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

Introduction: Smoking is known to affect whole-blood expression and methylation profiles. Although whole-genome methylation studies indicated that effects observed in blood may be driven by changes within leukocyte subtypes, these phenomena have not been explored using expression profiling. Material and methods: This study reanalyzed data from the Correlated Expression and Disease Association Research (CEDAR) patient cohort recruited by Momozawa et al. (E-MTAB-6667). Data from gene expression profiling of immunomagnetically sorted CD4+, CD8+, CD14+, CD15+, and CD19+ cells were processed. Differential expression analyses were conducted in each immune cell type, followed by gene ontology analysis and supplementary investigations. Results: Ninety-four differentially expressed genes were found (CD8+ n = 58, CD14+ n = 20, CD4+ n = 14, CD19+ n = 2). Two key smoking-related genes were overexpressed in specific cell types: LRRN3 (CD4+, CD8+) and MMP25 (CD8+, CD14+). In CD4+ cells smoking was associated with reduced expression of the NK cell receptor KLRB1, suggesting CD4+ subpopulation shifts and differences in interferon signaling (reduced IRF1 and IL18RAP in smokers). Key results and their integration with an immune protein-protein interaction network revealed that smoking influences integrins in CD8+ cells (ITGB7, ITGAL, ITGAM, ITGB2). C-type lectin CLEC4A was reduced in CD8+ cells and CLEC10A was increased in CD14+ cells from smokers; moreover, CLEC5A (CD8+), CLEC7A (CD8+) and CLEC9A (CD19+) were related to smoking in supplementary analyses. CD14+ cells from smokers exhibited overexpression of LDLR and the formyl peptide receptor FPR3. Conclusions: Smoking specifically alters vital immune regulation genes in lymphocyte subtypes, especially CD4+, CD8+ and CD14+ cells.

Waiting in the wings: RUNX3 reveals hidden depths of immune regulation with potential implications for inflammatory bowel disease.

BACKGROUND: Complex interactions between the environment and the mucosal immune system underlie inflammatory bowel disease (IBD). The involved cytokine signalling pathways are modulated by a number of transcription factors, one of which is runt-related transcription factor 3 (RUNX3). OBJECTIVE: To systematically review the immune roles of RUNX3 in immune regulation, with a focus on the context of IBD. METHODS: Relevant articles and reviews were identified through a Scopus search in April 2020. Information was categorized by immune cell types, analysed and synthesized. IBD transcriptome data sets and FANTOM5 regulatory networks were processed in order to complement the literature review. RESULTS: The available evidence on the immune roles of RUNX3 allowed for its description in twelve cell types: intraepithelial lymphocyte, Th1, Th2, Th17, Treg, double-positive T, cytotoxic T, B, dendritic, innate lymphoid, natural killer and macrophages. In the gut, the activity of RUNX3 is multifaceted and context-dependent: it may promote homeostasis or exacerbated reactions via cytokine signalling and regulation of receptor expression. RUNX3 is mostly engaged in pathways involving ThPOK, T-bet, IFN-γ, TGF-ß/IL-2Rß, GATA/CBF-ß, SMAD/p300 and a number of miRNAs. RUNX3 targets relevant to IBD may include RAG1, OSM and IL-17B. Moreover, in IBD RUNX3 expression correlates positively with GZMM, and negatively with IFNAR1, whereas in controls, it strongly associates with TGFBR3. CONCLUSIONS: Dysregulation of RUNX3, mostly in the form of deficiency, likely contributes to IBD pathogenesis. More clinical research is needed to examine RUNX3 in IBD.

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 (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.

Methylation of RUNX3 Promoter 2 in the Whole Blood of Children with Ulcerative Colitis.

Ulcerative colitis (UC) results from a complex interplay between the environment, gut microbiota, host genetics, and immunity. Runt-related transcription factor 3 (RUNX3) regulates Th1/Th2 balance and, thus, the synthesis of cytokines and inflammation. We aimed to analyze the dependence of RUNX3 promoter 2 (P2) methylation level on: age, sex, body mass index (BMI), C-reactive protein (CRP), serum albumin, disease duration, Pediatric Ulcerative Colitis Activity Index (PUCAI), the Paris classification, and exposure to medications. This multicenter, cross-sectional study recruited hospitalized children with UC. Methylation of RUNX3 P2 was measured with methylation-sensitive restriction enzymes in the whole blood DNA. Sixty-four children were enrolled, with a mean age of 14.5 ± 2.8 years. Half of them were female (51.6%), and the average BMI Z-score was -0.44 ± 1.14. The mean methylation of RUNX3 P2 was 54.1 ± 13.3%. The methylation level of RUNX3 P2 did not correlate with age, sex, nutritional status, CRP, albumin, PUCAI, or the extent of colitis (Paris E1-E4). RUNX3 P2 methylation did not differ between patients recruited within two and a half months of diagnosis and children who had UC for at least a year. Current or past exposure to biologics, immunosuppressants, or steroids was not associated with RUNX3 P2 methylation. Methylation of RUNX3 promoter 2 in whole blood DNA does not seem to be associated with the characteristics of UC in children.

