Stress-responses of microbes in oil reservoir under high tetracycline exposure and their environmental risks.

As the groundwater ecosystem is connected with surface, antibiotics and antibiotic resistance genes (ARGs) in aquatic environments will gradually infiltrate into the deep environment, posing a potential threat to groundwater ecosystem. However, knowledge on the environmental risk of antibiotics and ARGs in groundwater ecosystem and their ecological process still remains unexplored. In this study, lab-scale oil reservoirs under high tetracycline stress were performed to evaluate the dynamics of microbial communities, ARGs and potential functions by using 16S rRNA gene sequencing and metagenomics analysis. Although the presence of antibiotics remarkably reduced the microbial abundance and diversity in a short term, but remain stable or even increased after a long-term incubation. Antibiotic stress caused a greater diversity and abundance of ARGs, and higher numbers of ARGs-related species with the capacity to transfer ARGs to other microbes through horizontal gene transfer. Thus, a much more frequent associations of microbial community at both node- and network-level and a selective pressure on enrichment of antibiotic resistant bacteria related to "anaerobic n-alkane degradation" and "methylotrophic methanogenesis" were observed. It is important to emphasize that high antibiotic stress could also prevent some microbes related to "Sulfate reduction", "Fe(II) oxidation", "Nitrate reduction", and "Xylene and Toluene degradation". This study provides an insight into the long-term stress-responses of microbial communities and functions in oil reservoir under tetracycline exposure, which may help to elucidate the effect of antibiotic stress on biogeochemical cycling with microbial involvement in groundwater ecosystem.

CnnPOGTP: a novel CNN-based predictor for identifying the optimal growth temperatures of prokaryotes using only genomic k-mers distribution.

SUMMARY: Temperature is very important for the growth of microorganisms. Appropriate temperature conditions can improve the possibility for isolation of currently uncultured microorganisms. The development of metagenomic binning technology had dramatically increased the availability of genomic information of prokaryotes, providing convenience to infer the optimal growth temperature (OGT). Here, we proposed CnnPOGTP, a predictor for OGTs of prokaryotes based on deep learning method using only k-mers distribution derived from genomic sequence. This method was annotation free, and the predicted OGT could be obtained by simply providing the genome sequence to the CnnPOGTP website. AVAILABILITY AND IMPLEMENTATION: http://www.orgene.net/CnnPOGTP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic and Comparative Genome Analysis of Exiguobacterium aurantiacum SW-20, a Petroleum-Degrading Bacteria with Salt Tolerance and Heavy Metal-Tolerance Isolated from Produced Water of Changqing Oilfield, China.

The genome of Exiguobacterium aurantiacum SW-20 (E. aurantiacum SW-20), a salt-tolerant microorganism with petroleum hydrocarbon-degrading ability isolated from the Changqing Oilfield, was sequenced and analyzed. Genomic data mining even comparative transcriptomics revealed that some genes existed in SW-20 might be related to the salt tolerance. Besides, genes related to petroleum hydrocarbon degradation discovered in genomic clusters were also found in the genome, indicating that these genes have a certain potential in the bioremediation of petroleum pollutants. Multiple natural product biosynthesis gene clusters were detected, which was critical for survival in the extreme conditions. Transcriptomic studies revealed that some genes were significantly up-regulated as salinity increased, implying that these genes might be related to the salt tolerance of SW-20 when living in a high salt environment. In our study, gene clusters including salt tolerance, heavy metal tolerance and alkane degradation were all compared. When the same functional gene clusters from different strains, it was discovered that the gene composition differed. Comparative genomics and in-depth analysis provided insights into the physiological features and adaptation strategies of E. aurantiacum SW-20 in the oilfield environment. Our research increased the understanding of niches adaption of SW-20 at genomic level.