Functional metaproteomics is a powerful approach to discovering new enzymes from microbial communities that can be used for medical or biotechnological applications. This method simultaneously analyses the functional metaproteome and the metagenome, overcoming the need for bacterial cultivability, while still directly measuring the desired enzyme activity. In a functional metaproteomics approach proteins from environmental samples are screened in in-gel-based assays for the desired activity and then selectively identified by mass spectrometry, using microbial metagenome information as template for a computational protein identification.

Here, we optimize and benchmark an in-gel zymography-based activity assay to identify microbial enzymes that catalyze the hydrolytic cleavage of short chain fatty acid (SCFA) esters. For this, a commercially available lipase from Aspergillus oryzae (AOL) and the fluorogenic substrates 4-methylumbelliferyl butyrate (4-MUB) and 4-methylumbelliferyl propionate (4-MUP) were selected. The evaluation of the sensitivity, reproducibility and efficiency of our approach shows that an amount as low as 62.5 ng of AOL can be detected. Overall, we could detect and quantify activity over three orders of magnitude.

Using our method to analyze metaproteomes from a variety of sources, we could determine the presence of SCFA ester-degrading enzymes. Since there is increasing evidence that reduced levels of SCFAs can be associated with pathogenesis of autoimmune and neurodegenerative diseases like multiple sclerosis, identifying compositional alterations of microbiota-derived SCFA-degrading enzymes could contribute to the understanding of the role of the gut microbiome in these pathologies.