The Genomics Integration Core will advance metabolomics by making improvements in tools for interpreting and using metabolic data, specifically in the context of biochemical pathways and networks, but also by integrating data generated from genomics research, such as results from SNP genotyping, microbial genomics, or transcript and protein expression studies, into metabolomics studies. The core will be pivotal for conducting regional pilot and feasibility projects and critical for the success of the training and educational mission of the Promotion and Outreach Core. The Genomics Integration Core will be comprised of four different laboratories: the Weimer metagenomics laboratory, the Karp pathway informatics research group, the Pollard statistical genomics research group and the Lin and Perroud bioinformatics services core within the UC Davis Genome Center. The core will be responsible for both advancing the content of diverse metabolomics databases and tools and integrating the use of those tools and tools from other disciplines, particularly genomics, into metabolic studies such as pathway mapping. Newly developed tools will be employed in the Genome Center's bioinformatics service core as determined in coordination with the WC3MRC's Central Service Core. Specifically, the Genomics Integration Core will provide comprehensive bioinformatics and statistical tools for interpreting metabolomic data. The Core will collaborate with regional scientists on study design, data analysis and genomic interpretation of metabolomic data. The core will test and compare existing tools for linking genomic and metabolomic data, such as pathway mapping. Specifically, scientists in this core will work to advance genomics and pathway tools for metabolomic studies. The Genomics Integration Core will focus on advancing a range of existing tools in order to connect genomic pathways and disease phenotype data. Gene-enzyme annotations in the HumanCyc pathway database, integration of text-mining results into Cytoscape representations of metabolic networks, extension of current pathway enrichment approaches to include full metabolomic network statistics, development of tools for visualizing metabolite-centric network graphs with genomic information on demand, or other appropriate technologies will be explored in these efforts. The Genomics Integration Core will develop and test improvements in such tools and validate their utility and user friendliness by collaborating with regional scientists in clinical and preclinical research projects. Finally, this core will provide training and education in conjunction with the Promotion and Outreach Core.
Sequencing of the human genome has opened new avenues for understanding health and the progression of diseases. We now begin to learn which parts of the genome enable metabolic responses in humans, and which parts of metabolism may be contributed by gut microbiota living in symbiosis. Integration of pathway modeling will enable a far better understanding and treatment of human diseases.
|Jung, Jae Hun; You, Sungyong; Oh, Jae Won et al. (2018) Integrated proteomic and phosphoproteomic analyses of cisplatin-sensitive and resistant bladder cancer cells reveal CDK2 network as a key therapeutic target. Cancer Lett 437:1-12|
|Barupal, Dinesh Kumar; Fan, Sili; Wancewicz, Benjamin et al. (2018) Generation and quality control of lipidomics data for the alzheimer's disease neuroimaging initiative cohort. Sci Data 5:180263|
|Fong, Louise Y; Jing, Ruiyan; Smalley, Karl J et al. (2018) Human-like hyperplastic prostate with low ZIP1 induced solely by Zn deficiency in rats. Proc Natl Acad Sci U S A 115:E11091-E11100|
|Pedersen, Theresa L; Newman, John W (2018) Establishing and Performing Targeted Multi-residue Analysis for Lipid Mediators and Fatty Acids in Small Clinical Plasma Samples. Methods Mol Biol 1730:175-212|
|Ha, Yun-Sok; Kim, Yeon-Yong; Yu, Na Hee et al. (2018) Down-regulation of transient receptor potential melastatin member 7 prevents migration and invasion of renal cell carcinoma cells via inactivation of the Src and Akt pathway. Investig Clin Urol 59:263-274|
|Gao, Bei; Gallagher, Tara; Zhang, Ying et al. (2018) Tracking Polymicrobial Metabolism in Cystic Fibrosis Airways: Pseudomonas aeruginosa Metabolism and Physiology Are Influenced by Rothia mucilaginosa-Derived Metabolites. mSphere 3:|
|Killion, Elizabeth A; Reeves, Andrew R; El Azzouny, Mahmoud A et al. (2018) A role for long-chain acyl-CoA synthetase-4 (ACSL4) in diet-induced phospholipid remodeling and obesity-associated adipocyte dysfunction. Mol Metab 9:43-56|
|Garratt, Michael; Lagerborg, Kim A; Tsai, Yi-Miau et al. (2018) Male lifespan extension with 17-? estradiol is linked to a sex-specific metabolomic response modulated by gonadal hormones in mice. Aging Cell :e12786|
|Nagy-Szakal, Dorottya; Barupal, Dinesh K; Lee, Bohyun et al. (2018) Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Sci Rep 8:10056|
|Gifford, Isaac; Battenberg, Kai; Vaniya, Arpana et al. (2018) Distinctive Patterns of Flavonoid Biosynthesis in Roots and Nodules of Datisca glomerata and Medicago spp. Revealed by Metabolomic and Gene Expression Profiles. Front Plant Sci 9:1463|
Showing the most recent 10 out of 184 publications