The involvement of vitamins and other micronutrients in intermediary metabolism was elucidated in the mid 1900’s at the level of individual biochemical reactions. Biochemical pathways remain the foundational knowledgebase for understanding how micronutrient adequacy modulates health in all life stages. Current daily recommended intakes were usually established on the basis of the association of a single nutrient to a single, most sensitive adverse effect and thus neglect interdependent and pleiotropic effects of micronutrients on biological systems. Hence, the understanding of the impact of overt or sub-clinical nutrient deficiencies on biological processes remains incomplete. Developing a more complete view of the role of micronutrients and their metabolic products in protein-mediated reactions is of importance. We thus integrated and represented cofactor-protein interaction data from multiple and diverse sources into a multi-layer network representation that links cofactors, cofactor-interacting proteins, biological processes, and diseases. Network representation of this information is a key feature of the present analysis and enables the integration of data from individual biochemical reactions and protein-protein interactions into a systems view, which may guide strategies for targeted nutritional interventions aimed at improving health and preventing diseases.
Recently, expression quantitative loci (eQTL) mapping studies, where expression levels of thousands of genes are viewed as quantitative traits, have been used to provide greater insight into the biology of gene regulation. Originally, eQTLs were detected by applying standard QTL detection tools (using a “one gene at-a-time” approach), but this method ignores many possible interactions between genes. Several other methods have proposed to overcome these limitations, but each of them has some specific disadvantages. In this paper, we present an integrated hierarchical Bayesian model that jointly models all genes and SNPs to detect eQTLs. We propose a model (named iBMQ) that is specifically designed to handle a large number G of gene expressions, a large number S of regressors (genetic markers) and a small number n of individuals in what we call a “large G, large S, small n” paradigm. This method incorporates genotypic and gene expression data into a single model while 1) specifically coping with the high dimensionality of eQTL data (large number of genes), 2) borrowing strength from all gene expression data for the mapping procedures, and 3) controlling the number of false positives to a desirable level. To validate our model, we have performed simulation studies and showed that it outperforms other popular methods for eQTL detection, including QTLBIM, R-QTL, remMap and M-SPLS. Finally, we used our model to analyze a real expression dataset obtained in a panel of mice BXD Recombinant Inbred (RI) strains. Analysis of these data with iBMQ revealed the presence of multiple hotspots showing significant enrichment in genes belonging to one or more annotation categories.
Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.
Little is known about the functions of chromosome Y (chrY) genes beyond their effects on sex and reproduction. In hearts, postpubertal testosterone affects the size of cells and the expression of genes differently in male C57BL/6J than in their C57.Y(A) counterparts, where the original chrY has been substituted with that from A/J mice. We further compared the 2 strains to better understand how chrY polymorphisms may affect cardiac properties, the latter being sexually dimorphic but unrelated to sex and reproduction. Genomic regions showing occupancy with androgen receptors (ARs) were identified in adult male hearts from both strains by chromatin immunoprecipitation. AR chromatin immunoprecipitation peaks (showing significant enrichment for consensus AR binding sites) were mostly strain specific. Measurements of anogenital distances in male pups showed that the biologic effects of perinatal androgens were greater in C57BL/6J than in C57.Y(A). Although perinatal endocrine manipulations showed that these differences contributed to the strain-specific differences in the response of adult cardiac cells to testosterone, the amounts of androgens produced by fetal testes were not different in each strain. Nonetheless, chrY polymorphisms associated in newborn pups' hearts with strain-specific differences in genomic regions showing either AR occupancy, accessible chromatin sites, or trimethylation of histone H3 Lysine 4 marks, as well as with differential expression of 2 chrY-encoded histone demethylases. In conclusion, the effects of chrY on adult cardiac phenotypes appeared to result from an interaction of this chromosome with the organizational programming effects exerted by the neonatal testosterone surge and show several characteristics of being mediated by an epigenetic remodeling of chromatin.
