Evaluating the biodegradation of aromatic hydrocarbons by monitoring of several functional genes

Fine-scale degrader community profiling over an aerobic/anaerobic redox gradient in a toluene-contaminated aquifer

Hydrocarbon contaminants in groundwater can be degraded by microbes under different redox settings, forming hot spots of degradation especially at the fringes of contaminant plumes. At a tar-oil-contaminated aquifer in Germany, it was previously shown that the distribution of anaerobic toluene degraders as traced via catabolic and ribosomal marker genes is highly correlated to zones of increased anaerobic degradation at the lower fringe of the plume. Here, we trace the respective distribution of aerobic toluene degraders over a fine-scale depth transect of sediments taken at the upper fringe of the plume and below, based on the analysis of 16S rRNA genes as well as catabolic markers in intervals of 3-10 cm. Well-defined small-scale distribution maxima of typical aerobic degrader lineages within the Pseudomonadaceae, Comamonadaceae and Burkholderiaceae are revealed over the redox gradient. An unexpected maximal abundance of 9.2 × 10⁶ toluene monooxygenase (tmoA) genes per g of sediment was detected in the strongly reduced plume core, and gene counts did not increase towards the more oxidized upper plume fringe. This may point towards unusual ecological controls of these yet unidentified aerobic degraders, and indicates that competitive niche partitioning between aerobic and anaerobic hydrocarbon degraders in the field is not yet fully understood. These findings demonstrate the potential of catabolic marker gene assays in elaborating the ecology of contaminant plumes, which is a prerequisite for developing integrated monitoring strategies for natural attenuation.

Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis

In the past decade, over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms. These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism, leading to greater understanding of observed phenotypes. These methods have been sparsely applied to comparisons between multiple organisms, however, due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared. To circumvent this obstacle, we developed a novel process, termed metabolic network reconciliation, whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based. This process was applied to two organisms of great importance to disease and biotechnological applications, Pseudomonas aeruginosa and Pseudomonas putida, respectively. The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences (to the degree that is currently known), rather than artifacts of the reconstruction process. The reconstructions were re-validated with various experimental data after reconciliation. With the reconciled and validated reconstructions, we performed a genome-wide comparison of metabolic flexibility between P. aeruginosa and P. putida that generated significant new insight into the underlying biology of these important organisms. Through this work, we provide a novel methodology for reconciling models, present new genome-scale reconstructions of P. aeruginosa and P. putida that can be directly compared at a network level, and perform a network-wide comparison of the two species. These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads, and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species.