Identifying Molecular Effects of Diet through Systems Biology: Influence of Herring Diet on Sterol Metabolism and Protein Turnover in Mice
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE22532 | GEO | 2011/01/24
SECONDARY ACCESSION(S): PRJNA128501
REPOSITORIES: GEO
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Project description:Identifying molecular effects between herring and beef diet in Ldlr-/- mice The transcriptome of the three tissues, which are liver, muscle and adipose tissue, were used for indentify the influence of diet on metabolism. Ldlr-/- mice were fed with either a beef diet or a herring diet.
2011-01-24 | E-GEOD-22532 | biostudies-arrayexpress
Project description:BackgroundChanges in lifestyle have resulted in an epidemic development of obesity-related diseases that challenge the healthcare systems worldwide. To develop strategies to tackle this problem the focus is on diet to prevent the development of obesity-associated diseases such as cardiovascular disease (CVD). This will require methods for linking nutrient intake with specific metabolic processes in different tissues.Methodology/principal findingLow-density lipoprotein receptor-deficient (Ldlr -/-) mice were fed a high fat/high sugar diet to mimic a westernized diet, being a major reason for development of obesity and atherosclerosis. The diets were supplemented with either beef or herring, and matched in macronutrient contents. Body composition, plasma lipids and aortic lesion areas were measured. Transcriptomes of metabolically important tissues, e.g. liver, muscle and adipose tissue were analyzed by an integrated approach with metabolic networks to directly map the metabolic effects of diet in these different tissues. Our analysis revealed a reduction in sterol metabolism and protein turnover at the transcriptional level in herring-fed mice.ConclusionThis study shows that an integrated analysis of transcriptome data using metabolic networks resulted in the identification of signature pathways. This could not have been achieved using standard clustering methods. In particular, this systems biology analysis could enrich the information content of biomedical or nutritional data where subtle changes in several tissues together affects body metabolism or disease progression. This could be applied to improve diets for subjects exposed to health risks associated with obesity.
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Project description:BackgroundThe oleaginous yeast Cutaneotrichosporon oleaginosus represents one of the most promising microbial platforms for resource-efficient and scalable lipid production, with the capacity to accept a wide range of carbohydrates encapsulated in complex biomass waste or lignocellulosic hydrolysates. Currently, data related to molecular aspects of the metabolic utilisation of oligomeric carbohydrates are sparse. In addition, comprehensive proteomic information for C. oleaginosus focusing on carbohydrate metabolism is not available.ResultsIn this study, we conducted a systematic analysis of carbohydrate intake and utilisation by C. oleaginosus and investigated the influence of different di- and trisaccharide as carbon sources. Changes in the cellular growth and morphology could be observed, depending on the selected carbon source. The greatest changes in morphology were observed in media containing trehalose. A comprehensive proteomic analysis of secreted, cell wall-associated, and cytoplasmatic proteins was performed, which highlighted differences in the composition and quantity of secreted proteins, when grown on different disaccharides. Based on the proteomic data, we performed a relative quantitative analysis of the identified proteins (using glucose as the reference carbon source) and observed carbohydrate-specific protein distributions. When using cellobiose or lactose as the carbon source, we detected three- and five-fold higher diversity in terms of the respective hydrolases released. Furthermore, the analysis of the secreted enzymes enabled identification of the motif with the consensus sequence LALL[LA]L[LA][LA]AAAAAAA as a potential signal peptide.ConclusionsRelative quantification of spectral intensities from crude proteomic datasets enabled the identification of new enzymes and provided new insights into protein secretion, as well as the molecular mechanisms of carbo-hydrolases involved in the cleavage of the selected carbon oligomers. These insights can help unlock new substrate sources for C. oleaginosus, such as low-cost by-products containing difficult to utilize carbohydrates. In addition, information regarding the carbo-hydrolytic potential of C. oleaginosus facilitates a more precise engineering approach when using targeted genetic approaches. This information could be used to find new and more cost-effective carbon sources for microbial lipid production by the oleaginous yeast C. oleaginosus.
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2014-06-30 | E-GEOD-48977 | biostudies-arrayexpress
Project description:BACKGROUND: With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. RESULTS: In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. CONCLUSIONS: Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.
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