Methods development for metaproteomics-guided bioprospecting of novel enzymes

Abstract: Industrial biotechnology has been announced by several organizations and governments as a key enabling technology for the enhanced economic growth in a low-carbon and knowledge-based bioeconomy. An important goal to promote an environment friendly and sustainable industrial biotechnology is the discovery of new enzymes.To date, almost all enzymes used in industry have been discovered by pure culturing of microorganisms, however, it is known that less than 1% of all microorganisms can be obtained in pure cultures. The remaining majority of microorganisms is only viable by close biological interactions provided in microbial communities and is not available for enzyme discovery using the classical pure culture approaches. The investigation of microbial communities, which can be viewed as metaorganisms, has been enabled during the last two decades by refining established methods for the analysis of genes, mRNA or proteins and are called metagenomics, metatranscriptomics and metaproteomics, respectively. To date, these techniques have mostly been used in the field of microbial ecology for the understanding of the composition, function and metabolism of microbial communities but not for the purpose of bioprospecting for novel enzymes. Identification of genes that code for possible enzyme candidates is hindered, due to the fact that 30-40% of the sequenced metagenomes contain genes coding for unidentified proteins. Additionally, the -omics techniques generate large amounts of data that need to be analyzed and the outcome of the analysis does not necessarily lead to the discovery of novel applicable enzymes.The work presented in this thesis describes the establishment of the necessary conditions for a metaproteomics-based method that allows for a straightforward and targeted identification of novel enzymes with desired activity from microbial communities. The approach provides a valuable alternative to the incomplete and inefficient analysis of non-targeting data and laborious workflow, which is typically generated by the established meta-omics techniques. In developing the methods presented in this thesis, microbial communities in constructed environments were established, which allowed for the controlled expression of extracellular hydrolytic enzymes under defined conditions. By combination and modulation of advanced metaproteomics and metagenomics techniques, we were able to directly identify the enzymes and the corresponding gene sequences of several cellulolytic enzymes as a first example for the feasibility of this approach.

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