A multi-omic screening approach for the discovery of thermoactive glycoside hydrolases

Next-generation sequencing and computational biology have facilitated the implementation of new combinatorial screening approaches to discover novel enzymes of biotechnological interest. In this study, we describe the successful establishment of a multi-omic approach for the identification of thermostable hydrolase-encoding genes by determination of gene expression levels. We applied this combinatorial approach using an anaerobic enrichment culture from an Azorean hot spring sample grown on green coffee beans as recalcitrant substrate. An in-depth analysis of the microbial community resulted in microorganisms capable of metabolizing the selected substrate, such as the genera Caloramator, Dictyoglomus and Thermoanaerobacter as active and abundant microorganisms. To discover glycoside hydrolases, 90,342 annotated genes were screened for specific reaction types. A total number of 106 genes encoding cellulases (EC 3.2.1.4), beta-glucosidases (EC 3.2.1.21) and endo-1,4-beta-mannosidases (EC 3.2.1.78) were selected. Mapping of RNA-Seq reads to the related metagenome led to expression levels for each gene. Amongst those, 14 genes, encoding glycoside hydrolases, showed highest expression values, and were used for further cloning. Four proteins were biochemically characterized and were identified as thermoactive glycoside hydrolases with a broad substrate range. This work demonstrated that a combinatory omic approach is a suitable strategy identifying unique thermoactive enzymes from environmental samples.