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.

Deep mining decreases the microbial taxonomic and functional diversity of subsurface oil reservoirs.

Microbes in subsurface oil reservoirs play important roles in elemental cycles and biogeochemical processes. However, the community assembly pattern of indigenous microbiome and their succession under long-term human activity remain poorly understood. Here we studied the microbial community assembly in underground sandstone cores from 190 to 2050 m in northeast China and their response to long-term oil recovery (10-50 years). Indigenous microbiome in subsurface petroleum reservoirs were dominated by Gammaproteobacteria, Firmicutes, Alphaproteobacteria, Bacteroidetes, and Actinobacteria, which exhibited a higher contribution of homogenizing dispersal assembly and different taxonomy distinct ecological modules when compared with perturbed samples. Specifically, the long-term oil recovery reduced the bacterial taxonomic- and functional-diversity, and increased the community co-occurrence associations in subsurface oil reservoirs. Moreover, distinguished from the perturbed samples, both variation partition analysis and structural equation model revealed that the contents of quartz, NO3- and Cl- significantly structured the α- and ß-diversity in indigenous subsurface bacterial communities. These findings first provide the holistic picture of microbiome in the deep oil reservoirs, which demonstrate the significant impact of human activity on microbiome in deep continental subsurface.

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.

Disentangling the distinct mechanisms shaping the subsurface oil reservoir bacterial and archaeal communities across northern China.

Microbes in surface ecosystem exhibit strong biogeographic patterns, and are less apparent after human management. However, in contrast with the considerable knowledge on the surface ecosystem, the microbial biogeographic patterns in deep subsurface ecosystem under artificial disturbance is poorly understood. Here, we explored the spatial scale-dependence patterns of bacterial and archaeal communities in oil reservoirs under different artificial flooding duration and environmental conditions across northern China. Bacterial and archaeal communities of oil reservoirs exhibited distinct assembly patterns with a stronger distance-decay relationship in archaeal communities than bacterial communities, as different environmental factors linked to the diversity of bacteria and archaea. Specifically, bacterial and archaeal network properties revealed a significant correlation with spatial reservoir isolation by distinct co-occurrence patterns. The co-occurrences of bacterial communities were more complex in high temperature and alkaline pH, while archaeal co-occurrences were more frequent in low temperature and neutral pH. Potential functions in bacterial communities were more connected with chemoheterotrophy, whereas methanogenesis was abundant in archaeal communities, as confirmed by both keystone taxa and main ecological clusters in networks. This revealed that different mechanisms underlain geography and co-occurrence patterns of bacteria and archaea in oil reservoirs, providing a new insight for understanding biogeography and coexistence theory in deep subsurface ecosystem.

Stochastic assembly process dominates bacterial succession during a long-term microbial enhanced oil recovery.

Microbial enhanced oil recovery (MEOR) has been successfully used in oil exploitation to increase oil production. However, the mechanisms of microbial interactions and community assembly related to oil production performance along MEOR process are poorly understood. Here, we investigated the microbiome of an oil reservoir for a period of 5 years under three phases of different treatments with the injection of a mixture of microbes, nutrients, and air at different intensity. During the MEOR process, amplification of functional genes revealed an increase of genes related to hydrocarbon degradation linked to methanogenesis, supported by stable isotope analysis for confirmation of the methanogenesis activity. Meanwhile, a lower contribution of the ubiquitous/common taxa, closer and more positive associations, and lower modularity were observed in bacterial co-occurrence networks, with the rare taxa being the keystone taxa. The null model analysis and structural equation modeling revealed that the contribution of stochastic processes affected by functional groups and co-occurrence patterns to bacterial community increased significantly with the increase of oil production. This provides new insight that stochastic assembly in bacterial community increased along with MEOR process, and it is worthwhile paying attention to the uncertain consequences caused by random evolution since the treatment effect of MEOR is closely related to the in-situ community in oil reservoir.