Francesco Falciani

Dosage Compensation in the Mouse Balances Up-Regulation and Silencing of X-Linked Genes

Dosage compensation in mammals involves silencing of one X chromosome in XX females and requires expression, in cis, of Xist RNA. The X to be inactivated is randomly chosen in cells of the inner cell mass (ICM) at the blastocyst stage of development. Embryonic stem (ES) cells derived from the ICM of female mice have two active X chromosomes, one of which is inactivated as the cells differentiate in culture, providing a powerful model system to study the dynamics of X inactivation. Using microarrays to assay expression of X-linked genes in undifferentiated female and male mouse ES cells, we detect global up-regulation of expression (1.4- to 1.6-fold) from the active X chromosomes, relative to autosomes. We show a similar up-regulation in ICM from male blastocysts grown in culture. In male ES cells, up-regulation reaches 2-fold after 2–3 weeks of differentiation, thereby balancing expression between the single X and the diploid autosomes. We show that silencing of X-linked genes in female ES cells occurs on a gene-by-gene basis throughout differentiation, with some genes inactivating early, others late, and some escaping altogether. Surprisingly, by allele-specific analysis in hybrid ES cells, we also identified a subgroup of genes that are silenced in undifferentiated cells. We propose that X-linked genes are silenced in female ES cells by spreading of Xist RNA through the X chromosome territory as the cells differentiate, with silencing times for individual genes dependent on their proximity to the Xist locus.

Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: hurdles, achievements, and future outlook.

Background In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the U.K. Natural Environment Research Council (Fish Toxicogenomics—Moving into Regulation and Monitoring, held 21–23 April 2008 at the Pacific Environmental Science Centre, Vancouver, BC, Canada). Objectives The consortium from government agencies, academia, and industry addressed three topics: progress in ecotoxicogenomics, regulatory perspectives on roadblocks for practical implementation of toxicogenomics into risk assessment, and dealing with variability in data sets. Discussion Participants noted that examples of successful application of omic technologies have been identified, but critical studies are needed to relate molecular changes to ecological adverse outcome. Participants made recommendations for the management of technical and biological variation. They also stressed the need for enhanced interdisciplinary training and communication as well as considerable investment into the generation and curation of appropriate reference omic data. Conclusions The participants concluded that, although there are hurdles to pass on the road to regulatory acceptance, omics technologies are already useful for elucidating modes of action of toxicants and can contribute to the risk assessment process as part of a weight-of-evidence approach.

Identifying health impacts of exposure to copper using transcriptomics and metabolomics in a fish model.

Copper (Cu) is a micronutrient essential for the biochemical functioning of numerous processes in vertebrates but is also often present in the aquatic environment at concentrations able to cause adverse health effects in aquatic organisms. This study investigated the signaling pathways mediating the effects of exposure to Cu using a toxicogenomic approach in a fish model, the stickleback ( Gasterosteus aculeatus ). Freshwater-acclimated male fish were exposed via the water to Cu, including at environmentally relevant concentrations (3.2-128 microg of Cu/L for 4 days), and the biological responses explored through analyses of the hepatic transcriptome and metabolome and phenotypic end points, including assessment of DNA damage in blood cells. The Cu exposures resulted in DNA strand breaks in blood cells at all exposure concentrations and alterations in hepatic gene expression and metabolite concentrations in a concentration-dependent manner (from 10 microg of Cu/L). Genes associated with the cholesterol biosynthesis pathway were significantly over-represented and consistently down-regulated (at 128 microg of Cu/L), similar to that occurring in a mouse model for Wilsons disease. Additionally, inductions in metallothionein and catalase were also observed. The concentrations of NAD(+) and lactate increased significantly with the Cu exposure, consistent with a shift toward anaerobic metabolism, and these aligned closely with changes observed in gene expression. The pathways of Cu toxicity identified in our study support the conserved mechanisms of Cu toxicity from lower vertebrates to mammals, provide novel insights into the deleterious effects of Cu in fish, and further demonstrate the utility of fish as environmental sentinels for chemical impacts on both environmental and human health.

