TITLE:
Towards a Comprehensive Search of Putative Chitinases Sequences in Environmental Metagenomic Databases
AUTHORS:
Aline S. Romão-Dumaresq, Adriana M. Fróes, Rafael R. C. Cuadrat, Floriano P. Silva, Alberto M. R. Dávila
KEYWORDS:
Chitinase; Metagenome; pHMM; Sequence Search
JOURNAL NAME:
Natural Science,
Vol.6 No.5,
March
13,
2014
ABSTRACT: Chitinases catalyze the hydrolysis of chitin,
a linear homopolymer of β-(1,4)-linked N-acetylglucosamine. The broad range of applications of
chitinolytic enzymes makes their identification and study very promising.
Metagenomic approaches offer access to functional genes in uncultured
representatives of the microbiota and hold great potential in the discovery of
novel enzymes, but tools to extensively explore these data are still scarce. In
this study, we develop a chitinase mining pipeline to facilitate the
comprehensive search of these enzymes in environmental metagenomic databases
and also to explore phylogenetic relationships among the retrieved sequences.
In order to perform the analyses, UniprotKB fungal and bacterial chitinases
sequences belonging to the glycoside hydrolases (GH) family-18, 19 and 20 were
used to generate 15 reference datasets, which were then used to generate high
quality seed alignments with the MAFFT program. Profile Hidden Markov Models
(pHMMs) were built from each seed alignment using the hmmbuild program of HMMER v3.0 package. The best-hit
sequences returned by hmmsearch against two environmental metagenomic databases (Community
Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis—CAMERA and
Integrated Microbial Genomes—IMG/M) were retrieved and further analyzed. The NJ
trees generated for each chitinase dataset showed some variability in the
catalytic domain region of the metagenomic sequences and revealed common
sequence patterns among all the trees. The scanning of the retrieved metagenomic
sequences for chitinase conserved domains/signatures using both the
InterPro and the RPS-BLAST tools confirmed the efficacy and sensitivity of our pHMM-based
approach in detecting putative chitinases sequences. These analyses provide
insight into the potential reservoir of novel molecules in metagenomic
databases while supporting the chitinase mining pipeline developed in this
work. By using our chitinase mining pipeline, a larger number of previously
unannotated metagenomic chitinase sequences can be classified, enabling further
studies on these enzymes.