Toward a Quantitative Estimate of Future Heat Wave Mortality under Global Climate Change

Background: Climate change is anticipated to affect human health by changing the distribution of known risk factors. Heat waves have had debilitating effects on human mortality, and global climate models predict an increase in the frequency and severity of heat waves. The extent to which climate change will harm human health through changes in the distribution of heat waves and the sources of uncertainty in estimating these effects have not been studied extensively. Objectives: We estimated the future excess mortality attributable to heat waves under global climate change for a major U.S. city. Methods: We used a database comprising daily data from 1987 through 2005 on mortality from all nonaccidental causes, ambient levels of particulate matter and ozone, temperature, and dew point temperature for the city of Chicago, Illinois. We estimated the associations between heat waves and mortality in Chicago using Poisson regression models. Results: Under three different climate change scenarios for 2081–2100 and in the absence of adaptation, the city of Chicago could experience between 166 and 2,217 excess deaths per year attributable to heat waves, based on estimates from seven global climate models. We noted considerable variability in the projections of annual heat wave mortality; the largest source of variation was the choice of climate model. Conclusions: The impact of future heat waves on human health will likely be profound, and significant gains can be expected by lowering future carbon dioxide emissions

Significance testing in ridge regression for genetic data.

Published versio

Quantification of Cell Signaling Networks Using Kinase Activity Chemosensors

The ability to directly determine endogenous kinase activity in tissue homogenates provides valuable insights into signaling aberrations that underlie disease phenotypes. When activity data is collected across a panel of kinases, a unique “signaling fingerprint” is generated that allows for discrimination between diseased and normal tissue. Here we describe the use of peptide-based kinase activity sensors to fingerprint the signaling changes associated with disease states. This approach leverages the phosphorylation-sensitive sulfonamido-oxine (Sox) fluorophore to provide a direct readout of kinase enzymatic activity in unfractionated tissue homogenates from animal models or clinical samples. To demonstrate the application of this technology, we focus on a rat model of nonalcoholic fatty liver disease (NAFLD). Sox-based activity probes allow for the rapid and straightforward analysis of changes in kinase enzymatic activity associated with disease states, providing leads for further investigation using traditional biochemical approaches

Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level

Reference values for methacholine reactivity (SAPALDIA study)

BACKGROUND: The distribution of airway responsiveness in a general population of non-smokers without respiratory symptoms has not been established, limiting its use in clinical and epidemiological practice. We derived reference equations depending on individual characteristics (i.e., sex, age, baseline lung function) for relevant percentiles of the methacholine two-point dose-response slope. METHODS: In a reference sample of 1567 adults of the SAPALDIA cross-sectional survey (1991), defined by excluding subjects with respiratory conditions, responsiveness during methacholine challenge was quantified by calculating the two-point dose-response slope (O'Connor). Weighted L1-regression was used to estimate reference equations for the 95(th ), 90(th ), 75(th )and 50(th )percentiles of the two-point slope. RESULTS: Reference equations for the 95(th ), 90(th ), 75(th )and 50(th )percentiles of the two-point slope were estimated using a model of the form a + b* Age + c* FEV(1 )+ d* (FEV(1))(2 ), where FEV(1 )corresponds to the pre-test (or baseline) level of FEV(1). For the central half of the FEV(1 )distribution, we used a quadratic model to describe the dependence of methacholine slope on baseline FEV(1). For the first and last quartiles of FEV(1), a linear relation with FEV(1 )was assumed (i.e., d was set to 0). Sex was not a predictor term in this model. A negative linear association with slope was found for age. We provide an Excel file allowing calculation of the percentile of methacholine slope of a subject after introducing age – pre-test FEV(1 )– and results of methacholine challenge of the subject. CONCLUSION: The present study provides equations for four relevant percentiles of methacholine two-point slope depending on age and baseline FEV(1 )as basic predictors in an adult reference population of non-obstructive and non-atopic persons. These equations may help clinicians and epidemiologists to better characterize individual or population airway responsiveness

Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene

BACKGROUND: There is a need to develop robust and clinically applicable gene expression signatures. Hypoxia is a key factor promoting solid tumour progression and resistance to therapy; a hypoxia signature has the potential to be not only prognostic but also to predict benefit from particular interventions. METHODS: An approach for deriving signatures that combine knowledge of gene function and analysis of in vivo co-expression patterns was used to define a common hypoxia signature from three head and neck and five breast cancer studies. Previously validated hypoxia-regulated genes (seeds) were used to generate hypoxia co-expression cancer networks. RESULTS: A common hypoxia signature, or metagene, was derived by selecting genes that were consistently co-expressed with the hypoxia seeds in multiple cancers. This was highly enriched for hypoxia-regulated pathways, and prognostic in multivariate analyses. Genes with the highest connectivity were also the most prognostic, and a reduced metagene consisting of a small number of top-ranked genes, including VEGFA, SLC2A1 and PGAM1, outperformed both a larger signature and reported signatures in independent data sets of head and neck, breast and lung cancers. CONCLUSION: Combined knowledge of multiple genes' function from in vitro experiments together with meta-analysis of multiple cancers can deliver compact and robust signatures suitable for clinical application

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

Knowledge driven decomposition of tumor expression profiles

<p>Abstract</p> <p>Background</p> <p>Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.</p> <p>Results</p> <p>We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples.</p> <p>Conclusion</p> <p>The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.</p