Genetic susceptibility to ageing-associated diseases

The constant and rapid increase of life expectancy in western countries is associated with a major ageing of our populations. In these conditions, we can expect an epidemic progression of most chronic diseases, especially cardiovascular, neurodegenerative and metabolic disorders, the main causes of death in the world. The global burden of these diseases will have a dramatic impact on the health and on the socio-economical context of our societies. From a global point of view, the occurrence and progression of these multifactorial diseases rely upon the nature and intensity of the environmental determinants we are exposed to all life long, but also to our individual genetic susceptibility. Through the determination of this higher susceptibility to an environmental risk factor and the understanding of its mechanisms of action, prevention and management efforts will be better focused. In such multifactorial affections, the development and the transmission of the disease do not follow the simple laws of monogenic Mendelian models. The complexity of this transmission is associated with the influence, at various degrees, of several genes and of a close interaction between this particular genetic susceptibility and environmental risk factors. With the recent development of automated and high throughput molecular biology techniques and their use in epidemiological studies, gene expression regulation and post genomic studies, the determination of sub-groups facing a higher individual genetic susceptibility has begun. This determination will offer new clues for a better-targeted disease management.     

Bio-array images processing and genetic networks modelling

The new tools available for gene expression studies are essentially the bio-array methods using a large variety of physical detectors (isotopes, fluorescent markers, ultrasounds...). Here we present first rapidly an image-processing method independent of the detector type, dealing with the noise and with the peaks overlapping, the peaks revealing the detector activity (isotopic in the presented example), correlated with the gene expression. After this primary step of bio-array image processing, we can extract information about causal influence (activation or inhibition) a gene can exert on other genes, leading to clusters of genes co-expression in which we extract an interaction matrix M and an associated interaction graph G explaining the genetic regulatory dynamics correlated to the studied tissue function. We give two examples of such interaction matrices and graphs (the flowering genetic regulatory network of Arabidopsis thaliana and the lytic/lysogenic operon of the phage Mu) and after some theoretical rigorous results recently obtained concerning the asymptotic states generated by the genetic networks having a given interaction matrix and reciprocally concerning the minimal (in the sense of having a minimal number of non-zero coefficients) matrices having given stationary stable states.     

Hardware (DNA) circuits

A scheme is presented whereby a new genetic control circuit can be introduced into an organism, permitting the experimenter to turn the expression of a given gene (or set of genes) on or off at will. The proposed scheme involves a positive feedback loop--here, a positive regulator, the CII protein of phage lambda, with its structural gene engineered so as to require CII for its expression. This feedback loop creates the possibility of two stable steady states, with gene cII ON or OFF. Genes added downstream of cII and lacking a promoter will follow the same expression as cII. Two additional circuits allow the experimenter to switch at will between the ON and OFF states.     

A simple framework to describe the regulation of gene expression in prokaryotes

Based on the bimolecular mass action law and the derived mass conservation laws, we propose a mathematical framework in order to describe the regulation of gene expression in prokaryotes. It is shown that the derived models have all the qualitative properties of the activation and inhibition regulatory mechanisms observed in experiments. The basic construction considers genes as templates for protein production, where regulation processes result from activators or repressors connecting to DNA binding sites. All the parameters in the models have a straightforward biological meaning. After describing the general properties of the basic mechanisms of positive and negative gene regulation, we apply this framework to the self-regulation of the trp operon and to the genetic switch involved in the regulation of the lac operon. One of the consequences of this approach is the existence of conserved quantities depending on the initial conditions that tune bifurcations of fixed points. This leads naturally to a simple explanation of threshold effects as observed in some experiments.     

Multiplicative genetic effects in scrapie disease susceptibility

Despite experimental evidence that scrapie is an infectious disease of sheep, variations of the occurrence of the natural disease suggest an influence of host genetic factors. It has been established that the genetic polymorphism of the prion protein (PrP) gene is correlated to the incidence of scrapie and to the survival time: five polymorphisms have been described by variations at amino-acid codons 136, 154 and 171. In this paper we study the effect on scrapie susceptibility of the pairing of the five allelic variants known to exist: we show that scrapie susceptibility is given by the produce of the elementary allelic factors. This first well-documented evidence of a multiplicative property of genetic risk factors could give hints on the underlying mechanisms of prion-induced neurodegenerative diseases.     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Biochemical research on oogenesis. RNA accumulation during oogenesis of the dogfish Scyliorhinus caniculus

Four biochemical mechanisms have been shown to operate in the oocytes of amphibians and teleosts: (1) amplification of the 28 S and 18 S genes, (2) noncoordinate accumulation of 5 S RNA and 28 S + 18 S RNA, (3) storage of 5 S and transfer RNA made in excess by small oocytes within nucleoprotein particles, (4) expression of different 5 S genes in oocytes and somatic cells. We have tried to extend these observations to another group of vertebrates, i.e., selacians (Chondrichthya). Our data suggest that ribosomal gene amplification is low or absent in the oocytes of the dogfish Scyliorhinus caniculus. However, previtellogenic oocytes of this species accumulate more 5 S RNA than needed for ribosome assembly. Transfer and 5 S RNA present in small oocytes are probably not free in the cell sap. A substantial fraction of these RNAs sediments at 10 S when homogenates of immature ovaries are centrifuged in sucrose density gradients. In contrast to what we observed in amphibians and teleosts, 5 S RNA from ovaries of S. caniculus is identical in sequence to 5 S RNA from liver. Among the four mechanisms mentioned above, the second and probably the third one are used by the oocytes of S. caniculus. Mechanism (4) is absent in this species. No definitive conclusion can be drawn concerning mechanism (1), i.e., ribosomal gene amplification.     

Gene expression changes associated with cytotoxicity identified using cDNA arrays

In order to investigate gene expression changes associated with cytotoxicity, we used cDNA arrays to monitor the expression of over 5,000 genes in response to toxic stress in the HepG2 liver cell line. Cells were treated with cytotoxic doses of acetaminophen, caffeine or thioacetamide for nine time points ranging from 1 to 24 h. Samples of mRNA from each time point were used to prepare radiolabeled cDNA, which was hybridized to nylon-membrane-based cDNA arrays. High-stringency washes were applied to reduce cross-hybridization. Analysis of spot intensities revealed that each compound led to approximately 150-250 gene expression changes that were sustained over at least three adjacent time points. The affected genes could be classified into clusters based on their temporal patterns of differential expression. A common set of 44 genes showed similar expression changes in response to all three compounds. Of these changes, 90% could be confirmed by quantitative RT-PCR analysis. The results indicate that detailed array-based time-course studies, coupled with a sensitive and highly specific confirmation assay, provide a powerful means of identifying cytotoxicity-associated gene expression changes. Electronic Publication     

Analysis of Differential Expression of Barley ESTs during Cold Acclimatization Using Microarray Technology

Abstract: Genomics adds a new dimension to genetic analysis, shifting the focus from the study of a single gene to the whole genome. We have successfully applied the genomics approach based on microarray to the study of genes involved in barley responses to cold stress. About 900 EST clones from barley were obtained from a cDNA library of cold acclimatized leaves of cv. Nure and arrayed, and gene expression analysis done on cold acclimatized vs. control plants. The system allowed for reliable detection of differences in mRNA expression levels, and was confirmed by the finding that numerous previously reported cold-related genes were differentially expressed in treated and untreated plants when evaluated in our arrays. The expression profiles of a sample of genes analysed by the array were confirmed by quantitative RT-PCR.
Previously, identification of novel plant genes was achieved considering a few genes at a time; now many genes can be found as up- or down-regulated based on a one step procedure. Many of the genes we found to be up- or down-regulated do not have an assigned function. This includes 15 of the 78 up-regulated and 8 of the 45 down-regulated clones. Our results add new genes to the group of cold-regulated genes and provide the opportunity to better understand the complex mechanism of stress tolerance.     

A new method for gene discovery in large-scale microarray data

Microarrays are an effective tool for monitoring genome-wide gene expression levels. In current microarray analyses, the majority of genes on arrays are frequently eliminated for further analysis because the changes in their expression levels (ratios) are considered to be not significant. This strategy risks failure to discover whole sets of genes related to a quantitative trait of interest, which is generally controlled by several loci that make various contributions. Here, we describe a high-throughput gene discovery method based on correspondence analysis with a new index for expression ratios [arctan (1/ratio)] and three artificial marker genes. This method allows us to quickly analyze the whole microarray dataset and discover up-/down-regulated genes related to a trait of interest. We employed an example dataset to show the theoretical advantage of this method. We then used the method to identify 88 cancer-related genes from a published microarray data from patients with breast cancer. This method also allows us to predict the phenotype of a given sample from the gene expression profile. This method can be easily performed and the result is also visible in 3D viewing software that we have developed.     

Genetics of human obesity

We present the knowledge acquired in the field of the genetics of human obesity. The molecular approach proved to be powerful to define new syndromes associated to obesity. The pivotal role of leptin and melanocortin pathways were recognized but in rare obesity cases. In the commoner form of obesities, a multitude of polymorphisms located in genes and candidate regions participate in an individual susceptibility to weight gain in a permissive environment. The effects are often uncertain and the results not always confirmed. It is now necessary to integrate data of various origins (environment, genotype, expression) to clarify the domain.     

