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Viral drug resistance is an everlasting topic for HIV/AIDS professionals from clinical, laboratory and public health perspectives [...].Viral drug resistance is an everlasting topic for HIV/AIDS professionals from clinical, laboratory and public health perspectives [...].

Vaccines against SARS-CoV-2 have shown high efficacy in clinical trials, yet a full immunologic characterization of these vaccines, particularly within the human upper respiratory tract, is less well known. Here, we enumerate and phenotype T cells in nasal mucosa and blood using flow cytometry before and after vaccination with the Pfizer-BioNTech COVID-19 vaccine ( n  = 21). Tissue-resident memory (Trm) CD8 + T cells expressing CD69 + CD103 + increase in number ~12 days following the first and second doses, by 0.31 and 0.43 log 10 cells per swab respectively ( p  = 0.058 and p  = 0.009 in adjusted linear mixed models). CD69 + CD103 + CD8 + T cells in the blood decrease post-vaccination. Similar increases in nasal CD8 + CD69 + CD103 − T cells are observed, particularly following the second dose. CD4 + cells co-expressing CCR6 and CD161 are also increased in abundance following both doses. Stimulation of nasal CD8 + T cells with SARS-CoV-2 spike peptides elevates expression of CD107a at 2- and 6-months ( p  = 0.0096) post second vaccine dose, with a subset of donors also expressing increased cytokines. These data suggest that nasal T cells may be induced and contribute to the protective immunity afforded by this vaccine. Whether mRNA SARS-CoV-2 vaccines promote T cells within the nasal mucosa of vaccine recipients is not known. Here the authors show that after mRNA SARS-CoV-2 vaccination, antigen specific T cells can be measured in the nasal mucosa and that these T cells may be localised to respond to a subsequent virus infection. Clinical trial registration NCT04713163

Higher accessibility and decreasing costs of next generation sequencing (NGS), availability of commercial kits, and development of dedicated analysis pipelines, have allowed an increasing number of laboratories to adopt this technology for HIV drug resistance (HIVDR) genotyping. Conventional HIVDR genotyping is traditionally carried out using population-based Sanger sequencing, which has a limited capacity for reliable detection of variants present at intra-host frequencies below a threshold of approximately 20%. NGS has the potential to improve sensitivity and quantitatively identify low-abundance variants, improving efficiency and lowering costs. However, some challenges exist for the standardization and quality assurance of NGS-based HIVDR genotyping. In this paper, we highlight considerations of these challenges as related to laboratory, clinical, and implementation of NGS for HIV drug resistance testing. Several sources of variation and bias occur in each step of the general NGS workflow, i.e., starting material, sample type, PCR amplification, library preparation method, instrument and sequencing chemistry-inherent errors, and data analysis options and limitations. Additionally, adoption of NGS-based HIVDR genotyping, especially for clinical care, poses pressing challenges, especially for resource-poor settings, including infrastructure and equipment requirements and cost, logistic and supply chains, instrument service availability, personnel training, validated laboratory protocols, and standardized analysis outputs. The establishment of external quality assessment programs may help to address some of these challenges and is needed to proceed with NGS-based HIVDR genotyping adoption.

Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparison of available pipelines are lacking, yet may impact subsequent HIVDR interpretation and other downstream applications. Here we compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Virology Quality Assurance (VQA) program. Ten VQA panel specimens were genotyped by each of six international laboratories using their own in-house NGS assays. Raw NGS data were then processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DEEPGEN. All pipelines detected amino acid variants (AAVs) at full range of frequencies (1~100%) and demonstrated good linearity as compared to the reference frequency values. While the sensitivity in detecting low abundance AAVs, with frequencies between 1~20%, is less a concern for all pipelines, their specificity dramatically decreased at AAV frequencies <2%, suggesting that 2% threshold may be a more reliable reporting threshold for ensured specificity in AAV calling and reporting. More variations were observed among the pipelines when low abundance AAVs are concerned, likely due to differences in their NGS read quality control strategies. Findings from this study highlight the need for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority HIVDR variants.

Conventional HIV drug resistance (HIVDR) genotyping utilizes Sanger sequencing (SS) methods, which are limited by low data throughput and the inability of detecting low abundant drug resistant variants (LADRVs). Here we present a next generation sequencing (NGS)-based HIVDR typing platform that leverages the advantages of Illumina MiSeq and HyDRA Web. The platform consists of a fully validated sample processing protocol and HyDRA web, an open web portal that allows automated customizable NGS-based HIVDR data processing. This platform was characterized and validated using a panel of HIV-spiked plasma representing all major HIV-1 subtypes, pedigreed plasmids, HIVDR proficiency specimens and clinical specimens. All examined major HIV-1 subtypes were consistently amplified at viral loads of ≥1,000 copies/ml. The gross error rate of this platform was determined at 0.21%, and minor variations were reliably detected down to 0.50% in plasmid mixtures. All HIVDR mutations identifiable by SS were detected by the MiSeq-HyDRA protocol, while LADRVs at frequencies of 1~15% were detected by MiSeq-HyDRA only. As compared to SS approaches, the MiSeq-HyDRA platform has several notable advantages including reduced cost and labour, and increased sensitivity for LADRVs, making it suitable for routine HIVDR monitoring for both patient care and surveillance purposes.

