Prenatal and postnatal correlated responses in maternal traits of mice assessed by crossfostering

International audienc

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Conditions for the Evolution of Gene Clusters in Bacterial Genomes

Genes encoding proteins in a common pathway are often found near each other along bacterial chromosomes. Several explanations have been proposed to account for the evolution of these structures. For instance, natural selection may directly favour gene clusters through a variety of mechanisms, such as increased efficiency of coregulation. An alternative and controversial hypothesis is the selfish operon model, which asserts that clustered arrangements of genes are more easily transferred to other species, thus improving the prospects for survival of the cluster. According to another hypothesis (the persistence model), genes that are in close proximity are less likely to be disrupted by deletions. Here we develop computational models to study the conditions under which gene clusters can evolve and persist. First, we examine the selfish operon model by re-implementing the simulation and running it under a wide range of conditions. Second, we introduce and study a Moran process in which there is natural selection for gene clustering and rearrangement occurs by genome inversion events. Finally, we develop and study a model that includes selection and inversion, which tracks the occurrence and fixation of rearrangements. Surprisingly, gene clusters fail to evolve under a wide range of conditions. Factors that promote the evolution of gene clusters include a low number of genes in the pathway, a high population size, and in the case of the selfish operon model, a high horizontal transfer rate. The computational analysis here has shown that the evolution of gene clusters can occur under both direct and indirect selection as long as certain conditions hold. Under these conditions the selfish operon model is still viable as an explanation for the evolution of gene clusters

T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection.

The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory 'help' signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The 'non-exhausted' T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities

Standing genetic variation and compensatory evolution in transgenic organisms: a growth-enhanced salmon simulation

Genetically modified strains usually are generated within defined genetic backgrounds to minimize variation for the engineered characteristic in order to facilitate basic research investigations or for commercial application. However, interactions between transgenes and genetic background have been documented in both model and commercial agricultural species, indicating that allelic variation at transgene-modifying loci are not uncommon in genomes. Engineered organisms that have the potential to allow entry of transgenes into natural populations may cause changes to ecosystems via the interaction of their specific phenotypes with ecosystem components and services. A transgene introgressing through natural populations is likely to encounter a range of natural genetic variation (among individuals or sub-populations) that could result in changes in phenotype, concomitant with effects on fitness and ecosystem consequences that differ from that seen in the progenitor transgenic strain. In the present study, using a growth hormone transgenic salmon example, we have modeled selection of modifier loci (single and multiple) in the presence of a transgene and have found that accounting for genetic background can significantly affect the persistence of transgenes in populations, potentially reducing or reversing a “Trojan gene” effect. Influences from altered life history characteristics (e.g., developmental timing, age of maturation) and compensatory demographic/ecosystem controls (e.g., density dependence) also were found to have a strong influence on transgene effects. Further, with the presence of a transgene in a population, genetic backgrounds were found to shift in non-transgenic individuals as well, an effect expected to direct phenotypes away from naturally selected optima. The present model has revealed the importance of understanding effects of selection for background genetics on the evolution of phenotypes in populations harbouring transgenes

Stunned Silence: Gene Expression Programs in Human Cells Infected with Monkeypox or Vaccinia Virus

Poxviruses use an arsenal of molecular weapons to evade detection and disarm host immune responses. We used DNA microarrays to investigate the gene expression responses to infection by monkeypox virus (MPV), an emerging human pathogen, and Vaccinia virus (VAC), a widely used model and vaccine organism, in primary human macrophages, primary human fibroblasts and HeLa cells. Even as the overwhelmingly infected cells approached their demise, with extensive cytopathic changes, their gene expression programs appeared almost oblivious to poxvirus infection. Although killed (gamma-irradiated) MPV potently induced a transcriptional program characteristic of the interferon response, no such response was observed during infection with either live MPV or VAC. Moreover, while the gene expression response of infected cells to stimulation with ionomycin plus phorbol 12-myristate 13-acetate (PMA), or poly (I-C) was largely unimpaired by infection with MPV, a cluster of pro-inflammatory genes were a notable exception. Poly(I-C) induction of genes involved in alerting the innate immune system to the infectious threat, including TNF-alpha, IL-1 alpha and beta, CCL5 and IL-6, were suppressed by infection with live MPV. Thus, MPV selectively inhibits expression of genes with critical roles in cell-signaling pathways that activate innate immune responses, as part of its strategy for stealthy infection

Treatment options in end-stage heart failure: where to go from here?

Chronic heart failure is a major healthcare problem associated with high morbidity and mortality. Despite significant progress in treatment strategies, the prognosis of heart failure patients remains poor. The golden standard treatment for heart failure is heart transplantation after failure of medical therapy, surgery and/or cardiac resynchronisation therapy. In order to improve patients’ outcome and quality of life, new emerging treatment modalities are currently being investigated, including mechanical cardiac support devices, of which the left ventricular assist device is the most promising treatment option. Structured care for heart failure patients according to the most recent international heart failure guidelines may further contribute to optimal decision-making. This article will review the conventional and novel treatment modalities of heart failure

Comparison of gene expression during in vivo and in vitro postnatal retina development

Retina explants are widely used as a model of neural development. To define the molecular basis of differences between the development of retina in vivo and in vitro during the early postnatal period, we carried out a series of microarray comparisons using mouse retinas. About 75% of 8,880 expressed genes from retina explants kept the same expression volume and pattern as the retina in vivo. Fewer than 6% of the total gene population was changed at two consecutive time points, and only about 1% genes showed more than a threefold change at any time point studied. Functional Gene Ontology (GO) mapping for both changed and unchanged genes showed similar distribution patterns, except that more genes were changed in the GO clusters of response to stimuli and carbohydrate metabolism. Three distinct expression patterns of genes preferentially expressed in rod photoreceptors were observed in the retina explants. Some genes showed a lag in increased expression, some showed no change, and some continued to have a reduced level of expression. An early downregulation of cyclin D1 in the explanted retina might explain the reduction in numbers of precursors in explanted retina and suggests that external factors are required for maintenance of cyclin D1. The global view of gene profiles presented in this study will help define the molecular changes in retina explants over time and will provide criteria to define future changes that improve this model system

Impact of Cellular miRNAs on Circulating miRNA Biomarker Signatures

Effective diagnosis and surveillance of complex multi-factorial disorders such as cancer can be improved by screening of easily accessible biomarkers. Highly stable cell free Circulating Nucleic Acids (CNA) present as both RNA and DNA species have been discovered in the blood and plasma of humans. Correlations between tumor-associated genomic/epigenetic/transcriptional changes and alterations in CNA levels are strong predictors of the utility of this biomarker class as promising clinical indicators. Towards this goal microRNAs (miRNAs) representing a class of naturally occurring small non-coding RNAs of 19–25 nt in length have emerged as an important set of markers that can associate their specific expression profiles with cancer development. In this study we investigate some of the pre-analytic considerations for isolating plasma fractions for the study of miRNA biomarkers. We find that measurement of circulating miRNA levels are frequently confounded by varying levels of cellular miRNAs of different hematopoietic origins. In order to assess the relative proportions of this cell-derived class, we have fractionated whole blood into plasma and its ensuing sub-fractions. Cellular miRNA signatures in cohorts of normal individuals are catalogued and the abundance and gender specific expression of bona fide circulating markers explored after calibrating the signal for this interfering class. A map of differentially expressed profiles is presented and the intrinsic variability of circulating miRNA species investigated in subsets of healthy males and females