Simple discovery of bacterial biocatalysts from environmental samples through functional metaproteomics
© The Author(s). 2017
Received: 20 December 2016
Accepted: 25 February 2017
Published: 3 March 2017
Bacterial biocatalysts play a key role in our transition to a bio-based, post-petroleum economy. However, the discovery of new biocatalysts is currently limited by our ability to analyze genomic information and our capacity of functionally screening for desired activities. Here, we present a simple workflow that combines functional metaproteomics and metagenomics, which facilitates the unmediated and direct discovery of biocatalysts in environmental samples. To identify the entirety of lipolytic biocatalysts in a soil sample contaminated with used cooking oil, we detected all proteins active against a fluorogenic substrate in sample’s metaproteome using a 2D-gel zymogram. Enzymes’ primary structures were then deduced by tryptic in-gel digest and mass spectrometry of the active protein spots, searching against a metagenome database created from the same contaminated soil sample. We then expressed one of the novel biocatalysts heterologously in Escherichia coli and obtained proof of lipolytic activity.
KeywordsZymogram Lipase Biocatalyst Metagenomics Metaproteomics
A conceptually straightforward way to identify new microbial biocatalysts is the screening of a multitude of organisms isolated from an environmental sample for a desired enzymatic activity . However, due to our inability to cultivate the vast majority of microorganisms in the lab, such a screening will miss potentially more than 99% of organisms present in a given environmental sample . To counter this problem, DNA-based, culture-independent approaches have now become the state-of-the-art in biocatalyst discovery. These methods rely on library-based screening efforts, where an expression library from environmental DNA is screened for a certain activity. Coining the term metagenome, this concept was introduced by Handelsmann and co-workers  and has been used e.g., in large-scale projects to identify lipolytic enzymes from soil metagenomes . This approach typically involves screening hundreds of thousands of clones, and the number of biocatalytically active proteins discovered is dependent on the library size. An alternative is the in silico search for homologs of known biocatalysts in metagenomic datasets, a method we have recently employed ourselves , and which is comprehensively reviewed in . This method uses known structural motifs to find novel enzymes in sequence databases. Rapid advances in sequence-based metagenomics and a plethora of publicly available DNA data have led to a widespread adoption in the scientific community. However, it can be argued that in silico screening loses the immediacy of an activity-based, i.e., structurally unbiased, discovery by adding an additional layer of abstraction in the form of DNA-sequence data.
Here, we present a functional metaproteomic approach as a method for rapid enzyme discovery. This method combines the immediacy of an activity-based screening with the independence from lab-cultivability of “meta-omic” approaches. This approach is conceptually comprehensive as it has the potential to discover all enzymes that exhibit an activity that can be screened for in an environmental sample, in principle facilitating the discovery of novel structure-function pairs. The method does not rely on a comprehensive evaluation of the metagenome and metaproteome data but rather utilizes both to simplify the discovery of proteins exhibiting a desired enzyme activity.
Metaproteomics is quickly becoming a well-established high-throughput “meta-omic” approach to study microbial ecology, as recently reviewed in  and . Metaproteomics was developed by Bond and Wilmes to mine microbiomes for novel proteins from previously uncultured organisms , and one of its earliest applications was the functional study of biocatalysts that degrade organochloride pollutants .
To verify the biocatalytic activity of the uncharacterized hydrolase ML-005, its DNA sequence was synthetized based on the metagenome data and cloned into Escherichia coli in an IPTG-inducible pBR322 -based expression vector with a tac promotor. The protein was then heterologously expressed in E. coli and its lipolytic activity confirmed through in-gel zymography (Fig. 2b, c). Furthermore, crude extract of E. coli expressing ML-005 showed high activity in a standard lipase/esterase enzyme assay, using p-nitrophenyl butyrate as a substrate (Fig. 2d). Lipid hydrolyzing enzymes can be categorized as lipase or esterases, with esterases typically preferring short-chain and lipases preferring long-chain fatty acid esters as substrates. We thus cloned the gene encoding ML-005 into a pET-based expression vector containing a T7-promotor, fusing a C-terminal His6-tag to the protein. We then expressed ML-005 in E. coli BL21 and purified it to homogeneity to test its reactivity towards para-nitrophenyl esters with fatty acids of differing chain-lengths. While ML-005 was effective in hydrolyzing short-chain (C4) and medium-chain length (C8) esters, we were not able detect any activity towards the long-chain p-nitrophenyl palmitate (C16), indicating that ML-005 is an esterase (Fig. 2e).
In conclusion, functional metaproteomics is an efficient tool to directly discover biocatalytic activity in the proteome of an environmental sample. The limitations of our approach pertain to the difficulties inherent in the isolation of proteins and DNA from environmental samples [20–24]. The complete phylogenetic diversity of a sample could only be harnessed if all DNA and all proteins expressed in the sample would be isolated. The method furthermore depends on an effective in-gel refolding of the biocatalyst and the availability of zymographic assays  that can be adapted to screen environmental samples for a certain biocatalytic activity.
Our results show that a simple workflow that combines 2D gel-based proteomics, functional screening, and metagenome-based protein identification makes it possible to identify novel lipolytic enzymes, an important class of biocatalysts, on the protein level, harnessing the phylogenetic diversity found in an environmental sample from a used cooking oil disposal site. We validated our approach by the heterologous expression and purification of the newly discovered and previously unknown esterase ML-005.
We thank Ulrich Kück for his support at the Department of General and Molecular Botany.
Principal funding was provided by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC Grant agreement no 281384–FuMe to L.I.L. J.E.B. acknowledges funding from the State of North Rhine-Westphalia (Synapt G2-S mass spectrometer). M.N. acknowledges funding from the DFG (NO407/5-1).
Availability of data and materials
Sequencing data of the sample was submitted to the European Nucleotide Archive (www.ebi.ac.uk/ena) under project number PRJEB16064 and sample accession number ERP017906. The mass spectrometry proteomics data and the customized database have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD005148 .
LIL, JEB, and PS designed the experiments. PS and SS performed the experiments. PS and SS evaluated the mass spec data. MN, PS, and AK annotated the metagenomics data and assembled the database. PS and LIL wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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