Environmental activity-based protein profiling for function-driven enzyme discovery from natural communities

Abstract

Background: Microbial communities are important drivers of global biogeochemical cycles, xenobiotic detoxification, as well as organic matter decomposition. Their major metabolic role in ecosystem functioning is ensured by a unique set of enzymes, providing a tremendous yet mostly hidden enzymatic potential. Exploring this enzymatic repertoire is therefore not only relevant for a better understanding of how microorganisms function in their natural environment, and thus for ecological research, but further turns microbial communities, in particular from extreme habitats, into a valuable resource for the discovery of novel enzymes with potential applications in biotechnology. Different strategies for their uncovering such as bioprospecting, which relies mainly on metagenomic approaches in combination with sequence-based bioinformatic analyses, have emerged; yet accurate function prediction of their proteomes and deciphering the in vivo activity of an enzyme remains challenging.

Results: Here, we present environmental activity-based protein profiling (eABPP), a multi-omics approach that extends genome-resolved metagenomics with mass spectrometry-based ABPP. This combination allows direct profiling of environmental community samples in their native habitat and the identification of active enzymes based on their function, even without sequence or structural homologies to annotated enzyme families. eABPP thus bridges the gap between environmental genomics, correct function annotation, and in vivo enzyme activity. As a showcase, we report the successful identification of active thermostable serine hydrolases from eABPP of natural microbial communities from two independent hot springs in Kamchatka, Russia.

Conclusions: By reporting enzyme activities within an ecosystem in their native state, we anticipate that eABPP will not only advance current methodological approaches to sequence homology-guided enzyme discovery from environmental ecosystems for subsequent biocatalyst development but also contributes to the ecological investigation of microbial community interactions by dissecting their underlying molecular mechanisms.

Keywords: Activity-based protein profiling; Chemical proteomics; Click chemistry; Environmental microbial communities; Hot springs; Metagenomics; Metaproteomics; Serine hydrolases; Target identification.

Fig. 1

Environmental ABPP workflow. Workflow of the established environmental ABPP (eABPP) approach for the function-based identification of serine hydrolases. This approach can be divided into four different blocks, i.e., sampling and in vivo labeling of an environmental microbial community, metagenomics, target protein identification by LC-MS/MS, and enzyme characterization of a protein of interest

Fig. 2

Location of the sampled springs and two-step chemical labeling of serine hydrolases. (a) The map shows the location of the two sampled springs ‘Arkashin shurf’ (KAM3811) and ‘Helicopter spring’ (KAM3808) in the Uzon Caldera region, Kamchatka Peninsula, Russia. (b, c) A representative picture is shown along with the exact coordinates and physicochemical properties of both springs. (d) Chemical structure of the employed FP-alkyne probe and overview of the reaction mechanism taking place at the active site of serine hydrolases during activity-based in vivo labeling of microbial communities from the two springs. (e) Attachment of the trifunctional reporter 5/6-TAMRA-azide-biotin via a copper-catalyzed azide-alkyne cycloaddition (CuAAC) for downstream target enzyme enrichment in a second in vitro step after protein extraction from sediments

Fig. 3

Distribution of microorganisms across KAM3811 and KAM3808. (a) The proportion of Bacteria (cyan) and Archaea (magenta) within the sediments sampled for eABPP is depicted as pie diagrams for KAM3811 and KAM3808. An overview of the relative distribution of representative phyla from these domains is given respectively based on the GTDB taxonomy. (b) Phylogenetic tree displaying the relationship between the microorganisms found across both springs as calculated with GTDB-Tk based on the dereplicated, binned, and curated metagenomes from KAM3811 and KAM3808. Relative abundances of microorganisms based on the coverage of genomes are given for KAM3811 (magenta) and KAM3808 (light blue), respectively, along with their genome completeness (light green), contamination (purple), GC content (light red), and genome length (dark cyan) as calculated via checkM. The yellow stars indicate the genomes from which predicted serine hydrolases were confidently identified with the applied eABPP approach

Fig. 4

Predicted serine hydrolases identified from the sampled hot springs. Log2-fold enrichment of identified proteins labeled with FP-alkyne compared to the DMSO control for the sediments sampled from (a) KAM3811 and (b) KAM3808. Proteins predicted as serine hydrolases are displayed as colored dots (green: p-value ≤ 0.05, orange: p-value ≥ 0.05). The protein chosen for function validation is circled in red. Hits lying above the dotted line were more than two-fold enriched with the probe and were therefore considered primary hits. Each treatment group comprised three biological replicates

Fig. 5

Characterization of the putative esterase. (a) Predicted structure of the UPF0227 protein with the secondary structures visualized in red (alpha helices) and purple (beta strands). The five-stranded parallel beta-sheet consists of the strands β1, β2, β3, β6 and β7. (b) Surface-displaying structure of the putative esterase. The close-up displays the conserved serine hydrolase motif GxSxG, comprising the residues G63, T64, S65, L66, and G67, which is located in a substrate pocket (depicted in red). Structure prediction was performed with AlphaFold and the output was processed in Chimera. (c) Kinetic characterization of the esterase using the pNP-substrates pNP-acetate, -butyrate, -octanoate, -decanoate, and -dodecanoate at concentrations up to 0.7 mM at pH 8.0 and 70 °C and calculation of V_max and K_m. Values represent the mean of three technical replicates ± SD. (d) In vitro labeling of varying amounts of the esterase with FP-alkyne in the presence or absence of paraoxon