Biosensors in environmental biotechnology – the useful tools of contaminants monitoring

Metabolism and available substrates are a major driving force for shaping microbial communities, and a rational interference with microbial community structure for degradation of pollutants necessitates knowledge on metabolic potential and activity of the community members. Recent years have seen tremendous efforts to understand the natural diversity of biodegradation of aromatic pollutants, with the aim of exploiting these findings for bioremediation purposes. These efforts comprise the analysis of metabolic routes, of enzymes and genes involved in degradation, of the variability and diversity in given gene families and a detailed understanding of enzyme mechanisms and substrate specificity determinants. In addition, the genomes of several microbes relevant to biodegradation are being analyzed providing the opportunity to gain global insights into the potential of specific microorganisms. Third, but not least, approaches to analyze and assess biodegradation processes have been shifting towards the application of culture-independent methodologies to characterize natural and engineered pollutant-degrading microbial associations. We will report here on novel insights into the microbial metabolic diversity for aromatic pollutant degradation including representatives of crucial previously uncharacterized enzyme families and give examples of insights into structure–function relationships. The broad knowledge acquired using isolated organisms and has allowed us to use culture-independent studies to assess the diversity and quantity of catabolic genes in response to pollution, rather than simple changes in community structure. The α-subunits of the catalytic ironsulfur proteins of Rieske-type non-heme iron oxygenases represent useful molecular targets for culture-independent functional genotype analyses as they are responsible for substrate specificity and thus for shaping the metabolic net for pollutant degradation in environmental communities. We will report here on their diversity in a polluted environment and on the identification of gene variants which give rise to a new and unexpected degradative phenotype. To detect the selection of catabolic gene types upon selective pressure in eDNA, a microarray was designed targeting crucial gene families which helps to direct efforts to evolutionary branches that are being selected, avoiding the circumstantial targeting of a narrow evolutionary window. The outlined strategy could be implemented for virtually any gene family appropriately represented in the database, and is complementary to activity-based metagenomic screenings.