The buried China-Russia Crude Oil Pipeline (CRCOP) across the permafrost-associated cold ecosystem in northeastern China carries a risk of contamination to the deep active layers and upper permafrost in case of accidental rupture of the embedded pipeline or migration of oil spills. As many soil microbes are capable of degrading petroleum, knowledge about the intrinsic degraders and the microbial dynamics in the deep subsurface could extend our understanding of the application of in-situ bioremediation. In this study, an experiment was conducted to investigate the bacterial communities in response to simulated contamination to deep soil samples by using 454 pyrosequencing amplicons. The result showed that bacterial diversity was reduced after 8-weeks contamination. A shift in bacterial community composition was apparent in crude oil-amended soils with Proteobacteria (esp. α-subdivision) being the dominant phylum, together with Actinobacteria and Firmicutes. The contamination led to enrichment of indigenous bacterial taxa like Novosphingobium, Sphingobium, Caulobacter, Phenylobacterium, Alicylobacillus and Arthrobacter, which are generally capable of degrading polycyclic aromatic hydrocarbons (PAHs). The community shift highlighted the resilience of PAH degraders and their potential for in-situ degradation of crude oil under favorable conditions in the deep soils.

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Methanogenic archaea are important for the global greenhouse gas budget since they produce methane under anoxic conditions in numerous natural environments such as oceans, estuaries, soils, and lakes. Whether and how environmental change will propagate into methanogenic assemblages of natural environments remains largely unknown owing to a poor understanding of global distribution patterns and environmental drivers of this specific group of microorganisms. In this study, we performed a meta-analysis targeting the biogeographic patterns and environmental controls of methanogenic communities using 94 public mcrA gene datasets. We show a global pattern of methanogenic archaea that is more associated with habitat filtering than with geographical dispersal. We identify salinity as the control on methanogenic community composition at global scale whereas pH and temperature are the major controls in non-saline soils and lakes. The importance of salinity for structuring methanogenic community composition is also reflected in the biogeography of methanogenic lineages and the physiological properties of methanogenic isolates. Linking methanogenic alpha-diversity with reported values of methane emission identifies estuaries as the most diverse methanogenic habitats with, however, minor contribution to the global methane budget. With salinity, temperature and pH our study identifies environmental drivers of methanogenic community composition facing drastic changes in many natural environments at the moment. However, consequences of this for the production of methane remain elusive owing to a lack of studies that combine methane production rate with community analysis.

Abstract. The rewetting of drained peatlands alters peat geochemistry and often leads to sustained elevated methane emission. Although this methane is produced entirely by microbial activity, the distribution and abundance of methane-cycling microbes in rewetted peatlands, especially in fens, is rarely described. In this study, we compare the community composition and abundance of methane-cycling microbes in relation to peat porewater geochemistry in two rewetted fens in northeastern Germany, a coastal brackish fen and a freshwater riparian fen, with known high methane fluxes. We utilized 16S rRNA high-throughput sequencing and quantitative polymerase chain reaction (qPCR) on 16S rRNA, mcrA, and pmoA genes to determine microbial community composition and the abundance of total bacteria, methanogens, and methanotrophs. Electrical conductivity (EC) was more than 3 times higher in the coastal fen than in the riparian fen, averaging 5.3 and 1.5 mS cm−1, respectively. Porewater concentrations of terminal electron acceptors (TEAs) varied within and among the fens. This was also reflected in similarly high intra- and inter-site variations of microbial community composition. Despite these differences in environmental conditions and electron acceptor availability, we found a low abundance of methanotrophs and a high abundance of methanogens, represented in particular by Methanosaetaceae, in both fens. This suggests that rapid (re)establishment of methanogens and slow (re)establishment of methanotrophs contributes to prolonged increased methane emissions following rewetting.

Oil spills from pipeline ruptures are a major source of terrestrial petroleum pollution in cold regions. However, our knowledge of the bacterial response to crude oil contamination in cold regions remains to be further expanded, especially in terms of community shifts and potential development of hydrocarbon degraders. In this study we investigated changes of microbial diversity, population size and keystone taxa in permafrost soils at four different sites along the China-Russia crude oil pipeline prior to and after perturbation with crude oil. We found that crude oil caused a decrease of cell numbers together with a reduction of the species richness and shifts in the dominant phylotypes, while bacterial community diversity was highly site-specific after exposure to crude oil, reflecting different environmental conditions. Keystone taxa that strongly co-occurred were found to form networks based on trophic interactions, that is co-metabolism regarding degradation of hydrocarbons (in contaminated samples) or syntrophic carbon cycling (in uncontaminated samples). With this study we demonstrate that after severe crude oil contamination a rapid establishment of endemic hydrocarbon degrading communities takes place under favorable temperature conditions. Therefore, both endemism and trophic correlations of bacterial degraders need to be considered in order to develop effective cleanup strategies.

