Great Abilities of Shinella zoogloeoides Strain from a Landfarming Soil for Crude Oil Degradation and a Synergy Model for Alginate-Bead-Entrapped Consortium Efficiency

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Abstract

Oil contamination is of great concern worldwide and needs to be properly addressed. The present work aimed to contribute to the development of bacterial consortia for oil recovery. We investigated the community structure of a landfarming-treated soil (LF2) by metagenomics to unravel the presence of hydrocarbon degraders. Moreover, we isolated Shinella zoogloeoides LFG9 and Bacillus swezeyi LFS15 from LF2 and combined them with Pseudomonas guguanensis SGPP2 isolated from an auto mechanic workshop soil to form the mixed consortium COG1. Bacterial isolates were tested for biosurfactant production. Additionally, the bioremediation potential of COG1 was studied as free and entrapped consortia by gas chromatography-mass spectrometry, in comparison to the single strains. Results revealed the presence of Actinobacteria (66.11%), Proteobacteria (32.21%), Gammaproteobacteria (5.39%), Actinomycetales (65.15%), Burkholderiales (13.92%), and Mycobacterium (32.22%) taxa, indicating the presence of hydrocarbon degraders in soil LF2. All three isolated strains were biosurfactant producers capable of degrading crude oil components within 14 days. However, Shinella zoogloeoides LFG9 performed best and was retained as candidate for further bioremediation investigation. In addition, COG1 performed better when immobilized, with entrapment effectiveness manifested by increased fatty acids and aromatic compound degradation. Attempt to improve crude oil biodegradation by adding surfactants failed as sodium dodecyl sulfate restrained the immobilized consortium performance.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2023)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph.

Dinghua Li, Chi-Man Liu, Ruibang Luo … (2015)

MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252 Gbps in 44.1 and 99.6 h on a single computing node with and without a graphics processing unit, respectively. MEGAHIT assembles the data as a whole, i.e. no pre-processing like partitioning and normalization was needed. When compared with previous methods on assembling the soil data, MEGAHIT generated a three-time larger assembly, with longer contig N50 and average contig length; furthermore, 55.8% of the reads were aligned to the assembly, giving a fourfold improvement. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.