The identification of novel enzymes for use in industrial biotechnology is an important goal in enzyme discovery. The need for novel biocatalysts for sustainable and efficient bioenergy production and the development of new biomaterials especially gives rise to new strategic opportunities of proteomic research. Most industrially relevant enzymes to date have been isolated from pure cultured microorganisms. It is however well established that only a small fraction of all existing microorganisms can be obtained in pure cultures, thus limiting the potential of finding novel enzymes. The possibility to identify valuable enzymes directly from complete microbial communities would therefore potentially give access to a huge number of novel enzyme candidates.
Metaproteomics, or “the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time” has hitherto mainly been used to understand ecosystem function. In order to reach our goals we have instead used the dynamics of metaproteomics to develop a method based on “induced differential metaproteomics”, in which a desired enzyme activity is induced in a microbial population and compared to a non-induced reference of the very same population. In a first example the goal was to induce, select and identify cellulases from a methanogenic community, maintained in a biogas reactor at a metabolic steady-state in a chemically defined medium.
Two aliquots were subtracted from the reactor, of which one was treated to induce cellulase activity. At the peak of cellulase activity and biogas production in the induced sample, proteins from the liquid phase of the two samples were prepared for 2D-DIGE of the extra-cellular proteins. Out of several hundred protein spots generated by the microbial community and visible in the 2D-DIGE experiment, 95 could be identified as up-regulated in the induced sample by image analysis, as compared to the references (thus representing potential cellulases). In-gel digestion and tandem mass-spectrometry of located and selected up-regulated proteins revealed that 18 out of 30 proteins could be assigned as cellulases or associated to cellulolytic activity giving a remarkable hit-rate of 60 % and thus demonstrating the feasibility of the approach.
These cellulases found can be expected to be highly active and stable at the conditions in which they are naturally produced (pH, temp., salinity etc.). A strategic objective of research, both in academia and in thebiotechnology industry, is to identify novel, highly active microbial enzymes that are stable at the different conditions of various industrial applications. Thus, one of our future prospects includes to further employ the described methodology to identify novel enzymes from microbial communities originating from more extreme environments.