Genetic Diversity and Acquired Drug Resistance Mutations Detected by Deep Sequencing in Virologic Failures among Antiretroviral Treatment Experienced Human Immunodeficiency Virus-1 Patients in a Pastoralist Region of Ethiopia

Identification of a Clade-Specific HLA-C*03:02 CTL Epitope GY9 Derived from the HIV-1 p17 Matrix Protein

Efforts towards an effective HIV-1 vaccine have remained mainly unsuccessful. There is increasing evidence for a potential role of HLA-C-restricted CD8+ T cell responses in HIV-1 control, including our recent report of HLA-C*03:02 among African children. However, there are no documented optimal HIV-1 CD8+ T cell epitopes restricted by HLA-C*03:02; additionally, the structural influence of HLA-C*03:02 on epitope binding is undetermined. Immunoinformatics approaches provide a fast and inexpensive method to discover HLA-restricted epitopes. Here, we employed immunopeptidomics to identify HLA-C*03:02 CD8+ T cell epitopes. We identified a clade-specific Gag-derived GY9 (GTEELRSLY) HIV-1 p17 matrix epitope potentially restricted to HLA-C*03:02. Residues E62, T142, and E151 in the HLA-C*03:02 binding groove and positions p3, p6, and p9 on the GY9 epitope are crucial in shaping and stabilizing the epitope binding. Our findings support the growing evidence of the contribution of HLA-C molecules to HIV-1 control and provide a prospect for vaccine strategies.

HIV1 drug resistance among patients experiencing first-line treatment failure in Ethiopia: protocol for a systematic review and meta-analysis

BACKGROUND

The emergence of HIV drug resistance presents a substantial challenge. Current antiretroviral treatments, along with current classes, face the danger of becoming partially or entirely inactive. As a result, alternative treatment regimens are limited, and treatment choices are complicated. According to the recommendation of the WHO, nations should consider changing their first-line ART regimen if HIV drug resistance exceeds 10%. In spite of the fact that a number of primary studies have been performed on HIV drug resistance in Ethiopia, their pooled prevalence rate has not been determined in a systematic review and meta-analysis, which may provide stronger evidence. Therefore, the objective of this systematic review and meta-analysis will be to estimate the pooled prevalence rate of HIV1 drug resistance in patients with first-line treatment failure in Ethiopia.

METHODS

Primary studies will be identified from PubMed/MEDLINE, Scopus, Embase, Web of Science Core Collection, and Google Scholar. The period of search will be from 01 April to 30 June 2024. Studies identified through the search strategies will first be screened by titles and abstracts. Included studies meeting established criteria will be evaluated for risk of bias using the JBI checklist. Data will be extracted, and the pooled prevalence rate of HIV drug resistance will be computed using STATA 14 software. Random effect models will be used when heterogeneity is suspected. The I2 statistic and its corresponding P value will be checked to distinguish heterogeneity. Additionally, publication bias and heterogeneity will be checked using visual funnel plots, Egger's test, trim-and-fill tests, meta-regression, and subgroup analysis. To present and synthesize the results, narrative synthesis will be performed to describe study characteristics and findings, and forest plots will be used to visually represent effect sizes and confidence intervals from individual studies.

DISCUSSION

Estimating the pooled prevalence rate of HIV drug resistance through a systematic review and meta-analysis improves the reliability of the evidence, the availability of effective HIV treatment options, and the ability to assist in making decisions for both clinical practice and public health policy in Ethiopia.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42024533975.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Establishment and application of a method of tagged-amplicon deep sequencing for low-abundance drug resistance in HIV-1

In the latest HIV-1 global drug resistance report released by WHO, countries are advised to strengthen pre-treatment monitoring of drug resistance in AIDS patients. In this study, we established an NGS-based segmented amplification HIV-1 drug resistance mutation detection method. The pol region of HIV-1 was divided into three short fragments for NGS. The entire amplification and sequencing panel were more cost-effective and batched by using the barcode sequence corresponding to the sample. Each parameter was evaluated using samples with known resistance variants frequencies. The nucleotide sequence error rate, amino acid error rate, and noise value of the NGS-based segmented amplification method were both less than 1%. When the threshold was 2%, the consensus sequences of the HIV-1 NL4-3 strain were completely consistent with the Sanger sequences. This method can detect the minimum viral load of the sample at 10² copies/ml, and the input frequency and detection frequency of HIV-1 resistance mutations within the range of 1%–100% had good conformity (R² = 0.9963; R² = 0.9955). This method had no non-specific amplification for Hepatitis B and C. Under the 2% threshold, the incidence of surveillance drug resistance mutations in ART-naive HIV-infected patients was 20.69%, among which NRTIs class resistance mutations were mainly.