Gut microbiota play key roles in host nutrition and metabolism. However, little is known about the relationship between host genetics, gut microbiota and metabolic profiles. Here, we used high-throughput sequencing and gas chromatography/mass spectrometry approaches to characterize the microbiota composition and the metabolite profiles in the gut of five cyprinid fish species with three different feeding habits raised under identical husbandry conditions. Our results showed that host species and feeding habits significantly affect not only gut microbiota composition but also metabolite profiles (ANOSIM, p ≤ 0.05). Mantel test demonstrated that host phylogeny, gut microbiota, and metabolite profiles were significantly related to each other (p ≤ 0.05). Additionally, the carps with the same feeding habits had more similarity in gut microbiota composition and metabolite profiles. Various metabolites were correlated positively with bacterial taxa involved in food degradation. Our results shed new light on the microbiome and metabolite profiles in the gut content of cyprinid fishes, and highlighted the correlations between host genotype, fish gut microbiome and putative functions, and gut metabolite profiles.
In the research of complex networks, structural analysis can be explained as finding the information hidden in the network’s topological structure. Thus, the way and the range of the structural information collection decide what kinds of information can be found in the structural analysis. In this work, based on the definition of Shannon entropy and the changeable range of structural information collecting (changeable local network for each node), the local structural information (LSI) of nodes in complex networks is proposed. According to the definition, when the range of the local network converges to the node itself, the LSI is their original structural properties, e.g. node’s degree, betweenness and clustering coefficient, but when the range of the local network extends to the whole network (order of the local network equal to the diameter of networks), the LSI is equivalent to the structural entropy of the entire static network, e.g. degree structural entropy, betweenness structural entropy. We also find that the local degree structural information can be used to classify the nodes in the network, and the proportion of the “bridge” nodes in the network is a new indicator of the network’s robustness, the bigger this proportion of bridge nodes in the network, the more robust the network. This finding also explains why the regular networks or the lattice is so stable, as almost all the nodes in those systems are the “bridge” nodes that are identified by the local degree structural information.
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