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.

Public Health Relevance

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.

Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California Davis
Department
Type
DUNS #
City
Davis
State
CA
Country
United States
Zip Code
95618
Barkal, Layla J; Theberge, Ashleigh B; Guo, Chun-Jun et al. (2016) Microbial metabolomics in open microscale platforms. Nat Commun 7:10610
Cajka, Tomas; Fiehn, Oliver (2016) Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. Anal Chem 88:524-45
Shih, P B; Yang, J; Morisseau, C et al. (2016) Dysregulation of soluble epoxide hydrolase and lipidomic profiles in anorexia nervosa. Mol Psychiatry 21:537-46
Kind, Tobias; Cho, Eunho; Park, Taeeun D et al. (2016) Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis. Sci Rep 6:39227
Fahrmann, Johannes F; Grapov, Dmitry; DeFelice, Brian C et al. (2016) Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer. Cancer Biomark 16:609-17
Shahin, Mohamed H; Gong, Yan; McDonough, Caitrin W et al. (2016) A Genetic Response Score for Hydrochlorothiazide Use: Insights From Genomics and Metabolomics Integration. Hypertension 68:621-9
Chen, Zhaohui; Kim, Jayoung (2016) Urinary proteomics and metabolomics studies to monitor bladder health and urological diseases. BMC Urol 16:11
Zhao, Yan; Wan, Debin; Yang, Jun et al. (2016) Catalytic Activities of Tumor-Specific Human Cytochrome P450 CYP2W1 Toward Endogenous Substrates. Drug Metab Dispos 44:771-80
Fiehn, Oliver (2016) Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling. Curr Protoc Mol Biol 114:30.4.1-30.4.32
Fahrmann, Johannes F; Grapov, Dmitry; Phinney, Brett S et al. (2016) Proteomic profiling of lung adenocarcinoma indicates heightened DNA repair, antioxidant mechanisms and identifies LASP1 as a potential negative predictor of survival. Clin Proteomics 13:31

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