IntroductionMultifunctional pro-inflammatory cytokine tumor necrosis factor-α (TNF-α) has been implicated in a variety of inflammatory diseases including rheumatoid arthritis (RA). TNF-α polymorphisms are mostly located in its promoter region and play a significant role in disease susceptibility and severity. We therefore sought to investigate TNFA –863C/A (rs1800630) polymorphism association with RA activity in our Pakistani study group.Material and methodsA total of 268 human subjects were enrolled. Among them, 134 were RA patients and 134 were controls. In this study the physical parameters of RA patients were collected, and the disease activity was measured by DAS28. The genotypes were determined following the allele-specific PCR along with the pre-requisite internal amplification controls. Subsequently, data were analyzed statistically for any significant association including χ2/Fisher’s exact test using GraphPad prism 6 software.ResultsWe found that the TNF-α –863 C/A (rs1800630) variant was not differentially segregated between cases and controls in either genotype frequency, with χ2 of 2.771 and a p-value of 0.2502, or allele frequency, with χ2 of 2.741 and a p-value of 0.0978, with an odds ratio (95% CI) of 0.7490 (0.5317–1.055).ConclusionsThe lack of positive association of TNF-α –863(rs1800630) polymorphism in our study group implies that TNF-α –863 polymorphism is not a susceptible marker to RA and cannot serve as a genetic factor for screening RA patients in Pakistan. There might be other factors that may influence disease susceptibility. However, further investigations on additional larger and multi-regional population samples are required to determine the consequences of genetic variations for disease prognosis.

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The study demonstrated -857C/T (rs1799724) polymorphism may not have influenced RA susceptibility in our study group. However, investigations of genetic variability influence on disease outcome in large prospective cohorts are required, so the complicated interconnection of genetic and environmental elements can be emulated for better understanding.

Background and Objective Smoking disturbs the bronchial‐mucus‐barrier. This study assesses the cellular composition and gene expression shifts of the bronchial‐mucus‐barrier with smoking to understand the mechanism of mucosal damage by cigarette smoke exposure. We explore whether single‐cell‐RNA‐sequencing (scRNA‐seq) based cellular deconvolution (CD) can predict cell‐type composition in RNA‐seq data. Methods RNA‐seq data of bronchial biopsies from three cohorts were analysed using CD. The cohorts included 56 participants with chronic obstructive pulmonary disease [COPD] (38 smokers; 18 ex‐smokers), 77 participants without COPD (40 never‐smokers; 37 smokers) and 16 participants who stopped smoking for 1 year (11 COPD and 5 non‐COPD‐smokers). Differential gene expression was used to investigate gene expression shifts. The CD‐derived goblet cell ratios were validated by correlating with staining‐derived goblet cell ratios from the COPD cohort. Statistics were done in the R software (false discovery rate p‐value < 0.05). Results Both CD methods indicate a shift in bronchial‐mucus‐barrier cell composition towards goblet cells in COPD and non‐COPD‐smokers compared to ex‐ and never‐smokers. It shows that the effect was reversible within a year of smoking cessation. A reduction of ciliated and basal cells was observed with current smoking, which resolved following smoking cessation. The expression of mucin and sodium channel (ENaC) genes, but not chloride channel genes, were altered in COPD and current smokers compared to never smokers or ex‐smokers. The goblet cell‐derived staining scores correlate with CD‐derived goblet cell ratios. Conclusion Smoking alters bronchial‐mucus‐barrier cell composition, transcriptome and increases mucus production. This effect is partly reversible within a year of smoking cessation. CD methodology can predict goblet‐cell percentages from RNA‐seq.