Cardiorespiratory Fitness, Alcohol Intake, and Metabolic Syndrome Incidence in Men

Purpose To prospectively examine the independent and joint effects of alcohol consumption and cardiorespiratory fitness on the incidence of metabolic syndrome in a cohort of men. Methods A prospective examination of 3,411 apparently healthy men at baseline, who came to the Cooper Clinic (Dallas, Texas) for at least 2 preventive visits (1979–2010). Primary exposure variables were cardiorespiratory fitness and alcohol intake; the outcome measure was metabolic syndrome (MetS) and the components thereof. Cox proportional hazard models were computed to assess the relationship between the exposure variables and the incidence of MetS while adjusting for confounders. Results Over a mean follow-up period of 9 years (SD=7.8), 276 men developed MetS. In multivariable analysis, a dose-response relationship was observed between increased levels of fitness and reduced MetS risk (moderate fitness: HR=0.60, 95%CI 0.43–0.82; high fitness: HR=0.49, 95%CI 0.35–0.69). When examining the independent effects of alcohol, light drinking increased the risk for MetS by 66% (HR=1.66, 95%CI 1.11–2.48). No statistically significant interaction effect was observed between alcohol and fitness in relation to MetS (P = 0.32). When assessing the relation between each exposure and the components of MetS, higher fitness consistently reduced the risk of all components; whereas lower alcohol intake reduced the risk of elevated glucose and blood pressure and increased the risk for low HDL-c. Conclusions Among this cohort of men, higher fitness levels reduced the risk for MetS and its components. The relation between alcohol intake levels and metabolic risk was more complex and not reflected when examining MetS as a whole

Neuronspecific expression of the rat gonadotropin-releasing hormone gene is conferred by interactions of a defined promoter element with the enhancer in GT1–7 cells

Neuroendocrine control of the reproductive cascade is mediated by GnRH, which in mammals is produced by a subset of neurons scattered throughout the hypothalamus and forebrain. Utilizing a cultured cell model of GnRH neurons (GT1-7 cells), two regulatory regions in the rat GnRH 5 flanking DNA were identified as essential for cell-type specificity: a 300-bp enhancer and a 173-bp conserved proximal promoter. Using transient transfections to compare expression in GT1-7 cells to a non-GnRH-expressing cell type (NIH 3T3), we show that the GnRH enhancer and the proximal promoter each play roles in conferring this specificity. Deletion of footprint 2 (FP2; ؊26 to ؊76) from the promoter when coupled to the GnRH enhancer diminishes reporter activity in GT1-7 cells more strongly than in NIH 3T3 cells. Furthermore, deletion of FP2 from the promoter when coupled to the heterologous Rous sarcoma virus 5-long terminal repeat promoter abolishes the difference in reporter activity between GT1-7 and NIH 3T3 cells, suggesting that FP2 of the GnRH promoter is necessary for cell-specific expression. In addition, FP2 alone is sufficient to confer cell-specific expression and can interact with the GnRH enhancer to augment reporter gene expression specifically in GT1-7 cells. Finally, a 31-bp sequence from within FP2 (؊63 to ؊33) synergistically activates transcription when coupled with the GnRH enhancer in GT1-7 cells but not in NIH 3T3 cells. Thus, this 31-bp region contains elements necessary for interaction between the GnRH enhancer and promoter. We show that two of five protein complexes that bind to the ؊63 to ؊33 region are GT1-7 cell specific, and both of them appear to be homeodomain proteins. The identification of a cell-specific element in the GnRH proximal promoter significantly advances our understanding of the transcriptional basis for neuron-specific GnRH gene expression. (Molecular Endocrinology 14: 1509-1522, 2000

Methodology for the development of a taxonomy and toolkit to evaluate health-related habits and lifestyle (eVITAL)

<p>Abstract</p> <p>Background</p> <p>Chronic diseases cause an ever-increasing percentage of morbidity and mortality, but many have modifiable risk factors. Many behaviors that predispose or protect an individual to chronic disease are interrelated, and therefore are best approached using an integrated model of health and the longevity paradigm, using years lived without disability as the endpoint.</p> <p>Findings</p> <p>This study used a 4-phase mixed qualitative design to create a taxonomy and related online toolkit for the evaluation of health-related habits. Core members of a working group conducted a literature review and created a framing document that defined relevant constructs. This document was revised, first by a working group and then by a series of multidisciplinary expert groups. The working group and expert panels also designed a systematic evaluation of health behaviors and risks, which was computerized and evaluated for feasibility. A demonstration study of the toolkit was performed in 11 healthy volunteers.</p> <p>Discussion</p> <p>In this protocol, we used forms of the community intelligence approach, including frame analysis, feasibility, and demonstration, to develop a clinical taxonomy and an online toolkit with standardized procedures for screening and evaluation of multiple domains of health, with a focus on longevity and the goal of integrating the toolkit into routine clinical practice.</p> <p>Trial Registration</p> <p>IMSERSO registry 200700012672</p

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies