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ARS Home Pacific West Area Davis, California Western Human Nutrition Research Center Immunity and Disease Prevention Research Research





- Lemay, Danielle
- Human Nutrition (107)

Research Project: **Location:
                            Immunity and Disease Prevention Research** Project Number: 2032-10700-002-003-R
                        
                        Project Type: Reimbursable Cooperative Agreement Start Date: Apr 1, 2020
                        
                        End Date: Mar 31, 2025 **Objective:**
                        
                        The objective of the USDA is to (a) sequence and analyze 1000 fecal metagenomes derived from a Danish clinical trial and (b) develop models using machine learning to determine gut microbiota-mediated effects of calcium and/or inulin supplements on calcium absorption and bone mineral density. **Approach:**
                        
                        For the shotgun metagenomics aspect, ARS will work with University of Copenhagen to jointly oversee all aspects of metagenomics data generation, integration, and analysis. The team at the University of Copenhagen will be responsible for sample collection, DNA extraction and assessment, and bioinformatics of sequencing results to determine microbial taxonomy. ARS will be responsible for overseeing library preparation and sequencing (at the University of California (UC) Davis Genome Center) and bioinformatics to determine functional contributions of the metagenomes, particularly for lactose and inulin fermentation. ARS will mentor the UC Davis graduate student to create a custom database of relevant fermentation pathway genes and to use this database to analyze sequencing data. ARS will also host University of Copenhagen post-doctoral scholar, as needed. Together, the teams will define the microbial taxonomy and relevant microbial genes that are differently abundant between the five interventions (placebo, non-dairy calcium, calcium-rich whey permeate, inulin, calcium-rich whey permeate + inulin) and determine which microbial differences are associated with the primary outcome (bone mineral density). 

For the machine learning aspect, ARS will work with a UC Davis computer science junior specialist to develop and assess models for the project-wide data to determine which "features" of clinical, microbial, supplement, habitual diet, etc. best predict baseline calcium absorption and bone mineral density after one year intervention.


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