See also our honours and PhD projects
Genome diversity, metagenomics and lateral gene transfer
Genome sequences have now been completed for more than 500 microbial genomes. Perhaps one of the most surprising findings from the genomics era is the apparent extent of lateral gene transfer in shaping microbial genomes, and the amount of genomic variability between even closely related strains of the same species.
In pursuing these issues, I have led the sequencing of more than twenty microbial genomes. Some of my groups' recent efforts have focused on lateral gene transfer and niche adaptation in the marine cyanobacterium Synechococcus. In collaboration with Dr. Brian Palenik at Scripps Institute of Oceanography we have sequenced four complete Synechococcus genomes, and comparison of these genomes has identified a variety of novel genomic islands. Microarray expression analysis has been used to investigate differences in gene regulation between these strains and to investigate functions of genes encoded within novel islands, and comparative genome hybridization has been used to examine conservation of genomic islands amongst different isolates.
Recently, we have started to apply a targeted metagenomic approach, by undertaking 454 sequencing of the metagenomes of Synechococcus cyanobacteria sorted from a complex community by flow cytometry and compare these data to the completed genomes of strains previously isolated from these environments to gain a better insight into cyanobacterial genome diversity in the "real world".
Fig. Synechococcus genome alignments
Systems Biology analysis of membrane transport and metabolism
The deluge of genome data has lead to an ever increasing reliance on bioinformatic predictions, but the accuracy of such predictions remains unclear. My group has been applying "high throughput" functional genomics to characterize the functional of novel transport and metabolic genes, with the objectives of 1) validating the accuracy of our bioinformatics predictions, and 2) developing a complete "systems biology" understanding of microbial physiology.
We have developed an annotation pipeline for automatically identifying and classifying membrane transporters in sequenced genomes and have systematically analyzed every available complete genome. These analyses are available in our relational database, TransportDB, which is publicly accessible at www.membranetransport.org. We have applied these analyses to metabolic reconstruction to build models of the metabolic and transport networks for a variety of sequenced genomes.
My group has also been pursuing ways of validating such models through the use of gene knockouts and microarrays. We have utilized Biolog Phenotype Microrrays to identify phenotypes of gene knockout mutants in a relatively high throughput fashion.
Fig. Metabolic pathways in E. coli
Applying Genomics to Medicine
I have also been interested in utilizing genome sequence data for direct practical applications. One example has been in using genomes for vaccine development using reverse vaccinology approaches: the bioinformatic identification of potential vaccine candidates, followed by high throughput cloning, protein purification and screening offers an attractive approach for the rapid development of vaccines or diagnostics. For instance, we have applied such an approach to vaccine development for the human and animal pathogen Brucella suis in collaboration with Dr. Tom Ding at the Walter Reed Army Institute of Research.
Fig. Genome sequence and microarray of clinical and agricultural bugs
Infant gut microbiota results
Firstly, to the volunteers, thank you very much for volunteering! I have now finished analysing data and have the results!! As you might remember we used the samples collected from your babies as a starting inoculum to culture the gut microbiota in the presence of four different baby cereal products. The cereal products we used are available in the market and they are sourced from wheat, sorghum, rice and oats (unfortunately, I can not disclose the names just yet). However, the effect of these cereal products on the in vitro cultured infant gut microbiota were very similar, this might suggest that feeding babies with any of these products could have similar effects. Each baby is labeled with the unique code provided at the sample submission. Overall, the compositions of the gut microbiota of all the babies looked normal for the age and the diet. We could see more fibre digesting bacterial groups (i.e. Bacteroidaceae) in older babies, while younger babies had more bacteria capable of digesting breast milk (i.e. Bifidobactericeae).
Gut microbiota compositions
This shows the percentage abundance of each group. The names of bacteria are indicated at the family level of bacterial nomenclature. I have labelled bacteria with less than 1% abundance as “Other”. Bacterial groups that were not assigned to a family are labelled as “Unassigned”.