Frequencies of Gag-restricted T-cell escape "footprints" differ across HIV-1 clades A1 and D chronically infected Ugandans irrespective of host HLA B alleles

Evolutionary dynamics of HIV-1 subtype C in Brazil

The extensive genetic diversity of HIV-1 is a major challenge for the prevention and treatment of HIV-1 infections. Subtype C accounts for most of the HIV-1 infections in the world but has been mainly localized in Southern Africa, Ethiopia and India. For elusive reasons, South Brazil harbors the largest HIV-1 subtype C epidemic in the American continent that is elsewhere dominated by subtype B. To investigate this topic, we collected clinical data and viral sequences from 2611 treatment-naïve patients diagnosed with HIV-1 in Brazil. Molecular epidemiology analysis supported 35 well-delimited transmission clusters of subtype C highlighting transmission within South Brazil but also from the South to all other Brazilian regions and internationally. Individuals infected with subtype C had lower probability to be deficient in CD4⁺ T cells when compared to subtype B. The HIV-1 epidemics in the South was characterized by high female-to-male infection ratios and women-to-child transmission. Our results suggest that HIV-1 subtype C probably takes advantage of longer asymptomatic periods to maximize transmission and is unlikely to outcompete subtype B in settings where the infection of women is relatively less relevant. This study contributes to elucidate factors possibly underlying the geographical distribution and expansion patterns of the most spread HIV-1 subtypes.

Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort

Background

Identifying immunogens that induce HIV-1-specific immune responses is a lengthy process that can benefit from computational methods, which predict T-cell epitopes for various HLA types.

Methods

We tested the performance of the NetMHCpan4.0 computational neural network in re-identifying 93 T-cell epitopes that had been previously independently mapped using the whole proteome IFN-γ ELISPOT assays in 6 HLA class I typed Ugandan individuals infected with HIV-1 subtypes A1 and D. To provide a benchmark we compared the predictions for NetMHCpan4.0 to MHCflurry1.2.0 and NetCTL1.2.

Results

NetMHCpan4.0 performed best correctly predicting 88 of the 93 experimentally mapped epitopes for a set length of 9-mer and matched HLA class I alleles. Receiver Operator Characteristic (ROC) analysis gave an area under the curve (AUC) of 0.928. Setting NetMHCpan4.0 to predict 11-14mer length did not improve the prediction (37–79 of 93 peptides) with an inverse correlation between the number of predictions and length set. Late time point peptides were significantly stronger binders than early peptides (Wilcoxon signed rank test: p = 0.0000005). MHCflurry1.2.0 similarly predicted all but 2 of the peptides that NetMHCpan4.0 predicted and NetCTL1.2 predicted only 14 of the 93 experimental peptides.

Conclusion

NetMHCpan4.0 class I epitope predictions covered 95% of the epitope responses identified in six HIV-1 infected individuals, and would have reduced the number of experimental confirmatory tests by > 80%. Algorithmic epitope prediction in conjunction with HLA allele frequency information can cost-effectively assist immunogen design through minimizing the experimental effort.

Prevalence of Non-B HIV-1 Subtypes in North Italy and Analysis of Transmission Clusters Based on Sequence Data Analysis

HIV-1 diversity is increasing in European countries due to immigration flows, as well as travels and human mobility, leading to the circulation of both new viral subtypes and new recombinant forms, with important implications for public health. We analyzed 710 HIV-1 sequences comprising protease and reverse-transcriptase (PR/RT) coding regions, sampled from 2011 to 2017, from naive patients in Spedali Civili Hospital, Brescia, Italy. Subtyping was performed by using a combination of different tools; the phylogenetic analysis with a structured coalescence model and Makarov Chain Monte Carlo was used on the datasets, to determine clusters and evolution. We detected 304 (43%) patients infected with HIV-1 non-B variants, of which only 293 sequences were available, with four pure subtypes and five recombinant forms; subtype F1 (17%) and CRF02_AG (51.1%) were most common. Twenty-five transmission clusters were identified, three of which included >10 patients, belonging to subtype CRF02_AG and subtype F. Most cases of alleged transmission were between heterosexual couples. Probably due to strong migratory flows, we have identified different subtypes with particular patterns of recombination or, as in the case of the subtype G (18/293, 6.1%), to a complete lack of relationship between the sequenced strains, revealing that they are all singletons. Continued HIV molecular surveillance is most important to analyze the dynamics of the boost of transmission clusters in order to implement public health interventions aimed at controlling the HIV epidemic.

