Environment dependent microbial co-occurrences across a cyanobacterial bloom in a freshwater lake.

Microbial taxon-taxon co-occurrences may directly or indirectly reflect the potential relationships between the members within a microbial community. However, to what extent and the specificity by which these co-occurrences are influenced by environmental factors remains unclear. In this report, we evaluated how the dynamics of microbial taxon-taxon co-occurrence is associated with the changes of environmental factors in Nan Lake at Wuhan city, China with a Modified Liquid Association method. We were able to detect more than 1000 taxon-taxon co-occurrences highly correlated with one or more environmental factors across a phytoplankton bloom using 16S rRNA gene amplicon community profiles. These co-occurrences, referred to as environment dependent co-occurrences (ED_co-occurrences), delineate a unique network in which a taxon-taxon pair exhibits specific, and potentially dynamic correlations with an environmental parameter, while the individual relative abundance of each may not. Microcystis involved ED_co-occurrences are in important topological positions in the network, suggesting relationships between the bloom dominant species and other taxa could play a role in the interplay of microbial community and environment across various bloom stages. Our results may broaden our understanding of the response of a microbial community to the environment, particularly at the level of microbe-microbe associations.

MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation.

BACKGROUND: Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. RESULTS: In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. CONCLUSIONS: Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.

Exposure to Formaldehyde Perturbs the Mouse Gut Microbiome.

Exposure to Formaldehyde (FA) results in many pathophysiological symptoms, however the underlying mechanisms are not well understood. Given the complicated modulatory role of intestinal microbiota on human health, we hypothesized that interactions between FA and the gut microbiome may account for FA's toxicity. Balb/c mice were allocated randomly to three groups: a control group, a methanol group (0.1 and 0.3 ng/mL MeOH subgroups), and an FA group (1 and 3 ng/mL FA subgroups). Groups of either three or six mice were used for the control or experiment. We applied high-throughput sequencing of 16S ribosomal RNA (rRNA) gene approaches and investigated possible alterations in the composition of mouse gut microbiota induced by FA. Changes in bacterial genera induced by FA exposure were identified. By analyzing KEGG metabolic pathways predicted by PICRUSt software, we also explored the potential metabolic changes, such as alpha-Linolenic acid metabolism and pathways in cancer, associated with FA exposure in mice. To the best of our knowledge, this preliminary study is the first to identify changes in the mouse gut microbiome after FA exposure, and to analyze the relevant potential metabolisms. The limitation of this study: this study is relatively small and needs to be further confirmed through a larger study.