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.

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IntroductionNext‐generation sequencing (NGS) has several advantages over conventional Sanger sequencing for HIV drug resistance (HIVDR) genotyping, including detection and quantitation of low‐abundance variants bearing drug resistance mutations (DRMs). However, the high HIV genomic diversity, unprecedented large volume of data, complexity of analysis and potential for error pose significant challenges for data processing. Several NGS analysis pipelines have been developed and used in HIVDR research; however, the absence of uniformity in data processing strategies results in lack of consistency and comparability of outputs from different pipelines. To fill this gap, an international symposium on bioinformatic strategies for NGS‐based HIVDR testing was held in February 2018 in Winnipeg, Canada, convening laboratory scientists, bioinformaticians and clinicians involved in four recently developed, publicly available NGS HIVDR pipelines. The goal of this symposium was to establish a consensus on effective bioinformatic strategies for NGS data management and its use for HIVDR reporting.DiscussionEssential functionalities of an NGS HIVDR pipeline were divided into five analytic blocks: (1) NGS read quality control (QC)/quality assurance (QA); (2) NGS read alignment and reference mapping; (3) HIV variant calling and variant QC; (4) NGS HIVDR reporting; and (5) extended data applications and additional considerations for data management. The consensuses reached among the participants on all major aspects of these blocks are summarized here. They encompass not only recommended data management and analysis strategies, but also detailed bioinformatic approaches that help ensure accuracy of the derived HIVDR analysis outputs for both research and potential clinical use.ConclusionsWhile NGS is being adopted more broadly in HIVDR testing laboratories, data processing is often a bottleneck hindering its generalized application. The proposed standardization of NGS read QC/QA, read alignment and reference mapping, variant calling and QC, HIVDR reporting and relevant data management strategies in this “Winnipeg Consensus” may serve as a starting guideline for NGS HIVDR data processing that informs the refinement of existing pipelines and those yet to be developed. Moreover, the bioinformatic strategies presented here may apply more broadly to NGS data analysis of microbes harbouring significant genomic diversity.