The microbes that inhabit human bodies outnumber the human cells by an order of magnitude, and impact many aspects of health and disease including obesity, vaginosis, and Crohn's disease. Understanding this endogenous microbiota is emerging as a key extension of efforts to understand the human genome and the role of genetic variation on health and disease. The Human Microbiome Project (HMP) will characterize microbial communities in a large number of individual healthy humans using metagenomic sequencing. Consequently, new methods for interpreting sequence data to understand microbial community composition and dynamics are urgently needed. This project unites disciplines ranging from ecology to evolutionary biology to applied mathematics, to develop new methods for understanding which body habitats are more or less similar in terms of their microbial communities, by evaluating measures of microbial diversity and change, and creating needed new metrics of community composition. This will enable understanding of how clinically relevant parameters such as age, sex, or the pH of specific body habitats affect these communities, and of how the dynamics of change in microbial communities within an individual, in transmission between individuals, and in transmission between humans and the environment. This project is directly responsive to the Roadmap RFA for Development of New tools for Computational Analysis of Human Microbiome Project Data.
The specific aims of this proposal are:
Aim 1. Develop, characterize, and apply enriched descriptors of microbial community diversity.
Aim 2. Develop methods for describing how human microbial communities vary over time and space.
Aim 3. Develop new methods for tracing the flow of organisms among different communities. Some key aspects of the proposed work are: the development of new statistical methods for estimating microbial diversity within a body habitat; development of enriched methods for describing microbial community diversity; exhaustive validation of methods for comparing microbial communities through large-scale simulations and by using the largest available data sets that characterize microbial communities empirically; and the development of new methods for tracing the sources of the microbes that inhabit the human body using both marker genes and whole-metagenome data. Key outcomes include the ability to help determine the extent to which there is a core human microbiome, and how best to sample human microbial diversity. All methods developed will be made available under open source licenses and will be deposited with the HMP Data Analysis and Coordination Center (DACC). The investigators intend to work closely with other researchers involved in the HMP in order to ensure rapid progress.
Microbial communities associated with the human body play critical roles in human health and disease. This project will provide methods that help establish the nature and variability of microbial communities in healthy human individuals. Using these individuals as a baseline, this work will will pave the way for studies of a wide range of medical conditions that these communities affect by looking for abnormal communities associated with specific disease states, allowing the development of new diagnostics and therapeutic approaches. ? ? ? ?
| Tripathi, Anupriya; Marotz, Clarisse; Gonzalez, Antonio et al. (2018) Are microbiome studies ready for hypothesis-driven research? Curr Opin Microbiol 44:61-69 |
| Gonzalez, Antonio; Navas-Molina, Jose A; Kosciolek, Tomasz et al. (2018) Qiita: rapid, web-enabled microbiome meta-analysis. Nat Methods 15:796-798 |
| Sinha, Rashmi; Abu-Ali, Galeb; Vogtmann, Emily et al. (2017) Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat Biotechnol 35:1077-1086 |
| Lloyd-Price, Jason; Mahurkar, Anup; Rahnavard, Gholamali et al. (2017) Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550:61-66 |
| Weiss, Sophie; Xu, Zhenjiang Zech; Peddada, Shyamal et al. (2017) Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27 |
| Charepalli, Venkata; Reddivari, Lavanya; Radhakrishnan, Sridhar et al. (2017) Pigs, Unlike Mice, Have Two Distinct Colonic Stem Cell Populations Similar to Humans That Respond to High-Calorie Diet prior to Insulin Resistance. Cancer Prev Res (Phila) 10:442-450 |
| Weiss, Sophie; Van Treuren, Will; Lozupone, Catherine et al. (2016) Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J 10:1669-81 |
| Metcalf, Jessica L; Xu, Zhenjiang Zech; Weiss, Sophie et al. (2016) Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351:158-62 |
| Weiss, Sophie; Carter, David O; Metcalf, Jessica L et al. (2016) Carcass mass has little influence on the structure of gravesoil microbial communities. Int J Legal Med 130:253-63 |
| Rosenbaum, Michael; Knight, Rob; Leibel, Rudolph L (2015) The gut microbiota in human energy homeostasis and obesity. Trends Endocrinol Metab 26:493-501 |
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