Estimating the respective contributions of human and viral genetic variation to HIV control

Bartha, István; McLaren, Paul J.; Brumme, Chanson J.; Harrigan, Richard; Telenti, Amalio; Fellay, Jacques

doi:10.1101/029017

Cited by 14 publications

(17 citation statements)

References 14 publications

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“…While within-host evolutionary dynamics are dominated by selective forces and competitive fitness ( Figure 2) [2], between-host evolution is considered to be largely shaped by neutral processes and multiple HIV-1 variants co-exist at the epidemiological level, although recent findings might imply more selective action at this level than generally assumed [16,17]. Circulating 40 viral diversity at population level is an intrinsic reflection of transmission dynamics within the epidemic [2], but virus and host genetics additionally impact between-host dynamics [10,18,19,5,20,2,21]. As a result, a complex interplay of multi-scale evolutionary processes exists and the selective advantage of viral traits differs at the within-host and between-host level as conflicting 2.…”

Section: Introductionmentioning

confidence: 99%

The impact of HIV-1 within-host evolution on transmission dynamics

Theys

Libin

Pineda-Peña

et al. 2017

Preprint

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The adaptive potential of HIV-1 is a vital mechanism to evade host immune responses and antiviral treatment. However, high evolutionary rates during persistent infection can impair transmission efficiency and alter disease progression in the new host, resulting in a delicate trade-off between within-host virulence and between-host infectiousness. This trade-off is visible in the disparity in evolutionary rates at within-host and between-host levels, and preferential transmission of ancestral donor viruses. Understanding the impact of withinhost evolution for epidemiological studies is essential for the design of preventive and therapeutic measures. Herein, we review recent theoretical and experimental work that generated new insights into the complex link between within-host evolution and between-host fitness, revealing temporal and selective processes underlying the structure and dynamics of HIV-1 transmission.$ Contributed equally

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Section: Introductionmentioning

confidence: 99%

The impact of HIV-1 within-host evolution on transmission dynamics

Theys

Libin

Pineda-Peña

et al. 2017

Preprint

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“…We found that a model that incorporates HLA similarities as prior information, outperforms a model that treats each allele as an independent predictor. Similar methods were presented by Sharon et al [39] and Bartha et al [1]. In particular, Bartha et al [1] use the GCTA method [45], which is similar to our multivariate normal model.…”

Section: Discussionmentioning

confidence: 76%

“…Similar methods were presented by Sharon et al [39] and Bartha et al [1]. In particular, Bartha et al [1] use the GCTA method [45], which is similar to our multivariate normal model. Bartha Our method could still benefit from a number of improvements.…”

Section: Discussionmentioning

confidence: 76%

“…The covariance of two HLA molecules is equal to the depth (i.e. the distance to the root) of the "most recent common ancestor" of the molecules The HLA molecule #1 corresponds to node 2, and the contribution to the SPVL of HLA #1 is given by α (0,1) + α (1,2) . Likewise, HLA #2 corresponds to node 13 and its contribution to the SPVL is α (0,6) + α (6,9) + α (9,13) .…”

Section: Clustering Hla Moleculesmentioning

confidence: 99%

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Estimating HLA disease associations using similarity trees

Dorp

Keşmir

2018

Preprint

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The human leukocyte antigen (HLA) is associated with many (infectious) disease outcomes. These associations are perhaps best documented for HIV-1. For example, the HLA-B*58:01 allele is associated with control of the virus, while HLA-B*18:01 is considered detrimental. In HLA disease association studies, it is often ignored that certain HLA molecules are functionally very similar to others. For instance, HLA-B*18:03 differs "only" at 3 positions in its peptide binding site from HLA-B*18:01, and not surprisingly, HLA-B*18:03 is also associated with fast progression to AIDS.Here, we present a Bayesian method that takes functional HLA similarities into account to find HLA associations with quantitative traits such as HIV-1 viral load. The method is based on the so-called phylogenetic mixed model (a model for the evolution of a quantitative trait on the branches of a phylogeny), and can easily be modified to study a wide range of research questions, like the role of the heterozygote advantage, or KIR ligands on disease outcomes. We show that in the case of HIV-1, our model is significantly better at predicting set-point virus load than a model that ignores HLA similarities altogether. Furthermore, our method provides a comprehensible visualization of HLA associations. The software is available online at www.github.com/chvandorp/MHCshrubs

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“…It is, however, unclear how these polygenic molecular systems in different organisms interact to alter higherorder phenotypes such as virulence, or even more direct phenotypes like the transcriptome of both species. There is some conflicting evidence on the balance of the system, with some studies and traits suggesting that genetic variation in the pathogen dominates the system (Bartha, McLaren et al 2017, Wang, Roux et al 2018, while others suggest a balanced contribution of plant and pathogen genetics (Corwin, Copeland et al 2016, Fordyce, Soltis et al 2018, Soltis, Atwell et al 2019. Thus, there is a need to develop genomic approaches to understand how polygenic information is transmitted between the pathogen and the host to shift the genomic response of both organisms.…”

Section: Introductionmentioning

confidence: 99%

Pathogen genetic control of transcriptome variation in the Arabidopsis thaliana – Botrytis cinerea pathosystem

Soltis¹,

Zhang

Corwin³

et al. 2019

Preprint

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Disease symptoms arise from the interaction of the host and pathogen genomes. However, little is known about how genetic variation in the interaction modulates both organisms' transcriptomes, especially in polygenic interactions like those between generalist pathogens and their plant hosts. To begin mapping how polygenic pathogen variation influences both organisms' transcriptomes, we used the Botrytis cinerea -Arabidopsis thaliana pathosystem.We measured the co-transcriptome across a genotyped and genetically diverse collection of 96 B. cinerea isolates infected on the Arabidopsis wildtype, Col-0. Using the B. cinerea genomic variation, we performed genome-wide association (GWA) for each of 23,947 measurable transcripts in the host, and 9,267 measurable transcripts in the pathogen. Unlike other eGWA studies, there was a relative absence of cis-eQTL that is likely explained by structural variants and allelic heterogeneity within the pathogen's genome. This analysis identified mostly trans-eQTL in the pathogen with eQTL hotspots dispersed across the pathogen genome that altered the pathogen's transcripts, the host's transcripts, or both the pathogen and the host. Gene membership in the trans-eQTL hotspots suggests links to several known and many novel virulence mechanisms in the plant-pathogen interaction. Genes annotated to these hotspots provide potential targets for blocking manipulation of the host response by this ubiquitous generalist pathogen. This shows that genetic control over the co-transcriptome is polygenic, similar to the virulence outcome in the interaction of Botrytis cinerea on Arabidopsis thaliana.

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Estimating the respective contributions of human and viral genetic variation to HIV control

Cited by 14 publications

References 14 publications

The impact of HIV-1 within-host evolution on transmission dynamics

The impact of HIV-1 within-host evolution on transmission dynamics

Estimating HLA disease associations using similarity trees

Pathogen genetic control of transcriptome variation in the Arabidopsis thaliana – Botrytis cinerea pathosystem

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