were significantly more likely to acquire them than patients without the alleles.

Overall, the conclusions of this paper are well supported by data. The statistical methods used are rigorous and straightforward to apply to cohort data for other populations. However, the description of the DRM and genotyping data is minimal, and its presentation is somewhat confusing. While the three identified HLA-DRM pairs were analyzed extensively, it is not clear how frequent these genotypes and DRMs (independently of each other) are in the patient cohort. As currently written, the manuscript lacks context for assessing the contribution of immune-driven DRMs relative to transmission-acquired DRMs in the treatment-naïve HIV+ population. Overall, however, this paper provides a valuable contribution to the field as a proof-of-concept demonstration of the emergence of immune-derived DRMs.

The Results section would greatly benefit from a first paragraph that puts the subsequent analysis into context. Currently it is challenging to get any sense for the distribution of DMRs and HLA-types in the population. It would help to specify the number of different HLA-types, the relative frequencies of the predominant HLA-types, the number of different drug resistance variants found in ART-naïve patients and their relative frequency in the population. What proportion of the patients that were included in the study had any drug resistance mutations at all? Did any patients have multiple DRMs?

In my opinion, the Results section would flow more logically if the purpose of the cross-sectional and survival analyses were discussed before presenting the results. E.g. A cross-sectional analysis was performed to assess "…" and a survival analysis to assess "…".

Specifying "literature search" as an analysis methodology seems a little clunky. Why not simply state that the B18 HLA type has been shown to bind to the epitope containing the E138 DR mutation in several previous studies [listing citations]?

Would the cross-section analysis have more power if the two potential HLA alleles associated with the PR-E138 DRM were compared simultaneously against a reference group containing all other alleles? In the current analysis, the two potentially important alleles are essentially compared against each other (as part of the "other" category).

It would be helpful if the frequencies in Figure 3A would be spelled out in text rather than just shown in the table. E.g. what proportion of patients had the DRM-associated HLA alleles, and what fraction of these patients subsequently acquired the DRMs?

Reviewer #1:

[…] This study was extremely clearly written and I believe the scientific conclusions are justified by the analysis. One area of interest would be viral loads among study participants with specific HLA/drug resistance pairs. If possible and if data is available, then it would be interesting to see whether onset of a new mutation as seen in the survival analysis is associated with a decrease in viral load due to a fitness cost. There would be a natural comparison to make versus viral load changes in all other participants, as well as those with relevant HLA types who do not develop new drug resistance mutations. A similar approach with CD4 T cell count trajectories would also be interesting and in theory easy to perform.

While this is a very relevant question and would undoubtedly add to the study, we are unfortunately limited by power. For 2 of the pairs: RT-E138:HLA-A24 and RT-V179:HLA-B35, there are 2 and 3 mutation events respectively among those with the queried HLA type, and 8 and 1 respectively for those with another HLA type. This makes a paired T-test comparing the mean HIV viral load or CD4 count before and after the mutation nearly impossible to detect any difference, even if there is one.

To illustrate this, we did perform the comparison for RT-E138:HLA-B18:

The average viral load right after the mutation for those with HLA-B18 was 51 489, compared to the much higher 97 098 viral load mean before the mutation arose. Although the viral load is apparently halved, the fact that this was pooled from the four out of the five individuals with a post and pre-mutation viral load measurement, meant that the p value of the paired T-test (after log-transforming the viral load values), was only 0.122. While this indicates a probable relationship (particularly in contrast to the increase among the 5 non-HLA-B18 individuals who developed the mutation [mean viral load before: 23 036, after: 35 756, p value=0.137), we would need more patients to definitively demonstrate this. There was no change observed for CD4 count.

Because of this, we regretfully decided not to include this into the manuscript.

