Shuzhao Li, Ph.D. - US grants

Project Summary/Abstract Abstract The immune system manifests itself through various cellular and molecular events at different scales dynamically. The data accumulated at NIH ImmPort repository provide a great opportunity to deepen our understanding of the immune system, in the contexts of vaccine studies, transplantation, infectious diseases, etc. Novel bioinformatics tools are important to integrate and interpret these data, obtained from different technologies and some of high-dimensions. We propose Multiscale, Multifactorial Immune Networks (MMIN) as a framework to effectively integrate different data types. This work will build on our popular tools, blood transcription modules (BTMs) and mummichog, to combine intelligent dimension reduction techniques with partial least square regression to construct association networks that enable cross-type data queries. The MMIN will be provided as a free web service, with the option to be fully integrated into ImmPort. Selected datasets from ImmPort will be used for testing, documentation and reanalysis. We will further apply MMIN to examine the involvement of heme biosynthesis in the immune responses across studies.

Project Summary Human evolution has created complex metabolism systems to transform and eliminate potentially harmful chemicals to which we are exposed. Available evidence indicates that these systems generate a million or more different chemical metabolites, most of which are completely uncharacterized. Widespread use of mass spectrometry-based metabolomics methods shows that many unidentified mass spectral features are significantly associated with human diseases. Substantial epidemiological research implicates environmental contributions to many disease processes, and we believe that many of the unidentified mass spectral features are metabolites of environmental chemicals. We have an established and successful human exposome research center focused on improving the understanding of environmental contributions to disease. The present proposal is to build upon this foundation to develop powerful new chemical identification tools that can be scaled to identify hundreds of thousands of foreign chemical metabolites in the human body. We have assembled an exposome research team of analytical scientists with expertise in mass spectrometry, xenobiotic metabolism, computational chemistry and robotic methods, to develop and test new chemical identification tools to identify hundreds of thousands of foreign chemical metabolites. Our approach relies upon expertise in 1) computational chemistry to predict possible xenobiotic metabolites, respective adduct forms and ion dissociation patterns in mass spectrometry, 2) use of enzymatic and cellular xenobiotic biotransformation systems, which allows creation of multi-well panels containing specific biotransformation systems to generate xenobiotic metabolites, 3) ion fragmentation mass spectrometry and NMR spectroscopy methods to confirm chemical identities and 4) expertise with robotic systems which can be used to scale the approach to identify hundreds of thousands of metabolites of environmental chemicals. An Administrative Core will maintain an organizational structure and coordinate activities between the Experimental Core and the Computational Core, NIH and the Stakeholder Engagement and Program Coordination Center (SEPCC). The Experimental Core will develop and provide compound identification capability with ultra-high-resolution mass spectrometry support. The Computational Core will develop a predicted xenobiotic metabolite database to support metabolite identification. The Administrative Core will maintain interactions with HERCULES Exposome Research Center and support interactions with prospective Core users. Milestones are established to monitor progress toward goals to establish tools for compound identification that can be scaled to identify hundreds of thousands of foreign chemical metabolites. The results will catalyze metabolomics research by providing new ways to identify unknown metabolites of environmental chemicals, and also support identification of a broader range of metabolites of drugs, food, microbiome, dietary supplements and commercial products.

Computational Core ?Project Summary The Computational Core will develop tools supporting the Experimental Core and creation of an informatic compound identification resource that greatly expands available biological and analytical characterization of xenobiotic compounds. This includes the development of computational tools that maximize information capture from the Experimental Core and uses this information to develop informatic compound identification resources for high-resolution mass spectrometry (HRMS) platforms. The Core is structured to deliver key analytics for mega-scale mass spectral data processing and improved workflows for chemical identification using high-throughput arrays. The team has extensive expertise in systems biology, computational metabolomics, multiomic integration, database management and HRMS spectral processing, and will leverage informatic and machine learning expertise in the NIEHS-funded HERCULES Environmental Health Data Sciences Core (EHDSC) at Emory University. The Computational Core will provide sustained impact for the Metabolomic Consortium through development of an open-source, platform independent software pipeline and cloud-based xenobiotic databases. Throughout the pipeline creation and implementation process we will work closely with the Metabolomics Consortium Stakeholder Engagement and Program Coordination Center (SEPCC) to provide consistent identification metrics and annotation best practices, in addition to eliciting feedback from the National Metabolomics Data Repository for maximizing synergy metabolomic datasets. Because of the unique needs of this project to develop improved algorithms for prediction of in silico biotransformation products and ion dissociation patterns and processing tools for the large amount of metabolite data generated by the Experimental Core, we have identified key milestones and deliverables to meet ECIDC objectives. These will be accomplished through aims designed to process MS/MS spectra for thousands to hundreds of thousands of metabolites generated by the Experimental Core in a time and cost- effective manner by developing a semi-automated workflow that combines visual scripting, computational prediction of enzymatic biotransformation products and MS/MS spectral deconvolution that utilizes correlation across samples to isolate high-purity dissociation patterns. We will build upon the mega-biotransformation- identification pipeline to 1) calibrate and enhance in silico prediction of biotransformation products using parent compounds, 2) calibrate and enhance in silico prediction MS/MS dissociation patterns, 3) LC retention time and adduct prediction tools for reducing false matches, 4) a combined cloud-based database containing experimental and predicted MS/MS spectral patterns for xenobiotics and metabolites, and 5) exposome-based metabolic pathway maps to rapidly assess xenobiotic exposure enrichment in human populations using untargeted, HRMS profiling data. These tools will be scalable to different instruments and number of samples to support the goal to provide mega-scale identification of xenobiotic metabolites.

Abstract Understanding immunometabolism at the systemic level is important to vaccine development and cancer immunotherapy. While new data are coming out, methodical measurements of small molecules using metabolomics are still limited. Our team recently reported a multi-omics study of herpes zoster vaccine (Zostavax), which identified major metabolic influences to vaccine induced immunity, including inositol phosphate metabolism and steroid metabolism. To fully test the metabolite predictors of immune responses, we have acquired samples from a larger Zostavax study, and propose to perform advanced, high-coverage metabolomics analysis. Predictive models of immune responses will be developed on one cohort and tested on the other. These data will also be compared to a newer and more efficacious zoster vaccine. Together, this study will establish how metabolite profiles determine the immune responses of individuals.