Developing treatments or vaccines for HIV is challenging because the genetic makeup of the virus is constantly changing in an effort to outwit the human immune system. Moreover, the immune system is highly variable as a result of the long-standing co-evolution of humans and microbes. Each individual will try to oppose the invading virus in a unique way, forcing the virus to acquire specific mutations that can be interpreted as the genetic signature of this one-against-one battle.

To explore the influence of co-evolution on HIV, Bartha et al. took samples of both human and viral genomes from 1071 individuals infected with HIV, the AIDS virus, and used genotyping and sequencing technology to obtain a comprehensive description of the genetic variation in both. Computational techniques were then used to search for links between variants in the human DNA sequences and variants in the viral sequences.

The most common type of genetic variation found in the human genome is a single nucleotide polymorphism, or SNP for short: a SNP is produced when a single nucleotide – an A, C, G or T – is replaced by a different nucleotide. Bartha et al. found that SNPs within the human DNA sequences in their study were linked to variations in 48 amino acids in HIV. Moreover, all these SNPs were found within a group of genes known as the HLA (human leukocyte antigen) system, which encodes for proteins that play a vital role in the immune response. This work identified the areas of the human genome that put pressure on the AIDS virus, and the regions of the virus that serve to escape human control.

The approach developed by Bartha et al. allows the interactions between a microbe and a human host to be studied by looking at the genome of the microbe and the genome of the infected person. It also differentiates host-induced mutations that limit the capacity of the virus to do harm from those that are tolerated by the pathogen. A similar strategy could be used to study other infectious diseases.

Through multiple rounds of selection and escape, host and pathogen genomes are imprinted with signatures of co-evolution that are governed by Darwinian forces. On the host side, well-characterized anti-retroviral restriction factors, such as TRIM5α, APOBEC3G and BST2, harbor strong signals of selection in primate genomes, clear examples of retroviral pressure (Ortiz et al., 2009). On the virus side, obvious signs of selection are observable in the HIV-1 genome: escape mutations and reversions have been described in epitopes restricted by human leukocyte antigen (HLA) class I molecules and targeted by cytotoxic T lymphocyte (CTL) responses (Goulder et al., 2001; Kawashima et al., 2009). Sequence polymorphisms have also been reported recently in regions targeted by killer immunoglobulin-like receptors (KIR), suggesting evasion from immune pressure by natural killer (NK) cells (Alter et al., 2011). Evidence for the remodeling of retroviral genomes by host genetic pressure also comes from simian immunodeficiency virus (SIV) infection studies in rhesus macaques, where escape from restrictive TRIM5α alleles has been observed in the viral capsid upon cross-species transmission of SIVsm (Kirmaier et al., 2010). In contrast, human alleles of TRIM5α do not result in escape mutations, likely because of adaptation of the pathogen to the host (Rahm et al., 2013). Sequence adaptation is also a known feature of cross-species transmission. For example, a methionine in the matrix protein (Gag-30) in SIV_cpzPtt changed to arginine in lineages leading to HIV-1 and reverted to methionine when HIV-1 was passaged through chimpanzees (Wain et al., 2007).

To date, combined analyses of human and HIV-1 genetic data have addressed the association of HLA and KIR genes with variants in the retroviral genome (Moore et al., 2002; Brumme et al., 2007; Bhattacharya et al., 2007; Kawashima et al., 2009; Alter et al., 2011; Carlson et al., 2012; Wright et al., 2012). Additionally, genome-wide association studies (GWAS) performed in the host have focused on various HIV-related clinical phenotypes (Fellay et al., 2007; Fellay et al., 2009; Pereyra et al., 2010). In parallel, large amounts of HIV-1 sequence data have been generated for phylogenetic studies, which shed new light on viral transmission and evolution (Kouyos et al., 2010; Alizon et al., 2010; Von Wyl et al., 2011), or allow clinically driven analyses of viral genes targeted by antiretroviral drugs (resistance testing) (Von Wyl et al., 2009).