T he highly used C57BL/6 mouse inbred strain is the preferred choice for mouse transgenic and knockout studies 1 and was the first strain whose genome was fully sequenced. 2 However, several C57BL6 substrains have emerged over the years, each showing genomic differences because of genetic drift and displaying various phenotypic differences. 1,3 One example is the difference between the C57BL/6J and C57BL/6N substrains. The C57BL/6 strain was initially developed at The Jackson Laboratory, and mice from that colony are identified as C57BL/6J. In 1951, some mice were separated from the original colony to initiate a new colony at the National Institutes of Health, the latter being identified as C57BL/6N. 1 Despite the recognition that genetic drift between mouse strains may compromise the reproducibility of experimental data over time and place, 4 there are still many publications where the substrain of origin of C57BL/6 mice is not mentioned. However, a recent study reported that the cardiac output of C57BL/6N male mice is higher than that of their C57BL/6J counterparts.5 Likewise, the effects of transverse aortic constriction on survival and the remodeling of left ventricles (LV) are much greater in C57BL/6N than in C57BL/6J mice. 6 Thus, evidence indicates that genetic drift can significantly alter cardiac phenotypes in the established C57BL/6 inbred strain.Despite indications that hearts from the C57BL/6N and C57BL/6J mouse substrains differ in terms of their contractility, as well as their responses to stress-induced overload (including survival rate, maintenance of cardiac function, and development of hypertrophy), no information is available about the underlying molecular and cellular mechanisms. We compared the effects of either subacute (48 hours) and chronic (14 days) infusions of angiotensin II (Ang II) on LV remodeling in both substrains. As we observed substrainspecific differences in expression for some genes known to be specific for particular cell-types, we further confirmed Abstract-Despite indications that hearts from the C57BL/6N and C57BL/6J mouse substrains differ in terms of their contractility and their responses to stress-induced overload, no information is available about the underlying molecular and cellular mechanisms. We tested whether subacute (48 hours) and chronic (14 days) administration of angiotensin II (500 ng/kg per day) had different effects on the left ventricles of male C57BL/6J and C57BL/6N mice. Despite higher blood pressure in C57BL/6J mice, chronic angiotensin II induced fibrosis and increased the left ventricular weight/body weight ratio and cardiac expression of markers of left ventricular hypertrophy to a greater extent in C57BL/6N mice. Subacute angiotensin II affected a greater number of cardiac genes in C57BL/6N than in C57BL/6J mice. Some of the most prominent differences were observed for markers of (1) macrophage activation and M2 polarization, including 2 genes (osteopontin and galectin-3) whose inactivation was reported as sufficient to prevent angiotensin...
Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multiomics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.
Background and ScopeWeight loss success is dependent on the ability to refrain from regaining the lost weight in time. This feature was shown to be largely variable among individuals, and these differences, with their underlying molecular processes, are diverse and not completely elucidated. Altered plasma metabolites concentration could partly explain weight loss maintenance mechanisms. In the present work, a systems biology approach has been applied to investigate the potential mechanisms involved in weight loss maintenance within the Diogenes weight-loss intervention study.Methods and ResultsA genome wide association study identified SNPs associated with plasma glycine levels within the CPS1 (Carbamoyl-Phosphate Synthase 1) gene (rs10206976, p-value = 4.709e-11 and rs12613336, p-value = 1.368e-08). Furthermore, gene expression in the adipose tissue showed that CPS1 expression levels were associated with successful weight maintenance and with several SNPs within CPS1 (cis-eQTL). In order to contextualize these results, a gene-metabolite interaction network of CPS1 and glycine has been built and analyzed, showing functional enrichment in genes involved in lipid metabolism and one carbon pool by folate pathways.ConclusionsCPS1 is the rate-limiting enzyme for the urea cycle, catalyzing carbamoyl phosphate from ammonia and bicarbonate in the mitochondria. Glycine and CPS1 are connected through the one-carbon pool by the folate pathway and the urea cycle. Furthermore, glycine could be linked to metabolic health and insulin sensitivity through the betaine osmolyte. These considerations, and the results from the present study, highlight a possible role of CPS1 and related pathways in weight loss maintenance, suggesting that it might be partly genetically determined in humans.
Although gene coexpression domains have been reported in most eukaryotic organisms, data available to date suggest that coexpression rarely concerns more than doublets or triplets of adjacent genes in mammals. Using expression data from hearts of mice from the panel of AxB/BxA recombinant inbred mice, we detected (according to window sizes) 42−53 loci linked to the expression levels of clusters of three or more neighboring genes. These loci thus formed “cis-expression quantitative trait loci (eQTL) clusters” because their position matched that of the genes whose expression was linked to the loci. Compared with matching control regions, genes contained within cis-eQTL clusters showed much greater levels of coexpression. Corresponding regions showed: (1) a greater abundance of polymorphic elements (mostly short interspersed element retrotransposons), and (2) significant enrichment for the motifs of binding sites for various transcription factors, with binding sites for the chromatin-organizing CCCTC-binding factor showing the greatest levels of enrichment in polymorphic short interspersed elements. Similar cis-eQTL clusters also were detected when we used data obtained with several tissues from BxD recombinant inbred mice. In addition to strengthening the evidence for gene expression domains in mammalian genomes, our data suggest a possible mechanism whereby noncoding polymorphisms could affect the coordinate expression of several neighboring genes.
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