Functional metagenomic approach in identify cellulose-degrading enzymes from Malaysian palm oil mill effluent

Abstract

Major source of enzymes is microbes and during cultivation step, more than 85% of them resist cultivation and their genetic patrimony is loss. This work sought to bioprospect cellulose-degrading enzymes from microbiota of the palm oil mill effluent (POME). To get access to this great microbial diversity and to discover the biocatalysts behind, this research has adopted metagenomic approach which technically escapes the cultivation step and screens the microbial DNA for desired enzymatic activity. Metagenomics consists of the creation and screening of metagenomic DNA libraries. In vivo identification of cellulose-degrading enzymes was carried out with high-throughput screening and in silico identification of genes encoded the enzymes was performed with algorithm-based methods while the results validation was executed by recombinant enzymes expression, purification and characterisation. Culture-enrichment strategy based on natural selection principle was used in the early stage to enhance the screening hit rate. Metagenomic DNA was extracted from enriched and non-enriched sample to construct 109,824 fosmids (4.49 Gb) metagenomic DNA library. In this library, pCC1FOS fosmid and E. coli EPI300T1R are the vector and surrogate host of the cloning system, respectively. A high throughput functional metagenomic screen was developed and applied to search for cellulose-degrading enzymes within the library clones using 4-methylumbelliferyl-ß-D-glucopyranoside (MUGlc) and 4-methylumbelliferyl-ß-D-cellobioside (MUC) fluorogenic substrates. The screens were normalised using robust z-score and highest rated clones (100) were then selected. Their fosmids were isolated and sequenced with Hiseq (Illumina) of next-generation sequencing strategy. For quality control of the reads, SolexaQA and FastQC tools were used. Poor quality bases were removed with DynamicTrim algorithm, all bases with Qphred less than 20 were trimmed, and the LengthSort algorithm was used to remove sequences less than 50 bp. de Bruijn graph of de novo assembly algorithm has organised the reads on k-mers to build contigs, and Velvet optimiser has selected the optimum k-mers. These contigs were the input of SSPACE algorithm used to locate and orient contigs. Codon DNA sequences (CDS) were identified with PRODIGAL software. The genes identification was carried out following Blastp and SmartBLAST. Seventeen of bioprospected putative cellulose-degrading enzymes were cloned into pBAD-TOPO plasmid and expressed in TOP10 E.coli cloning. Enzymes were purified with HisTrap HP column with the aid of a FPLC system. Two putative glucanases and two putative ß-glucosidases were then biochemically characterised for optimal pH and temperature in the presence of substrates MUGlc and MUC, pNPG and pNPC and CMC as well. In NGS-data analysis step, 4900 contigs and 3540 scaffolds were constructed. 42,247 CDS were detected and 96 potential cellulose-degrading enzymes were identified which evinces the richness of POME metagenome on biocatalysts. The protein sequences of 15 cellulose-degrading enzymes are 100% similar to protein sequences available in protein databases while 40 enzymes show (80-99%) similarities, 24 enzymes (60-79%), 14 enzymes (40-59%) and 3 enzymes show less than 40% similarity, this reflects the qualification of functional metagenomics to bioprospect untapped enzymes. The potential types of enzymes are 19.20% glucanases, 31.32% glucosidases and 46.48% glucoside hydrolases with cellulose-degrading enzyme conserved domains. For the 17 expressed enzymes, three different glycoside hydrolase families, (enzyme 1, 2, 10 and 21) from GH3, (enzyme 6, 12 and 20) from GH5, (enzyme 3) from GH8 and other glycoside hydrolase families. Enzyme 3 is probably an example of untapped enzyme; it was active toward MUC and MUGlc. The optimum catalysis activity by enzyme 3 occurred at 50 °C and pH 4. For enzymes 4, 11 and 13, no enzymatic activity was detected due to low expression level. This research was very challenging but rewarding. It lays the foundation of diverse and untapped biocatalysts discovery. The bioprospected enzymes found in this metagenomic DNA library can be produced and optimised to be used in different industrial applications. In addition, the NGS-analysed-data can be usd to study the diversity of POME.