Building on the unprecedented possibility to acquire and combine paired human and viral genomic information from the same infected individuals; we employ an innovative strategy for global genome-to-genome host–pathogen analysis. By simultaneously testing for associations between genome-wide human variation, HIV-1 sequence diversity, and plasma viral load (VL), our approach allows the mapping of all sites of host–pathogen genomic interaction, the correction for both host and viral population stratification, and the assessment of the respective impact of human and HIV-1 variation on a clinical outcome (Figure 1).

Figure 1

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HIV-1 host genomic studies performed so far have focused on clinically defined outcomes (resistance to infection, clinical presentation, disease progression or death) or on pathogen-related laboratory results (such as CD4+ T cell counts and VL set point). While useful, these phenotypes have significant drawbacks. First, consistency of phenotypic determination can be hard to achieve, and such inconsistency can adversely affect power in large-scale genetic studies performed across multiple centers (Evangelou et al., 2011). Second, a relatively long follow-up in the absence of antiretroviral treatment is necessary to obtain informative data about the natural history of infection. However, international guidelines now propose an early start of antiretroviral therapy in most HIV-1 infected individuals (Thompson et al., 2012), making the collection of large numbers of long-term untreated patients not only unrealistic but also ethically questionable.

To overcome these limitations, we developed a novel approach for host genetic studies of infectious diseases, built on the unprecedented possibility to obtain paired genome-wide information from hosts and pathogens. We combined human polymorphism and HIV-1 sequence diversity in the same analytical framework to search for sites of human-virus genomic conflict, effectively using variation in HIV-1 amino acids as an ‘intermediate phenotype’ for association studies. Intermediate phenotypes have recently been shown to be useful in uncovering association signals that are not detectable using more complex clinical endpoints: illustrative examples include metabolomic biomarkers in cardiovascular research (Suhre et al., 2011), serum IgE concentration in the study of asthma (Moffatt et al., 2010), or neuroimaging-based phenotypes in psychiatry genetics (Rasetti and Weinberger, 2011). Variation in the pathogen sequence is an as-yet-untapped intermediate phenotype, specific by nature to genomic research in infectious diseases. Importantly, it depends on sequencing the pathogen, which could prove in many cases easier and more standardized than obtaining detailed clinical phenotypes.

Our approach allowed the mapping of host genetic pressure on the HIV-1 genome. The strongest association signals genome-wide were observed between human SNPs tagging HLA class I alleles and viral mutations in their corresponding CTL epitopes. Additional association signals were observed outside of optimally defined CTL epitopes, which could indicate novel epitopes, or represent secondary (compensatory) mutations. In a single experiment, these results recapitulate extensive epidemiological and immunogenetic research and represent a proof-of-concept that biologically meaningful association signals are identifiable using a hypothesis-free strategy. Indeed, host factors leading to viral adaptation can be uncovered by searching for associated imprints in the viral genome. Of note, the International HIV Controllers Study demonstrated the importance of specific amino acid positions in the HLA-B binding groove on a clinical outcome (elite control) (Pereyra et al., 2010). We here extend this observation to the HLA-A and C grooves, emphasizing the similarity in mechanism of host pressure on the viral proteome that is not necessarily translated into observable clinical outcomes.

We found a higher density of amino acid positions under selection in Gag and Nef compared with the rest of the HIV proteome. This is consistent with earlier findings that indicate the importance of Gag p24-specific CTL responses in slower progression to AIDS (Borghans et al., 2007; Brennan et al., 2012) or controller status (Dyer et al., 2008). Moreover, this further demonstrates that mapping host pressure on the pathogen proteome can reveal biologically relevant effects.

Analyses were performed using samples from clinically well-characterized patients, most of them with repeated and reliable HIV-1 VL measurements in the absence of antiretroviral therapy. We were thus able to compare the results of GWAS assessing human genetic determinants of mean VL, a standard clinical correlate of HIV-1 control, and genome-to-genome GWAS on amino acid variants in the viral proteome. The use of HIV-1 variation as outcome resulted in a considerable gain in power to detect host factors: the lowest p-values were observed for SNPs mapping to the HLA class I region in both approaches, but associations were much stronger with HIV-1 amino acid variation than for HIV-1 VL (2.7 × 10⁻⁶⁶ vs 1 × 10⁻⁰⁸), even when accounting for the increased number of multiple tests.

In addition to identifying sites of interaction between the host and the pathogen, the study design allowed the scoring of biological consequences of such interaction, by assessing associations between host-driven escape at viral sites and an in vivo phenotype (VL). For example, we decomposed the effect of rs2395029 (a marker of HLA-B*57:01) on VL to the effects of the multiple viral amino acid variants that are associated with that SNP. While some HIV-1 amino acid changes individually associate with decrease in VL, the compound image that emerges is one of a multiplicity of modest effects distributed across many residues. Correlations between host-associated variants and VL are difficult to interpret, because they may reflect fitness costs or compensation, the existence of strong (Iversen et al., 2006; Carlson et al.,, 2012) or novel (Almeida et al., 2011) immune responses, or the indirect impact of specific HLA class I alleles. Nevertheless, the observation that the majority of host-associated HIV-1 mutations do not correlate with any detectable change in VL confirms HIV’s remarkable capacity to adapt and compensate to immune pressure, often without measurable fitness cost.

A significant confounder in both human and viral genomic analyses is the existence of population stratification, where shared ancestry between infected individuals, stratification by ethnic groups, non-random distribution of HIV-1 subtypes, or clusters of viral transmission can all have an influence on the population frequencies of specific mutations, and thus create spurious associations if not carefully controlled for. Previous studies usually controlled for viral population substructure but were limited in the control of human population stratification (Moore et al., 2002; Bhattacharya et al., 2007). Our approach offers the opportunity to correct for both factors, thanks to the availability of extensive host and viral genomic information.

The present sample size provided approximately 80% power to detect a common human variant (minor allele frequency of 10%) with an odds ratio of 4.2 in the genome-to-genome analysis (Study B) and a viral amino acid explaining approximately 4% of the variation in plasma viral load (Study C) at the respective significance thresholds (Purcell et al., 2003). Consistent with most studies performed in HIV-1 host genetics over the past few years (reviewed in Telenti and Johnson (2012)), we did not identify previously unknown host genetic loci involved in host-viral interaction and HIV-1 restriction. The proposed approach can only detect polymorphic host factors that leave an imprint on the virus, which may exclude mediators of immunopathogenesis or genes involved in the establishment of tolerance (Medzhitov et al., 2012). An additional limitation is the incomplete nature of genomic information available both on the host side (common genotypes from GWAS) and on the viral side (near full-length consensus sequence; gp120 was not included in the analyses). Finally, the multiple hypothesis burden of a genome-to-genome scan is extremely high. It is conceivable that larger studies, or studies that focus on a subgroup of predefined host genes, would have power to detect novel associations. A comprehensive, but computationally challenging description of host–pathogen genomic interactions would require human genome sequencing, coupled with deep sequencing of intra-host retroviral subpopulations.

In summary, we used a genome-to-genome, hypothesis-free approach to identify associations between host polymorphisms and HIV-1 genomic variation. This strategy allows a global assessment of host–pathogen interactions at the genome level and reveals sites of genomic conflict. Comparable approaches are immediately applicable to explore other important infectious diseases, as long as polymorphic host factors exert sufficient selective pressure to trigger escape mutations in the pathogen. The observation that pathogen sequence variation, used as an intermediate phenotype, is more powerful than clinical and laboratory outcomes to identify some host factors allows smaller-scale studies and encourages analyses of less prevalent infectious diseases. Researchers involved in pathogen genome studies and host genetic studies should strongly consider the gathering of paired host–pathogen data.

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Author details

1. István Bartha

   1. School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Institute of Microbiology, University Hospital and University of Lausanne, Lausanne, Switzerland
   3. Research Group of Theoretical Biology and Evolutionary Ecology, Eötvös Loránd University and the Hungarian Academy of Sciences, Budapest, Hungary
   4. Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   IB, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Jonathan M Carlson

   eScience Group, Microsoft Research, Los Angeles, United States

   Contribution

   JMC, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Chanson J Brumme and Paul J McLaren

   Competing interests

   The authors declare that no competing interests exist.

