Identification and Characterization of a Novel Carboxylesterase Belonging to Family VIII with Promiscuous Acyltransferase Activity Toward Cyanidin-3-O-Glucoside from a Soil Metagenomic Library

Association of Bacterial Communities with Psychedelic Mushroom and Soil as Revealed in 16S rRNA Gene Sequencing

Microbial communities' resident in the mushroom fruiting body and the soil around it play critical roles in the growth and propagation of the mushroom. Among the microbial communities associated with psychedelic mushrooms and the rhizosphere soil, bacterial communities are considered vital since their presence greatly influences the health of the mushrooms. The present study aimed at finding the microbiota present in the psychedelic mushroom Psilocybe cubensis and the soil the mushroom inhabits. The study was conducted at two different locations in Kodaikanal, Tamil Nadu, India. The composition and structure of microbial communities in the mushroom fruiting body and the soil were deciphered. The genomes of the microbial communities were directly assessed. High-throughput amplicon sequencing revealed distinct microbial diversity in the mushroom and the related soil. The interaction of environmental and anthropogenic factors appeared to have a significant impact on the mushroom and soil microbiome. The most abundant bacterial genera were Ochrobactrum, Stenotrophomonas, Achromobacter, and Brevundimonas. Thus, the study advances the knowledge of the composition of the microbiome and microbial ecology of a psychedelic mushroom, and paves the way for in-depth investigation of the influence of microbiota on the mushroom, with special emphasis on the impact of bacterial communities on mushroom growth. Further studies are required for a deeper understanding of the microbial communities that influence the growth of P. cubensis mushroom.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.