An Algorithm for Clustered Data Generalized Additive Modelling with S-PLUS

We present a set of functions in S-PLUS to implement the clustered data generalized additive marginal modelling (CDGAM) strategy proposed by Berhane and Tibshirani (1998). A variety of working correlation structures are supported, and the regression basis may include components from the family of smoothing splines.

An Algorithm for Clustered Data Generalized Additive Modelling with S-PLUS

We present a set of functions in S-PLUS to implement the clustered data generalized additive marginal modelling (CDGAM) strategy proposed by Berhane and Tibshirani (1998). A variety of working correlation structures are supported, and the regression basis may include components from the family of smoothing splines

Short‐term hyperglycemia produces oxidative damage and apoptosis in neurons

Dorsal root ganglia neurons in culture die through programmed cell death when exposed to elevated glucose, providing an in vitro model system for the investigation of the mechanisms leading to diabetic neuropathy. This study examines the time course of programmed cell death induction, regulation of cellular antioxidant capacity, and the protective effects of antioxidants in neurons exposed to hyperglycemia. We demonstrate that the first 2 h of hyperglycemia are sufficient to induce oxidative stress and programmed cell death. Using fluorimetric analysis of reactive oxygen species (ROS) production, in vitro assays of antioxidant enzymes, and immunocytochemical assays of cell death, we demonstrate superoxide formation, inhibition of aconitase, and lipid peroxidation within 1 h of hyperglycemia. These are followed by caspase‐3 activation and DNA fragmentation. Antioxidant potential increases by 3–6 h but is insufficient to protect these neurons. Application of the antioxidant α‐lipoic acid potently prevents glucose‐induced oxidative stress and cell death. This study identifies cellular therapeutic targets to prevent diabetic neuropathy. Since oxidative stress is a common feature of the micro‐ and macrovascular complications of diabetes, the present findings have broad application to the treatment of diabetic patients.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154304/1/fsb2fj042513fje.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154304/2/fsb2fj042513fje-sup-0001.pd

Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes

<p>Abstract</p> <p>Background</p> <p>Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait.</p> <p>Results</p> <p>Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway.</p> <p>Conclusions</p> <p>Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.</p

Dengue virus induces mitochondrial elongation through impairment of Drp1-triggered mitochondrial fission

Mitochondria are highly dynamic organelles that undergo continuous cycles of fission and fusion to maintain essential cellular functions. An imbalance between these two processes can result in many pathophysiological outcomes. Dengue virus (DENV) interacts with cellular organelles, including mitochondria, to successfully replicate in cells. This study used live-cell imaging and found an increase in mitochondrial length and respiration during DENV infection. The level of mitochondrial fission protein, Dynamin-related protein 1 (Drp1), was decreased on mitochondria during DENV infection, as well as Drp1 phosphorylated on serine 616, which is important for mitochondrial fission. DENV proteins NS4b and NS3 were also associated with subcellular fractions of mitochondria. Induction of fission through uncoupling of mitochondria or overexpression of Drp1 wild-type and Drp1 with a phosphomimetic mutation (S616D) significantly reduced viral replication. These results demonstrate that DENV infection causes an imbalance in mitochondrial dynamics by inhibiting Drp1-triggered mitochondrial fission, which promotes viral replication

Combining effects from rare and common genetic variants in an exome-wide association study of sequence data

Recent breakthroughs in next-generation sequencing technologies allow cost-effective methods for measuring a growing list of cellular properties, including DNA sequence and structural variation. Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Because for a given gene both rare and common causal variants can coexist and have independent effects on a trait, strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. To date, little work has been done on integrating signals from common and rare variants into powerful statistics for finding disease genes in genome-wide association studies. In this analysis of the Genetic Analysis Workshop 17 data, we evaluate various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals. We show that the analysis of common variants only using classical approaches can achieve higher power to detect causal genes than recently proposed rare variant methods and that strategies that combine association signals derived independently in rare and common variants can slightly increase the power compared to strategies that focus on the effect of either the rare variants or the common variants

Power enhancement via multivariate outlier testing with gene expression arrays

Motivation: As the use of microarrays in human studies continues to increase, stringent quality assurance is necessary to ensure accurate experimental interpretation. We present a formal approach for microarray quality assessment that is based on dimension reduction of established measures of signal and noise components of expression followed by parametric multivariate outlier testing

The Spitzer Warm Mission Science Prospects

After exhaustion of its cryogen, the Spitzer Space telescope will still have a fully functioning two-channel mid-IR camera that will have sensitivities better than any other ground or space-based telescopes until the launch of JWST. This document provides a description of the expected capabilities of Spitzer during its warm mission phase, and provides brief descriptions of several possible very large science programs that could be conducted. This information is intended to serve as input to a wide ranging discussion of the warm mission science, leading up to the Warm Mission Workshop in June 2007