The complex relationship between humans and their microbiome and the changes of the microbiome during onset of human disease are poorly understood. We propose to form a multiomics Center for the detailed longitudinal analysis of both the microbiome, its activity, and its interconnected relationship with the host during healthy and disease states by omics profiling. Building upon our broad expertise, we will analyze several human microbiomes (fecal, nasal, and exogenous viral) in conjunction with host blood and urine components. Samples will be collected and analyzed during healthy and viral infections from the same individuals over the course of at least three years. Through analysis of the microbiome and host biological activities as measured through a variety of omics approaches (metagenome, genome, transcriptome, proteome, metabolome), we will follow the dynamic changes in the microbiome and host pathways that occur during viral infections and other potential stresses, and obtain an unprecedented view of the molecular pathways that change during this period. We will focus on subjects at risk for diabetes, and we will correlate the molecular changes in microbiome (endogenous and viral) activity with changes in host glucose levels and diabetes onset. Overall, more than 1080 different physiological states will be analyzed in omic detail and the microbiome and corresponding host information will be deposited in a public repository and serve as an invaluable resource to the scientific community. To accomplish this goal we have assembled a uniquely qualified team.
Both viral and endogenous microbiomes are thought to have an enormous influence on human health but exactly how this occurs is not known. By analyzing in detail the gut and nasal microbiome and how its activity changes during viral infection in people we hope to understand in detail its influence on human physiology, particularly those at risk for diabetes. This information may help in understand how diabetes is acquired and ultimately may be useful for disease detection and treatment.
|Araya, Carlos L; Cenik, Can; Reuter, Jason A et al. (2016) Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations. Nat Genet 48:117-25|
|Chennamsetty, Indumathi; Coronado, Michael; Contrepois, KÃ©vin et al. (2016) Nat1 Deficiency Is Associated with Mitochondrial Dysfunction and Exercise Intolerance in Mice. Cell Rep 17:527-540|
|Kuleshov, Volodymyr; Jiang, Chao; Zhou, Wenyu et al. (2016) Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome. Nat Biotechnol 34:64-9|
|Contrepois, KÃ©vin; Liang, Liang; Snyder, Michael (2016) Can Metabolic Profiles Be Used as a Phenotypic Readout of the Genome to Enhance Precision Medicine? Clin Chem 62:676-8|
|Wu, Linfeng; Snyder, Michael (2015) Impact of allele-specific peptides in proteome quantification. Proteomics Clin Appl 9:432-6|
|Reuter, Jason A; Spacek, Damek V; Snyder, Michael P (2015) High-throughput sequencing technologies. Mol Cell 58:586-97|
|Contrepois, KÃ©vin; Jiang, Lihua; Snyder, Michael (2015) Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)-Mass Spectrometry. Mol Cell Proteomics 14:1684-95|
|Sharon, Donald; Snyder, Michael (2014) Serum profiling using protein microarrays to identify disease related antigens. Methods Mol Biol 1176:169-78|
|Kuleshov, Volodymyr; Xie, Dan; Chen, Rui et al. (2014) Whole-genome haplotyping using long reads and statistical methods. Nat Biotechnol 32:261-6|
|Tilgner, Hagen; Grubert, Fabian; Sharon, Donald et al. (2014) Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc Natl Acad Sci U S A 111:9869-74|
Showing the most recent 10 out of 14 publications