Host genetic variation at a locus near CHD1L impacts HIV sequence diversity in a South African population.

IMPORTANCE: It has been previously shown that genetic variants near CHD1L on chromosome 1 are associated with reduced HIV VL in African populations. However, the impact of these variants on viral diversity and how they restrict viral replication are unknown. We report on a regional association analysis in a South African population and show evidence of selective pressure by variants near CHD1L on HIV RT and gag. Our findings provide further insight into how genetic variability at this locus contributes to host control of HIV in a South African population.

Human Immunotypes Impose Selection on Viral Genotypes Through Viral Epitope Specificity.

BACKGROUND: Understanding the genetic interplay between human hosts and infectious pathogens is crucial for how we interpret virulence factors. Here, we tested for associations between HIV and host genetics, and interactive genetic effects on viral load (VL) in HIV-positive antiretroviral treatment-naive clinical trial participants. METHODS: HIV genomes were sequenced and the encoded amino acid (AA) variants were associated with VL, human single nucleotide polymorphisms (SNPs), and imputed HLA alleles using generalized linear models with Bonferroni correction. RESULTS: Human (388 501 SNPs) and HIV (3010 variants) genetic data were available for 2122 persons. Four HIV variants were associated with VL (P < 1.66 × 10-5). Twelve HIV variants were associated with a range of 1-512 human SNPs (P < 4.28 × 10-11). We found 46 associations between HLA alleles and HIV variants (P < 1.29 × 10-7). HIV variants and immunotypes when analyzed separately were associated with lower VL, whereas the opposite was true when analyzed in concert. Epitope binding predictions supported our observations. CONCLUSIONS: Our results show the importance of immunotype specificity on viral antigenic determinants, and the identified genetic interplay emphasizes that viral and human genetics should be studied in the context of each other.Clinical Trials Registration: NCT00867048.

Interferon lambda 4 impacts the genetic diversity of hepatitis C virus.

Hepatitis C virus (HCV) is a highly variable pathogen that frequently establishes chronic infection. This genetic variability is affected by the adaptive immune response but the contribution of other host factors is unclear. Here, we examined the role played by interferon lambda-4 (IFN-λ4) on HCV diversity; IFN-λ4 plays a crucial role in spontaneous clearance or establishment of chronicity following acute infection. We performed viral genome-wide association studies using human and viral data from 485 patients of white ancestry infected with HCV genotype 3a. We demonstrate that combinations of host genetic variants, which determine IFN-λ4 protein production and activity, influence amino acid variation across the viral polyprotein - not restricted to specific viral proteins or HLA restricted epitopes - and modulate viral load. We also observed an association with viral di-nucleotide proportions. These results support a direct role for IFN-λ4 in exerting selective pressure across the viral genome, possibly by a novel mechanism.

Adaptation of hepatitis C virus to interferon lambda polymorphism across multiple viral genotypes.

Genetic polymorphism in the interferon lambda (IFN-λ) region is associated with spontaneous clearance of hepatitis C virus (HCV) infection and response to interferon-based treatment. Here, we evaluate associations between IFN-λ polymorphism and HCV variation in 8729 patients (Europeans 77%, Asians 13%, Africans 8%) infected with various viral genotypes, predominantly 1a (41%), 1b (22%) and 3a (21%). We searched for associations between rs12979860 genotype and variants in the NS3, NS4A, NS5A and NS5B HCV proteins. We report multiple associations in all tested proteins, including in the interferon-sensitivity determining region of NS5A. We also assessed the combined impact of human and HCV variation on pretreatment viral load and report amino acids associated with both IFN-λ polymorphism and HCV load across multiple viral genotypes. By demonstrating that IFN-λ variation leaves a large footprint on the viral proteome, we provide evidence of pervasive viral adaptation to innate immune pressure during chronic HCV infection.

Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach.

The emergence and re-emergence of plant pathogenic microorganisms are processes that imply perturbations in both host and pathogen ecological niches. Global change is largely assumed to drive the emergence of new etiological agents by altering the equilibrium of the ecological habitats which in turn places hosts more in contact with pathogen reservoirs. In this context, the number of epidemics is expected to increase dramatically in the next coming decades both in wild and crop plants. Under these considerations, the identification of the genetic variants underlying natural variation of resistance is a pre-requisite to estimate the adaptive potential of wild plant populations and to develop new breeding resistant cultivars. On the other hand, the prediction of pathogen's genetic determinants underlying disease emergence can help to identify plant resistance alleles. In the genomic era, whole genome sequencing combined with the development of statistical methods led to the emergence of Genome Wide Association (GWA) mapping, a powerful tool for detecting genomic regions associated with natural variation of disease resistance in both wild and cultivated plants. However, GWA mapping has been less employed for the detection of genetic variants associated with pathogenicity in microbes. Here, we reviewed GWA studies performed either in plants or in pathogenic microorganisms (bacteria, fungi and oomycetes). In addition, we highlighted the benefits and caveats of the emerging joint GWA mapping approach that allows for the simultaneous identification of genes interacting between genomes of both partners. Finally, based on co-evolutionary processes in wild populations, we highlighted a phenotyping-free joint GWA mapping approach as a promising tool for describing the molecular landscape underlying plant - microbe interactions.