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Principal Investigator: Xiao Tan
Organization: MASSACHUSETTS GENERAL HOSPITAL
Fiscal Year: 2024
Award: $169,560
Funding agency: National Institute of Diabetes and Digestive and Kidney Diseases
Inflammatory bowel disease (IBD) affects over 1.2 million patients in the United States and causes significant
morbidity and healthcare expenditures. Studies have associated the development of IBD with changes in the
human gut microbiome, which together with genetic and environmental factors alter immune responses to gut
flora and cause chronic inflammation. The surface and secreted proteins and glycans of gut microbes mediates
many aspects of these immune interactions. However, the study of these molecules is limited by the extreme
complexity of the gut environment. Standard proteomic techniques only capture a small fraction of the predicted
microbial gene products while metagenomic analyses using automated annotations fail to identify functions for
nearly half of all predicted proteins. The dietary, host, and microbial contributions to the diverse carbohydrate
pool also makes the analysis of microbial glycans in stool samples highly challenging. The incomplete evaluation
of microbial surface and secreted proteins and microbial glycans impedes the discovery of new biological insights
into IBD. Machine learning algorithms, and especially advancements in natural language processing (NLP)
based on deep neural networks, have enabled major improvements in the accuracy of a number of tasks related
to human speech and written text. These deep neural networks function by analyzing massive collections of texts
and then creating high-dimensional vectors to represent the semantic meaning of words without the need for
specific labels. Biological polymers such as DNA, proteins, and glycans are also long complex sequences, and
application of NLP techniques enabled accurate predictions of the functional and structural characteristics of
proteins and glycans from primary sequence. We hypothesize that machine learning methods incorporating deep
neural networks can be successfully applied to the analysis of microbial metagenomes and glycomes to identify
previously unknown perturbations in IBD. We will test this hypothesis with the following aims: 1) Develop and
adapt deep learning algorithms to analyze the surface-associated and secreted gut microbial metaproteome in
IBD; 2) Create and apply deep learning algorithms to analyze the fecal microbial glycome in IBD; 3)
Experimentally validate the functions of a subset of novel microbial proteins and glycans that are altered in IBD.
The long-term goal of this project is to discover new biological insights into the pathogenesis, progression, and
treatment of IBD. This proposal comprises a five-year research career development program focused on the
creation and adaptation of deep learning algorithms to the analysis of gut microbial metagenomic and glycomic
data. The candidate is an Instructor of Medicine at Harvard Medical School and the Division of Gastroenterology
at Massachusetts General Hospital. He has assembled an outstanding group of collaborators and advisors with
deep expertise in machine learning, glycobiology, microbiome analysis, and IBD. Under the guidance of his
mentors Dr. James Collins and Dr. Tristan Bepler, the proposed experiments and training will equip the candidate
with a unique set of skills that will enable him to transition to independence as a physician-scientist.
Terms: <Affect><Algorithms><American><Automated Annotation><Bacterial Gene Products><Bacterial Gene Proteins><Bacterial Proteins><Binding><Biochemical><Biological><Biological Function><Biological Process><Biomedical Research><Carbohydrates><Catalogs><Cell Body><Cell surface><Cells><Characteristics><Chemicals><Chronic><Chronic Disease><Chronic Illness><Classification><Collection><Complex><Computing Methodologies><DNA><Data><Data Bases><Databases><Deoxyribonucleic Acid><Development><Digestive Diseases><Digestive System Diseases><Digestive System Disorders><Disease><Disorder><Environment><Environmental Exposure><Environmental Factor><Environmental Risk Factor><Enzyme Gene><Enzymes><Esteroproteases><Evaluation><Event><Functional Metagenomics><GI microbiome><GI microbiota><GI tract disorder><Gastroenterology><Gastrointestinal microbiota><General Hospitals><General Taxonomy><Genes><Genetic><Glycans><Glycobiology><Goals><Health Expenditures><Human><Human Genetics><Immune><Immune response><Immunes><Immunity><Immunochemical Immunologic><Immunologic><Immunological><Immunological response><Immunologically><Immunologics><Inflammation><Inflammatory Bowel Diseases><Inflammatory Bowel Disorder><Information Retrieval><Information extraction><Intestinal><Intestines><Investigation><Label><Life><Machine Learning><Massachusetts><Mediating><Medicine><Membrane><Membrane Protein Gene><Membrane Proteins><Membrane-Associated Proteins><Mentors><Metagenomics><Methods><Microbe><Modern Man><Modification><Molecular Interaction><Morbidity><Morbidity - disease rate><Natural Language Processing><Nucleic Acids><Pathogenesis><Pathogenicity><Pathway interactions><Patients><Peptidases><Peptide Hydrolases><Performance><Physicians><Polymers><Polysaccharides><Process><Program Development><Protease Gene><Proteases><Protein Secretion><Proteinases><Proteins><Proteolytic Enzymes><Proteome><Proteomics><Regulation><Research><Scientist><Semantics><Series><Source><Speech><Structure><Surface><Surface Proteins><Systematics><Taxonomy><Techniques><Testing><Text><Training><Translations><United States><Validation><Writing><analyze microbiome><annotation schema><anti-microbial peptide><biologic><bowel><career development><catalog><chronic disorder><colitis mouse model><colitis murine model><complex data><computational annotation><computational methodology><computational methods><computational pipelines><computer annotation><computer based method><computer methods><computing method><data base><deep learning><deep learning algorithm><deep learning based neural network><deep learning method><deep learning neural network><deep learning strategy><deep neural net><deep neural network><density><design><designing><developmental><dietary><digestive disorder><digestive tract disease><digestive tract microbiome><enteric microbial community><enteric microbiome><enteric microbiota><environmental risk><experiment><experimental research><experimental study><experiments><fecal sample><gastrointestinal microbial flora><gastrointestinal microbiome><gastrointestinal tract disease><gastrointestinal tract disorder><gene product><gut commensal><gut community><gut flora><gut microbe community><gut microbes><gut microbial community><gut microbial composition><gut microbial consortia><gut microbial species><gut microbiome><gut microbiota><gut microbiotic><gut microflora><gut-associated microbiome><health care expenditure><healthcare expenditure><high dimensional data><high dimensionality><host microbe association><host microbe relationship><host response><host-microbe interactions><host-microbial interactions><host-microorganism interactions><immune system response><immunogenicity><immunoresponse><improved><in vitro Assay><in vivo><inflammatory disease of the intestine><inflammatory disorder of the intestine><insight><instructor><intestinal autoinflammation><intestinal biome><intestinal flora><intestinal microbes><intestinal microbiome><intestinal microbiota><intestinal microflora><intestinal tract microflora><large data sets><large datasets><machine based learning><machine learned algorithm><machine learning algorithm><machine learning based algorithm><machine learning based method><machine learning method><machine learning methodologies><medical college><medical expenditure><medical schools><membrane structure><metabolism measurement><metabolomics><metabonomics><metagenome><microbial><microbiome analysis><mouse colitis><multidimensional data><multidimensional datasets><murine colitis><natural language understanding><novel><pathway><polymer><polymeric><protein function><school of medicine><skills><stool sample><stool specimen><sugar><tool><translation><validation studies><validations><vector>