Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort

Nowak, Jan; Dybska, Emilia; Adams, Alex; Walkowiak, Jarosław

doi:10.5114/ceji.2022.120618

Cited by 4 publications

(2 citation statements)

References 75 publications

(111 reference statements)

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“…60 Besides, smoking can increase the expression of CLEC5A in immune cells. 61 Wortham et al demonstrated that CLEC5A was highly expressed on alveolar macrophages in mouse models of COPD and human longterm smokers, while it was almost not expressed in macrophages of healthy controls, 62 which was consistent with our findings. What's interesting was that Sung et al found that thrombotic inflammation was attenuated dramatically in clec5a−/− mice, 63 based on which we dared to speculate that perhaps the high expression of CLEC5A is a major risk factor for embolism in COPD patients.…”

Section: Discussionsupporting

confidence: 92%

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Zhang,

Yu,

Wang

et al. 2023

JIR

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Background This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods In this study, the “CIBERSORT” algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The “limma” package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the “Boruta” and “LASSO” algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.

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Section: Discussionsupporting

confidence: 92%

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Zhang,

Yu,

Wang

et al. 2023

JIR

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“…They also related to IL1 signaling: possible recruitment of IKK complex by interleukin 1 receptor associated kinase 1 ( IRAK1 ) and interleukin 18 binding protein ( IL18BP ) interference with signaling via IL18 (which belongs to IL1 family). One of the top positive correlates of RUNX3 was C-type lectin domain containing 16A ( CLEC16A ) of a group of proteins related to (auto)immunity and IBD [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic Context of RUNX3 Expression in Monocytes: A Cross-Sectional Analysis

2023

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The runt-related transcription factor 3 (RUNX3) regulates the differentiation of monocytes and their response to inflammation. However, the transcriptomic context of RUNX3 expression in blood monocytes remains poorly understood. We aim to learn about RUNX3 from its relationships within transcriptomes of bulk CD14+ cells in adults. This study used immunomagnetically sorted CD14+ cell gene expression microarray data from the Multi-Ethnic Study of Atherosclerosis (MESA, n = 1202, GSE56047) and the Correlated Expression and Disease Association Research (CEDAR, n = 281, E-MTAB-6667) cohorts. The data were preprocessed, subjected to RUNX3-focused correlation analyses and random forest modeling, followed by the gene ontology analysis. Immunity-focused differential ratio analysis with intermediary inference (DRAIMI) was used to integrate the data with protein–protein interaction network. Correlation analysis of RUNX3 expression revealed the strongest positive association for EVL (rmean = 0.75, pFDR-MESA = 5.37 × 10−140, pFDR-CEDAR = 5.52 × 10−80), ARHGAP17 (rmean = 0.74, pFDR-MESA = 1.13 × 10−169, pFDR-CEDAR = 9.20 × 10−59), DNMT1 (rmean = 0.74, pFDR-MESA = 1.10 × 10−169, pFDR-CEDAR = 1.67 × 10−58), and CLEC16A (rmean = 0.72, pFDR-MESA = 3.51 × 10−154, pFDR-CEDAR = 2.27 × 10−55), while the top negative correlates were C2ORF76 (rmean = −0.57, pFDR-MESA = 8.70 × 10−94, pFDR-CEDAR = 1.31 × 10−25) and TBC1D7 (rmean = −0.55, pFDR-MESA = 1.36 × 10−69, pFDR-CEDAR = 7.81 × 10−30). The RUNX3-associated transcriptome signature was involved in mRNA metabolism, signal transduction, and the organization of cytoskeleton, chromosomes, and chromatin, which may all accompany mitosis. Transcriptomic context of RUNX3 expression in monocytes hints at its relationship with cell growth, shape maintenance, and aspects of the immune response, including tyrosine kinases.

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Association of smoking with neurocognition, inflammatory and myeloid cell activation profiles in people with HIV on antiretroviral therapy

Yadav,

Gionet,

Karaj

et al. 2024

Aids

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Objective: People with HIV (PWH) experience excess comorbidities, including neurocognitive disorders, which are linked to inflammation, particularly monocyte-macrophage activation. Smoking contributes to morbidity and mortality in well-treated PWH. We investigated associations between smoking, neurocognitive function, and inflammation in PWH on ART. Design: We used baseline data on cognition and inflammation from a longitudinal study of virologically-suppressed PWH who do and do not smoke. Methods: Participants completed 4 neurocognitive tests (7 measures), with a composite score as the primary measure. Inflammatory markers were plasma sCD14, sCD163, and CCL2/MCP-1; %CD14+ monocytes expressing CD16, CD163, and CCR2; and %CD8+ T cells co-expressing CD38/HLA-DR. Exploratory analyses included a plasma cytokine/chemokine panel, neurofilament light chain (NFL), hsCRP and monocyte transcriptomes by RNAseq. Results: We recruited 58 PWH (26 current smoking [PWH/S], 32 no current smoking [PWH/NS]). Mean composite and individual neurocognitive scores did not differ significantly by smoking status except for the color shape task; PWH/S exhibited worse cognitive flexibility, with adjusted mean times 317.2 (95%CI 1.4, 632.9) msec longer than PWH/NS. PWH/S had higher plasma sCD14 than PWH/NS (median(IQR) 1820(1678, 2105) versus 1551(1284, 1760) ng/ml, p=0.009). Other inflammatory markers were not significantly different between PWH/S and PWH/NS. Monocyte transcriptomes showed several functions, regulators and gene sets that differed by smoking status. Conclusions: sCD14, a marker of monocyte activation, is elevated in PWH who smoke. While neurocognitive measures and other inflammatory markers did not generally differ, these data implicate smoking-related myeloid activation and monocyte gene dysregulation in the HIV/smoking synergy driving HIV-associated comorbidities.

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Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort

Cited by 4 publications

References 75 publications

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Transcriptomic Context of RUNX3 Expression in Monocytes: A Cross-Sectional Analysis

Association of smoking with neurocognition, inflammatory and myeloid cell activation profiles in people with HIV on antiretroviral therapy

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