Open AccessMicrobial characterisation and Cold-Adapted Predicted Protein (CAPP) database construction from the active layer of Greenland's permafrost
Author(s) -
Gilda Varliero,
Muhammad Rafiq,
Swati Singh,
Annabel Summerfield,
Fotis Sgouridis,
Donald A. Cowan,
Gary Barker
Publication year - 2021
Publication title -
fems microbiology ecology/fems microbiology, ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.377
H-Index - 155
eISSN - 1574-6941
pISSN - 0168-6496
DOI - 10.1093/femsec/fiab127
Subject(s) - metagenomics , permafrost , transect , biology , microbial population biology , protein engineering , phylogenetic tree , computational biology , database , ecology , gene , enzyme , biochemistry , genetics , computer science , bacteria
Permafrost represents a reservoir for the biodiscovery of cold-adapted proteins which are advantageous in industrial and medical settings. Comparisons between different thermo-adapted proteins can give important information for cold-adaptation bioengineering. We collected permafrost active layer samples from 34 points along a proglacial transect in southwest Greenland. We obtained a deep read coverage assembly (>164x) from nanopore and Illumina sequences for the purposes of i) analysing metagenomic and metatranscriptomic trends of the microbial community of this area, and ii) creating the Cold-Adapted Predicted Protein (CAPP) database. The community showed a similar taxonomic composition in all samples along the transect, with a solid permafrost-shaped community, rather than microbial trends typical of proglacial systems. We retrieved 69 high- and medium-quality metagenome-assembled clusters, 213 complete biosynthetic gene clusters and more than three million predicted proteins. The latter constitute the CAPP database that can provide cold-adapted protein sequence information for protein- and taxon-focused amino acid sequence modifications for the future bioengineering of cold-adapted enzymes. As an example, we focused on the enzyme polyphenol oxidase, and demonstrated how sequence variation information could inform its protein engineering.