Reviewer #2:

[…] The Results section would greatly benefit from a first paragraph that puts the subsequent analysis into context. Currently it is challenging to get any sense for the distribution of DRMs and HLA-types in the population. It would help to specify the number of different HLA-types, the relative frequencies of the predominant HLA-types, the number of different drug resistance variants found in ART-naïve patients and their relative frequency in the population. What proportion of the patients that were included in the study had any drug resistance mutations at all? Did any patients have multiple DRMs?

This is a very valid point. Two tables have now been added to indicate the most frequent HLA-I alleles and DRMs observed in the study population (Tables 2-3, pg 14-15). A new subsection (lines 270-280) has been added to the Results to summarize them, before moving on to the analyses themselves (the DRM breakdown originally in the Discussion has also been moved to this subsection):

“The most commonly found HLA-I types are summarized in Table 2. […] As for the two DRMs of interest, 145had a DRM at RT-E138: 124 RT-E138A, 14 RT-E138G, 6 RT-E138K, 1 RT-E138Q. Eighty-two were found at RT-V179: 68 RT-V179D, 13 RT-V179E, 1 RT-V179F.”

In my opinion, the Results section would flow more logically if the purpose of the cross-sectional and survival analyses were discussed before presenting the results. E.g. A cross-sectional analysis was performed to assess "…" and a survival analysis to assess "…".

An introduction to each analysis has been added to the beginning of each analysis subsection. (lines 294-297, 319-321, 345-346):

“To examine the effect of having a given HLA-I allele on the presence of the DRM in question, we created for each candidate pair a logistic regression model predicting the presence of that specific DRM (at the earliest resistance testing), given presence/absence of the queried HLA-I type. […] These also indicated weakened HLA binding to the DRM-peptide (i.e. supporting the putative association) for two of the three candidate pairs: RT-E138:HLA-B18 and RT-V179:HLA-B35 (Supplementary Table S2).”

Specifying "literature search" as an analysis methodology seems a little clunky. Why not simply state that the B18 HLA type has been shown to bind to the epitope containing the E138 DR mutation in several previous studies [listing citations]?

Methods:

“To examine if there was any mechanistic plausibility to the associations found in the above analyses, we utilized the program server NetMHCpan 4.1 to predict the binding affinity of the HLA allele to the all 9-mer peptides including the mutation position, with either the wildtype amino acid at the position or one of the three most common mutated amino acids observed (24). […] Additionally, we searched the Los Alamos HIV Molecular Immunology Database to corroborate the candidate pairs with prior experimental studies indicating the HLA-epitope match (25).”

Results:

“NetMHCpan predictions of HLA binding were performed to gauge the mechanistic plausibility of the effects observed in the first two analyses. […] Prior literature indicating experimentally verified epitope binding of the HIV proteome to HLA also exists for these two pairs (13, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36).”

Would the cross-section analysis have more power if the two potential HLA alleles associated with the PR-E138 DRM were compared simultaneously against a reference group containing all other alleles? In the current analysis, the two potentially important alleles are essentially compared against each other (as part of the "other" category).

While combining the two HLA alleles would provide more statistical power, the issue is that one pair with RT-E138 ( with HLA-B18), already demonstrates a very strong relationship, while the other (with HLA-A24) shows none in our analysis. A pooled analysis would very likely show a relationship, but it would almost certainly be due to the RT-E138:HLA-B18’s strongly significant association “overpowering” the RT-E138:HLA-A24’s null association, and misleadingly yielding a statistically significant association overall.

It would be helpful if the frequencies in Figure 3A would be spelled out in text rather than just shown in the table. E.g. what proportion of patients had the DRM-associated HLA alleles, and what fraction of these patients subsequently acquired the DRMs?

The manuscript has now been updated to clearly explain these numbers (and percentages) within the text itself (lines 324-332):

“For RT-E138:HLA-B18, 63 (7.7%) of the 813 patients without an RT-E138 mutation were HLA-B18, among which 5 (7.9%) developed it before ART initiation, compared to the 5 (0.7%) out of the 750 with another HLA-B18 type (HR: 12.211, 95% CI: 3.523-42.318 [p value <0.001]) (Figure 3). […] Of the 150 (18.3%) of the 821 patients with HLA-B35 (initially without an HLA-B35 mutation), 3 (2.0%) developed a mutation at RT-V179, compared to only 1 (0.1%) of the 671 with another HLA-B type (HR: 16.116, 95% CI: 1.673-155.216 [p value = 0.016]).”

