Mining of novel thermo-stable cellulolytic genes from a thermophilic cellulose-degrading consortium by metagenomics

Abstract

In this study, metagenomics was applied to characterize the microbial community and to discover carbohydrate-active genes of an enriched thermophilic cellulose-degrading sludge. The 16S analysis showed that the sludge microbiome was dominated by genus of cellulolytic Clostridium and methanogenesis Methanothermobacter. In order to retrieve genes from the metagenome, de novo assembly of the 11,930,760 Illumina 100 bp paired-end reads (totally 1.2 Gb) was carried out. 75% of all reads was utilized in the de novo assembly. 31,499 ORFs (Open Reading Frame) with an average length of 852 bp were predicted from the assembly; and 64% of these ORFs were predicted to present full-length genes. Based on the Hidden Markol Model, 253 of the predicted thermo-stable genes were identified as putatively carbohydrate-active. Among them the relative dominance of GH9 (Glycoside Hydrolase) and corresponding CBM3 (Carbohydrate Binding Module) revealed a cellulosome-based attached metabolism of polysaccharide in the thermophilic sludge. The putative carbohydrate-active genes ranged from 20% to 100% amino acid sequence identity to known proteins in NCBI nr database, with half of them showed less than 50% similarity. In addition, the coverage of the genes (in terms of ORFs) identified in the sludge were developed into three clear trends (112×, 29× and 8×) in which 85% of the high coverage trend (112×) mainly consisted of phylum of Firmicutes while 49.3% of the 29× trend was affiliated to the phylum of Chloroflexi.

Figure 1. Plot of the number of reads aligned to each ORF as a function of the length of the ORF.

The ORFs were generated from contigs longer than 1000 bp. The number of reads aligned to each ORF was determined by SAMTools package. The ORFs were colored according to their taxonomy classification by MEGAN’s LCA algorithm at phylum level. The number of ORFs assigned to each phylum was listed following the phylum name. Insert: taxonomy distribution of ORFs in the three coverage trends demonstrated in the outside frame.

Figure 2. Taxonomy classification of the metagenome at class level based on RMORF approach.

ORFs were assigned by default MEGAN LCA algorithm; only nodes with over 5 ORFs and 1000 reads assigned are shown. The circles are drawn based on the number of reads assigned to the particular node. The number after description denotes, respectively, the sum of reads and ORFs assigned below the particular node. The circles are colored according to its classification at phylum level as in Figure 1. Insert: the relative distribution of annotated reads and ORFs in the major phyla.

Figure 3. ORF and Reads assignment to KEGG Methanogenesis Pathway.

Blue square indicates this enzyme has at least one ORF assigned; Yellow square indicates this enzyme has at least one read assigned. Insert: numbers of ORFs and reads assigned to enzymes in the pathway. Metabolism modules are highlighted in different colors: blue, “Formate to Methane”; green, “Acetate to Methane”; purple, “Methanol to Methane”; yellow, “Coenzyme M synthesis”; red, enzymes shared among different modules.

Figure 4. Similarity distribution of predicted ORFs with thermo-stable carbohydrate-active genes against NCBI nr database by BLASTp (E-value ≤1E-5).

Figure 5. Comparison of predicted carbohydrate-active genes (top chart) and carbohydrate-binding modules (bottom chart) in three cellulosic materials fed metagenomes: rumen microbiome

, termite hindgut microbiome and the enriched thermophilic cellulolytic sludge microbiome from this study. Glycoside hydrolase (GH) families are assigned to different categories based on the classification published by Pope et al. PFAMs associated with particular GHs and CBMs are listed in Table S3 and S4. Gene counts include both complete ORFs and ORF fragments.