Biomarker discovery in microarray gene expression data with Gaussian processes

MOTIVATION In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumour cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework. RESULTS The usefulness of the proposed algorithm for ordinal labels is demonstrated by the gene expression signature associated with the Gleason score for prostate cancer data. Our results demonstrate how multi-gene markers that may be initially developed with a diagnostic or prognostic application in mind are also useful as an investigative tool to reveal associations between specific molecular and cellular events and features of tumour physiology. Our algorithm can also be applied to microarray data with binary labels with results comparable to other methods in the literature.

Global gene expression profiles in fibroblasts from synovial, skin and lymphoid tissue reveals distinct cytokine and chemokine expression patterns

We investigated the extent to which fibroblasts isolated from diverse tissues differ in their capacity to modulate inflammation by comparing the global gene expression profiles of cultured human fibroblasts from skin, acute and chronically inflamed synovium, lymph node and tonsil. The responses of these fibroblasts to TNF-alpha, IFN-gamma and IL-4 stimulation were markedly different, as revealed by hierarchical cluster analysis and principal component analysis. In the absence of exogenous cytokine, synovial and skin fibroblasts exhibited similar patterns of gene expression. However their transcriptional profiles diverged upon treatment with TNF-alpha. This proved to be biologically relevant, as TNF-alpha induced the secretion of different patterns and amounts of IL-6, IL-8 and CCL2 (MCP-1) in the two fibroblast types. Co-culture of skin or synovial fibroblasts with synovial fluid-derived mononuclear cells provided further evidence that these transcriptional differences were functionally significant in an ex vivo setting. Interestingly, the transcriptional response of skin fibroblasts to IL-4 converged with that of TNF-alpha-treated synovial fibroblasts, suggesting resident tissue fibroblasts and their blood-borne precursors may be imprinted by inflammatory cytokines that are characteristic of different tissues. Our data supports the concept that fibroblasts are heterogeneous, and that they contribute to the tissue-specificity of inflammatory reactions. Fibroblasts are therefore likely to play an active role in the persistence of chronic inflammatory reactions.

Global transcriptional response to vancomycin in Mycobacterium tuberculosis.

In order to gain additional understanding of the physiological mechanisms used by bacteria to maintain surface homeostasis and to identify potential targets for new antibacterial drugs, we analysed the variation of the Mycobacterium tuberculosis transcriptional profile in response to inhibitory and subinhibitory concentrations of vancomycin. Our analysis identified 153 genes differentially regulated after exposing bacteria to a concentration of the drug ten times higher than the MIC, and 141 genes differentially expressed when bacteria were growing in a concentration of the drug eightfold lower than the MIC. Hierarchical clustering analysis indicated that the response to these different conditions is different, although with some overlap. This approach allowed us to identify several genes whose products could be involved in the protection from antibiotic stress targeting the envelope and help to confer the basal level of M. tuberculosis resistance to antibacterial drugs, such as Rv2623 (UspA-like), Rv0116c, PE20-PPE31, PspA and proteins related to toxin-antitoxin systems. Moreover, we also demonstrated that the alternative sigma factor sigma(E) confers basal resistance to vancomycin, once again underlining its importance in the physiology of the mycobacterial surface stress response.

GALGO: an R package for multivariate variable selection using genetic algorithms

SUMMARY The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.

Regulators encoded in the Escherichia coli type III secretion system 2 gene cluster influence expression of genes within the locus for enterocyte effacement in enterohemorrhagic E. coli O157 : H7

