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Innate responses following infection with enteric microbes

肠道微生物感染后的先天反应

基本信息

批准号：
10177672
负责人：
Soumita Das
金额：
$ 35.45万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10177672
关键词：
2019-nCoV Abdominal Pain Address Affect Aftercare Anatomy Animals Artificial Intelligence Autopsy Biological Assay COVID-19 COVID-19 pandemic Cell model Cells China Chronic Disease Collaborations Colon Communities Consumption Cytokine Gene DNA Damage Data Data Set Diabetes Mellitus Diarrhea Disease Drug Screening Drug usage Duodenum Emergency Situation Enteral Enzyme-Linked Immunosorbent Assay Esophagus Feces Formalin Functional disorder Gastrointestinal tract structure Gene Expression Profile Genes Goals Healthcare Systems Human IL8 gene Immune response Immunohistochemistry Infection Inflammatory Inflammatory Bowel Diseases Interleukin-15 Interleukin-6 Intestines Knowledge Literature Lower Gastrointestinal Tract Lung Machine Learning Microbe Modeling Morbidity - disease rate Natural Immunity Nature Nausea Neutral Amino Acid Transport Systems Nomenclature Nutrient Nutritional Oral Organ Organoids Pathogenicity Pathology Pathway interactions Patients Pattern Pharmaceutical Preparations Pneumonia Probiotics Process Prodrugs Publishing Quantitative Reverse Transcriptase PCR RNA Rectum Reporting Research Route Sampling Severe Acute Respiratory Syndrome Severities Severity of illness Signal Transduction Small Intestines Specimen Stomach Supportive care Symptoms Testing Therapeutic Therapeutic Effect Time VIP gene Viral Virus Diseases Work base biobank biosafety level 3 facility cohort combat complex data coronavirus disease cytokine cytokine release syndrome dietary supplements drug candidate drug discovery gastrointestinal gastrointestinal infection gastrointestinal symptom genetic signature gut microbiota ileum intestinal epithelium microbial monolayer mortality pandemic disease prebiotics predictive modeling response therapeutic target tool transcriptome sequencing transcriptomics translational impact transmission process viral RNA virtual

项目摘要

ABSTRACT As the rapidly unfolding COVID-19 pandemic claims its victims around the world, it has also inspired the scientific community to come up with solutions to meet an urgent and unmet need —i.e., ameliorate the severity of Covid- 19 and reduce mortality. Two obstacles make that task difficult—First, the pathophysiology of Covid-19 remains a mystery; the emerging reports generally agree that the disease has a very slow onset and that those who succumb typically mount a ‘cytokine storm’, i.e., an overzealous immune response. However, despite being implicated as a culprit behind the observed mortality and morbidity in COVID-19, we know virtually nothing about what constitutes (nature, extent) or contributes to (cell or origin) such an overzealous response. A significant number of patients have GI symptoms. The treatment goals in COVID-19 have been formulated largely as a ‘trial and error’-approach; this is reflected in the mixed results of the trials that have concluded. Second, the process of drug discovery is comprised of time-consuming steps; to avoid delays, we need to define the nature of the fatal cellular response before deciding how to model it in animals or matching therapeutics to curb it. Our preliminary work has helped us define the aberrant host cellular response in COVID-19. We used machine learning tools that can look beyond interindividual variability to extract underlying gene expression patterns within complex data across multiple cohorts of viral pandemics, including COVID-19. The resultant pattern, i.e., signature, was subsequently exploited as a predictive model to navigate COVID-19 for GI symptoms. Surprisingly, the 166-gene signature was conserved in all viral pandemics, including COVID-19, inspiring the nomenclature-- (ViP)-signature. The ViP signature identified and predicted the disease severity of SARS-CoV2-infected patients. We hypothesized that the ViP signature provides a quantitative and qualitative framework for titrating the cellular response in viral pandemics and could serve as a powerful unbiased tool in our armamentarium to vet candidate drugs rapidly. In this proposal, our predicted model, experimental datasets and the information from published literature will be used to screen drugs/nutritional components/probiotics in the GI organoid derived monolayers in a semi-HTP format. We will experimentally validate the effect of the therapeutics predicted in ViP gene signature of the host. The following two aims will provide a translational impact on the COVID-19 emergency. Aim 1: Identify the gastro-intestinal pathogenic pathways during COVID-19. Aim 2: Determine the impact of drugs, nutrients and supplements in gut-in-a-dish model of COVID-19. Impact: This proposal will identify the gastro-intestinal pathways (in healthy and patients with chronic diseases) involved in the GI symptoms of COVID 19 and provide new treatment options in COVID-19.