Comparing three methods for variance estimation with duplicated high density oligonucleotide arrays

Microarray experiments are being increasingly used in molecular biology. A common task is to detect genes with differential expression across two experimental conditions, such as two different tissues or the same tissue at two time points of biological development. To take proper account of statistical variability, some statistical approaches based on the t-statistic have been proposed. In constructing the t-statistic, one needs to estimate the variance of gene expression levels. With a small number of replicated array experiments, the variance estimation can be challenging. For instance, although the sample variance is unbiased, it may have large variability, leading to a large mean squared error. For duplicated array experiments, a new approach based on simple averaging has recently been proposed in the literature. Here we consider two more general approaches based on nonparametric smoothing. Our goal is to assess the performance of each method empirically. The three methods are applied to a colon cancer data set containing 2,000 genes. Using two arrays, we compare the variance estimates obtained from the three methods. We also consider their impact on the t-statistics. Our results indicate that the three methods give variance estimates close to each other. Due to its simplicity and generality, we recommend the use of the smoothed sample variance for data with a small number of replicates. Electronic Publication     

Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology

In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis. Toward this goal, we propose a methodology for the analysis of high-density oligonucleotide arrays. The gene expression measures are modeled as censored data to account for the quantification limits of the technology, and two gene selection criteria based on contrasts from an analysis of covariance (ANCOVA) model are presented. The model is formulated in a hierarchical Bayesian framework, which in addition to making the fit of the model straightforward and computationally efficient, allows us to borrow strength across genes. The elicitation of hierarchical priors, as well as issues related to parameter identifiability and posterior propriety, are discussed in detail. We examine the performance of our proposed method on simulated data, then present a detailed case study of an endometrial cancer dataset.     

Analyzing Multiple‐Probe Microarray: Estimation and Application of Gene Expression Indexes

Summary Gene expression index estimation is an essential step in analyzing multiple probe microarray data. Various modeling methods have been proposed in this area. Amidst all, a popular method proposed in Li and Wong (2001) is based on a multiplicative model, which is similar to the additive model discussed in Irizarry et al. (2003a) at the logarithm scale. Along this line, Hu et al. (2006) proposed data transformation to improve expression index estimation based on an ad hoc entropy criteria and naive grid search approach. In this work, we re‐examined this problem using a new profile likelihood‐based transformation estimation approach that is more statistically elegant and computationally efficient. We demonstrate the applicability of the proposed method using a benchmark Affymetrix U95A spiked‐in experiment. Moreover, We introduced a new multivariate expression index and used the empirical study to shows its promise in terms of improving model fitting and power of detecting differential expression over the commonly used univariate expression index. As the other important content of the work, we discussed two generally encountered practical issues in application of gene expression index: normalization and summary statistic used for detecting differential expression. Our empirical study shows somewhat different findings from the MAQC project ( MAQC, 2006 ).     

FGX: a frequentist gene expression index for Affymetrix arrays

We consider a new frequentist gene expression index for Affymetrix oligonucleotide DNA arrays, using a similar probe intensity model as suggested by Hein and others (2005), called the Bayesian gene expression index (BGX). According to this model, the perfect match and mismatch values are assumed to be correlated as a result of sharing a common gene expression signal. Rather than a Bayesian approach, we develop a maximum likelihood algorithm for estimating the underlying common signal. In this way, estimation is explicit and much faster than the BGX implementation. The observed Fisher information matrix, rather than a posterior credibility interval, gives an idea of the accuracy of the estimators. We evaluate our method using benchmark spike-in data sets from Affymetrix and GeneLogic by analyzing the relationship between estimated signal and concentration, i.e. true signal, and compare our results with other commonly used methods.     

A note on joint versus gene-specific mixed model analysis of microarray gene expression data

Currently, linear mixed model analyses of expression microarray experiments are performed either in a gene-specific or global mode. The joint analysis provides more flexibility in terms of how parameters are fitted and estimated and tends to be more powerful than the gene-specific analysis. Here we show how to implement the gene-specific linear mixed model analysis as an exact algorithm for the joint linear mixed model analysis. The gene-specific algorithm is exact, when the mixed model equations can be partitioned into unrelated components: One for all global fixed and random effects and the others for the gene-specific fixed and random effects for each gene separately. This unrelatedness holds under three conditions: (1) any gene must have the same number of replicates or probes on all arrays, but these numbers can differ among genes; (2) the residual variance of the (transformed) expression data must be homogeneous or constant across genes (other variance components need not be homogeneous) and (3) the number of genes in the experiment is large. When these conditions are violated, the gene-specific algorithm is expected to be nearly exact.