Metasecretome-selective phage display approach for mining the functional potential of a rumen microbial community

Milica Ciric, Christina D. Moon, Sinead C Leahy, Christopher J Creevey, Eric Altermann, Graeme T. Attwood, Jasna Rakonjac, Dragana Gagic

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

14 Dyfyniadau (Scopus)
159 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Background
In silico, secretome proteins can be predicted from completely sequenced genomes using various available algorithms that identify membrane-targeting sequences. For metasecretome (collection of surface, secreted and transmembrane proteins from environmental microbial communities) this approach is impractical, considering that the metasecretome open reading frames (ORFs) comprise only 10% to 30% of total metagenome, and are poorly represented in the dataset due to overall low coverage of metagenomic gene pool, even in large-scale projects.

Results
By combining secretome-selective phage display and next-generation sequencing, we focused the sequence analysis of complex rumen microbial community on the metasecretome component of the metagenome. This approach achieved high enrichment (29 fold) of secreted fibrolytic enzymes from the plant-adherent microbial community of the bovine rumen. In particular, we identified hundreds of heretofore rare modules belonging to cellulosomes, cell- surface complexes specialised for recognition and degradation of the plant fibre.

Conclusions
As a method, metasecretome phage display combined with next-generation sequencing has a power to sample the diversity of low-abundance surface and secreted proteins that would otherwise require exceptionally large metagenomic sequencing projects. As a resource, metasecretome display library backed by the dataset obtained by next-generation sequencing is ready for i) affinity selection by standard phage display methodology and ii) easy purification of displayed proteins as part of the virion for individual functional analysis.
Iaith wreiddiolSaesneg
Rhif yr erthygl356
CyfnodolynBMC Genomics
Cyfrol15
Rhif cyhoeddi1
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 12 Mai 2014

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