Dataset Information

Cloning and identification of novel hydrolase genes from a dairy cow rumen metagenomic library and characterization of a cellulase gene.

ABSTRACT: Interest in cellulose degrading enzymes has increased in recent years due to the expansion of the ellulosic biofuel industry. The rumen is a highly adapted environment for the degradation of cellulose and a promising source of enzymes for industrial use. To identify cellulase enzymes that may be of such use we have undertaken a functional metagenomic screen to identify cellulase enzymes from the bacterial community in the rumen of a grass-hay fed dairy cow.Twenty five clones specifying cellulose activity were identified. Subcloning and sequence analysis of a subset of these hydrolase-positive clones identified 10 endoglucanase genes. Preliminary characterization of the encoded cellulases was carried out using crude extracts of each of the subclones. Zymogram analysis using carboxymethylcellulose as a substrate showed a single positive band for each subclone, confirming that only one functional cellulase gene was present in each. One cellulase gene, designated Cel14b22, was expressed at a high level in Escherichia coli and purified for further characterization. The purified recombinant enzyme showed optimal activity at pH 6.0 and 50°C. It was stable over a broad pH range, from pH 4.0 to 10.0. The activity was significantly enhanced by Mn2+ and dramatically reduced by Fe3+ or Cu2+. The enzyme hydrolyzed a wide range of beta-1,3-, and beta-1,4-linked polysaccharides, with varying activities. Activities toward microcrystalline cellulose and filter paper were relatively high, while the highest activity was toward Oat Gum.The present study shows that a functional metagenomic approach can be used to isolate previously uncharacterized cellulases from the rumen environment.

SUBMITTER: Gong X

PROVIDER: S-EPMC3545987 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cloning and identification of novel hydrolase genes from a dairy cow rumen metagenomic library and characterization of a cellulase gene.

Gong Xia X Gruninger Robert J RJ Qi Meng M Paterson Lyn L Forster Robert J RJ Teather Ron M RM McAllister Tim A TA

BMC research notes 20121013

<h4>Background</h4>Interest in cellulose degrading enzymes has increased in recent years due to the expansion of the ellulosic biofuel industry. The rumen is a highly adapted environment for the degradation of cellulose and a promising source of enzymes for industrial use. To identify cellulase enzymes that may be of such use we have undertaken a functional metagenomic screen to identify cellulase enzymes from the bacterial community in the rumen of a grass-hay fed dairy cow.<h4>Results</h4>Twen ...[more]

PMID: 23062472

Similar Datasets

Project description:BackgroundGoat rumen microbial communities are perceived as one of the most potential biochemical reservoirs of multi-functional enzymes, which are applicable to enhance wide array of bioprocesses such as the hydrolysis of cellulose and hemi-cellulose into fermentable sugar for biofuel and other value-added biochemical production. Even though, the limited understanding of rumen microbial genetic diversity and the absence of effective screening culture methods have impeded the full utilization of these potential enzymes. In this study, we applied culture independent metagenomics sequencing approach to isolate, and identify microbial communities in goat rumen, meanwhile, clone and functionally characterize novel cellulase and xylanase genes in goat rumen bacterial communities.ResultsBacterial DNA samples were extracted from goat rumen fluid. Three genomic libraries were sequenced using Illumina HiSeq 2000 for paired-end 100-bp (PE100) and Illumina HiSeq 2500 for paired-end 125-bp (PE125). A total of 435gb raw reads were generated. Taxonomic analysis using Graphlan revealed that Fibrobacter, Prevotella, and Ruminococcus are the most abundant genera of bacteria in goat rumen. SPAdes assembly and prodigal annotation were performed. The contigs were also annotated using the DOE-JGI pipeline. In total, 117,502 CAZymes, comprising endoglucanases, exoglucanases, beta-glucosidases, xylosidases, and xylanases, were detected in all three samples. Two genes with predicted cellulolytic/xylanolytic activities were cloned and expressed in E. coli BL21(DE3). The endoglucanases and xylanase enzymatic activities of the recombinant proteins were confirmed using substrate plate assay and dinitrosalicylic acid (DNS) analysis. The 3D structures of endoglucanase A and endo-1,4-beta xylanase was predicted using the Swiss Model. Based on the 3D structure analysis, the two enzymes isolated from goat's rumen metagenome are unique with only 56-59% similarities to those homologous proteins in protein data bank (PDB) meanwhile, the structures of the enzymes also displayed greater stability, and higher catalytic activity.ConclusionsIn summary, this study provided the database resources of bacterial metagenomes from goat's rumen fluid, including gene sequences with annotated functions and methods for gene isolation and over-expression of cellulolytic enzymes; and a wealth of genes in the metabolic pathways affecting food and nutrition of ruminant animals.

Project description:Application of manure from antibiotic-treated animals to crops facilitates the dissemination of antibiotic resistance determinants into the environment. However, our knowledge of the identity, diversity, and patterns of distribution of these antibiotic resistance determinants remains limited. We used a new combination of methods to examine the resistome of dairy cow manure, a common soil amendment. Metagenomic libraries constructed with DNA extracted from manure were screened for resistance to beta-lactams, phenicols, aminoglycosides, and tetracyclines. Functional screening of fosmid and small-insert libraries identified 80 different antibiotic resistance genes whose deduced protein sequences were on average 50 to 60% identical to sequences deposited in GenBank. The resistance genes were frequently found in clusters and originated from a taxonomically diverse set of species, suggesting that some microorganisms in manure harbor multiple resistance genes. Furthermore, amid the great genetic diversity in manure, we discovered a novel clade of chloramphenicol acetyltransferases. Our study combined functional metagenomics with third-generation PacBio sequencing to significantly extend the roster of functional antibiotic resistance genes found in animal gut bacteria, providing a particularly broad resource for understanding the origins and dispersal of antibiotic resistance genes in agriculture and clinical settings. IMPORTANCE The increasing prevalence of antibiotic resistance among bacteria is one of the most intractable challenges in 21st-century public health. The origins of resistance are complex, and a better understanding of the impacts of antibiotics used on farms would produce a more robust platform for public policy. Microbiomes of farm animals are reservoirs of antibiotic resistance genes, which may affect distribution of antibiotic resistance genes in human pathogens. Previous studies have focused on antibiotic resistance genes in manures of animals subjected to intensive antibiotic use, such as pigs and chickens. Cow manure has received less attention, although it is commonly used in crop production. Here, we report the discovery of novel and diverse antibiotic resistance genes in the cow microbiome, demonstrating that it is a significant reservoir of antibiotic resistance genes. The genomic resource presented here lays the groundwork for understanding the dispersal of antibiotic resistance from the agroecosystem to other settings.

Dataset Information

Cloning and identification of novel hydrolase genes from a dairy cow rumen metagenomic library and characterization of a cellulase gene.

Publications

Cloning and identification of novel hydrolase genes from a dairy cow rumen metagenomic library and characterization of a cellulase gene.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets