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

Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies

Background: Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. Methods: We explored SAH-IA links using machine learning and bioinformatics, identifying 66 IA-related SAH genes. KEGG analysis highlighted pathways like NF-κB, TNF, and COVID-19. Results: Two immune-related genes (ZNF281, LRRN3) were identified, and a ceRNA network was constructed. Ten potential SAH-IA drugs were screened via CMAP. Conclusion: ZNF281 and LRRN3 may regulate immune pathways (T cells, NK cells, macrophages), influencing IA-related SAH development, and could serve as therapeutic targets.

Smoking-Associated Changes in Gene Expression in Coronary Artery Disease Patients Using Matched Samples

Smoking is a well known risk factor for coronary artery disease (CAD). However, the effects of smoking on gene expression in the blood of CAD subjects in Hungary have not been extensively studied. This study aimed to identify differentially expressed genes (DEGs) associated with smoking in CAD subjects. Eleven matched samples based on age and gender were selected for analysis in this study. All subjects were non-obese, non-alcoholic, non-diabetic, and non-hypertensive and had moderate to severe stenosis of one or more coronary arteries, confirmed by coronary angiography. Whole blood samples were collected using PAXgene tubes. Next-generation sequencing was employed using the NextSeq 500 system to generate high-throughput sequencing data for transcriptome profiling. The differentially expressed genes were analyzed using the R programming language. Results: The study revealed that smokers exhibited non-significant higher levels of total cholesterol, low-density lipoprotein-cholesterol, and triglycerides compared to non-smokers (p > 0.05), although high-density lipoprotein-cholesterol was also elevated. Despite this, the overall lipid profile of smokers remained less favorable. Non-smokers had a higher BMI (p = 0.02). Differential gene expression analysis identified 58 DEGs, with 38 upregulated in smokers. The key upregulated genes included LILRB5 (log2FC = 2.88, p = 1.05 × 10-5) and RELN (log2FC = 3.31, p = 0.024), while RNF5_2 (log2FC = -5.29, p = 0.028) and IGHV7-4-1_1 (log2FC = -2.86, p = 0.020) were notably downregulated. Heatmap analysis showed a distinct clustering of gene expression profiles between smokers and non-smokers. However, GO analysis did not identify significant biological pathways associated with the DEGs. Conclusions: This research illuminates smoking's biological effects, aiding personalized medicine for predicting and treating smoking-related diseases.

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

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 antiretroviral therapy (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 four neurocognitive tests (seven 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% confidence interval (CI) 1.4-632.9] ms longer than PWH/NS. PWH/S had higher plasma sCD14 than PWH/NS [median (IQR) 1820 (1678-2105) vs. 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.

CONCLUSION

sCD14, a marker of monocyte activation, is elevated in PWH who smoke. Although 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.

Active and Passive Smoking as Catalysts for Cognitive Impairment in Rural Indonesia: A Population-based Study

Objectives

Research indicates that active smokers are at risk of cognitive impairment. However, the correlation between chronic passive smoking and the risk of cognitive impairment remains underexplored. This study aimed to determine the association between smoking, passive smoking, and cognitive impairment and examined the dose-response effect.

Methods

This population-based two-year survey was conducted in four rural public health centers from 2021 to 2023 in Central Java, Indonesia, each center caters to approximately 30 000 people. The participants were selected using simple random sampling from the health center visitors aged 30-60 years. Smoking and passive smoking were determined by self-assessment. Mini-Mental State Examination was used to evaluate cognitive impairment. The potential impact of confounding variables such as lifestyle, sociodemographic factors, and chronic diseases were considered and excluded during statistical analysis.

Results

The participants were 409 individuals aged 30-60 years. The majority were men (264; 64.5%). Among them, 308 (75.3%) were active smokers, 271 (66.3%) were passive smokers, and 138 (33.7%) were not exposed to tobacco smoke. There was a significant relationship between cognitive impairment and increasing pack years of active smoking. The highest and most significant risk was observed in those who smoked ≥ 20 pack-years with an adjusted odds ratio (aOR) of 1.61 and 95% CI: 0.98-2.31. Passive smokers had a slightly lower risk of cognitive impairment than those who did not smoke and never smoked (aOR = 2.01; 95% CI: 1.37-2.70). They were comparable with OR of 10-19 pack-years total exposure to active smoking (aOR = 1.86; 95% CI: 1.24-2.42).

Conclusions

There was a dose-response relationship between smoking and cognitive impairment with a significant effect on ≥ 20 pack-years of exposure. Passive smoking also indicated a significant risk of cognitive impairment equivalent to an estimated 10-19 pack-years of active smoking.

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

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.

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

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 (r_mean = 0.75, p_FDR-MESA = 5.37 × 10^-140, p_FDR-CEDAR = 5.52 × 10^-80), ARHGAP17 (r_mean = 0.74, p_FDR-MESA = 1.13 × 10^-169, p_FDR-CEDAR = 9.20 × 10^-59), DNMT1 (r_mean = 0.74, p_FDR-MESA = 1.10 × 10^-169, p_FDR-CEDAR = 1.67 × 10^-58), and CLEC16A (r_mean = 0.72, p_FDR-MESA = 3.51 × 10^-154, p_FDR-CEDAR = 2.27 × 10^-55), while the top negative correlates were C2ORF76 (r_mean = -0.57, p_FDR-MESA = 8.70 × 10^-94, p_FDR-CEDAR = 1.31 × 10^-25) and TBC1D7 (r_mean = -0.55, p_FDR-MESA = 1.36 × 10^-69, p_FDR-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.