"Month related variability in immunological test results; implications for immunological follow-up studies."

This longitudinal study was originally designed to detect changes in the in vitro immune response of healthy subjects as a result of a psychological intervention. In this study a significant proportion, about 70%, of the immunological variability in the test results was accounted for by the differences in immunological response levels of the subjects. Apart from this between-subject-effect, a significant proportion of the variability in test results was related to the month of data sampling. The month-effect was computed in such a way that the between-subject variation was taken into account. This resulted in a more accurate estimation of the month-effect. Even after correction for the intervention, i.e. the defence of the PhD thesis, the effect of month of data sampling remains significant for mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration, percentage of CD4 and CD8 cells, and for the response to the mitogens phytohaemagglutinin, pokeweed mitogen and concanavalin A as well as the results for the mixed lymphocyte culture for one pool out of three. In contrast, no significant month-effect was observed for the whole blood cell counts, for the differential white blood cell counts as determined by monoclonal antibody staining for cell surface markers CD3, CD16, TAC and OKM1, nor for the immunoglobulin IgM and IgG serum levels. Likewise the cell-mediated lympholysis activities measured against three pools of stimulator cells remained unaltered. We discuss the implications for future immunological follow-up studies of the observation that a significant proportion of the variability in immunological test results is related to differences between subjects and to the month of data sampling

Refined mapping of autoimmune disease associated genetic variants with gene expression suggests an important role for non-coding RNAs

Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases. (C) 2016 The Authors. Published by Elsevier Ltd.</p