About

Project Description

Plant cell wall (fiber) hydrolysis must be improved, if there is to be a more effective and profitable use of crop residues and other fibrous materials for the production of meat, milk, draft animal power and second generation biofuels. The goals for our Consortium are to elucidate the genetics and molecular biology of the predominant species of fibrolytic ruminal bacteria, improve feed digestibility and reduce animal waste, and thereby renew a more positive image for animal agriculture and its impact on the environment. Furthermore, we are interested in exploiting this knowledge to support efforts that seek to improve the saccharification of lignocellulosic biomass, for the cost-efficient production of second-generation biofuels.

The diversity of microbes present in the rumen is great, but the major part of fiber degradation is carried out by only a select group of anaerobic bacteria. So our first objective was to develop a genetic "blueprint" of ruminal fiber degradation by sequencing the genomes of four different bacteria, utilizing the facilities and expertise provided by The Institute for Genomic Research. The selected bacteria not only have a dominant role in ruminal fiber degradation, but also possess features that are of fundamental scientific interest, making them worthy candidates for genome sequencing projects. Our second objective was to utilize Suppressive Subtractive Hybridization to examine the genomic differences present in a larger group of related fibrolytic ruminal bacteria. These approaches were used at a time when DNA sequencing technologies were still too costly to undertake the large scale projects now underway.

Consortium Members

Acknowledgements

  • Bill Nelson - University of Southern California - Original database and website curation
  • Emmanuel Mongodin - The University of Maryland School of Medicine, Institute for Genome Sciences - Genome analysis and Microarray development
  • Sean Daugherty - The University of Maryland School of Medicine, Institute for Genome Sciences - Data analysis