As Told By Stephen Dixon

An Unusual Interaction

Recently, David Lloyd, a TA, came across an interesting study which analyzed the capacity of bacteria to degrade naphthenic acids. According to the study, both Pseudomonas putidas and Pseudomonas fluorescens are capable of degrading small amounts of naphthenic acids, but when put in co-cultures with each other, their capacity increases to 95% elimination. Whats more is that this degradation occurs in mixtures consisting of a large variety of naphthenic acids including those with one, two, and three rings. The inference we drew from this effect is that there are some unique genes within each bacteria that, when allowed to interact with each other, are responsible for the degradation of naphthenic acids. We hypothesized that it is possible to narrow down the candidates for this pathway by using a bioinformatic survey.

Strategy for the Bioinformatic Survey

The goal of the bioinformatic survey is to provide leads to the experimental side (or wet lab) of the project. Two assumptions were made at the beginning of the survey. The first is that the two genomes are homologous enough to eliminate a substantial portion of each genome from consideration. The other assumption is that the gene of interest is located within the non-homologous regions of the either genome. If both assumptions are correct, then it should be possible to create a short list of candidate genes involved in the degradation pathway. Knowing what genes are involved in the degradation means that wet lab can simply look upstream for a naphthenic acid promoter.

However, it is also possible that the degradation pathway is the same, and that the organisms are simply conferring a survival advantage to each other. In that case, (INSERT STRATEGY HERE)

Preliminary Research

My initial thinking is that both genomes consist of two parts: parts that have a homology to the other genome, and parts that don't. Eliminating the parts that do by definition would immediately reveal the parts that aren't. Since homology tends to be between similar sequences rather than exact matches, a statistical approach could be used that determines homology based on the significance of the finding.

Patrick Wu, my colleague, started off by looking for software that we could reuse for our application. DNA manipulation is sufficicently complicated enough that it makes no sense to reinvent the wheel; after some digging around, he found an open-source DNA alignment tool called MUMmer. MUMmer uses a suffix tree (a type of data structure) that works in O(n) time to rapidly align whole genomes; in the process, MUMmer provides information on single nucleotide changes, translocations, and homologous/similar genes.

Currently, we are looking into how MUMmer can be used to compare the homology of the genomes. If it turns out that the homology is not significant enough, then some other criteria must be used to narrow down the list of unique genes.