The risk for major depression is both genetically and environmentally determined. It has been proposed that epigenetic mechanisms could mediate the lasting increases in depression risk following exposure to adverse life events and provide a mechanistic framework within which genetic and environmental factors can be integrated. Epigenetics refers to processes affecting gene expression and translation that do not involve changes in the DNA sequence and include NA methylation (NAm) and microNAs (miNAs) as well as histone modifications. ere we review evidence for a role of epigenetics in the pathogenesis of depression from studies investigating DNAm, miRNAs, and histone modifications using different tissues and various experimental designs. From these studies, a model emerges where underlying genetic and environmental risk factors, and interactions between the two, could drive aberrant epigenetic mechanisms targeting stress response pathways, neuronal plasticity, and other behaviorally relevant pathways that have been implicated in major depression.
A substantial proportion of colorectal cancers (CRCs) are interval CRCs (I-CRCs; i.e., CRCs diagnosed soon after a colonoscopy). Chromosomal instability (CIN) is defined as an increase in the rate of which whole chromosomes/large chromosomal fragments are gained or lost and is observed in 85% of non-hereditary CRCs. The contribution of CIN to the etiology of I-CRCs remains unknown. We established a fluorescence in situ hybridization (FISH) approach to characterize CIN by enumerating specific chromosomes and determined the prevalence of numerical CIN in a population-based cohort of I-CRCs and control (sporadic) CRCs. Using the population-based Manitoba Health administrative databases and Manitoba Cancer Registry, we identified an age, sex, and colonic site of CRC matched cohort of I-CRCs and controls and retrieved their archived paraffin-embedded tumor samples. FISH chromosome enumeration probes specifically recognizing the pericentric regions of chromosomes 8, 11, and 17 were first used on cell lines and then CRC tissue microarrays to detect aneusomy, which was then used to calculate a CIN score (CS). The 15th percentile CS for control CRC was used to define CIN phenotype. Mean CSs were similar in the control CRCs and I-CRCs; 82% of I-CRCs exhibited a CIN phenotype, which was similar to that in the control CRCs. This study suggests that CIN is the most prevalent contributor to genomic instability in I-CRCs. Further studies should evaluate CIN and microsatellite instability (MSI) in the same cohort of I-CRCs to corroborate our findings and to further assess concomitant contribution of CIN and MSI to I-CRCs.
Epithelial ovarian cancer (EOC) is the most prevalent form of ovarian cancer and has the highest mortality rate. Novel insight into EOC is required to minimize the morbidity and mortality rates caused by recurrent, drug resistant disease. Although numerous studies have evaluated genome instability in EOC, none have addressed the putative role chromosome instability (CIN) has in disease progression and drug resistance. CIN is defined as an increase in the rate at which whole chromosomes or large parts thereof are gained or lost, and can only be evaluated using approaches capable of characterizing genetic or chromosomal heterogeneity within populations of cells. Although CIN is associated with numerous cancer types, its prevalence and dynamics in EOC is unknown. In this study, we assessed CIN within serial samples collected from the ascites of five EOC patients, and in two well-established ovarian cancer cell models of drug resistance (PEO1/4 and A2780s/cp). We quantified and compared CIN (as measured by nuclear areas and CIN Score (CS) values) within and between serial samples to glean insight into the association and dynamics of CIN within EOC, with a particular focus on resistant and recurrent disease. Using quantitative, single cell analyses we determined that CIN is associated with every sample evaluated and further show that many EOC samples exhibit a large degree of nuclear size and CS value heterogeneity. We also show that CIN is dynamic and generally increases within resistant disease. Finally, we show that both drug resistance models (PEO1/4 and A2780s/cp) exhibit heterogeneity, albeit to a much lesser extent. Surprisingly, the two cell line models exhibit remarkably similar levels of CIN, as the nuclear areas and CS values are largely overlapping between the corresponding paired lines. Accordingly, these data suggest CIN may represent a novel biomarker capable of monitoring changes in EOC progression associated with drug resistance.
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