SNAPPy: A snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing

Human immunodeficiency virus 1 (HIV-1) genome sequencing is routinely done for drug resistance monitoring in hospitals worldwide. Subtyping these extensive datasets of HIV-1 sequences is a critical first step in molecular epidemiology and evolution studies. The clinical relevance of HIV-1 subtypes is increasingly recognized. Several studies suggest subtype-related differences in disease progression, transmission route efficiency, immune evasion, and even therapeutic outcomes. HIV-1 subtyping is mainly done using web-servers. These tools have limitations in scalability and potential noncompliance with data protection legislation. Thus, the aim of this work was to develop an efficient method for large-scale local HIV-1 subtyping. We designed SNAPPy: a snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing. It contains several tasks of phylogenetic inference and BLAST queries, which can be executed sequentially or in parallel, taking advantage of multiple-core processing units. Although it was built for subtyping, SNAPPy is also useful to perform extensive HIV-1 alignments. This tool facilitates large-scale sequence-based HIV-1 research by providing a local, resource efficient and scalable alternative for HIV-1 subtyping. It is capable of analyzing full-length genomes or partial HIV-1 genomic regions (GAG, POL, and ENV) and recognizes more than ninety circulating recombinant forms. SNAPPy is freely available at: https://github.com/PMMAraujo/snappy/releases.

Characterization of a large cluster of HIV-1 A1 infections detected in Portugal and connected to several Western European countries

HIV-1 subtypes associate with differences in transmission and disease progression. Thus, the existence of geographic hotspots of subtype diversity deepens the complexity of HIV-1/AIDS control. The already high subtype diversity in Portugal seems to be increasing due to infections with sub-subtype A1 virus. We performed phylogenetic analysis of 65 A1 sequences newly obtained from 14 Portuguese hospitals and 425 closely related database sequences. 80% of the A1 Portuguese isolates gathered in a main phylogenetic clade (MA1). Six transmission clusters were identified in MA1, encompassing isolates from Portugal, Spain, France, and United Kingdom. The most common transmission route identified was men who have sex with men. The origin of the MA1 was linked to Greece, with the first introduction to Portugal dating back to 1996 (95% HPD: 1993.6–1999.2). Individuals infected with MA1 virus revealed lower viral loads and higher CD4⁺ T-cell counts in comparison with those infected by subtype B. The expanding A1 clusters in Portugal are connected to other European countries and share a recent common ancestor with the Greek A1 outbreak. The recent expansion of this HIV-1 subtype might be related to a slower disease progression leading to a population level delay in its diagnostic.

Genotypic and Mechanistic Characterization of Subtype-Specific HIV Adaptation to Host Cellular Immunity

The extent to which viral genetic context influences HIV adaptation to human leukocyte antigen (HLA) class I-restricted immune pressures remains incompletely understood. The Ugandan HIV epidemic, where major pandemic group M subtypes A1 and D cocirculate in a single host population, provides an opportunity to investigate this question. We characterized plasma HIV RNA gag, pol, and nef sequences, along with host HLA genotypes, in 464 antiretroviral-naive individuals chronically infected with HIV subtype A1 or D. Using phylogenetically informed statistical approaches, we identified HLA-associated polymorphisms and formally compared their strengths of selection between viral subtypes. A substantial number (32%) of HLA-associated polymorphisms identified in subtype A1 and/or D had previously been reported in subtype B, C, and/or circulating recombinant form 01_AE (CRF01_AE), confirming the shared nature of many HLA-driven escape pathways regardless of viral genetic context. Nevertheless, 34% of the identified HLA-associated polymorphisms were significantly differentially selected between subtypes A1 and D. Experimental investigation of select examples of subtype-specific escape revealed distinct underlying mechanisms with important implications for vaccine design: whereas some were attributable to subtype-specific sequence variation that influenced epitope-HLA binding, others were attributable to differential mutational barriers to immune escape. Overall, our results confirm that HIV genetic context is a key modulator of viral adaptation to host cellular immunity and highlight the power of combined bioinformatic and mechanistic studies, paired with knowledge of epitope immunogenicity, to identify appropriate viral regions for inclusion in subtype-specific and universal HIV vaccine strategies.IMPORTANCE The identification of HIV polymorphisms reproducibly selected under pressure by specific HLA alleles and the elucidation of their impact on viral function can help identify immunogenic viral regions where immune escape incurs a fitness cost. However, our knowledge of HLA-driven escape pathways and their functional costs is largely limited to HIV subtype B and, to a lesser extent, subtype C. Our study represents the first characterization of HLA-driven adaptation pathways in HIV subtypes A1 and D, which dominate in East Africa, and the first statistically rigorous characterization of differential HLA-driven escape across viral subtypes. The results support a considerable impact of viral genetic context on HIV adaptation to host HLA, where HIV subtype-specific sequence variation influences both epitope-HLA binding and the fitness costs of escape. Integrated bioinformatic and mechanistic characterization of these and other instances of differential escape could aid rational cytotoxic T-lymphocyte-based vaccine immunogen selection for both subtype-specific and universal HIV vaccines.