3. Chanson J Brumme

   BC Centre for Excellence in HIV/AIDS, Vancouver, Canada

   Contribution

   CJB, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Jonathan M Carlson and Paul J McLaren

   Competing interests

   The authors declare that no competing interests exist.

4. Paul J McLaren

   1. School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Institute of Microbiology, University Hospital and University of Lausanne, Lausanne, Switzerland
   3. Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   PJM, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Jonathan M Carlson and Chanson J Brumme

   Competing interests

   The authors declare that no competing interests exist.

5. Zabrina L Brumme

   1. BC Centre for Excellence in HIV/AIDS, Vancouver, Canada
   2. Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada

   Contribution

   ZLB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

6. Mina John

   Institute of Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia

   Contribution

   MJ, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

7. David W Haas

   Vanderbilt University Medical Center, Nashville, United States

   Contribution

   DWH, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Javier Martinez-Picado

   1. AIDS Research Institute IrsiCaixa, Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
   2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

   Contribution

   JM-P, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Judith Dalmau

   AIDS Research Institute IrsiCaixa, Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain

   Contribution

   JD, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Cecilio López-Galíndez

    Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain

    Contribution

    CL-G, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Concepción Casado

    Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain

    Contribution

    CC, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Andri Rauch

    Clinic of Infectious Diseases, University of Bern & Inselspital, Bern, Switzerland

    Contribution

    AR, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

13. Huldrych F Günthard

    Division of Infectious Diseases and Hospital Epidemiology, University Hospital and University of Zürich, Zürich, Switzerland

    Contribution

    HFG, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. Enos Bernasconi

    Division of Infectious Diseases, Regional Hospital of Lugano, Lugano, Switzerland

    Contribution

    EB, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. Pietro Vernazza

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

    Contribution

    PV, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

16. Thomas Klimkait

    Department of Biomedicine, University of Basel, Basel, Switzerland

    Contribution

    TK, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

17. Sabine Yerly

    Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland

    Contribution

    SY, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

18. Stephen J O’Brien

    Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia

    Contribution

    SJO’B, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

19. Jennifer Listgarten

    eScience Group, Microsoft Research, Los Angeles, United States

    Contribution

    JL, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

20. Nico Pfeifer

    eScience Group, Microsoft Research, Los Angeles, United States

    Present address

    Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany

    Contribution

    NP, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

21. Christoph Lippert

    eScience Group, Microsoft Research, Los Angeles, United States

    Contribution

    CL, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

22. Nicolo Fusi

    eScience Group, Microsoft Research, Los Angeles, United States

    Contribution

    NF, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

23. Zoltán Kutalik

    1. Swiss Institute of Bioinformatics, Lausanne, Switzerland
    2. Institute of Social and Preventive Medicine, University Hospital and University of Lausanne, Lausanne, Switzerland

    Contribution

    ZK, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

24. Todd M Allen

    Ragon Institute of MGH, MIT, and Harvard, Massachusetts General Hospital, Boston, United States

    Contribution

    TMA, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

25. Viktor Müller

    Research Group of Theoretical Biology and Evolutionary Ecology, Eötvös Loránd University and the Hungarian Academy of Sciences, Budapest, Hungary

    Contribution

    VM, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

26. P Richard Harrigan

    1. BC Centre for Excellence in HIV/AIDS, Vancouver, Canada
    2. Faculty of Medicine, University of British Columbia, Vancouver, Canada

    Contribution

    PRH, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

27. David Heckerman

    eScience Group, Microsoft Research, Los Angeles, United States

    Contribution

    DH, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

28. Amalio Telenti

    Institute of Microbiology, University Hospital and University of Lausanne, Lausanne, Switzerland

    Contribution

    AT, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    Amalio.Telenti@chuv.ch

    Competing interests

    The authors declare that no competing interests exist.

29. Jacques Fellay

    1. School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Institute of Microbiology, University Hospital and University of Lausanne, Lausanne, Switzerland
    3. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Contribution

    JF, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    jacques.fellay@epfl.ch

    Competing interests

    The authors declare that no competing interests exist.

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