Physical mapping of four porcine 20S proteasome core complex genes (PSMA1, PSMA2, PSMA3 and PSMA6)

Distinct pathways associated with chromosomal aberration frequency in a cohort exposed to genotoxic compounds compared to general population

Non-specific structural chromosomal aberrations (CAs) observed in peripheral blood lymphocytes of healthy individuals can be either chromosome-type aberrations (CSAs) or chromatid-type aberrations (CTAs) depending on the stage of cell division they are induced in and mechanism of formation. It is important to study the genetic basis of chromosomal instability as it is a marker of genotoxic exposure and a predictor of cancer risk. For that purpose, we conducted two genome-wide association studies (GWASs) on healthy individuals in the presence and absence of apparent genotoxic exposure from the Czech Republic and Slovakia. The pre-GWAS cytogenetic analysis reported the frequencies of CSA, CTA and total CA (CAtot). We performed both linear and binary logistic regression analysis with an arbitrary cut-off point of 2% for CAtot and 1% for CSA and CTA. Using the statistical threshold of 1.0 × 10-5, we identified five loci with in silico predicted functionality in the reference group and four loci in the exposed group, with no overlap between the associated regions. A meta-analysis on the two GWASs identified further four loci with moderate associations in each of the studies. From the reference group mainly loci within genes related to DNA damage response/repair were identified. Other loci identified from both the reference and exposed groups were found to be involved in the segregation of chromosomes and chromatin modification. Some of the discovered regions in each group were implicated in tumourigenesis and autism.

Identification of differential modules in ankylosing spondylitis using systemic module inference and the attract method

The objective of the present study was to identify differential modules in ankylosing spondylitis (AS) by integrating network analysis, module inference and the attract method. To achieve this objective, four steps were conducted. The first step was disease objective network (DON) for AS, and healthy objective network (HON) inference dependent on gene expression data, protein-protein interaction networks and Spearman's correlation coefficient. In the second step, module detection was performed by utilizing a clique-merging algorithm, which comprised of exploring maximal cliques by clique algorithm and refining or merging maximal cliques with a high overlap. The third part was seed module evaluation through module pair matches by Jaccard score and module correlation density (MCD) calculation. Finally, in the fourth step, differential modules between the AS and healthy groups were identified based on a gene set enrichment analysis-analysis of variance model in the attract method. There were 5,301 nodes and 28,176 interactions both in DON and HON. A total of 20 and 21 modules were detected for the AS and healthy group, respectively. Notably, six seed modules across two groups were identified with Jaccard score ≥0.5, and these were ranked in descending order of differential MCD (ΔC). Seed module 1 had the highest ΔC of 0.077 and Jaccard score of 1.000. By accessing the attract method, one differential module between the AS group and healthy group was identified. In conclusion, the present study successfully identified one differential module for AS that may be a potential marker for AS target therapy and provide insights for future research on this disease.