Methylation of cytosines at CpG sites is a common epigenetic DNA modification that can be measured by a large number of methods, now even in a genome-wide manner for hundreds of thousands of sites. The application of DNA methylation analysis is becoming widely popular in complex disorders, for example, to understand part of the “missing heritability”. The DNA samples most readily available for methylation studies are derived from whole blood. However, blood consists of many functionally and developmentally distinct cell populations in varying proportions. We studied whether such variation might affect the interpretation of methylation studies based on whole blood DNA. We found in healthy male blood donors there is important variation in the methylation profiles of whole blood, mononuclear cells, granulocytes, and cells from seven selected purified lineages. CpG methylation between mononuclear cells and granulocytes differed for 22% of the 8252 probes covering the selected 343 genes implicated in immune-related disorders by genome-wide association studies, and at least one probe was differentially methylated for 85% of the genes, indicating that whole blood methylation results might be unintelligible. For individual genes, even if the overall methylation patterns might appear similar, a few CpG sites in the regulatory regions may have opposite methylation patterns (i.e., hypo/hyper) in the main blood cell types. We conclude that interpretation of whole blood methylation profiles should be performed with great caution and for any differences implicated in a disorder, the differences resulting from varying proportions of white blood cell types should be considered.

Estrogen receptor (ER)-negative tumors represent 20–30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry1. The etiology2 and clinical behavior3 of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition4. To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10−12 and LGR6, P = 1.4 × 10−8), 2p24.1 (P = 4.6 × 10−8) and 16q12.2 (FTO, P = 4.0 × 10−8), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.

Despite the high prevalence of nonalcoholic fatty liver disease (NAFLD), little is known of its pathogenesis based on study of human liver samples. By the use of Affymetrix GeneChips (17,601 genes), we investigated gene expression in the human liver of subjects with extreme steatosis due to NAFLD without histological signs of inflammation (liver fat 66.0 +/- 6.8%) and in subjects with low liver fat content (6.4 +/- 2.7%). The data were analyzed by using sequence-based reannotation of Affymetrix probes and a robust model-based normalization method. We identified genes involved in hepatic glucose and lipid metabolism, insulin signaling, inflammation, coagulation, and cell adhesion to be significantly associated with liver fat content. In addition, genes involved in ceramide signaling (MAP2K4) and metabolism (UGCG) were found to be positively associated with liver fat content. Genes involved in lipid metabolism (PLIN, ACADM), fatty acid transport (FABP4, CD36), amino acid catabolism (BCAT1), and inflammation (CCL2) were validated by real-time PCR and were found to be upregulated in subjects with high liver fat content. The data show that multiple changes in gene expression characterize simple steatosis.

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.

Estrogen receptor (ER)-negative breast cancer shows a higher incidence in women of African ancestry compared to women of European ancestry. In search of common risk alleles for ER-negative breast cancer, we combined genome-wide association study (GWAS) data from women of African ancestry (1,004 ER-negative cases and 2,745 controls) and European ancestry (1,718 ER-negative cases and 3,670 controls), with replication testing conducted in an additional 2,292 ER-negative cases and 16,901 controls of European ancestry. We identified a common risk variant for ER-negative breast cancer at the TERT-CLPTM1L locus on chromosome 5p15 (rs10069690: per-allele odds ratio (OR) = 1.18 per allele, P = 1.0 × 10−10). The variant was also significantly associated with triple-negative (ER-negative, progesterone receptor (PR)-negative and human epidermal growth factor-2 (HER2)-negative) breast cancer (OR = 1.25, P = 1.1 × 10−9), particularly in younger women (<50 years of age) (OR = 1.48, P = 1.9 × 10−9). Our results identify a genetic locus associated with estrogen receptor negative breast cancer subtypes in multiple populations.

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

EU and the Seventh Framework Programme (the MeDALL project).

The mechanisms of inflammation in acne are currently subject of intense investigation. This study focused on the activation of adaptive and innate immunity in clinically early visible inflamed acne lesions and was performed in two independent patient populations. Biopsies were collected from lesional and non-lesional skin of acne patients. Using Affymetrix Genechips, we observed significant elevation of the signature cytokines of the Th17 lineage in acne lesions compared to non-lesional skin. The increased expression of IL-17 was confirmed at the RNA and also protein level with real-time PCR (RT-PCR) and Luminex technology. Cytokines involved in Th17 lineage differentiation (IL-1β, IL-6, TGF-β, IL23p19) were remarkably induced at the RNA level. In addition, proinflammatory cytokines and chemokines (TNF-α, IL-8, CSF2 and CCL20), Th1 markers (IL12p40, CXCR3, T-bet, IFN-γ), T regulatory cell markers (Foxp3, IL-10, TGF-β) and IL-17 related antimicrobial peptides (S100A7, S100A9, lipocalin, hBD2, hBD3, hCAP18) were induced. Importantly, immunohistochemistry revealed significantly increased numbers of IL-17A positive T cells and CD83 dendritic cells in the acne lesions. In summary our results demonstrate the presence of IL-17A positive T cells and the activation of Th17-related cytokines in acne lesions, indicating that the Th17 pathway is activated and may play a pivotal role in the disease process, possibly offering new targets of therapy.