Novel probiotics to mitigate xenobiotic toxicity through microbial biotransformation

通过微生物生物转化减轻外源毒性的新型益生菌

基本信息

批准号：
10707553
负责人：
MOHAMMAD RK MOFRAD
金额：
$ 27.58万
依托单位：
NEXILICO, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10707553
关键词：
3-Dimensional Address Affect Arsenates Arsenic Arsenicals Arsenites Base Sequence Beta-glucuronidase Binding Biotechnology California Classification Communities Complex Computational Biology Computational Technique Computer Models Data Set Databases Diet Drug Metabolic Detoxication Environment Environmental Exposure Enzymes Evaluation Food Contamination Future Government Hazardous Substances Homologous Gene Hour Human Human Microbiome Hybrids Industry Lyase Mediating Metabolic Metabolic Biotransformation Metabolism Methods Microbe Microbiology Modeling Molecular Analysis Nitroreductases Organism Outcome Persons Pharmacology Phase Poison Probiotics Proteins Reaction Research Role Small Business Technology Transfer Research Sulfatases System Techniques Technology Testing Toxic effect Toxicology Universities Validation Water Pollutants Water Pollution Xenobiotic Metabolism Xenobiotics azoreductase commercialization cost effective deep learning disease registry drinking water environmental chemical enzyme substrate experience gut microbiome in silico in vitro Assay microbial microbiome microbiome analysis microbiome research microorganism molecular modeling novel primary outcome probiotic supplementation response risk mitigation screening

项目摘要

PROJECT SUMMARY / ABSTRACT The human gut microbiome consists of trillions of microorganisms which metabolize a variety of xenobiotic compounds, including environmental chemicals, thereby affecting their overall toxicity to the host organism. Gut microorganisms can either increase or decrease the toxicity of xenobiotic compounds based on reactions with microbial enzymes, which is referred to as Microbiome Modulation of Toxicity (MMT). Specific xenobiotic-metabolizing enzymes have been identified, such as azoreductases, nitroreductases, β-glucuronidases, sulfatases, and β-lyases. However, despite many studies that have demonstrated the significance of the gut microbiome in xenobiotic toxicity, the role of microbial biotransformation in toxicity response is largely ignored. Specifically, there is a significant lack of predictive methods to identify potential microbial strains that could mitigate xenobiotic toxicity, as probiotics, by transforming those compounds into metabolites with a reduced toxicity profile. This Phase I proposal seeks to address this critical need by developing a predictive computational-experimental platform to characterize the microbial biotransformation of xenobiotics and identify naturally occurring gut microbial strains that offer protection to the host from xenobiotic toxicity. We will employ advanced computational techniques and in vitro assays to test thousands of microbial enzymes and their associated microorganisms to identify species that could detoxify a set of targeted xenobiotics. Our hybrid platform combines the high-throughput capabilities of in silico methods with the accuracy of experimental techniques to provide cost-effective yet accurate and actionable predictions. The primary outcome of this project will be the identification, characterization, and validation of novel probiotics to protect humans from the toxicity of a range of xenobiotics, including environmental exposure, food contamination, and water pollution, by detoxifying them into metabolites with a reduced toxicity profile. For the proof-of-concept in Phase I, we will focus on the microbial metabolism of arsenicals, which have been the highest-ranked substances of concern on the US Agency for Toxic Substances and Disease Registry (ATSDR) and the US EPA’s Priority List of Hazardous Substances since 1977. The successful outcome of this project will provide a novel set of probiotics to mitigate the risks associated with a range of xenobiotics, including arsenicals, affecting millions of lives around the world. 1

项目摘要 /摘要人类肠道微生物组由数万亿微生物组成，这些微生物代谢多种异生物生物化合物，包括环境化学物质，从而影响其对宿主生物体的总体毒性。肠道微生物可以根据反应来增加或降低异生物化合物的毒性与微生物酶一起，称为毒性的微生物组调节（MMT）。具体的已经鉴定出异生物生物代谢酶，例如二氧化激酶，硝酸还原酶， β-葡萄糖醛酸酶，硫酸酶和β-裂解酶。但是，许多研究表明肠道微生物组在异种毒性中的重要性，微生物生物转化在毒性中的作用响应在很大程度上被忽略了。具体而言，存在明显缺乏预测方法来识别潜力通过将这些化合物转化为益生菌的微生物菌株可能会降低异生物毒性毒性代谢物的毒性特征降低。我提出的这一阶段旨在通过发展预测性来满足这一关键需求计算实验平台以表征异种生物的微生物生物转化并识别天然发生的肠道微生物菌株可为宿主提供保护免受异种毒性的保护。我们将雇用先进的计算技术和体外测定，以测试数千种微生物酶及其相关的微生物，以鉴定可以排毒一组靶向异生物的物种。我们的混合动力平台结合了计算机方法中的高通量功能和实验的准确性提供具有成本效益但准确且可操作的预测的技术。该项目的主要结果是新型益生菌的识别，表征和验证保护人类免受一系列异种生物的毒性，包括环境暴露，食物通过将其排毒成毒性降低的代谢产物，污染和水污染。为了在第一阶段的概念概念证明，我们将重点关注砷的微生物代谢，这是对美国有毒物质和疾病注册机构（ATSDR）关注的最高水平物质（ATSDR）以及美国EPA自1977年以来的危险物质的优先清单。该项目的成功结果将提供一套新型益生菌，以减轻与一系列异种生物相关的风险，包括砷，影响世界各地数百万的生命。 1