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      Phenotypic signatures of immune selection in HIV-1 reservoir cells

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          Abstract

          Human immunodeficiency virus 1 (HIV-1) reservoir cells persist lifelong despite antiretroviral treatment 1, 2 but may be vulnerable to host immune responses that could be exploited in strategies to cure HIV-1. Here we used a single-cell, next-generation sequencing approach for the direct ex vivo phenotypic profiling of individual HIV-1-infected memory CD4 + T cells from peripheral blood and lymph nodes of people living with HIV-1 and receiving antiretroviral treatment for approximately 10 years. We demonstrate that in peripheral blood, cells harbouring genome-intact proviruses and large clones of virally infected cells frequently express ensemble signatures of surface markers conferring increased resistance to immune-mediated killing by cytotoxic T and natural killer cells, paired with elevated levels of expression of immune checkpoint markers likely to limit proviral gene transcription; this phenotypic profile might reduce HIV-1 reservoir cell exposure to and killing by cellular host immune responses. Viral reservoir cells harbouring intact HIV-1 from lymph nodes exhibited a phenotypic signature primarily characterized by upregulation of surface markers promoting cell survival, including CD44, CD28, CD127 and the IL-21 receptor. Together, these results suggest compartmentalized phenotypic signatures of immune selection in HIV-1 reservoir cells, implying that only small subsets of infected cells with optimal adaptation to their anatomical immune microenvironment are able to survive during long-term antiretroviral treatment. The identification of phenotypic markers distinguishing viral reservoir cells may inform future approaches for strategies to cure and eradicate HIV-1.

          Abstract

          A proteogenomic profiling analysis of single cells from the blood and lymph nodes of individuals living with HIV-1 reveals that CD4 + memory T cells harbouring intact provirus show signatures associated with resistance to immune-mediated killing and cell survival.

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          Most cited references68

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                mlichterfeld@partners.org
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                4 January 2023
                4 January 2023
                2023
                : 614
                : 7947
                : 309-317
                Affiliations
                [1 ]GRID grid.461656.6, ISNI 0000 0004 0489 3491, Ragon Institute of MGH, MIT and Harvard, ; Cambridge, MA USA
                [2 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Infectious Disease Division, Brigham and Women’s Hospital, ; Boston, MA USA
                [3 ]National Institute of Allergies and Infectious Diseases, Bethesda, MD USA
                [4 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Infectious Disease Division, Massachusetts General Hospital, ; Boston, MA USA
                [5 ]GRID grid.413575.1, ISNI 0000 0001 2167 1581, Howard Hughes Medical Institute, ; Chevy Chase, MD USA
                [6 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Institute for Medical Engineering and Sciences and Department of Biology, Massachusetts Institute of Technology, ; Cambridge, MA USA
                Author information
                http://orcid.org/0000-0002-3775-790X
                http://orcid.org/0000-0001-5443-8292
                http://orcid.org/0000-0002-5979-3311
                http://orcid.org/0000-0002-6163-9452
                http://orcid.org/0000-0001-5153-7340
                http://orcid.org/0000-0001-6122-9245
                http://orcid.org/0000-0001-9865-8350
                Article
                5538
                10.1038/s41586-022-05538-8
                9908552
                36599977
                10c5221a-ee43-41d7-a218-ca5ec8001739
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 March 2022
                : 8 November 2022
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                © The Author(s), under exclusive licence to Springer Nature Limited 2023

                Uncategorized
                retrovirus,cell biology
                Uncategorized
                retrovirus, cell biology

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