Microbial pathogens have been selected for the capacity to evade or manipulate host responses in order to survive after infection. Chlamydia, an obligate intracellular pathogen and the causative agent for many human diseases, can escape T lymphocyte immune recognition by degrading host transcription factors required for major histocompatibility complex (MHC) antigen expression. We have now identified a chlamydial protease– or proteasome–like activity factor (CPAF) that is secreted into the host cell cytosol and that is both necessary and sufficient for the degradation of host transcription factors RFX5 and upstream stimulation factor 1 (USF-1). The CPAF gene is highly conserved among chlamydial strains, but has no significant overall homology with other known genes. Thus, CPAF represents a unique secreted protein produced by an obligate intracellular bacterial pathogen to interfere with effective host adaptive immunity.

We have previously shown that the obligate intracellular pathogen chlamydia can suppress interferon (IFN)-γ–inducible major histocompatibility complex (MHC) class II expression in infected cells by degrading upstream stimulation factor (USF)-1. We now report that chlamydia can also inhibit both constitutive and IFN-γ–inducible MHC class I expression in the infected cells. The inhibition of MHC class I molecule expression correlates well with degradation of RFX5, an essential downstream transcription factor required for both the constitutive and IFN-γ–inducible MHC class I expression. We further demonstrate that a lactacystin-sensitive proteasome-like activity identified in chlamydia-infected cell cytosolic fraction can degrade both USF-1 and RFX5. This proteasome-like activity is dependent on chlamydial but not host protein synthesis. Host preexisting proteasomes may not be required for the unique proteasome-like activity. These observations suggest that chlamydia-secreted factors may directly participate in the proteasome-like activity. Efforts to identify the chlamydial factors are underway. These findings provide novel information on the molecular mechanisms of chlamydial evasion of host immune recognition.

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.

Objective: To determine the correlation between polymorphisms in the IL-4 gene cluster and resistance to HIV-1 infection. Design: A cross-sectional genetic analysis of polymorphisms within the IL-4 gene cluster was conducted in a well-described female sex worker cohort from Nairobi, Kenya, known to exhibit differential susceptibility to HIV-1 infection. Methods: Microsatellite genotyping was used to screen six microsatellite markers in the IL-4 gene cluster for associations with HIV-1 resistance. Further analysis of the interferon regulatory factor 1 (IRF-1) gene was conducted by genomic sequencing. Associations between IRF-1 gene polymorphisms and the HIV-1 resistance phenotype were determined using the chi-square test and Kaplan–Meier survival analysis. The functional consequence of IRF-1 polymorphism was conducted by quantitative Western blot. Results: Three polymorphisms in IRF-1, located at 619, the microsatellite region and 6516 of the gene, showed associations with resistance to HIV-1 infection. The 619A, 179 at IRF-1 microsatellite and 6516G alleles were associated with the HIV-1-resistant phenotype and a reduced likelihood of seroconversion. Peripheral blood mononuclear cells from patients with protective IRF-1 genotypes exhibited significantly lower basal IRF-1 expression and reduced responsiveness to exogenous IFN-γ stimulation. Conclusion: Polymorphisms in the IRF-1 gene are associated with resistance to infection by HIV-1 and a lowered level of IRF-1 protein expression. This study adds IRF-1, a transcriptional immunoregulatory gene, to the list of genetic correlates of altered susceptibility to HIV-1. This is the first report suggesting that a viral transcriptional regulator might contribute to resistance to HIV-1. Further functional analysis on the role of IRF-1 polymorphisms and HIV-1 resistance is underway.

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.