The China-Russia Crude Oil Pipeline (CRCOP) goes through 441 km permafrost soils in northeastern China. The bioremediation in case of oil spills is a major concern. So far, little is known about the indigenous bacteria inhabiting in the permafrost soils along the pipeline. A pilot 454 pyrosequencing analysis on the communities from four selected sites which possess high environment risk along the CRCOP is herein presented. The results reveal an immense bacterial diversity than previously anticipated. A total of 14448 OTUs with 84834 reads are identified, which could be assigned into 39 different phyla, and 223 families or 386 genera. Only five phyla sustain a mean OTU abundance more than 5% in all the samples, but they altogether account for 85.08% of total reads. Proteobacteria accounts for 41.65% of the total OTUs or 45% of the reads across all samples, and its proportion generally increases with soil depth, but OTUs numerically decline. Among Proteobacteria, the abundance of Beta-, Alpha-, Delta- and Gamma- subdivisions average to 38.7% (2331 OTUs), 37.5% (2257 OTUs), 10.35% (616 OTUs), and 6.21% (374 OTUs), respectively. Acidobacteria (esp. Acidobacteriaceae), Actinobacteria (esp. Intrasporangiaceae), Bacteroidetes (esp. Sphingobacteria and Flavobacteria) and Chloroflexi (esp. Anaerolineaceae) are also very common, accounting for 8.56% (1237 OTUs), 7.86% (1136 OTUs); 7.35% (1063 OTUs) and 8.27% (1195 OTUs) of total libraries, respectively. The ordination analysis indicates that bacteria communities in the upper active layer cluster together (similar), while bacterial consortia from the lower active layer and permafrost table scatter (less similar). The abundance of Rhodococcus (12 OTUs), Pseudomonas (71 OTUs) and Sphingomonas (87 OTUs) is even less (<0.01%). This effort to profile the background diversity may set the first stage for better evaluating the bacterial dynamics in response to accidental oil spills.

BackgroundThis study aimed to determine whether community-acquired pneumonia (CAP) had a metabolic profile and whether this profile can be used for disease severity assessment.MethodsA total of 175 individuals including 119 CAP patients and 56 controls were enrolled and divided into two cohorts. Serum samples from a discovery cohort (n = 102, including 38 non-severe CAP, 30 severe CAP, and 34 age and sex-matched controls) were determined by untargeted ultra-high-performance liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based metabolomics. Selected differential metabolites between CAP patients versus controls, and between the severe CAP group versus non-severe CAP group, were confirmed by targeted mass spectrometry assays in a validation cohort (n = 73, including 32 non-severe CAP, 19 severe CAP and 22 controls). Pearson’s correlation analysis was performed to assess relationships between the identified metabolites and clinical severity of CAP. The area under the curve (AUC), sensitivity and specificity of the metabolites for predicting the severity of CAP were also investigated.ResultsThe metabolic signature was markedly different between CAP patients and controls. Fifteen metabolites were found to be significantly dysregulated in CAP patients, which were mainly mapped to the metabolic pathways of sphingolipid, arginine, pyruvate and inositol phosphate. The alternation trends of five metabolites among the three groups including sphinganine, p-Cresol sulfate, dehydroepiandrosterone sulfate (DHEA-S), lactate and l-arginine in the validation cohort were consistent with those in the discovery cohort. Significantly lower concentrations of sphinganine, p-Cresol sulfate and DHEA-S were observed in CAP patients than in controls (p < 0.05). Serum lactate and sphinganine levels were positively correlated with confusion, urea level, respiratory rate, blood pressure, and age > 65 years (CURB-65), pneumonia severity index (PSI) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, while DHEA-S inversely correlated with the three scoring systems. Combining lactate, sphinganine and DHEA-S as a metabolite panel for discriminating severe CAP from non-severe CAP exhibited a better AUC of 0.911 (95% confidence interval 0.825–0.998) than CURB-65, PSI and APACHE II scores.ConclusionsThis study demonstrates that serum metabolomics approaches based on the LC-MS/MS platform can be applied as a tool to reveal metabolic changes during CAP and establish a metabolite signature related to disease severity.Trial registrationClinicalTrials.gov, NCT03093220. Registered retrospectively on 28 March 2017.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2049-2) contains supplementary material, which is available to authorized users.

Circular RNAs (circRNAs) have been reported as effective diagnostic and therapeutic biomarkers in many diseases, but the potential of using this easy‐to‐monitor and highly stable materials for diagnosing Community‐acquired pneumonia (CAP) remains unexplored. Here, aiming to identify potential CAP‐related circRNAs in peripheral blood and seeking to deepen the understanding of how circRNA‐miRNA‐mRNA regulatory networks may contribute to CAP, we applied microarrays profiling analysis and identified 8296 differentially expressed (DE) circRNAs between patients with CAP (n = 6) and healthy controls (n = 6). Subsequently, we validated the accumulation trends for the top 100 DE circRNAs based on qPCR in an independent validation cohort (30 patients vs 30 controls), and ultimately identified a panel of four circRNAs that perform extremely well as sensitive and specific biomarkers for diagnosing CAP: hsa_circ_0018429 (area under the curve [AUC] = 0.8216), hsa_circ_0026579 (AUC = 0.7733), hsa_circ_0125357 (AUC = 0.7730), and hsa_circ_0099188 (AUC = 0.6978); combined as a panel (AUC = 0.8776). In addition, hsa_circ_0026579 exhibited good performance in differentiating viral from bacterial or mixed infection, with an AUC of 0.863. We also identified 10 miRNAs that most likely to interact with these four circRNAs, and then predicted 205 mRNA target genes. The KEGG pathway enrichment analysis suggested highly plausible functional implications related to inflammation and to virus‐infection‐related signaling pathways (such as HTLV‐1 infection and hepatitis B infection). Thus, we generated a genetic network of potential CAP‐related regulatory interactions that should inform future hypothesis‐driven research into the causes and potential treatment of this widespread and frequently fatal disease.