Dataset Information

Mining of unexplored habitats for novel chitinases--chiA as a helper gene proxy in metagenomics.

ABSTRACT: The main objective of this study was to assess the abundance and diversity of chitin-degrading microbial communities in ten terrestrial and aquatic habitats in order to provide guidance to the subsequent exploration of such environments for novel chitinolytic enzymes. A combined protocol which encompassed (1) classical overall enzymatic assays, (2) chiA gene abundance measurement by qPCR, (3) chiA gene pyrosequencing, and (4) chiA gene-based PCR-DGGE was used. The chiA gene pyrosequencing is unprecedented, as it is the first massive parallel sequencing of this gene. The data obtained showed the existence across habitats of core bacterial communities responsible for chitin assimilation irrespective of ecosystem origin. Conversely, there were habitat-specific differences. In addition, a suite of sequences were obtained that are as yet unregistered in the chitinase database. In terms of chiA gene abundance and diversity, typical low-abundance/diversity versus high-abundance/diversity habitats was distinguished. From the combined data, we selected chitin-amended agricultural soil, the rhizosphere of the Arctic plant Oxyria digyna and the freshwater sponge Ephydatia fluviatilis as the most promising habitats for subsequent bioexploration. Thus, the screening strategy used is proposed as a guide for further metagenomics-based exploration of the selected habitats.

SUBMITTER: Cretoiu MS

PROVIDER: S-EPMC3353111 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Mining of unexplored habitats for novel chitinases--chiA as a helper gene proxy in metagenomics.

Cretoiu Mariana Silvia MS Kielak Anna Maria AM Abu Al-Soud Waleed W Abu Al-Soud Waleed W Sørensen Søren J SJ van Elsas Jan Dirk JD

Applied microbiology and biotechnology 20120425 5

The main objective of this study was to assess the abundance and diversity of chitin-degrading microbial communities in ten terrestrial and aquatic habitats in order to provide guidance to the subsequent exploration of such environments for novel chitinolytic enzymes. A combined protocol which encompassed (1) classical overall enzymatic assays, (2) chiA gene abundance measurement by qPCR, (3) chiA gene pyrosequencing, and (4) chiA gene-based PCR-DGGE was used. The chiA gene pyrosequencing is unp ...[more]

PMID: 22526805

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Project description:Small mammal species play an important role influencing vegetation primary productivity and plant species composition, seed dispersal, soil structure, and as predator and/or prey species. Species which experience population dynamics cycles can, at high population phases, heavily impact agricultural sectors and promote rodent-borne disease transmission. To better understand the drivers behind small mammal distributions and abundances, and how these differ for individual species, it is necessary to characterise landscape variables important for the life cycles of the species in question. In this study, a suite of Earth observation derived metrics quantifying landscape characteristics and dynamics, and in-situ small mammal trapline and transect survey data, are used to generate random forest species distribution models for nine small mammal species for study sites in Narati, China and Sary Mogul, Kyrgyzstan. These species distribution models identify the important landscape proxy variables driving species abundance and distributions, in turn identifying the optimal conditions for each species. The observed relationships differed between species, with the number of landscape proxy variables identified as important for each species ranging from 3 for Microtus gregalis at Sary Mogul, to 26 for Ellobius tancrei at Narati. Results indicate that grasslands were predicted to hold higher abundances of Microtus obscurus, E. tancrei and Marmota baibacina, forest areas hold higher abundances of Myodes centralis and Sorex asper, with mixed forest-grassland boundary areas and areas close to watercourses predicted to hold higher abundances of Apodemus uralensis and Sicista tianshanica. Localised variability in vegetation and wetness conditions, as well as presence of certain habitat types, are also shown to influence these small mammal species abundances. Predictive application of the Random Forest (RF) models identified spatial hot-spots of high abundance, with model validation producing R2 values between 0.670 for M. gregalis transect data at Sary Mogul to 0.939 for E. tancrei transect data at Narati. This enhances previous work whereby optimal habitat was defined simply as presence of a given land cover type, and instead defines optimal habitat via a combination of important landscape dynamic variables, moving from a human-defined to species-defined perspective of optimal habitat. The species distribution models demonstrate differing distributions and abundances of host species across the study areas, utilising the strengths of Earth observation data to improve our understanding of landscape and ecological linkages to small mammal distributions and abundances.

Dataset Information

Mining of unexplored habitats for novel chitinases--chiA as a helper gene proxy in metagenomics.

Publications

Mining of unexplored habitats for novel chitinases--chiA as a helper gene proxy in metagenomics.

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