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      HIV-2 mediated effects on target and bystander cells induce plasma proteome remodeling

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          Summary

          Despite low or undetectable plasma viral load, people living with HIV-2 (PLWH2) typically progress toward AIDS. The driving forces behind HIV-2 disease progression and the role of viremia are still not known, but low-level replication in tissues is believed to play a role. To investigate the impact of viremic and aviremic HIV-2 infection on target and bystander cell pathology, we used data-independent acquisition mass spectrometry to determine plasma signatures of tissue and cell type engagement. Proteins derived from target and bystander cells in multiple tissues, such as the gastrointestinal tract and brain, were detected at elevated levels in plasma of PLWH2, compared with HIV negative controls. Moreover, viremic HIV-2 infection appeared to induce enhanced release of proteins from a broader range of tissues compared to aviremic HIV-2 infection. This study expands the knowledge on the link between plasma proteome remodeling and the pathological cell engagement in tissues during HIV-2 infection.

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          Highlights

          • •

            Plasma proteome profiling of HIV negative, HIV-1, and HIV-2 infected individuals

          • •

            Secreted and leakage plasma protein signatures differ in HIV-1 and HIV-2 infection

          • •

            HIV-1 and HIV-2 viremia alter plasma protein profiles of target and bystander cells

          • •

            Protein signatures link bystander cell engagement and HIV-2 mediated inflammation

          Abstract

          Disease; Human specimen; Proteomics; Virology

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          Most cited references115

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)
            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              WGCNA: an R package for weighted correlation network analysis

              Peter Langfelder, Steve Horvath (2008)
              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Contributors
                Joakim Esbjörnsson
                Marianne Jansson
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 28 February 2024
                Publication date Collection: 19 April 2024
                Publication date (Electronic): 28 February 2024
                Volume: 27
                Issue: 4
                Electronic Location Identifier: 109344
                Affiliations
                [1 ]Department of Translational Medicine, Lund University, Lund, Sweden
                [2 ]Lund University Virus Centre, Lund, Sweden
                [3 ]Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
                [4 ]National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
                [5 ]Department of Clinical Sciences Lund, Lund University, Lund, Sweden
                [6 ]Department of Laboratory Medicine, Lund University, Lund, Sweden
                [7 ]BioMS – Swedish National Infrastructure for Biological Mass Spectrometry, Lund University, Lund, Sweden
                [8 ]National Public Health Laboratory, Bissau, Guinea-Bissau
                [9 ]Nuffield Department of Medicine, University of Oxford, Oxford, UK
                Author notes
                [∗ ]Corresponding author joakim.esbjornsson@ 123456med.lu.se
                [∗∗ ]Corresponding author marianne.jansson@ 123456med.lu.se
                [10]

                Senior author

                [11]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(24)00565-0 Publisher ID: 109344
                DOI: 10.1016/j.isci.2024.109344
                PMC ID: 10945182
                PubMed ID: 38500818
                SO-VID: 966e4d38-a1b1-4248-9f4a-a891d4554430
                Copyright © © 2024 The Authors
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 18 October 2023
                Date revision received : 23 November 2023
                Date accepted : 22 February 2024
                Categories
                Subject: Article

                Keywords: disease,human specimen,proteomics,virology
                Data availability:
                Keywords: disease, human specimen, proteomics, virology

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