Machine learning-based methods for the analysis of microbial glycomes and proteomes in inflammatory bowel disease.

基于机器学习的方法，用于分析炎症性肠病中微生物糖组和蛋白质组。

• 批准号: 10591842
• 负责人: Xiao Tan
• 金额: $ 16.96万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-04 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591842
• 关键词: Affect; Algorithms; American; Automated Annotation; Bacterial Proteins; Binding; Biochemical; Biological; Biological Process; Biomedical Research; Carbohydrates; Catalogs; Cell surface; Cells; Characteristics; Chemicals; Chronic; Chronic Disease; Classification; Collection; Complex; Computing Methodologies; DNA; Data; Databases; Development; Digestive System Disorders; Disease; Environment; Environmental Exposure; Environmental Risk Factor; Enzymes; Evaluation; Event; Gastroenterology; General Hospitals; Genes; Genetic; Glycobiology; Goals; Health Expenditures; Human; Human Genetics; Immune; Immune response; Immunity; Immunologics; Inflammation; Inflammatory Bowel Diseases; Information Retrieval; Intestines; Investigation; Label; Life; Machine Learning; Massachusetts; Mediating; Medicine; Membrane; Membrane Proteins; Mentors; Metagenomics; Methods; Microbe; Modification; Morbidity - disease rate; Natural Language Processing; Nucleic Acids; Pathogenesis; Pathogenicity; Pathway interactions; Patients; Peptide Hydrolases; Performance; Physicians; Polymers; Polysaccharides; Process; Program Development; Protein Secretion; Proteins; Proteome; Proteomics; Regulation; Research; Sampling; Scientist; Semantics; Series; Source; Speech; Structure; Surface; Taxonomy; Techniques; Testing; Text; Training; Translations; United States; Validation; Writing; antimicrobial peptide; career development; complex data; computational pipelines; deep learning; deep learning algorithm; deep neural network; density; design; dietary; experimental study; gene product; gut microbes; gut microbiome; gut microbiota; high dimensionality; host-microbe interactions; immunogenicity; improved; in vitro Assay; in vivo; insight; instructor; large datasets; machine learning algorithm; machine learning method; medical schools; metabolomics; metagenome; microbial; microbiome analysis; multidimensional data; murine colitis; novel; protein function; skills; stool sample; sugar; tool; validation studies; vector

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.

炎症性肠病（IBD）影响美国超过120万患者，并引起显著的肠道疾病。 发病率和医疗保健支出。研究表明，IBD的发生与 人类肠道微生物组与遗传和环境因素一起改变对肠道的免疫反应， 植物群并引起慢性炎症。肠道微生物的表面和分泌的蛋白质和聚糖介导 这些免疫相互作用的许多方面。然而，对这些分子的研究受到了极端的限制。 肠道环境的复杂性。标准的蛋白质组学技术只能捕获预测的一小部分。 微生物基因产物，而使用自动注释的宏基因组分析未能鉴定微生物基因产物的功能。 几乎一半的预测蛋白质。饮食、宿主和微生物对不同碳水化合物的贡献 混合物也使得粪便样本中微生物聚糖的分析极具挑战性。不完整的评价 微生物表面和分泌的蛋白质和微生物聚糖阻碍了新的生物学见解的发现 进入IBD机器学习算法，特别是自然语言处理（NLP）的进步 基于深度神经网络，已经实现了许多相关任务准确性的重大改进， 人类的语言和文字。这些深度神经网络通过分析大量文本集来发挥作用 然后创建高维向量来表示单词的语义，而不需要 具体的标签。生物聚合物如DNA、蛋白质和聚糖也是长的复杂序列， NLP技术的应用使得能够准确预测的功能和结构特征， 蛋白质和聚糖。我们假设，机器学习方法结合了深度 神经网络可以成功地应用于微生物宏基因组和糖组的分析， IBD中以前未知的扰动。我们将测试这个假设与以下目标：1）发展和 采用深度学习算法来分析肠道微生物的表面相关和分泌的元蛋白质组， IBD; 2）创建并应用深度学习算法来分析IBD中的粪便微生物糖组; 3） 实验验证IBD中改变的新型微生物蛋白质和聚糖子集的功能。 该项目的长期目标是发现新的生物学见解的发病机制，进展， IBD的治疗该提案包括一个为期五年的研究职业发展计划，重点是 创建和调整深度学习算法，以分析肠道微生物宏基因组和糖组学 数据候选人是哈佛医学院和胃肠病学分部的医学讲师 在马萨诸塞州总医院他召集了一批杰出的合作者和顾问， 在机器学习、糖生物学、微生物组分析和IBD方面拥有深厚的专业知识。在他的指导下， 导师博士詹姆斯柯林斯和博士特里斯坦贝普勒，拟议的实验和培训将装备候选人 他拥有一套独特的技能，使他能够作为一名物理学家和科学家独立工作。