A multi-omics approach to investigate the inflammatory response to life course socioeconomic position.

Aim: Inflammation represents a potential pathway through which socioeconomic position (SEP) is biologically embedded. Materials & methods: We analyzed inflammatory biomarkers in response to life course SEP by integrating multi-omics DNA-methylation, gene expression and protein level in 178 European Prospective Investigation into Cancer and Nutrition-Italy participants. Results & conclusion: We identified 61 potential cis acting CpG loci whose methylation levels were associated with gene expression at a Bonferroni correction. We examined the relationships between life course SEP and these 61 cis-acting regulatory methylation sites individually and jointly using several scores. Less-advantaged SEP participants exhibit, later in life, a lower inflammatory methylome score, suggesting an overall increased expression of the corresponding inflammatory genes or proteins, supporting the hypothesis that SEP impacts adult physiology through inflammation.

MagiCMicroRna: a web implementation of AgiMicroRna using shiny.

BACKGROUND: MicroRNA expression can be quantified using sequencing techniques or commercial microRNA-expression arrays. Recently, the AgiMicroRna R-package was published that enabled systematic preprocessing and statistical analysis for Agilent microRNA arrays. Here we describe MagiCMicroRna, which is a user-friendly web interface for this package, together with a new filtering approach. RESULTS: We used MagiCMicroRna to normalize and filter an Agilent miRNA microarray dataset of cancerous and normal tissues from 14 different patients. With the standard filtering procedure, 250 out of 817 microRNAs remained, whereas the new group-specific filtering approach resulted in broader datasets for further analysis in most groups (>279 microRNAs remaining). CONCLUSIONS: The user-friendly web interface of MagiCMicroRna enables researchers to normalize and filter Agilent microarrays by the click of one button. Furthermore, MagiCMicroRna provides flexibility in choosing the filtering method. The new group-specific filtering approach lead to an increased number and additional tissue-specific microRNAs remaining for subsequent analysis compared to the standard procedure. The MagiCMicroRna web interface and source code can be downloaded from https://bitbucket.org/mutgx/magicmicrorna.git.