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.

Industrial biotechnology is a large and growing industry as it is part of establishing a “greener” and more sustainable bioeconomy-based society. Using enzymes as biocatalysts is a viable alternative to chemicals and energy intense industrial processes and is en route to a more sustainable industry. Enzymes have been used in different areas for ages and are today used in many industrial processes such as biofuels production, food industry, tanning, chemical synthesis, pharmaceuticals etc. Enzymes are today a billion-dollar industry in itself and the demand for novel catalysts for various present and future processes of renewable resources are high and perfectly in line with converting to a more sustainable society.

Most enzymes used in industry today have been identified from isolated and pure cultured microorganisms with identified desirable traits and enzymatic capacities. However, it is known that less than 1% of all microorganisms can be can be obtained in pure cultures. Thus, if we were to rely solely on pure culturing, this would leave the 99% of the microorganisms that constitutes the “microbial dark matter” uninvestigated for their potential in coding for and producing valuable novel enzymes. Therefore, to investigate these “unculturable” microorganisms for novel and valuable enzymes, pure-culture independent methods are needed.

During the last two decades there has been a fast and extensive development in techniques and methods applicable for this purpose. Especially important has been the advancements made in mass spectrometry for protein identification and next generation sequencing of DNA. With these technical developments new research fields of proteomics and genomics have been developed, by which the complete protein complement of cells (the proteome) and all genes (the genome) of organisms can be investigated. When these techniques are applied to microbial communities these fields of research are known as meta-proteomics and meta-genomics.

However, when applied to complex microbial communities, difficulties different from those encountered in their original usage for analysis of single multicellular organisms or cell linages arises, and when used independently both methods have their own limitations and bottlenecks. In addition, both metaproteomics and metagenomics are largely non-targeting techniques. Thus, if the purpose is still to - somewhat contradictory – use these non-targeting methods for targeted identification of novel enzymes with certain desired activities and properties from within microbial communities, special measures need to be taken.

The work presented in this thesis describes the development of a method that combines

metaproteomics and metagenomics (i.e. metaproteogenomics) for the targeted discovery of novel enzymes with desired activities, and their correct coding genes, from within microbial communities. Thus, what is described is a method that can be used to circumvent the pure-culturing problem so that a much larger fraction of the microbial dark matter can be specifically investigated for the identification of novel valuable enzymes.