Physical Interactions and Expression Quantitative Traits Loci Identify Regulatory Connections for Obesity and Type 2 Diabetes Associated SNPs.

The mechanisms that underlie the association between obesity and type 2 diabetes are not fully understood. Here, we investigated the role of the 3D genome organization in the pathogeneses of obesity and type-2 diabetes. We interpreted the combined and differential impacts of 196 diabetes and 390 obesity associated single nucleotide polymorphisms (SNPs) by integrating data on the genes with which they physically interact (as captured by Hi-C) and the functional [i.e., expression quantitative trait loci (eQTL)] outcomes associated with these interactions. We identified 861 spatially regulated genes (e.g., AP3S2, ELP5, SVIP, IRS1, FADS2, WFS1, RBM6, HORMAD1, PYROXD2), which are enriched in tissues (e.g., adipose, skeletal muscle, pancreas) and biological processes and canonical pathways (e.g., lipid metabolism, leptin, and glucose-insulin signaling pathways) that are important for the pathogenesis of type 2 diabetes and obesity. Our discovery-based approach also identifies enrichment for eQTL SNP-gene interactions in tissues that are not classically associated with diabetes or obesity. We propose that the combinatorial action of active obesity and diabetes spatial eQTL SNPs on their gene pairs within different tissues reduces the ability of these tissues to contribute to the maintenance of a healthy energy metabolism.

Genome Modeling System: A Knowledge Management Platform for Genomics.

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Whole-genome analysis informs breast cancer response to aromatase inhibition.

To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.

Differences in outcome between obese and nonobese patients following severe blunt trauma are not consistent with an early inflammatory genomic response.

OBJECTIVES: Obesity has been demonstrated to alter a number of acute and chronic medical conditions. The effect of obesity on severely injured patients, however, remains incompletely defined. We sought to unravel potential physiologic and genomic alterations induced by obesity in severely injured blunt trauma patients. DESIGN: A retrospective review of clinical and genomic information contained in the Inflammation and the Host Response to Injury multicenter trauma-related database examining the relationship between body mass index and the early genomic response from peripheral blood leukocytes to patient outcome following severe blunt trauma was performed. SETTING: Multicenter collaboration between university-based academic trauma centers. PATIENTS: Severely injured blunt trauma patients enrolled in the database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Univariate analysis of 455 severely injured trauma patients using the National Institutes of Health/World Health Organization body mass index classification system revealed significant increases in morbidity, including longer intensive care unit stays and a greater number of ventilator days, cardiac arrests, episodes of acute renal failure, and patients developing multiple organ failure. Regression modeling identified body mass index class as being independently associated with adverse outcomes and increased morbidity but an inverse relationship with mortality in patients who suffered severe blunt traumatic injury. Initial leukocyte genomic expression patterns between 163 patients in the four different body mass index groupings did not differ; however, analysis of gene differences between body mass index classes occurring over time demonstrated significant changes in 513 probe sets with significant pathway differences being related to cellular metabolism. CONCLUSIONS: Increasing body mass index is associated with increased morbidity following severe blunt trauma. The initial blood leukocyte inflammatory response to blunt trauma does not appear to differ significantly between patients despite increasing body mass index. Resolution of the inflammatory response may differ between patients on the basis of body mass index; however, additional work is needed to clarify the potential causality of this finding.

Validation of the riboleukogram to detect ventilator-associated pneumonia after severe injury.

OBJECTIVE: We hypothesized that circulating leukocyte RNA profiles or "riboleukograms" detect ventilator-associated pneumonia after blunt trauma. SUMMARY BACKGROUND DATA: A pilot microarray study of 11 ventilator-associated pneumonia (VAP) patients suggested that 85 leukocyte genes can be used to diagnose VAP. Validation of this gene set to detect VAP was tested using data from an independent patient cohort. METHODS: A total of 158 intubated blunt trauma patients were enrolled at 5 centers, where 57 (36%) developed VAP. Patient age was 34.2 ± 11.1 years; 65% were male. Circulating leukocyte GeneChip U133 2.0 expression values were measured at time 0.5, 1, 4, 7, 14, 21, and 28 days after injury. DChip normalized leukocyte transcriptional profiles were analyzed using repeated measures logistic regression. A compound covariate model based on leukocyte gene transcriptional profiles in a training subset of patients was tested to determine predictive accuracy for VAP 4 days prior to clinical diagnosis in the test subset. RESULTS: Using gene expression values measured on each study day at an FDR <0.05, 27 (32%) of the 85 genes were associated with the diagnosis of VAP 1 to 4 days before diagnosis. However, the compound covariate model based on these 85-genes did not predict VAP in the test cohort better than chance (P = 0.27). In contrast, a compound covariate model based upon de novo transcriptional analysis of the 158 patients predicted VAP better than chance 4 days before diagnosis with a sensitivity of 57% and a specificity of 69%. CONCLUSION: Our results validate those described in a pilot study, confirming that riboleukograms are associated with the development of VAP days prior to clinical diagnosis. Similarly, a riboleukogram predictive model tested on a larger cohort of 158 patients was better than chance at predicting VAP days prior to clinical diagnosis.