Inflammatory biomarker relationships with helper T cell GPR15 expression and cannabis and tobacco smoking

Nicotine exacerbates liver damage in a mice model of Ehrlich ascites carcinoma through shifting SOD/NF-κB/caspase-3 pathways: ameliorating role of Chlorella vulgaris

Nicotine, a pervasive global environmental pollutant, is released throughout every phase of the tobacco's life cycle. This study examined the probable ameliorative role of Chlorella vulgaris (ChV) extract against nicotine (NIC)-induced hepatic injury in Ehrlich ascites carcinoma (EAC) bearing female Swiss mice. Sixty female Swiss mice were assigned to four equal groups orally gavaged 2% saccharin 0.2 mL/mouse (control group), orally intubated 100 mg ChV /kg (ChV group), orally intubated 100 µg/mL NIC in 2% saccharin (NIC group), and orally intubated NIC + ChV as in group 3 and 2 (NIC+ChV group). The dosing was daily for 4 weeks. Mice from all experimental groups were then inoculated intraperitoneally with viable tumor cells 2.5 × 106 (0.2 mL/mouse) in the fourth week, and the treatments were extended for another 2 weeks. The results have shown that NIC exposure significantly altered the serum levels of liver function indices, lipid profile, LDH, and ALP in the NIC-exposed group. NIC administration significantly increased hepatic inflammation, lipid peroxidation, and DNA damage-related biomarkers but reduced antioxidant enzyme activities. NIC exposure downregulated SOD1, SOD2, CAT, GPX1, and GPX2 but upregulated NF-κB hepatic gene expression. Notably, the presence of the EAC cells outside the liver was common in all mice groups. Liver tissue of the NIC-exposed group showed multifocal expansion of hepatic sinusoids by neoplastic cells. However, with no evidence of considerable infiltration of EAC cells inside the sinusoids or in periportal areas in the NIC + ChV groups. NIC significantly altered caspase-3, Bax, and BcL2 hepatic immune expression. Interestingly, ChV administration significantly mitigates NIC-induced alterations in hepatic function indices, lipid profile, and the mRNA expression of antioxidant and NF-κB genes and regulates the caspase-3, Bax, and BcL2 immunostaining. Finally, the in vivo protective outcomes of ChV against NIC-induced hepatic injury combined with EAC in female Swiss mice could suggest their helpful role for cancer patients who are directly or indirectly exposed to NIC daily.

Dysregulation of immunity by cigarette smoking promotes inflammation and cancer: A review

Smoking is a serious global health issue. Cigarette smoking contains over 7000 different chemicals. The main harmful components include nicotine, acrolein, aromatic hydrocarbons and heavy metals, which play the key role for cigarette-induced inflammation and carcinogenesis. Growing evidences show that cigarette smoking and its components exert a remarkable impact on regulation of immunity and dysregulated immunity promotes inflammation and cancer. Therefore, this comprehensive and up-to-date review covers four interrelated topics, including cigarette smoking, inflammation, cancer and immune system. The known harmful chemicals from cigarette smoking were summarized. Importantly, we discussed in depth the impact of cigarette smoking on the formation of inflammatory or tumor microenvironment, primarily by affecting immune effector cells, such as macrophages, neutrophils, and T lymphocytes. Furthermore, the main molecular mechanisms by which cigarette smoking induces inflammation and cancer, including changes in epigenetics, DNA damage and others were further summarized. This article will contribute to a better understanding of the impact of cigarette smoking on inducing inflammation and cancer.

Epigenomic profiling of isolated blood cell types reveals highly specific B cell smoking signatures and links to disease risk

BACKGROUND

Tobacco smoking alters the DNA methylation profiles of immune cells which may underpin some of the pathogenesis of smoking-associated diseases. To link smoking-driven epigenetic effects in specific immune cell types with disease risk, we isolated six leukocyte subtypes, CD14+ monocytes, CD15+ granulocytes, CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD56+ natural killer cells, from whole blood of 67 healthy adult smokers and 74 nonsmokers for epigenome-wide association study (EWAS) using Illumina 450k and EPIC methylation arrays.

RESULTS

Numbers of smoking-associated differentially methylated sites (smCpGs) at genome-wide significance (p < 1.2 × 10-7) varied widely across cell types, from 5 smCpGs in CD8+ T cells to 111 smCpGs in CD19+ B cells. We found unique smoking effects in each cell type, some of which were not apparent in whole blood. Methylation-based deconvolution to estimate B cell subtypes revealed that smokers had 7.2% (p = 0.033) less naïve B cells. Adjusting for naïve and memory B cell proportions in EWAS and RNA-seq allowed the identification of genes enriched for B cell activation-related cytokine signaling pathways, Th1/Th2 responses, and hematopoietic cancers. Integrating with large-scale public datasets, 62 smCpGs were among CpGs associated with health-relevant EWASs. Furthermore, 74 smCpGs had reproducible methylation quantitative trait loci single nucleotide polymorphisms (SNPs) that were in complete linkage disequilibrium with genome-wide association study SNPs, associating with lung function, disease risks, and other traits.

CONCLUSIONS

We observed blood cell-type-specific smCpGs, a naïve-to-memory shift among B cells, and by integrating genome-wide datasets, we identified their potential links to disease risks and health traits.

Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods

Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.