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.

The close monitoring of HIV drug resistance using genotypic HIV drug resistance testing (HIVDRT) has become essential for effective HIV/AIDS management at both individual and population levels. Over the years, a broad spectrum of analytes or specimens have been applied or attempted in HIVDRT; however, the suitability and performance of these analytes in HIVDRT and the clinical relevance of the results from them may vary significantly. This article provides a focused overview of the performance, strengths, and weaknesses of various analytes while used in HIVDRT, which may inform the optimal analytes selection in different application contexts.

HIV/AIDS is a global public health crisis that is yet to be contained. Effective management of HIV drug resistance (HIVDR) supported by close resistance monitoring is essential in achieving the WHO 95-95-95 targets, aiming to end the AIDS epidemic by 2030. Point-of-care tests (POCT) enable decentralized HIVDR testing with a short turnaround time and minimal instrumental requirement, allowing timely initiation of effective antiretroviral therapy (ART) and regimen adjustment as needed. HIVDR POCT is of particular significance in an era when ART access is scaling up at a global level and enhanced HIVDR monitoring is urgently needed, especially for low-to-middle-income countries. This article provides an overview of the currently available technologies that have been applied or potentially used in HIVDR POCT. It may also benefit the continued research and development efforts toward more innovative HIVDR diagnostics.

Over the past decade, there has been an increase in the adoption of next generation sequencing (NGS) technologies for HIV drug resistance (HIVDR) testing. NGS far outweighs conventional Sanger sequencing as it has much higher throughput, lower cost when samples are batched and, most importantly, significantly higher sensitivities for variants present at low frequencies, which may have significant clinical implications. Despite the advantages of NGS, Sanger sequencing remains the gold standard for HIVDR testing, largely due to the lack of standardization of NGS-based HIVDR testing. One important aspect of standardization includes external quality assessment (EQA) strategies and programs. Current EQA for Sanger-based HIVDR testing includes proficiency testing where samples are sent to labs and the performance of the lab conducting such assays is evaluated. The current methods for Sanger-based EQA may not apply to NGS-based tests because of the fundamental differences in their technologies and outputs. Sanger-based genotyping reports drug resistance mutations (DRMs) data as dichotomous, whereas NGS-based HIVDR genotyping also reports DRMs as numerical data (percent abundance). Here we present an overview of the need to develop EQA for NGS-based HIVDR testing and some unique challenges that may be encountered.

Human immunodeficiency virus (HIV) infections remain a significant public health concern worldwide. Over the years, sophisticated sequencing technologies such as next-generation sequencing (NGS) have emerged and been utilized to monitor the spread of HIV drug resistance (HIVDR), identify HIV drug resistance mutations, and characterize transmission dynamics. Similar applications also apply to the Hepatitis C virus (HCV), another bloodborne viral pathogen with significant intra-host genetic diversity. Several advantages to using NGS over conventional Sanger sequencing include increased data throughput, scalability, cost-effectiveness when batched sample testing is performed, and sensitivity for quantitative detection of minority resistant variants. However, NGS alone may fail to detect genomes from pathogens present in low copy numbers. As with all sequencing platforms, the primary determinant in achieving quality sequencing data is the quality and quantity of the initial template input. Samples containing degraded RNA/DNA and/or low copy number have been a consistent sequencing challenge. To overcome this limitation probe capture enrichment is a method that has recently been employed to target, enrich, and sequence the genome of a pathogen present in low copies, and for compromised specimens that contain poor quality nucleic acids. It involves the hybridization of sequence-specific DNA or RNA probes to a target sequence, which is followed by an enrichment step via PCR to increase the number of copies of the targeted sequences after which the samples are subjected to NGS procedures. This method has been performed on pathogens such as bacteria, fungus, and viruses and allows for the sequencing of complete genomes, with high coverage. Post NGS, data analysis can be performed through various bioinformatics pipelines which can provide information on genetic diversity, genotype, virulence, and drug resistance. This article reviews how probe capture enrichment helps to increase the likelihood of sequencing HIV and HCV samples that contain low viral loads and/or are compromised.