Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

As part of the Human Functional Genomics Project, which aims to understand the factors that determine the variability of immune responses, we investigated genetic variants affecting cytokine production in response to ex vivo stimulation in two independent cohorts of 500 and 200 healthy individuals. We demonstrate a strong impact of genetic heritability on cytokine production capacity after challenge with bacterial, fungal, viral, and non-microbial stimuli. In addition to 17 novel genome-wide significant cytokine QTLs (cQTLs), our study provides a comprehensive picture of the genetic variants that influence six different cytokines in whole blood, blood mononuclear cells, and macrophages. Important biological pathways that contain cytokine QTLs map to pattern recognition receptors (TLR1-6-10 cluster), cytokine and complement inhibitors, and the kallikrein system. The cytokine QTLs show enrichment for monocyte-specific enhancers, are more often located in regions under positive selection, and are significantly enriched among SNPs associated with infections and immune-mediated diseases. PAPERCLIP.

SummaryEffective immunity requires a complex network of cellular and humoral components that interact with each other and are influenced by different environmental and host factors. We used a systems biology approach to comprehensively assess the impact of environmental and genetic factors on immune cell populations in peripheral blood, including associations with immunoglobulin concentrations, from ∼500 healthy volunteers from the Human Functional Genomics Project. Genetic heritability estimation showed that variations in T cell numbers are more strongly driven by genetic factors, while B cell counts are more environmentally influenced. Quantitative trait loci (QTL) mapping identified eight independent genomic loci associated with leukocyte count variation, including four associations with T and B cell subtypes. The QTLs identified were enriched among genome-wide association study (GWAS) SNPs reported to increase susceptibility to immune-mediated diseases. Our systems approach provides insights into cellular and humoral immune trait variability in humans.

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

The immune response to pathogens varies substantially among people. While both genetic and non-genetic factors contribute to inter-person variation, their relative contributions and potential predictive power have remained largely unknown. By systematically correlating host factors in 534 healthy volunteers, including baseline immunological parameters and molecular profiles (genome, metabolome and gut microbiome), with cytokine-production capacity after stimulation with 20 pathogens, we identified distinct patterns of co-regulation. Among the 91 different cytokine–stimulus pairs, 11 categories of host factors together explained up to 67% of inter-individual variation in cytokine production induced by stimulation. A computational model based on genetic data predicted the genetic component of stimulus-induced cytokine-production (correlation 0.28-0.89), while non-genetic factors influenced cytokine production as well.

statistics from other independent studies 13,17,18 , we identify novel host-microbiota interactions. Furthermore, we explore the impact of potential confounding factors in modulating these genetic effects and identify potential diet-dependent host-microbiota interactions. We further assess the potential causal relationships between the gut microbiome and dietary habits, biomarkers and disease using Mendelian randomization (MR). Finally, we carry out a power analysis showing how microbiome studies, even at the current sample size, are underpowered to reveal the complex genetic architecture by which host genetics regulates the gut microbiome. ResultsGenome-wide associations with bacterial taxa and pathways. We investigated 5.5 million common (minor allele frequency (MAF) > 0.05) genetic variants on all autosomes and the X chromosome using linear mixed models 19 to test their association with 207 taxa and 205 bacterial pathways in 7,738 individuals from the DMP cohort (Methods and Supplementary Table 1) 19 . There was no evidence for test statistic inflation (median genomic lambda 1.002 (range, 0.75-1.03) for taxa and 1.004 (range, 0.87-1.04) for pathways). We identified 37 single nucleotide polymorphism (SNP)trait associations at 24 independent loci at a genome-wide P value threshold of 5 × 10 −8 (Fig. 1 and Supplementary Table 2). Genetic variants at two loci passed the more stringent study-wide threshold of 1.89 × 10 −10 that accounts for the number of independent tests performed (Methods).The strongest signal was seen for rs182549 located in an intron of MCM6, a perfect proxy of rs4988235 (r 2 = 1, 1000 Genomes Project European populations), one of the variants known to regulate the LCT gene and responsible for lactase persistence in adults (ClinVar accession RCV000008124). The T allele of rs182549, which confers lactase persistence through a dominant model of inheritance, was found to be associated with decreased abundances of the species Bifidobacterium adolescentis (P = 7.6 × 10 −14 ) and Bifidobacterium longum (P = 3.2 × 10 −08 ), as well as decreased abundances of higher-level taxa (Supplementary Table 2 (ref. 5 )). Associations at this locus were also seen for other taxa of the same genus but at lower levels of significance (Bifidobacterium catenulatum, P = 3.9 × 10 −5 ) and for species of the Collinsella genus (Extended Data Fig. 1). The genetic association at the LCT locus has been previously described, albeit only at the genus level, in Dutch, UK and US cohorts 6,8,14 , as well as in a recent large-scale meta-analysis 13 .The second locus that passed study-wide significance consisted of genetic variants near the ABO gene. ABO encodes the BGAT protein, a histo-blood group ABO system transferase. Associations found at this locus include species Bifidobacterium bifidum (rs8176645, p = 5.5 × 10 −15 ) and Collinsella aerofaciens (rs550057, P = 2.0 × 10 −8 , r 2 = 0.59 with rs8176645 in 1000 Genomes Project Europeans) and higher-order taxa (rs550057, genus Collinsella, P = 9.3 × 10 −11 ; family Coriobacteriac...

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Objective: Metabolic dysregulation and inflammation are important consequences of obesity and impact susceptibility to cardiovascular disease. Anti-inflammatory therapy in cardiovascular disease is being developed under the assumption that inflammatory pathways are identical in women and men, but it is not known if this is indeed the case. In this study, we assessed the sex-specific relation between inflammation and metabolic dysregulation in obesity. Approach and Results: Three hundred two individuals were included, half with a BMI 27 to 30 kg/m 2 and half with a BMI>30 kg/m 2 , 45% were women. The presence of metabolic syndrome was assessed according to the National Cholesterol Education Program-ATPIII criteria, and inflammation was studied using circulating markers of inflammation, cell counts, and ex vivo cytokine production capacity of isolated immune cells. Additionally, lipidomic and metabolomic data were gathered, and subcutaneous fat biopsies were histologically assessed. Metabolic syndrome is associated with an increased inflammatory profile that profoundly differs between women and men: women with metabolic syndrome show a lower concentration of the anti-inflammatory adiponectin, whereas men show increased levels of several pro-inflammatory markers such as IL (interleukin)-6 and leptin. Adipose tissue inflammation showed similar sex-specific associations with these markers. Peripheral blood mononuclear cells isolated from men, but not women, with metabolic syndrome display enhanced cytokine production capacity. Conclusions: We identified sex-specific pathways that influence inflammation in obesity. Excessive production of proinflammatory cytokines was observed in men with metabolic syndrome. In contrast, women typically showed reduced levels of the anti-inflammatory adipokine adiponectin. These different mechanisms of inflammatory dysregulation between women and men with obesity argue for sex-specific therapeutic strategies.