Faculty
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Patrick Schloss Visit the Schloss Lab website |
Host-microbiome interactions
My research group is interested at the interplay between humans and the microbes that reside on and within us - the human microbiome. Our microbiome is more than ten-time larger than the number of human cells and represents a collective genome that is more than 100-times more complex than our genome. Although many people are interested in pathogens, until recently, there has been little emphasis placed on determining how our microbiome keeps us healthy. Numerous lines of evidence indicate that our genetics, diet, behavior, and life experiences affect the structure of our microbiome. Yet, because of this daunting complexity, it is difficult to make statements about the precise interactions between us as hosts and specific bacterial groups within our microbiome. Our lab is pursuing two approaches to address this fascinating problem.
First, we are developing software to describe and compare microbial communities using molecular data. This software enables the scientific community to reach conclusions regarding host-microbiome interactions with statistical confidence and has effectively enabled the field to transition from discover-based to hypothesis-driven research. As part of this effort, we provide workshops to train microbial ecologists on the theory and practice of analyzing their data. Our program, mothur (http://www.mothur.org), is being widely used by microbial ecologists that have generated Sanger sequence data and pyrosequence tag sequences. We also collaborate with other investigators to conduct studies in humans to help design experiments and analyze data related to studying microbial ecology.
Second, to limit the complexity of the microbiome, we have developed a model system in the red flour beetle (Tribolium castaneum). T. castaneum has a diverse diet, genetic tools, and a simple microbial community. This model system will enable us to better understand the mechanisms of host-microbiome interactions. We are specifically interested in perturbing the microbial community via antibiotics and modifying host gene expression via transposon mutagenesis and RNAi. We use community profiling techniques, quantitative PCR, metagenomics, microscopy, and metagenomics to study the changes in the microbiome. Our goal is to one day translate the information gained from this model system to better understand the human microbiome.
Selected publications
Schloss, PD, Larget, BR, & Handelsman J. (2004). Integration of microbial ecology and statistics: a test to compare gene libraries. Applied and Environmental Microbiology. 70(9):5485-92.
Riesenfeld, CS, Schloss, PD & Handelsman, J. (2004). Metagenomics. Annual Reviews in Genetics. 38:525-52.
Schloss, PD & Handelsman, J. (2004). The status of the microbial census. Microbiology and Molecular Biology Reviews. 68(4):686-91.
Schloss, PD & Handelsman, J. (2005). Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Applied and Environmental Microbiology, 71(3):1501-6.
Schloss, PD & Handelsman J. (2005). Metagenomics for studying unculturable microorganisms: cutting the Gordian knot. Genome Biology, 6(8):229-33.
Schloss, PD & Handelsman, J. (2006). Introducing TreeClimber, a test to compare community structures. Applied and Environmental Microbiology, 72(4): 2379-84.
Schloss, PD, Delalibera, I. Jr., Handelsman, J, & Raffa, KF. (2006). Bacteria associated with the guts of invasive and native wood-boring beetles. Environmental Entomology, 35(3):625-9.
Schloss, PD & Handelsman, J. (2006). Toward a census of bacteria in soil. PLoS Computational Biology. 2(7):e92.
Schloss, PD & Handelsman, J. (2006) Introducing SONS, a tool for OTU-based comparisons of membership and structure between microbial communities. Applied and Environmental Microbiology. 72(10):6773-9.
Schloss, PD & Handelsman, J. (2007). The last word. Annual Reviews in Microbiology. 61:23-34.
Schloss, PD & Handelsman, J. (2008). A Statistical Toolbox for Metagenomics: Assessing Functional Diversity in Microbial Communities. BMC Bioinformatics. 9:34.
Schloss, PD. (2008). Evaluating different approaches that test whether microbial communities have the same structure. ISME Journal. 2:265-75.