Unexpected Actors in Inflammatory Bowel Disease Revealed by Machine Learning from Whole-Blood Transcriptomic Data.

Although big data from transcriptomic analyses have helped transform our understanding of inflammatory bowel disease (IBD), they remain underexploited. We hypothesized that the application of machine learning using lasso regression to transcriptomic data from IBD patients and controls can help identify previously overlooked genes. Transcriptomic data provided by Ostrowski et al. (ENA PRJEB28822) were subjected to a two-stage process of feature selection to discriminate between IBD and controls. First, a principal component analysis was used for dimensionality reduction. Second, the least absolute shrinkage and selection operator (lasso) regression was employed to identify genes potentially involved in the pathobiology of IBD. The study included data from 294 participants: 100 with ulcerative colitis (48 adults and 52 children), 99 with Crohn's disease (45 adults and 54 children), and 95 controls (46 adults and 49 children). IBD patients presented a wide range of disease severity. Lasso regression preceded by principal component analysis successfully selected interesting features in the IBD transcriptomic data and yielded 12 models. The models achieved high discriminatory value (range of the area under the receiver operating characteristic curve 0.61-0.95) and identified over 100 genes as potentially associated with IBD. PURA, GALNT14, and FCGR1A were the most consistently selected, highlighting the role of the cell cycle, glycosylation, and immunoglobulin binding. Several known IBD-related genes were among the results. The results included genes involved in the TGF-beta pathway, expressed in NK cells, and they were enriched in ontology terms related to immunity. Future IBD research should emphasize the TGF-beta pathway, immunoglobulins, NK cells, and the role of glycosylation.

Ileal Lactase Expression Associates with Lactase Persistence Genotypes.

(1) Background: Lactose digestion depends on persistence genotypes (including rs4988235), the frequency of which exhibits broad geographical variability. However, little is known about the relationship between lactase (LCT) genotypes and intestinal expression of LCT. We aimed to investigate ileal expression of LCT depending on main genetic polymorphisms (rs4988235, rs3754689, rs3739022), age, sex, smoking status, body mass index (BMI), and the expression of other genes; (2) Methods: phenotype, array-based genotype, and ileal mucosal biopsy expression data were obtained from the CEDAR study; (3) Results: analyses included 196 healthy Europeans (53.6% women) aged 53.0 ± 13.6 years with a mean BMI of 25.6 ± 4.2 kg/m2, of whom 17.4% were smoking. Ileal LCT expression was mostly independent of age, sex, BMI, or smoking. Rs4988235 homozygous minor allele (GG) associated with lower LCT expression (vs. AG p = 2.2 × 10-6, vs. AA p = 1.1 × 10-7). Homozygous major allele of rs3754689 (GG) was related to higher LCT expression (vs. AG p = 1.7 × 10-5, vs. AA p = 0.0074). Rs3754689 genotype did not modify LCT expression (GG vs. AG p = 0.051) in rs4988235-heterozygous subgroup. Interestingly, CD14, which is a marker of monocytes and macrophages, was the strongest negative transcriptomic correlate of LCT expression (r = -0.57, pFDR = 1.1 × 10-14); (4) Conclusions: both rs4988235 and rs3754689 associated with ileal LCT expression, which did not seem related to age, sex, smoking, or BMI. The inverse correlation between LCT and CD14 expression in the ileum is striking and requires further investigation.

DNA methylation analysis with methylation-sensitive high-resolution melting (MS-HRM) reveals gene panel for glioma characteristics.

INTRODUCTION: Local DNA hypermethylation is a potential source of cancer biomarkers. While the evaluation of single gene methylation has limited value, their selected panel may provide better information. AIMS: This study aimed to analyze the promoter methylation level in a 7-gene panel in brain tumors and verifies the usefulness of methylation-sensitive high-resolution melting (MS-HRM) for this purpose. METHODS: Forty-six glioma samples and one non-neoplastic brain sample were analyzed by MS-HRM in terms of SFRP1, SFRP2, RUNX3, CBLN4, INA, MGMT, and RASSF1A promoter methylation. The results were correlated with patients' clinicopathological features. RESULTS: DNA methylation level of all analyzed genes was significantly higher in brain tumor samples as compared to non-neoplastic brain and commercial, unmethylated DNA control. RASSF1A was the most frequently methylated gene, with statistically significant differences depending on the tumor WHO grade. Higher MGMT methylation levels were observed in females, whereas the levels of SFRP1 and INA promoter methylation significantly increased with patients' age. A positive correlation of promoter methylation levels was observed between pairs of genes, for example, CBLN4 and INA or MGMT and RASSF1A. CONCLUSIONS: Our 7-gene panel of promoter methylation can be helpful in brain tumor diagnosis or characterization, and MS-HRM is a suitable method for its analysis.