ABSTRACT Enterohemorrhagic Escherichia coli (EHEC) O157:H7 subverts host cells through a type III secretion system encoded by the locus for enterocyte effacement (LEE). Genome sequencing of this pathotype revealed the existence of a gene cluster encoding components of a second cryptic type III secretion system, E. coli type III secretion system 2 (ETT2). Recently, we showed that the ETT2 gene cluster is present in whole or in part in the majority of E. coli strains but is unable to encode a functional secretion system in most strains, including EHEC O157:H7. However, here we show that mutational inhibition of two regulatory genes (ECs3720 or etrA and ECs3734 or eivF) from the ETT2 cluster in EHEC O157:H7 leads to greatly increased secretion of proteins encoded by the LEE and to increased adhesion to human intestinal cells. Studies in which transcriptional fusions and microarrays were used indicated that EtrA and EivF exert profound negative effects on gene transcription within the LEE. Consistent with these observations, expression of these regulators in an EHEC O26:H- strain led to suppression of protein secretion under LEE-inducing conditions. These findings provide fresh examples of the influence of mobile genetic elements on regulation of the LEE and of cross talk between type III secretion system gene clusters. In addition, they provide a cautionary tale because they show that the effects of regulatory genes can outlive widespread decay of other genes in a functionally coherent gene cluster, a phenomenon that we have named the “Cheshire cat effect.” It also seems likely that variations in the ETT2 regulator repertoire might account for strain-to-strain variation in secretion of LEE-encoded proteins.

Hepatic transcriptomic profiles of European flounder (Platichthys flesus) from field sites and computational approaches to predict site from stress gene responses following exposure to model toxicants.

Genomic technologies offer opportunities to gain a more global assessment of the health status of an organism through an understanding of the functional pathways that are responding to pollutant exposure. We have developed a 13,000 clone cDNA toxicogenomics microarray for Platichthys flesus, the European flounder (EU-GENIPOL Project). We aimed to distinguish the origins of flounder taken from six sampling sites of different pollution status in Northern Europe according to their hepatic gene expression profile using bioinformatic approaches. To determine which gene expression differences may relate to pollutant impact, we have completed complementary laboratory exposures of flounder to selected toxicants and determined the associated gene expression profiles. Using multivariate variable selection coupled with a statistical modelling procedure (GALGO) we can predict geographical site but the accuracy is limited to specific sites. The search space for a combination of genes that effectively predicts class membership is very large, however, by combining the signatures derived from acute laboratory exposure to individual chemicals to limit the search space, a very accurate model for classification of all the different environmental sites was achieved. The final model utilised the expression profiles of 16 clones and validation with a qPCR array comprising these genes correctly assigned the site of origin for fish obtained from three of the sites in an independent sampling. These data would imply that the gene expression fingerprints obtained with these arrays are primarily attributable to variations in chemical pollutant responses at the different sites, indicating their potential utility in environmental impact assessment.

Hepatic transcriptomic and metabolomic responses in the Stickleback (Gasterosteus aculeatus) exposed to ethinyl-estradiol

An established three-spined stickleback (Gasterosteus aculeatus) cDNA array was expanded to 14,496 probes with the addition of hepatic clones derived from subtractive and normalized libraries from control males and males exposed to model toxicants. Microarrays and one-dimensional (1)H nuclear magnetic resonance (NMR) spectroscopy, together with individual protein and gene biomarkers were employed to investigate the hepatic responses of the stickleback to ethinyl-estradiol (EE(2)) exposure. Male fish were exposed via the water to EE(2), including environmentally relevant concentrations (0.1-100ng/l) for 4 days, and hepatic transcript and metabolite profiles, kidney spiggin protein and serum vitellogenin concentrations were determined in comparison to controls. EE(2) exposure did not significantly affect spiggin concentration but significantly induced serum vitellogenin protein at the threshold concentration of 32ng/l. (1)H NMR coupled with robust univariate testing revealed only limited changes, but these did support the predicted modulation of the amino acid profile by transcriptomics. Transcriptional induction was found for hepatic vitellogenins and choriogenins as expected, together with a range of other EE(2)-responsive genes. Choriogenins showed the more sensitive responses with statistically significant induction at 10ng/l. Real-time polymerase chain reaction (PCR) confirmed transcriptional induction of these genes. Phosvitinless vitellogenin C transcripts were highly expressed and represent a major form of the egg yolk precursors, and this is in contrast to other fish species where it is a minor component of vitellogenic transcripts. Differences in inducibility between the vitellogenins and choriogenins appear to be in accordance with the sequential formation of chorion and yolk during oogenesis in fish.