, 2010). Until now, sulfonate surfactants have been widely adopted as flooding agents in China (She et al., 2011). Biodegradation of hydrocarbon in petroleum reservoirs has adversely affected the majority of the world’s oil, making recovery and refining of that oil more costly (Jones et al., 2008). Microorganisms isolated from formation waters play a key role in the subsurface hydrocarbon degradation, however, the specific pathway occurring in oil reservoirs remains
poorly defined (Zhang et al., 2012). Previously, we isolated and characterized three indigenous microorganisms Nivolumab from a petroleum reservoir after polymer flooding (She et al., 2011). To further the characterization of microorganisms in petroleum reservoir after chemical flooding, currently, we isolated a Brevibacillus agri strain 5-2 (= CGMCC 5645) from a mixture of formation
water and petroleum in Changqing oilfield, China. Phylogenetic tree clearly showed that B. agri type strain NRRL NRS-1219 is most closely related to the strain 5-2 ( Fig. S1). Interestingly, strain 5-2 growing aerobically with tetradecane and hexadecane as the sole carbon, and was also found to have a capacity for metabolizing sulfonate. Previous studies selleckchem have documented the capability of hydrocarbon biodegradation in Brevibacillus borstelensis strain 707 ( Hadad et al., 2005), Brevibacillus sp. strain PDM-3 ( Reddy et al., 2010) and Brevibacillus panacihumi strain W25 ( Wang et al., 2014). However, no metabolism pathways involved in petroleum degradation was further characterized in B. agri. Therefore, B. agri strain 5-2 was subjected to the whole genome sequencing
for genomic analysis, and this can add more knowledge about the potential industrial applications of B. agri. The draft genome sequence of B. agri 5-2 strain was performed Morin Hydrate by using Illumina Hieseq 2000 genomic sequencer at BGI (Shenzhen, China). One 500-bp insert-size DNA library was generated then sequenced with Illumina Hiseq 2000 by using 2 × 100 bp pair end sequencing strategy. Filtered clean reads were assembled into scaffolds using the Velvet version 1.2.07 ( Zerbino and Birney, 2008), PAGIT flow was used to prolong the initial contigs and correct sequencing errors ( Swain et al., 2012). Predict genes were identified using Glimmer version 3.0 ( Delcher et al., 2007), tRNAscan-SE version 1.21 ( Lowe and Eddy, 1997) was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer version 1.2 ( Lagesen et al., 2007). KAAS server ( Moriya et al., 2007) was used to assign translated amino acids into KEGG Pathway with SBH (single-directional best hit) method ( Kanehisa et al., 2008). Translated genes were aligned with COG database ( Tatusov et al., 2003) using NCBI blastp (hits should have scores no less than 60, e value is no more than 1e-6).