A wide array of microorganisms survive and thrive in extreme environments. However, we know little about the patterns of, and controls over, their large-scale ecological distribution. To this end, we have applied a bar-coded 16S rRNA pyrosequencing technology to explore the phylogenetic differentiation among 59 microbial communities from physically and geochemically diverse acid mine drainage (AMD) sites across Southeast China, revealing for the first time environmental variation as the major factor explaining community differences in these harsh environments. Our data showed that overall microbial diversity estimates, including phylogenetic diversity, phylotype richness and pairwise UniFrac distance, were largely correlated with pH conditions. Furthermore, multivariate regression tree analysis also identified solution pH as a strong predictor of relative lineage abundance. Betaproteobacteria, mostly affiliated with the ‘Ferrovum' genus, were explicitly predominant in assemblages under moderate pH conditions, whereas Alphaproteobacteria, Euryarchaeota, Gammaproteobacteria and Nitrospira exhibited a strong adaptation to more acidic environments. Strikingly, such pH-dependent patterns could also be observed in a subsequent comprehensive analysis of the environmental distribution of acidophilic microorganisms based on 16S rRNA gene sequences previously retrieved from globally distributed AMD and associated environments, regardless of the long-distance isolation and the distinct substrate types. Collectively, our results suggest that microbial diversity patterns are better predicted by contemporary environmental variation rather than geographical distance in extreme AMD systems.

Little is known about the changes in soil microbial phosphorus (P) cycling potential during terrestrial ecosystem management and restoration, although much research aims to enhance soil P cycling. Here, we used metagenomic sequencing to analyse 18 soil microbial communities at a P-deficient degraded mine site in southern China where ecological restoration was implemented using two soil ameliorants and eight plant species. Our results show that the relative abundances of key genes governing soil microbial P-cycling potential were higher at the restored site than at the unrestored site, indicating enhancement of soil P cycling following restoration. The gcd gene, encoding an enzyme that mediates inorganic P solubilization, was predominant across soil samples and was a major determinant of bioavailable soil P. We reconstructed 39 near-complete bacterial genomes harboring gcd, which represented diverse novel phosphate-solubilizing microbial taxa. Strong correlations were found between the relative abundance of these genomes and bioavailable soil P, suggesting their contributions to the enhancement of soil P cycling. Moreover, 84 mobile genetic elements were detected in the scaffolds containing gcd in the 39 genomes, providing evidence for the role of phage-related horizontal gene transfer in assisting soil microbes to acquire new metabolic potential related to P cycling.

Summary• Arsenic (As) contamination of rice (Oryza sativa) is a worldwide concern and elucidating the molecular mechanisms of As accumulation in rice may provide promising solutions to the problem. Previous studies using microarray techniques to investigate transcriptional regulation of plant responses to As stress have identified numerous differentially expressed genes. However, little is known about the metabolic and regulatory network remodelings, or their interactions with microRNA (miRNA) in plants upon As(III) exposure.• We used Illumina sequencing to acquire global transcriptome alterations and miRNA regulation in rice under As(III) treatments of varying lengths of time and dosages.• We found that the response of roots was more distinct when the dosage was varied, whereas that of shoots was more distinct when the treatment time was varied. In particular, the genes involved in heavy metal transportation, jasmonate (JA) biosynthesis and signaling, and lipid metabolism were closely related to responses of rice under As(III) stress. Furthermore, we discovered 36 new As(III)-responsive miRNAs, 14 of which were likely involved in regulating gene expression in transportation, signaling, and metabolism.• Our findings highlight the significance of JA signaling and lipid metabolism in response to As(III) stress and their regulation by miRNA, which provides a foundation for subsequent functional research.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray–Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.