Author details

1. Huyen Nguyen

   1. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
   2. Institute of Medical Virology, Swiss National Center for Retroviruses, University of Zurich, Zurich, Switzerland

   Contribution

   Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   Huyen.Nguyen@usz.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1486-8970

2. Christian Wandell Thorball

   1. School of Life Sciences, École Polytechnique, Fédérale de Lausanne, Switzerland
   2. Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

   Contribution

   Resources, Data curation, Software, Validation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Jacques Fellay

   1. School of Life Sciences, École Polytechnique, Fédérale de Lausanne, Switzerland
   2. Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

   Contribution

   Resources, Data curation, Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8240-939X

4. Jürg Böni

   Institute of Medical Virology, Swiss National Center for Retroviruses, University of Zurich, Zurich, Switzerland

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Sabine Yerly

   Laboratory of Virology, Geneva University Hospital, University of Geneva, Geneva, Switzerland

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Matthieu Perreau

   Division of Immunology and Allergy, University Hospital Lausanne, University of Lausanne, Lausanne, Switzerland

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Hans H Hirsch

   1. Transplantation & Clinical Virology, Department of Biomedicine, University of Basel, Basel, Switzerland
   2. Infectious Diseases and Hospital Epidemiology, Department of Medicine, University Hospital Basel, Basel, Switzerland
   3. Clinical Virology, Laboratory Medicine, University Hospital Basel, Basel, Switzerland

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Katharina Kusejko

   1. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
   2. Institute of Medical Virology, Swiss National Center for Retroviruses, University of Zurich, Zurich, Switzerland

   Contribution

   Software, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4638-1940

9. Maria Christine Thurnheer

   University Clinic of Infectious Diseases, University Hospital of Bern, University of Bern, Bern, Switzerland

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

10. Manuel Battegay

    Infectious Diseases and Hospital Epidemiology, Department of Medicine, University Hospital Basel, Basel, Switzerland

    Contribution

    Resources, Data curation, Writing - review and editing

    Competing interests

    No competing interests declared

11. Matthias Cavassini

    Department of Infectious Diseases, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland

    Contribution

    Resources, Data curation, Writing - review and editing

    Competing interests

    has received research and travel grants for his institution from ViiV and Gilead.

12. Christian R Kahlert

    Division of Infectious Diseases and Hospital Epidemiology, Kantonsspital St. Gallen, St. Gallen, Switzerland

    Contribution

    Resources, Data curation, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0784-3276

13. Enos Bernasconi

    Division of Infectious Diseases, Regional Hospital, Lugano, Switzerland

    Contribution

    Resources, Data curation, Writing - review and editing

    Competing interests

    has received fees for his institution for participation to advisory board from MSD, Gilead Sciences, ViiV Healthcare, Abbvie and Janssen.

14. Huldrych F Günthard

    1. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
    2. Institute of Medical Virology, Swiss National Center for Retroviruses, University of Zurich, Zurich, Switzerland

    Contribution

    Resources, Data curation, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

    Competing interests

    HFG has received unrestricted research grants from Gilead Sciences and Roche; fees for data and safety monitoring board membership from Merck; consulting/advisory board membership fees from Gilead Sciences, Sandoz and Mepha; and travel reimbursement from Gilead.

15. Roger D Kouyos

    1. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
    2. Institute of Medical Virology, Swiss National Center for Retroviruses, University of Zurich, Zurich, Switzerland

    Contribution

    Conceptualization, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

    For correspondence

    roger.kouyos@usz.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9220-8348

16. The Swiss HIV Cohort Study

    Contribution

    Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

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