IIBR Informatics: Comprehensive Metabolism Phenotype Characterization and Interpretation

University Of Kentucky Research Foundation, Lexington KY

Investigators

Abstract

Metabolism is the combination of chemical processes associated with organisms. Comprehensive molecular characterization of metabolism, i.e. detecting, identifying, and measuring the amounts of metabolites which include feed molecules, intermediates, and product molecules of metabolism, can provide the most information-rich phenotypic description of the active state of an organism or an ecosystem. In particular, new high-end analytical instrumentation applied to experiments that enrich metabolites with stable isotopes (non-radioactive types of atoms like carbon-13) can generate detailed submolecular features representing isotopic flux through cellular and systemic metabolism for thousands of metabolites extracted from cells, tissue, biofluids, and environmental samples. The challenge now is in analyzing this complex data to derive new biological knowledge and in making this data and knowledge available in a highly reusable format in public scientific repositories. However, data analysis methods for these state-of-the-art technologies are lacking. This project will address this technical gap by developing new data analysis tools that enable effective analysis, integration, interpretation, and public deposition of large metabolomics analytical datasets collected from new high-end instruments. This project will provide research exposure and training for high school students, undergraduate students, graduate students, and postdoctoral fellows. Ultra-accurate-mass and high-resolution Fourier transform mass spectrometry (FTMS) applied in stable isotope-resolved metabolomics (SIRM) experiments can generate detailed isotopologue features representing isotopic flux through cellular and systemic metabolism for thousands of metabolites from cells, tissue, biofluids, and environmental samples. The challenge now is in analyzing this complex, submolecular data to derive new biological knowledge, since appropriate data analysis methods are lacking. This project will address this technical gap by developing new data analysis tools that enable effective analysis, integration, interpretation, and public deposition of untargeted SIRM and non-SIRM analytical data collected from high-end instrumentation. Objective 1 will develop a novel multi-scan peak characterization method that properly handles multiple data quality issues present in FTMS spectra, minimizing intra-scan variance while removing spectral artifacts that are dangerous to downstream data analyses. Objective 1 will also develop a singularly unique tool implementing Small Molecule Isotope Resolved Formula Enumeration, which will provide a truly untargeted isotope-specific molecular formula (IMF) assignment of FTMS peaks without using a database of known/expected metabolites. Objective 2 will develop a novel metabolic network placement method that utilizes SIRM isotopologue data for robust metabolite placement. An interoperable set of omics integration tools centered on a comprehensive atom-resolved interaction network will allow cell/tissue-specific and subcellular-specific metabolite network placement, modeling, and interpretation. Objective 3 will develop libraries and tools that create conformant depositions to an evolving mwTab format and standard of metadata quality, with isotope-resolved IUPAC International Chemical Identifiers (InChI) that greatly enhance reuse. The comprehensive characterization and interpretation of metabolism afforded by this proposal will have broad impact in the following specific ways: 1) Provide new metabolism research infrastructure that will be impactful for non-model organisms. 2) Broadly disseminate, via open-source code repositories (e.g. GitHub, Python Package Index, Bioconductor), highly-reusable, fully-documented, production-level software tools that enable novel metabolomics data analyses and public deposition. 3) Provide research training in data science, metabolomics, and systems biology to high school, undergraduate, and graduate students as well as postdoctoral fellows. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.