RAPID: Modeling the Host-Microbiome-Virome Interactions and their Impact on COVID-19 Severity.

RAPID:对宿主-微生物组-病毒组相互作用及其对 COVID-19 严重程度的影响进行建模。

基本信息

  • 批准号:
    2034995
  • 负责人:
  • 金额:
    $ 20万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-07-01 至 2022-06-30
  • 项目状态:
    已结题

项目摘要

COVID-19 is a complex disease. While sometimes there are no symptoms, many people suffer life-altering symptoms and side effects that can led to death. Age, gender, racial background, medical history, and lifestyle all can influence the rate of infection and its severity. To invade human cells, the virus attaches to certain macromolecules that are expressed at different levels in different tissues and organs. The local expression of these macromolecules can be influenced by the microbiome, i.e. the bacteria that grow in and around the tissues and organs. This project will analyze how the human microbiome affects the progression of COVID-19. Computational models will be developed that use microbiome compositions to predict outcomes of COVID-19 disease. Such models will provide a better understanding of the basic biology of SARS-COV-2 infection and lead to improved treatment and prevention strategies. The main hypothesis is that local microbiomes modulate niche-specific expression of macromolecules critical to COVID-19 development. These include the ACE2 receptor, TMPRSS2 serine protease, unidentified genes associated with SARS-COV-2 invasion and replication, and mediators of the excessive cytokine response. Further, we propose that these differences are associated with differential disease severity. The goal is to develop a statistical model for prediction of disease progression and severity of COVID-19 infection. COVID-19 inpatients are enrolled and nasopharyngeal, stool, buccal, urine and blood samples are collected longitudinally. Blood samples are processed for cytokine profiles and a selection of the remaining samples are analyzed by 16S rRNA taxonomic profiling to characterize the local microbiome and by metatranscriptomic sequencing to characterize gene expression profiles and the virus genome sequence. Viral loads are measured. These data will be augmented with metatranscriptomic data from community-collected testing samples which provide only a single nasopharyngeal sample. This dataset will be used to construct models aimed at predicting risk of COVID-19 disease progression and severity. Associations of the multi omic factors with disease severity will be analyzed using multifactorial modeling techniques that leverage the temporal dimension of the data and also incorporate racial and demographic factors. The constructed models will inform risk stratification in screening and increase our understanding of the host-and-microbiome factors impacting the trajectory of COVID-19 disease. Development of the disease model for COVID-19 may also inform the future development of predictive models for other viral infections. This project is being funding jointly between the Cellular and Biochemical Engineering Program in ENG/CBET and the Systems and Synthetic Biology Program in MCB/BIO.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
新冠肺炎是一种复杂的疾病。虽然有时没有症状,但许多人会出现改变生活的症状和副作用,可能会导致死亡。年龄、性别、种族背景、病史和生活方式都会影响感染率及其严重程度。为了入侵人类细胞,病毒附着在某些大分子上,这些大分子在不同的组织和器官中以不同的水平表达。这些大分子的局部表达会受到微生物组的影响,即生长在组织和器官内及其周围的细菌。这个项目将分析人类微生物群如何影响新冠肺炎的进展。将开发使用微生物组组成来预测新冠肺炎病结果的计算模型。这些模型将更好地了解SARS-COV-2感染的基本生物学,并导致改进治疗和预防策略。主要的假设是,当地的微生物群落调节着对新冠肺炎发展至关重要的大分子的特定生态位表达。这些包括ACE2受体、TMPRSS2丝氨酸蛋白酶、与SARS-COV-2入侵和复制相关的未知基因,以及过度细胞因子反应的媒介。此外,我们认为这些差异与疾病严重程度的不同有关。其目标是开发一个统计模型,用于预测疾病进展和新冠肺炎感染的严重程度。入选新冠肺炎住院患者,纵向采集鼻咽、大便、口腔、尿液和血样。对血液样本进行细胞因子谱处理,并选择其余样本进行16S rRNA分类谱分析,以确定当地微生物组的特征,并通过后转录测序,确定基因表达谱和病毒基因组序列。病毒载量是经过测量的。这些数据将用来自社区收集的只提供单一鼻咽样本的测试样本的元转录数据来扩充。该数据集将用于构建旨在预测新冠肺炎疾病进展和严重程度的风险模型。将使用多因素建模技术分析多种经济因素与疾病严重性的关联,这些技术利用数据的时间维度,并纳入种族和人口因素。所构建的模型将为筛查中的风险分层提供信息,并增加我们对影响新冠肺炎疾病发展轨迹的宿主和微生物组因素的理解。新冠肺炎疾病模型的开发也可能为其他病毒感染预测模型的未来发展提供信息。该项目是由ENG/CBET中的细胞和生化工程计划以及MCB/Bio中的系统和合成生物学计划共同资助的。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Real-valued Group Testing for Quantitative Molecular Assays
定量分子测定的实值组测试
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Gregory Buck其他文献

TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies
  • DOI:
    10.1186/s13059-024-03453-x
  • 发表时间:
    2024-12-19
  • 期刊:
  • 影响因子:
    9.400
  • 作者:
    Pixu Shi;Cameron Martino;Rungang Han;Stefan Janssen;Gregory Buck;Myrna Serrano;Kouros Owzar;Rob Knight;Liat Shenhav;Anru R. Zhang
  • 通讯作者:
    Anru R. Zhang
The collinear central configuration of n equal masses

Gregory Buck的其他文献

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{{ truncateString('Gregory Buck', 18)}}的其他基金

Assembling the Tree of Life: Phylum Euglenozoa
组装生命之树:眼虫门
  • 批准号:
    0830056
  • 财政年份:
    2008
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant
BBSI:The Bioinformatics and Bioengineering Summer Institute at VCU
BBSI:弗吉尼亚联邦大学生物信息学和生物工程暑期学院
  • 批准号:
    0609038
  • 财政年份:
    2006
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant
NIH-NSF BBSI: Virginia Commonwealth University
NIH-NSF BBSI:弗吉尼亚联邦大学
  • 批准号:
    0234101
  • 财政年份:
    2003
  • 资助金额:
    $ 20万
  • 项目类别:
    Continuing Grant
Physical Knots
物理结
  • 批准号:
    0107747
  • 财政年份:
    2001
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
NSF Minority Postdoctoral Research Fellowship for FY-1999
1999 财年 NSF 少数族裔博士后研究奖学金
  • 批准号:
    9904152
  • 财政年份:
    1999
  • 资助金额:
    $ 20万
  • 项目类别:
    Fellowship Award
Research at Undergraduate Institutions, Collaborative Research: Physical Knot Theory
本科院校研究、合作研究:物理结理论
  • 批准号:
    9706865
  • 财政年份:
    1997
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Research at Undergraduate Institutions Collaborative Research, Energy Functions for Knots
本科院校合作研究,结的能量函数
  • 批准号:
    9420088
  • 财政年份:
    1994
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant
Acquisition of Radioanalytic Imager
收购放射分析成像仪
  • 批准号:
    9016233
  • 财政年份:
    1991
  • 资助金额:
    $ 20万
  • 项目类别:
    Standard Grant

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Galaxy Analytical Modeling Evolution (GAME) and cosmological hydrodynamic simulations.
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职业:建模和解码牙龈组织中宿主-微生物组的相互作用
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急性弛缓性脊髓炎宿主易感因素建模
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先天性巨结肠相关小肠结肠炎中宿主-真菌相互作用的建模
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  • 批准号:
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