Pharmacogenomics: What is Next?

Pharmacogenomics is moving from a candidate gene strategy to large scale approaches. This is in line with the new paradigm of linking a trait to (a) pathway(s) rather than to single genes. In addition, breakthroughs in genomics offer a non-a priori assessment of implicated genes, expanding the possibilities in pharmacogenomics research. In this review, we discuss the pros and cons of new concepts in study design and on high throughput approaches to be implemented in the near future

Transfer transcriptomic signatures for infectious diseases

The modulation of the transcriptome is among the earliest responses to infection. However, defining the transcriptomic signatures of disease is challenging because logistic, technical, and cost factors limit the size and representativeness of samples in clinical studies. These limitations lead to a poor performance of signatures when applied to new datasets. Although the study focuses on infection, the central hypothesis of the work is the generalization of sets of signatures across diseases. We use a machine learning approach to identify common elements in datasets and then test empirically whether they are informative about a second dataset from a disease or process distinct from the original dataset. We identify sets of genes, which we name transfer signatures, that are predictive across diverse datasets and/or species (e.g., rhesus to humans). We demonstrate the usefulness of transfer signatures in two use cases: the progression of latent to active tuberculosis and the severity of COVID-19 and influenza A H1N1 infection. This indicates that transfer signatures can be deployed in settings that lack disease-specific biomarkers. The broad significance of our work lies in the concept that a small set of archetypal human immunophenotypes, captured by transfer signatures, can explain a larger set of responses to diverse diseases

Bioinformatics and HIV Latency

Despite effective treatment, HIV is not completely eliminated from the infected organism because of the existence of viral reservoirs. A major reservoir consists of infected resting CD4+ T cells, mostly of memory type, that persist over time due to the stable proviral insertion and a long cellular lifespan. Resting cells do not produce viral particles and are protected from viral-induced cytotoxicity or immune killing. However, these latently infected cells can be reactivated by stochastic events or by external stimuli. The present review focuses on novel genome-wide technologies applied to the study of integration, transcriptome, and proteome characteristics and their recent contribution to the understanding of HIV latency

Association of Pharmacogenetic Markers with Premature Discontinuation of First-line Anti-HIV Therapy: An Observational Cohort Study

Background. Poor tolerance and adverse drug reactions are main reasons for discontinuation of antiretroviral therapy (ART). Identifying predictors of ART discontinuation is a priority in HIV care. Methods. A genetic association study in an observational cohort to evaluate the association of pharmacogenetic markers with time to treatment discontinuation during the first year of ART. Analysis included 577 treatment-naive individuals initiating tenofovir (n = 500) or abacavir (n = 77), with efavirenz (n = 272), lopinavir/ritonavir (n = 184), or atazanavir/ritonavir (n = 121). Genotyping included 23 genetic markers in 15 genes associated with toxicity or pharmacokinetics of the study medication. Rates of ART discontinuation between groups with and without genetic risk markers were assessed by survival analysis using Cox regression models. Results. During the first year of ART, 190 individuals (33%) stopped 1 or more drugs. For efavirenz and atazanavir, individuals with genetic risk markers experienced higher discontinuation rates than individuals without (71.15% vs 28.10%, and 62.5% vs 14.6%, respectively). The efavirenz discontinuation hazard ratio (HR) was 3.14 (95% confidence interval (CI): 1.35-7.33, P = .008). The atazanavir discontinuation HR was 9.13 (95% CI: 3.38-24.69, P < .0001). Conclusions. Several pharmacogenetic markers identify individuals at risk for early treatment discontinuation. These markers should be considered for validation in the clinical settin

Estimating the Net Contribution of Interleukin-28B Variation to Spontaneous Hepatitis C Virus Clearance

The identification of associations between interleukin-28B (IL-28B) variants and the spontaneous clearance of hepatitis C virus (HCV) raises the issues of causality and the net contribution of host genetics to the trait. To estimate more precisely the net effect of IL-28B genetic variation on HCV clearance, we optimized genotyping and compared the host contributions in multiple- and single-source cohorts to control for viral and demographic effects. The analysis included individuals with chronic or spontaneously cleared HCV infections from a multiple-source cohort (n = 389) and a single-source cohort (n = 71). We performed detailed genotyping in the coding region of IL-28B and searched for copy number variations to identify the genetic variant or haplotype carrying the strongest association with viral clearance. This analysis was used to compare the effects of IL-28B variation in the two cohorts. Haplotypes characterized by carriage of the major alleles at IL-28B single-nucleotide polymorphisms (SNPs) were highly overrepresented in individuals with spontaneous clearance versus those with chronic HCV infections (66.1% versus 38.6%, P = 6 × 10−9). The odds ratios for clearance were 2.1 [95% confidence interval (CI) = 1.6-3.0] and 3.9 (95% CI = 1.5-10.2) in the multiple- and single-source cohorts, respectively. Protective haplotypes were in perfect linkage (r2 = 1.0) with a nonsynonymous coding variant (rs8103142). Copy number variants were not detected. Conclusion: We identified IL-28B haplotypes highly predictive of spontaneous HCV clearance. The high linkage disequilibrium between IL-28B SNPs indicates that association studies need to be complemented by functional experiments to identify single causal variants. The point estimate for the genetic effect was higher in the single-source cohort, which was used to effectively control for viral diversity, sex, and coinfections and, therefore, offered a precise estimate of the net host genetic contribution. (Hepatology 2011;53:1446-1454

Imprinted antibody responses against SARS-CoV-2 Omicron sublineages

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineages carry distinct spike mutations resulting in escape from antibodies induced by previous infection or vaccination. We show that hybrid immunity or vaccine boosters elicit plasma-neutralizing antibodies against Omicron BA.1, BA.2, BA.2.12.1, and BA.4/5, and that breakthrough infections, but not vaccination alone, induce neutralizing antibodies in the nasal mucosa. Consistent with immunological imprinting, most antibodies derived from memory B cells or plasma cells of Omicron breakthrough cases cross-react with the Wuhan-Hu-1, BA.1, BA.2, and BA.4/5 receptor-binding domains, whereas Omicron primary infections elicit B cells of narrow specificity up to 6 months after infection. Although most clinical antibodies have reduced neutralization of Omicron, we identified an ultrapotent pan-variant–neutralizing antibody that is a strong candidate for clinical development

Bioinformatics and HIV Latency

Despite effective treatment, HIV is not completely eliminated from the infected organism because of the existence of viral reservoirs. A major reservoir consists of infected resting CD4+ T cells, mostly of memory type, that persist over time due to the stable proviral insertion and a longcellular lifespan. Resting cells do not produce viral particles and are protected from viral-induced cytotoxicity or immune killing. However, these latently infected cells can be reactivated by stochastic events or by external stimuli. The present review focuses on novel genome-wide technologies applied to the study of integration, transcriptome, and proteome characteristics and their recent contribution to the understanding of HIV latency