Early Career: Improved Sensitivity and Utility of Metaproteomics Analyses
EMSL Project ID
48148
Abstract
In natural environments, microbes are constantly regulating protein function and metabolic activity. Metaproteomics, or analysis of the proteins from organisms in microbial communities, assays active metabolic functions and other biological processes to promote understanding the dynamic relationships between microbes and their environment. Understanding how organisms interact with their environment is key to the Department of Energy’s goal to use microbial communities for biofuel production.The objective of the proposed research is to dramatically improve the sensitivity of metaproteomics analyses via novel computational algorithms. Recent advances in mass spectrometry and biological separations have dramatically increased the depth of proteomic discovery. Unfortunately, traditional computational workflows are in many cases preventing researchers from realizing these benefits for microbial communities. Current metaproteomics algorithms have a dramatically reduced sensitivity, identifying too few proteins to make the technique truly useful. We propose to create a new generation of computational workflows to overcome the sensitivity limitations inherent in status quo data processing schemes.
As a model microbial community for the project, we will be using a cow gut rumen bioreactor, which is currently operating as a joint PNNL/WSU-Tri-Cities bioenergy project. The project focuses on the dynamics of a community in response to perturbation, specifically the ability of gut microbes to degrade different bio-materials. As the community adjusts to environmental perturbations, proteomic analyses will enhance our understanding of how it operates for sustained conversion of cellulose from different feedstocks under variable conditions, which is critical for our goal of sustainable bioenergy production.
There are three main aims of the proposal. First, to restore sensitivity, we propose to develop a multi-stage workflow built on the spectral networks paradigm. This will allow us to use information from commonly identified proteins to identify their counterparts in metaproteomics samples, revealing much of the physical and functional architecture of the microbial community. Second, to expand our ability to identify rare, unanticipated, or novel proteins, we will develop algorithms to directly annotate clusters of tandem mass spectra. Our last goal is to assign the metabolic reactions and cellular processes to the organism or set of organisms that are performing them, as opposed to applying the process to the entire community. In the context of a natural environment with thousands of different organisms present, this is a complex, yet crucial, output to aid in the systems-level reconstruction of the community.
Research results will be disseminated at conferences and in the peer-reviewed literature.
Project Details
Start Date
2013-10-10
End Date
2014-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Related Publications
Ting YS, SH Payne, JD Egertson, S Kim, B MacLean, L Kall, R Aebersold, RD Smith, W Noble, and M MacCoss. 2015. "Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data." Molecular and Cellular Proteomics . doi:10.1074/mcp.0114.047035 [In Press]