Maurizio Stefani

Differential variability analysis of gene expression and its application to human diseases

Motivation: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significantincrease or decrease in expression variability (variance). Asthese observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting. Results: Here we propose a novel analysis for changes in gene expression variability between groups of amples, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal. Availability: The R source code for differential variability analysis is available from the contact authors upon request. Contact: joshua@it.usyd.edu.au; mcharleston@it.usyd.edu.au

The effect of resveratrol on a cell model of human aging.

Abstract:u2002 The natural polyphenol resveratrol stimulates sirtuins and extends lifespan. Here resveratrol inhibited expression of replicative senescence marker INK4a in human dermal fibroblasts, and 47 of 19,000 genes from microarray experiments were differentially expressed. These included genes for growth, cell division, cell signaling, apoptosis, and transcription. Genes involved in Ras and ubiquitin pathways, Ras‐GRF1, RAC3, and UBE2D3, were downregulated. The changes suggest resveratrol might alter sirtuin‐regulated downstream pathways, rather than sirtuin activity. Serum deprivation and high confluency caused nuclear translocation of the SIRT1‐regulated transcription factor FOXO3a. Our data indicate resveratrols actions might cause FOXO recruitment to the nucleus.

Cardiac gene expression data and in silico analysis provide novel insights into human and mouse taste receptor gene regulation

G protein-coupled receptors are the principal mediators of the sweet, umami, bitter, and fat taste qualities in mammals. Intriguingly, the taste receptors are also expressed outside of the oral cavity, including in the gut, airways, brain, and heart, where they have additional functions and contribute to disease. However, there is little known about the mechanisms governing the transcriptional regulation of taste receptor genes. Following our recent delineation of taste receptors in the heart, we investigated the genomic loci encoding for taste receptors to gain insight into the regulatory mechanisms that drive their expression in the heart. Gene expression analyses of healthy and diseased human and mouse hearts showed coordinated expression for a subset of chromosomally clustered taste receptors. This chromosomal clustering mirrored the cardiac expression profile, suggesting that a common gene regulatory block may control the taste receptor locus. We identified unique domains with strong regulatory potential in the vicinity of taste receptor genes. We also performed de novo motif enrichment in the proximal promoter regions and found several overrepresented DNA motifs in cardiac taste receptor gene promoters corresponding to ubiquitous and cardiac-specific transcription factor binding sites. Thus, combining cardiac gene expression data with bioinformatic analyses, this study has provided insights into the noncoding regulatory landscape for taste GPCRs. These findings also have broader relevance for the study of taste GPCRs outside of the classical gustatory system, where understanding the mechanisms controlling the expression of these receptors may have implications for future therapeutic development.

Withdrawal of anti-epileptic medications during video EEG monitoring does not alter ECG parameters or HRV.

PURPOSEnTo assess if anti-epileptic drug (AED) withdrawal/cessation results in changes in ECG parameters and/or measures of heart rate variability (HRV), in patients having VEEG monitoring, that might affect susceptibility to sudden death in epilepsy.nnnMETHODSnIn this study, we included 36 patients with medically refractory epilepsy undergoing continuous video-EEG-ECG monitoring in hospital for pre-surgical assessment. We recorded and analysed multiple 10-min epochs of 2-lead ECG during periods when the subjects were awake and in REM and non-REM sleep, both on admission, and after the subjects had been partially or completely weaned from their AEDs. We compared ECG parameters and measures of HRV from these recorded epochs before and after AED reduction/cessation, with each patient acting as their own comparator. Epochs measured during awake, REM, and non-REM periods were analysed separately. In addition, we analysed a subgroup of patients who had been withdrawn from Na+-channel blocking medications specifically, to analyse the effect of this particular class of AEDs in isolation.nnnKEY FINDINGSnUpon AED withdrawal, we observed a small increase in heart rate and shortening of the QT interval, when subjects were awake, but no other changes in ECG parameters were detected, nor did we find changes in any measure of HRV. In addition, no significant changes were found during sleep. Similar results were found in the analysis of the subgroup of patients withdrawn from Na+-channel blocking AEDs.nnnSIGNIFICANCEnOur study does not support a prominent role for AEDs, and withdrawal/cessation of AEDs, in deranging cardiac physiology during video EEG monitoring in medically refractory epilepsy patients undergoing video EEG monitoring.

A model selection approach to discover age-dependent gene expression patterns using quantile regression models

BackgroundIt has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify interesting genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns.ResultsHere we present a novel model selection approach to discover genes with linear or non-linear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models.ConclusionIn this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks.

Actin and Its Binding Proteins in Heart Failure

Heart failure (HF) is one of the leading causes of combined morbidity and mortality among developed nations. It is the final clinical presentation of a variety of cardiovascular diseases and disorders, such as coronary artery disease, hypertension, valvular heart disease, myocarditis, diabetes, alcohol abuse, and familial cardiomyopathies (Narula et al. 1996). This pathophysiological state is characterized by progressive deterioration of ventricular function, usually in the left ventricle (LV). Pathologic stressors and increased demands for cardiac work result in compensatory mechanisms, such as hypertrophy of cardiomyocytes, increased sympathetic activity, and activation of the rennin–angiotensin–aldosterone system (Curtiss et al. 1978; Levine et al. 1982; Pfeffer et al. 1988; Anversa, Olivetti and Capasso 1991). These short-term adaptive mechanisms eventually become maladaptive and lead to HF. Understanding the mechanisms underlying HF has been difficult since it arises from the interaction between environmental factors and genetic susceptibility, and cannot be explained by a single gene or pathway (Seidman and Seidman 2001). Considerable research has been undertaken to explain why short-term compensatory mechanisms often become maladaptive in the long term. This chapter will focus on mutations in actin within the heart (both sarcomeric and cytoskeletal) and alterations in the actin binding proteins (ABPs) that interact with actin. (For a detailed discussion see the chapter by Sparrow and Laing.)