RATIONALE Tobacco smoking and air pollution are primary causes of chronic obstructive pulmonary disease (COPD). However, only a minority of smokers develop COPD. The mechanisms underlying the defense against nitrosative/oxidative stress in non-susceptible smokers to COPD remain largely unresolved. OBJECTIVES To investigate the defense mechanisms against nitrosative/oxidative stress that possibly prevent COPD development or progression. METHODS Four cohorts were investigated: (1) sputum samples (healthy, n=4; COPD, n=37), (2) lung tissue samples (healthy, n=13; smokers without COPD, n=10; smoker+COPD, n=17), (3) pulmonary lobectomy tissue samples (no/mild emphysema, n=6) and (4) blood samples (healthy, n=6; COPD, n=18). We screened 3-nitrotyrosine (3-NT) levels, as indication of nitrosative/oxidative stress, in human samples. We established a novel in vitro model of a cigarette smoke extract (CSE)-resistant cell line and studied 3-NT formation, antioxidant capacity, and transcriptomic profiles. Results were validated in lung tissue, isolated primary cells and an ex vivo model using adeno-associated virus-mediated gene transduction and human precision-cut lung slices (hPCLS). MEASUREMENTS AND MAIN RESULTS 3-NT levels correlate with COPD severity of patients. In CSE-resistant cells, nitrosative/oxidative stress upon CSE was attenuated, paralleled by profound upregulation of heme-oxygenase-1 (HO-1). We identified carcinoembryonic-antigen-related-cell-adhesion-molecule-6 (CEACAM6) as a negative regulator of HO-1-mediated nitrosative/oxidative stress defense in human alveolar type 2 epithelial cells (hAEC2). Consistently, inhibition of HO-1 activity in hAEC2 increased the susceptibility towards CSE-induced damage. Epithelium-specific CEACAM6 overexpression increased nitrosative/oxidative stress and cell death in hPCLS upon CSE treatment. CONCLUSIONS CEACAM6 expression determines the hAEC2 sensitivity to nitrosative/oxidative stress triggering emphysema development/progression in susceptible smokers.

MicroRNAs (miRNAs) are short non-coding RNAs that function in post-transcriptional gene silencing and mRNA regulation. Although the number of nucleotides of miRNAs ranges from 17 to 27, they are mostly made up of 22 nucleotides. The expression of miRNAs changes significantly in cancer, causing protein alterations in cancer cells by preventing some genes from being translated into proteins. In this research, a structural analysis of 587 miRNAs that are differentially expressed in myeloid cancer was carried out. Length distribution studies revealed a mean and median of 22 nucleotides, with an average of 21.69 and a variance of 1.65. We performed nucleotide analysis for each position where Uracil was the most observed nucleotide and Adenine the least observed one with 27.8% and 22.6%, respectively. There was a higher frequency of Adenine at the beginning of the sequences when compared to Uracil, which was more frequent at the end of miRNA sequences. The purine content of each implicated miRNA was also assessed. A novel motif analysis script was written to detect the most frequent 3–7 nucleotide (3–7n) long motifs in the miRNA dataset. We detected CUG (42%) as the most frequent 3n motif, CUGC (15%) as a 4n motif, AGUGC (6%) as a 5n motif, AAGUGC (4%) as a 6n motif, and UUUAGAG (4%) as a 7n motif. Thus, in the second part of our study, we further characterized the motifs by analyzing whether these motifs align at certain consensus sequences in our miRNA dataset, whether certain motifs target the same genes, and whether these motifs are conserved within other species. This thorough structural study of miRNA sequences provides a novel strategy to study the implications of miRNAs in health and disease. A better understanding of miRNA structure is crucial to developing therapeutic settings.

ABSTRACT Aberrant methylation is one of the driving forces of cancer genome development. Although the rate of methylation appears massively variable across the genome, it is mainly observed in histone modification, chromatin organization, DNA accessibility, or promoter sequence. Methylation of promoter sequence occurs mostly to cytosine nucleotides, which can affect transcription factors' binding affinities. In this study, we demonstrated that cytosine repeats (C types density), consisting of CC, CCC, CCCC, CCCCC, CCCCCC, CCCCCCC motifs and CpG islands density in 25 proto-oncogenes, tumor suppressor genes and control genes may play a role in the pathogenesis of acute myeloid leukemia. The promoter sequences were divided into a 100 nucleotide window from −500 to +100 nucleotides and 20 nucleotide window from −100 to +100. Each window is analyzed to find the higher C type and CpG islands density, which may cause the increased methylation in the promoter sequence. Our novel findings show that promoter sequence cytosine repeats and CpG density increase closer to transcription sites, especially just before and after the transcription start site (TSS). The results demonstrate that cytosine density increases while proto-oncogenes and TSG promoter sequences are closer to TSS 50.8% and 41.0% respectively, if (−500 to −200) and (−100 to +100) windows of the nucleotide sequences are compared. This proves that around TSS location has special nucleotide motifs and could be an important implication for our understanding of potential methylating locations in promoters.

The guanine rich locations are present in human genome. Previous studies have shown that the presence of G rich sequences and motifs may be significant for gene activity and function. We decided to focus our interest to identify G rich motifs in promoters of oncogenes and tumor suppressor genes. We used a set of 100 most common oncogenes and tumor suppressor genes (TSG) for this analysis. We collected 600nt long promoters with -500 and +100 TSS (transcription start site) from the oncogenes and TSG set. Using a computer program, we calculated the G densities using numbers and locations of G forms with 100nt moving widow. We included G numbers from 2 to 7 guanines. Analysis shows that G density increases from -500 to +100 and more from TSS. G density is found to be maximum within -/+100 of TSS. The results of G densities were compared with the expression data of the selected oncogenes and tumor suppressor genes in patients with colon cancer (n=174).