抽象的随着新型冠状病毒肺炎 (COVID-19) 疫情迅速蔓延，世界各地都有受害者，这也激发了科学界的研究兴趣。社区提出解决方案来满足紧急和未满足的需求——即减轻新冠病毒的严重性—— 19、降低死亡率。有两个障碍使这项任务变得困难——首先，Covid-19 的病理生理学仍然存在一个谜；新出现的报告普遍认为，这种疾病的发病非常缓慢，并且那些患有这种疾病的人屈服通常会引发“细胞因子风暴”，即过度狂热的免疫反应。然而，尽管是作为观察到的 COVID-19 死亡率和发病率背后的罪魁祸首，我们几乎一无所知是什么构成（性质、程度）或促成（细胞或起源）这种过度热心的反应。一个重要的许多患者有胃肠道症状。 COVID-19 的治疗目标主要被制定为“试验” 和错误的方法；这反映在已结束的审判的好坏参半的结果中。二、流程药物发现过程由耗时的步骤组成；为了避免延误，我们需要定义致命的细胞反应，然后决定如何在动物中模拟它或匹配疗法来遏制它。我们的初步工作帮助我们定义了 COVID-19 中的异常宿主细胞反应。我们用过机器学习工具可以超越个体差异来提取潜在的基因表达包括 COVID-19 在内的多个病毒大流行群体的复杂数据中的模式。由此产生的模式，即签名，随后被用作预测模型来导航 GI 的 COVID-19 症状。令人惊讶的是，166 个基因特征在所有病毒大流行中都得到了保留，包括 COVID-19、启发了命名法——（ViP）——签名。 ViP 签名可识别并预测疾病的严重程度 SARS-CoV2 感染患者。我们假设 ViP 签名提供了定量和定性的滴定病毒大流行中的细胞反应的框架，可以作为一个强大的、公正的工具我们的军备库可以快速审查候选药物。在这个提案中，我们的预测模型、实验数据集已发表文献的信息将用于筛选药物/营养成分/益生菌半 HTP 格式的 GI 类器官衍生单层。我们将通过实验验证其效果宿主 ViP 基因特征中预测的治疗方法。以下两个目标将产生转化影响关于 COVID-19 紧急情况。目标 1：确定 COVID-19 期间的胃肠道致病途径。目标 2：确定药物、营养素和补充剂对 COVID-19 培养皿模型的影响。影响：该提案将确定胃肠道途径（健康人和慢性病患者）涉及 COVID-19 的胃肠道症状，并为 COVID-19 提供新的治疗选择。

项目成果

期刊论文数量（34）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Boolean implication analysis unveils candidate universal relationships in microbiome data.

布尔含义分析在微生物组数据中揭示了候选人的普遍关系。

DOI：
10.1186/s12859-020-03941-4
发表时间：
2021-02-05
期刊：
BMC bioinformatics
影响因子：
3
作者：
Vo D;Singh SC;Safa S;Sahoo D
通讯作者：
Sahoo D

Regulation of Rac1 and Reactive Oxygen Species Production in Response to Infection of Gastrointestinal Epithelia.

DOI：
10.1371/journal.ppat.1005382
发表时间：
2016-01
期刊：
PLoS pathogens
影响因子：
6.7
作者：
den Hartog G;Chattopadhyay R;Ablack A;Hall EH;Butcher LD;Bhattacharyya A;Eckmann L;Harris PR;Das S;Ernst PB;Crowe SE
通讯作者：
Crowe SE

Inflammatory phenotype modulation in the respiratory tract and systemic circulation of e-cigarette users: a pilot study.

电子烟使用者呼吸道和体循环的炎症表型调节：一项试点研究。

DOI：
10.1152/ajplung.00363.2021
发表时间：
2021
期刊：
American journal of physiology. Lung cellular and molecular physiology
影响因子：
0
作者：
Sayed,IbrahimM;Masso-Silva,JorgeA;Mittal,Ankita;Patel,Arjun;Lin,Erica;Moshensky,Alex;Shin,John;Bojanowski,ChristineM;Das,Soumita;Akuthota,Praveen;CrottyAlexander,LauraE
通讯作者：
CrottyAlexander,LauraE

Modeling colorectal cancers using multidimensional organoids.