Using DGGE profiling to develop a novel culture medium suitable for oral microbial communities.

More than 700 bacterial species have been detected in the human oral cavity. They form highly organized microbial communities and are responsible for many oral infectious diseases, such as dental caries and periodontal disease. The prevention and treatment of these diseases require a comprehensive knowledge of oral microbial communities, which largely relies on culture-dependent methods to provide detailed phenotypic and physiological analysis of these communities. However, most of the currently available laboratory media can only selectively support the growth of a limited number of bacterial species within these communities, and fail to sustain the original oral microbial diversity. In this study, using denaturing gradient gel electrophoresis (DGGE) as an index to systematically survey and analyse the selectivity of commonly used laboratory media, we developed a new medium (SHI medium) by combining the ingredients of several selected media that can support different subpopulations within the original oral microbial community derived from pooled saliva. DGGE and 454 pyrosequencing analysis showed that SHI medium was capable of supporting a more diversified community with a microbial profile closer to that of the original oral microbiota. Furthermore, 454 pyrosequencing revealed that SHI medium supported the growth of many oral species that have not before been cultured. Crystal violet assay and the confocal laser scanning microscope analysis indicated that, compared with other media, SHI medium is able to support a more complex saliva-derived biofilm with higher biomass yield and more diverse species. This DGGE-guided method could also be used to develop novel media for other complex microbial communities.

Exploring expression data: identification and analysis of coexpressed genes.

Analysis procedures are needed to extract useful information from the large amount of gene expression data that is becoming available. This work describes a set of analytical tools and their application to yeast cell cycle data. The components of our approach are (1) a similarity measure that reduces the number of false positives, (2) a new clustering algorithm designed specifically for grouping gene expression patterns, and (3) an interactive graphical cluster analysis tool that allows user feedback and validation. We use the clusters generated by our algorithm to summarize genome-wide expression and to initiate supervised clustering of genes into biologically meaningful groups.

Better methods for solving parsimony and compatibility.

Evolutionary tree reconstruction is a challenging problem with important applications in biology and linguistics. In biology, one of the most promising approaches to tree reconstruction is to find the "maximum parsimony" tree, while in linguistics, the use of the "maximum compatibility" method has been very useful. However, these problems are NP-hard, and current approaches to solving these problems amount to heuristic searches through the space of possible tree topologies (a search which can, on large trees, take months to complete). In this paper, we present a new technique, Optimal Tree Refinement, for reconstructing very large trees. Our technique is motivated by recent experimental studies which have shown that certain polynomial time methods often return contractions of the true tree. We study the use of this technique in solving maximum parsimony and maximum compatibility, and present both hardness results and polynomial time algorithms.