Adiposity, its related biologic risk factors, and suicide: a cohort study of 542,088 Taiwanese adults

Recent studies in Western nations have shown inverse associations between body mass index (BMI, measured as weight (kg)/height (m)(2)) and suicide. However, it is uncertain whether the association is similar in non-Western settings, and the biologic pathways underlying the association are unclear. The authors investigated these issues in a cohort of 542,088 Taiwanese people 20 years of age or older who participated in a health check-up program (1994-2008); there were 573 suicides over a mean 8.1 years of follow up. There was a J-shaped association between BMI and suicide risk (P for the quadratic term = 0.033) but limited evidence of a linear association (adjusted hazard ratio per 1-standard-deviation increase = 0.95 (95% confidence interval: 0.85, 1.06)); compared with individuals whose BMI was 18.5-22.9, adjusted hazard ratios for those with a BMI /=35 were 1.56 (95% confidence interval: 1.07, 2.28) and 3.62 (95% confidence interval: 1.59, 8.22), respectively. A high waist-to-hip ratio was associated with an increased risk of suicide. There was some evidence for a reverse J-shaped association of systolic blood pressure and high density lipoprotein cholesterol with suicide and an association of higher triglyceride level with increased suicide risk; these associations did not appear to mediate the associations of BMI and waist-to-hip ratio with suicide.postprin

Rudimentary G-Quadruplex-Based Telomere Capping In Saccharomyces Cerevisiae

Telomere capping conceals chromosome ends from exonucleases and checkpoints, but the full range of capping mechanisms is not well defined. Telomeres have the potential to form G-quadruplex (G4) DNA, although evidence for telomere G4 DNA function in vivo is limited. In budding yeast, capping requires the Cdc13 protein and is lost at nonpermissive temperatures in cdc13-1 mutants. Here, we use several independent G4 DNA-stabilizing treatments to suppress cdc13-1 capping defects. These include overexpression of three different G4 DNA binding proteins, loss of the G4 DNA unwinding helicase Sgs1, or treatment with small molecule G4 DNA ligands. In vitro, we show that protein-bound G4 DNA at a 3\u27 overhang inhibits 5\u27-\u3e 3\u27 resection of a paired strand by exonuclease I. These findings demonstrate that, at least in the absence of full natural capping, G4 DNA can play a positive role at telomeres in vivo

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

Background: Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods: Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results: 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion: Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.Department of Veterans Affairs, Health Services Research and Development (DHA), American Lung Association (CI- 51755-N) awarded to DHA, the American Thoracic Society Fellow Career Development AwardPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84155/1/Cooke - ICD9 validity in COPD.pd

A Functional Taxonomy of Tumor Suppression in Oncogenic KRAS-Driven Lung Cancer

Cancer genotyping has identified a large number of putative tumor suppressor genes. Carcinogenesis is a multistep process, but the importance and specific roles of many of these genes during tumor initiation, growth, and progression remain unknown. Here we use a multiplexed mouse model of oncogenic KRAS–driven lung cancer to quantify the impact of 48 known and putative tumor suppressor genes on diverse aspects of carcinogenesis at an unprecedented scale and resolution. We uncover many previously understudied functional tumor suppressors that constrain cancer in vivo. Inactivation of some genes substantially increased growth, whereas the inactivation of others increases tumor initiation and/or the emergence of exceptionally large tumors. These functional in vivo analyses revealed an unexpectedly complex landscape of tumor suppression that has implications for understanding cancer evolution, interpreting clinical cancer genome sequencing data, and directing approaches to limit tumor initiation and progression. SIGNIFICANCE: Our high-throughput and high-resolution analysis of tumor suppression uncovered novel genetic determinants of oncogenic KRAS–driven lung cancer initiation, overall growth, and exceptional growth. This taxonomy is consistent with changing constraints during the life history of cancer and highlights the value of quantitative in vivo genetic analyses in autochthonous cancer models

Coevolved mutations reveal distinct architectures for two core proteins in the bacterial flagellar motor

Switching of bacterial flagellar rotation is caused by large domain movements of the FliG protein triggered by binding of the signal protein CheY to FliM. FliG and FliM form adjacent multi-subunit arrays within the basal body C-ring. The movements alter the interaction of the FliG C-terminal (FliGC) "torque" helix with the stator complexes. Atomic models based on the Salmonella entrovar C-ring electron microscopy reconstruction have implications for switching, but lack consensus on the relative locations of the FliG armadillo (ARM) domains (amino-terminal (FliGN), middle (FliGM) and FliGC) as well as changes during chemotaxis. The generality of the Salmonella model is challenged by the variation in motor morphology and response between species. We studied coevolved residue mutations to determine the unifying elements of switch architecture. Residue interactions, measured by their coevolution, were formalized as a network, guided by structural data. Our measurements reveal a common design with dedicated switch and motor modules. The FliM middle domain (FliMM) has extensive connectivity most simply explained by conserved intra and inter-subunit contacts. In contrast, FliG has patchy, complex architecture. Conserved structural motifs form interacting nodes in the coevolution network that wire FliMM to the FliGC C-terminal, four-helix motor module (C3-6). FliG C3-6 coevolution is organized around the torque helix, differently from other ARM domains. The nodes form separated, surface-proximal patches that are targeted by deleterious mutations as in other allosteric systems. The dominant node is formed by the EHPQ motif at the FliMMFliGM contact interface and adjacent helix residues at a central location within FliGM. The node interacts with nodes in the N-terminal FliGc α-helix triad (ARM-C) and FliGN. ARM-C, separated from C3-6 by the MFVF motif, has poor intra-network connectivity consistent with its variable orientation revealed by structural data. ARM-C could be the convertor element that provides mechanistic and species diversity.JK was supported by Medical Research Council grant U117581331. SK was supported by seed funds from Lahore University of Managment Sciences (LUMS) and the Molecular Biology Consortium

Gene set analysis for longitudinal gene expression data

<p>Abstract</p> <p>Background</p> <p>Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations.</p> <p>Results</p> <p>We provide a robust nonparametric approach to compare the expressions of longitudinally measured sets of genes under multiple treatments or experimental conditions. The limiting distributions of our statistics are derived when the number of genes goes to infinity while the number of replications can be small. When the number of genes in a gene set is small, we recommend permutation tests based on our nonparametric test statistics to achieve reliable type I error and better power while incorporating unknown correlations between and within-genes. Simulation results demonstrate that the proposed method has a greater power than other methods for various data distributions and heteroscedastic correlation structures. This method was used for an IL-2 stimulation study and significantly altered gene sets were identified.</p> <p>Conclusions</p> <p>The simulation study and the real data application showed that the proposed gene set analysis provides a promising tool for longitudinal microarray analysis. R scripts for simulating longitudinal data and calculating the nonparametric statistics are posted on the North Dakota INBRE website <url>http://ndinbre.org/programs/bioinformatics.php</url>. Raw microarray data is available in Gene Expression Omnibus (National Center for Biotechnology Information) with accession number GSE6085.</p