Living soil of fifty-year-old saw mill: Dawn bioresource with differential hydrolytic potentials

• Published in: Ecological genetics and genomics 2020-10, Vol.16, p.100061, Article 100061
• Main Authors: Pabbathi, Ninian Prem Prashanth, Mootapally, Chandrashekar, Velidandi, Aditya, Dave, Kaushambee, Nathani, Neelam M., Baadhe, Rama Raju
• Format: Article
• Language: English
• Subjects: Bacterial diversity; Cellulase; Metagenomics; Sawmill
• ISSN: 2405-9854; 2405-9854
• DOI: 10.1016/j.egg.2020.100061

Microbial prospection and evaluation of different novel cellulases requires a comprehensive biomass degrading microbial diversity. For studying microbial ecology of uncultivable microbes, the most potent approach would be high-throughput sequencing. In the present study, with the aim to identify potent cellulose degrading microbial diversity, samples were collected from a 50-year-old saw mill soil at two different depths of 4 cms (D1) and 12 cms (D2) from the ground level. Amplicon metagenomics was performed from the collected samples using Illumina Miseq platform followed by analysis using QIIME and MG-RAST. More than 75% of the microbiota was common to both the studied samples, with hits corresponding to 12 different phyla including unclassified bacteria as the most dominant followed by Proteobacteria. The most abundant genera were falling under four major phyla viz., Proteobacteria, Actinobacteria, Unclassified bacteria and Verrucomicrobia. Results revealed that there are 14 species unique to D1 and 9 unique to D2. D1 has abundant cellulose degraders (Bacillus foraminis, Byssovorax cruenta, Burkholderia tuberum, Bacteroides fragilis, Bacillus selenatarsenatis, Ammoniphilus oxalaticus etc.) when compared to D2 (Altererythrobacter epoxidivorans etc.). In context to the site under study, the results summarized in the work provide a primary snapshot of the microbial composition in sawmill soil and suggests that bio-prospection of the studied microbiome for carbohydrate degrading genes would yield potentially novel genes due to the abundance of unclassified and uncultured bacteria. Further, gene mining and expression study from the studied microbiota would yield potential enzymes for agroindustrial applications.