Abstract

The need for new antibiotics has sparked a search for the microbes that might potentially produce them. Current sequencing technologies allow us to explore the biotechnological potential of microbial communities in diverse environments without the need for cultivation, benefitting natural product discovery in diverse ways. A relatively recent method to search for the possible production of novel compounds includes studying the diverse genes belonging to polyketide synthase pathways (PKS), as these complex enzymes are an important source of novel therapeutics. In order to explore the biotechnological potential of the microbial community from the largest underground aquifer in the world located in the Yucatan, we used a polyphasic approach in which a simple, non-computationally intensive method was coupled with direct amplification of environmental DNA to assess the diversity and novelty of PKS type I ketosynthase (KS) domains. Our results suggest that the bioinformatic method proposed can indeed be used to assess the novelty of KS enzymes; nevertheless, this in silico study did not identify some of the KS diversity due to primer bias and stringency criteria outlined by the metagenomics pipeline. Therefore, additionally implementing a method involving the direct cloning of KS domains enhanced our results. Compared to other freshwater environments, the aquifer was characterized by considerably less diversity in relation to known ketosynthase domains; however, the metagenome included a family of KS type I domains phylogenetically related, but not identical, to those found in the curamycin pathway, as well as an outstanding number of thiolases. Over all, this first look into the microbial community found in this large Yucatan aquifer and other fresh water free living microbial communities highlights the potential of these previously overlooked environments as a source of novel natural products.

• Keywords: natural product discovery; polyketide synthase; secondary metabolites; Yucatan aquifer; microbial metagenomics