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      Deciphering the molecular regulatory of RAB32/GPRC5A axis in chronic obstructive pulmonary disease

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          Abstract

          Background

          Chronic obstructive pulmonary disease (COPD) is a significant public health problem characterized by persistent airflow limitation. Despite previous research into the pathogenesis of COPD, a comprehensive understanding of the cell-type-specific mechanisms in COPD remains lacking. Recent studies have implicated Rab GTPases in regulating chronic immune response and inflammation via multiple pathways. In this study, the molecular regulating mechanism of RAB32 in COPD was investigated by multiple bioinformatics mining and experimental verification.

          Methods

          We collected lung tissue surgical specimens from Zhongshan Hospital, Fudan University, and RT-qPCR and western blotting were used to detect the expression of Rabs in COPD lung tissues. Four COPD microarray datasets from the Gene Expression Omnibus (GEO) were analyzed. COPD-related epithelial cell scRNA-seq data was obtained from the GSE173896 dataset. Weighted gene co-expression network analysis (WGCNA), mfuzz cluster, and Spearman correlation analysis were combined to obtain the regulatory network of RAB32 in COPD. The slingshot algorithm was used to identify the regulatory molecule, and the co-localization of RAB32 and GPRC5A was observed with immunofluorescence.

          Results

          WGCNA identified 771 key module genes significantly associated with the occurrence of COPD, including five Rab genes. RAB32 was up-regulated in lung tissues from subjects with COPD as contrast to those without COPD on both mRNA and protein levels. Integrating the results of WGCNA, Mfuzz clusters, and Spearman analysis, nine potential interacting genes with RAB32 were identified. Among these genes, GPRC5A exhibited a similar molecular expression pattern to RAB32. Co-expression density analysis at the cell level demonstrated that the co-expression density of RAB32 and GPRC5A was higher in type I alveolar epithelial cells (AT1s) than in type II alveolar epithelial cells (AT2s). The immunofluorescence also confirmed the co-localization of RAB32 and GPRC5A, and the Pearson correlation analysis found the relationship between RAB32 and GPRC5A was significantly stronger in the COPD lungs ( r = 0.65) compared to the non-COPD lungs ( r = 0.33).

          Conclusions

          Our study marked endeavor to delineate the molecular regulatory axis of RAB32 in COPD by employing diverse methods and identifying GPRC5A as a potential interacting molecule with RAB32. These findings offered novel perspectives on the mechanism of COPD.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12931-024-02724-2.

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          Most cited references43

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          WGCNA: an R package for weighted correlation network analysis

          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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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                zhang.jing@zs-hospital.sh.cn
                Journal
                Respir Res
                Respir Res
                Respiratory Research
                BioMed Central (London )
                1465-9921
                1465-993X
                6 March 2024
                6 March 2024
                2024
                : 25
                : 116
                Affiliations
                [1 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, , Fudan University, ; Shanghai, China
                [2 ]GRID grid.417298.1, ISNI 0000 0004 1762 4928, Department of General Practice, , Xinqiao Hospital, Third Military Medical University, ; Chongqing, China
                Article
                2724
                10.1186/s12931-024-02724-2
                10919015
                38448858
                8cc305b4-783c-4bc4-a449-7b2caacea60b
                © The Author(s) 2024

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 5 November 2023
                : 11 February 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82270039
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Respiratory medicine
                chronic obstructive pulmonary disease,rab32,gprc5a,wgcna,single-cell rna sequencing

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