Smoking induces shifts in cellular composition and transcriptome within the bronchial mucus barrier.

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.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods.

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

How different viruses perturb host cellular machinery via short linear motifs.

The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.

Comprehensive Computational Analysis of Protein Phenotype Changes Due to Plausible Deleterious Variants of Human SPTLC1 Gene.

Genetic variations found in the coding and non-coding regions of a geneare known to influence the structure as well as the function of proteins. Serine palmitoyltransferase long chain subunit 1 a member of α-oxoamine synthase family is encoded by SPTLC1 gene which is a subunit of enzyme serine palmitoyltransferase (SPT). Mutations in SPTLC1 have been associated with hereditary sensory and autonomic neuropathy type I (HSAN-I). The exact mechanism through which these mutations elicit protein phenotype changes in terms of structure, stability and interaction with other molecules is unknown. Thus, we aimed to perform a comprehensive computational analysis of single nucleotide polymorphisms (SNPs) of SPTLC1 to prioritize a list of potential deleterious SNPs and to investigate the protein phenotype change due to functional polymorphisms. In this study, a diverse set of SPTLC1 SNPs were collected and scrutinized to categorize the potential deleterious variants. Our study concordantly identified 21 non- synonymous SNPs as pathogenic and deleterious that might induce alterations in protein structure, flexibility and stability. Moreover, evaluation of frameshift, 3' and 5' UTR variants shows c.*1302T> Gas effective. This comprehensive in silico analysis of systematically characterized list of potential deleterious variants could open avenues as primary filter to substantiate plausible pathogenic structural and functional impact of variants.

Lack of association of -863C/A (rs1800630) polymorphism of tumor necrosis factor-a gene with rheumatoid arthritis.

INTRODUCTION: Multifunctional 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 METHODS: A 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. RESULTS: We 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). CONCLUSIONS: The 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.

Lack of tumor necrosis factor alpha gene polymorphism -857c/t (rs1799724) association in Pakistani rheumatoid arthritis patients.

BACKGROUND: Rheumatoid arthritis (RA) is a common systemic autoimmune disease, influenced greatly by the pro-inflammatory cytokine tumor necrosis factor- alpha (TNF-α). Single nucleotide polymorphisms (SNPs) in regulatory regions of the TNF-α gene play a significant role in disease development and pathogenesis. The aim of this study was to investigate the association of TNF-α -857C/T (rs1799724) SNP with RA activity or severity in our Pakistani study group. METHODS: The study included 134 (116 women, 18 men) patients with RA and 134 ethnically matched healthy controls (108 women, 26 men). Each patient's disease activity was measured by Disease Activity Score of 28 joints. The genotypes were determined in all included individuals following allele-specific polymerase chain reaction along with the prerequisite internal amplification controls. Statistical analysis including chi-square/Fischer exact test and one-way analysis of variance; nonparametric Kruskal-Wallis test was employed using Graphpad Prism 6 software for association study. RESULTS: The prevalence of TNF-α -857C/T (rs1799724) polymorphism was not differentially distributed between RA patients and controls in either allele frequency, with odds ratio (95% CI) of 0.9661 (0.6714-1.390) and P-value of 0.8527, or genotype frequency with χ2 of 0.5015 and P-value of 0.7782. Moreover, no correlation was found when genotype frequency distribution was analyzed with disease severity (P = 0.6321 and Kruskal-Wallis statistics of 1.098). CONCLUSION: 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.