TET1 and TDG Suppress Inflammatory Response in Intestinal Tumorigenesis: Implications for Colorectal Tumors With the CpG Island Methylator Phenotype.

BACKGROUND & AIMS: Aberrant DNA methylation is frequent in colorectal cancer (CRC), but underlying mechanisms and pathologic consequences are poorly understood. METHODS: We disrupted active DNA demethylation genes Tet1 and/or Tdg from ApcMin mice and characterized the methylome and transcriptome of colonic adenomas. Data were compared to human colonic adenocarcinomas (COAD) in The Cancer Genome Atlas. RESULTS: There were increased numbers of small intestinal adenomas in ApcMin mice expressing the TdgN151A allele, whereas Tet1-deficient and Tet1/TdgN151A-double heterozygous ApcMin colonic adenomas were larger with features of erosion and invasion. We detected reduction in global DNA hypomethylation in colonic adenomas from Tet1- and Tdg-mutant ApcMin mice and hypermethylation of CpG islands in Tet1-mutant ApcMin adenomas. Up-regulation of inflammatory, immune, and interferon response genes was present in Tet1- and Tdg-mutant colonic adenomas compared to control ApcMin adenomas. This up-regulation was also seen in murine colonic organoids and human CRC lines infected with lentiviruses expressing TET1 or TDG short hairpin RNA. A 127-gene inflammatory signature separated colonic adenocarcinomas into 4 groups, closely aligned with their microsatellite or chromosomal instability and characterized by different levels of DNA methylation and DNMT1 expression that anticorrelated with TET1 expression. Tumors with the CpG island methylator phenotype (CIMP) had concerted high DNMT1/low TET1 expression. TET1 or TDG knockdown in CRC lines enhanced killing by natural killer cells. CONCLUSIONS: Our findings reveal a novel epigenetic regulation, linked to the type of genomic instability, by which TET1/TDG-mediated DNA demethylation decreases methylation levels and inflammatory/interferon/immune responses. CIMP in CRC is triggered by an imbalance of methylating activities over demethylating activities. These mice represent a model of CIMP CRC.

Act Locally, Act Globally-Microbiota, Barriers, and Cytokines in Atherosclerosis.

Atherosclerosis is a lipid-driven chronic inflammatory disease that is characterized by the formation and progressive growth of atherosclerotic plaques in the wall of arteries. Atherosclerosis is a major predisposing factor for stroke and heart attack. Various immune-mediated mechanisms are implicated in the disease initiation and progression. Cytokines are key mediators of the crosstalk between innate and adaptive immune cells as well as non-hematopoietic cells in the aortic wall and are emerging players in the regulation of atherosclerosis. Progression of atherosclerosis is always associated with increased local and systemic levels of pro-inflammatory cytokines. The role of cytokines within atherosclerotic plaque has been extensively investigated; however, the cell-specific role of cytokine signaling, particularly the role of cytokines in the regulation of barrier tissues tightly associated with microbiota in the context of cardiovascular diseases has only recently come to light. Here, we summarize the knowledge about the function of cytokines at mucosal barriers and the interplay between cytokines, barriers, and microbiota and discuss their known and potential implications for atherosclerosis development.

Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.

Estimation of interaction of drug-like compounds with antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R 2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.