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

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.

Chemoproteomics Reveals the Pan-HER Kinase Inhibitor Neratinib To Target an Arabidopsis Epoxide Hydrolase Related to Phytohormone Signaling.

Plant phytohormone pathways are regulated by an intricate network of signaling components and modulators, many of which still remain unknown. Here, we report a forward chemical genetics approach for the identification of functional SA agonists in Arabidopsis thaliana that revealed Neratinib (Ner), a covalent pan-HER kinase inhibitor drug in humans, as a modulator of SA signaling. Instead of a protein kinase, chemoproteomics unveiled that Ner covalently modifies a surface-exposed cysteine residue of Arabidopsis epoxide hydrolase isoform 7 (AtEH7), thereby triggering its allosteric inhibition. Physiologically, the Ner application induces jasmonate metabolism in an AtEH7-dependent manner as an early response. In addition, it modulates PATHOGENESIS RELATED 1 (PR1) expression as a hallmark of SA signaling activation as a later effect. AtEH7, however, is not the exclusive target for this physiological readout induced by Ner. Although the underlying molecular mechanisms of AtEH7-dependent modulation of jasmonate signaling and Ner-induced PR1-dependent activation of SA signaling and thus defense response regulation remain unknown, our present work illustrates the powerful combination of forward chemical genetics and chemical proteomics for identifying novel phytohormone signaling modulatory factors. It also suggests that marginally explored metabolic enzymes such as epoxide hydrolases may have further physiological roles in modulating signaling.

Identification of fungal lignocellulose-degrading biocatalysts secreted by Phanerochaete chrysosporium via activity-based protein profiling.

Activity-based protein profiling (ABPP) has emerged as a versatile biochemical method for studying enzyme activity under various physiological conditions, with applications so far mainly in biomedicine. Here, we show the potential of ABPP in the discovery of biocatalysts from the thermophilic and lignocellulose-degrading white rot fungus Phanerochaete chrysosporium. By employing a comparative ABPP-based functional screen, including a direct profiling of wood substrate-bound enzymes, we identify those lignocellulose-degrading carbohydrate esterase (CE1 and CE15) and glycoside hydrolase (GH3, GH5, GH16, GH17, GH18, GH25, GH30, GH74 and GH79) enzymes specifically active in presence of the substrate. As expression of fungal enzymes remains challenging, our ABPP-mediated approach represents a preselection procedure for focusing experimental efforts on the most promising biocatalysts. Furthermore, this approach may also allow the functional annotation of domains-of-unknown functions (DUFs). The ABPP-based biocatalyst screening described here may thus allow the identification of active enzymes in a process of interest and the elucidation of novel biocatalysts that share no sequence similarity to known counterparts.