Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis, colorectal cancer prescreening and therapeutic choices in melanoma. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic and cardiovascular diseases. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks.

b Sediment, a special realm in aquatic environments, has high microbial diversity. While there are numerous reports about the microbial community in marine sediment, freshwater and intertidal sediment communities have been overlooked. The present study determined millions of Illumina reads for a comparison of bacterial communities in freshwater, intertidal wetland, and marine sediments along Pearl River, China, using a technically consistent approach. Our results show that both taxon richness and evenness were the highest in freshwater sediment, medium in intertidal sediment, and lowest in marine sediment. The high number of sequences allowed the determination of a wide variety of bacterial lineages in all sediments for reliable statistical analyses. Principal component analysis showed that the three types of communities could be well separated from phylum to operational taxonomy unit (OTU) levels, and the OTUs from abundant to rare showed satisfactory resolutions. Statistical analysis (LEfSe) demonstrated that the freshwater sediment was enriched with Acidobacteria, Nitrospira, Verrucomicrobia, Alphaproteobacteria, and Betaproteobacteria. The intertidal sediment had a unique community with diverse primary producers (such as Chloroflexi, Bacillariophyta, Gammaproteobacteria, and Epsilonproteobacteria) as well as saprophytic microbes (such as Actinomycetales, Bacteroidetes, and Firmicutes). The marine sediment had a higher abundance of Gammaproteobacteria and Deltaproteobacteria, which were mainly involved in sulfate reduction in anaerobic conditions. These results are helpful for a systematic understanding of bacterial communities in natural sediment environments.

The microbial community plays an essential role in the high productivity in mangrove wetlands. A proper understanding of the spatial variations of microbial communities will provide clues about the underline mechanisms that structure microbial groups and the isolation of bacterial strains of interest. In the present study, the diversity and composition of the bacterial community in sediments collected from four locations, namely mudflat, edge, bulk, and rhizosphere, within the Mai Po Ramsar Wetland in Hong Kong, SAR, China were compared using the barcoded Illumina paired-end sequencing technique. Rarefaction results showed that the bulk sediment inside the mature mangrove forest had the highest bacterial α-diversity, while the mudflat sediment without vegetation had the lowest. The comparison of β-diversity using principal component analysis and principal coordinate analysis with UniFrac metrics both showed that the spatial effects on bacterial communities were significant. All sediment samples could be clustered into two major groups, inner (bulk and rhizosphere sediments collected inside the mangrove forest) and outer mangrove sediments (the sediments collected at the mudflat and the edge of the mangrove forest). With the linear discriminate analysis scores larger than 3, four phyla, namely Actinobacteria, Acidobacteria, Nitrospirae, and Verrucomicrobia, were enriched in the nutrient-rich inner mangrove sediments, while abundances of Proteobacteria and Deferribacterias were higher in outer mangrove sediments. The rhizosphere effect of mangrove plants was also significant, which had a lower α-diversity, a higher amount of Nitrospirae, and a lower abundance of Proteobacteria than the bulk sediment nearby.

BACKGROUND Extracellular vesicles (EVs) contain a rich cargo of different RNA species with specialized functions and clinical applications. However, the landscape and characteristics of extracellular vesicle long RNA (exLR) in human blood remain largely unknown. METHODS We presented an optimized strategy for exLR sequencing (exLR-seq) of human plasma. The sample cohort included 159 healthy individuals, 150 patients with cancer (5 cancer types), and 43 patients with other diseases. Bioinformatics approaches were used to analyze the distribution and features of exLRs. Support vector machine algorithm was performed to construct the diagnosis classifier, and diagnostic efficiency was evaluated by ROC analysis. RESULTS More than 10000 exLRs, including mRNA, circRNA, and lncRNA, were reliably detected in each exLR-seq sample from 1–2 mL of plasma. We observed that blood EVs contain a substantial fraction of intact mRNAs and a large number of assembling spliced junctions; circRNA was also enriched in blood EVs. Interestingly, blood exLRs reflected their tissue origins and the relative fractions of different immune cell types. Additionally, the exLR profile could distinguish patients with cancer from healthy individuals. We further showed that 8 exLRs can serve as biomarkers for hepatocellular carcinoma (HCC) diagnosis with high diagnostic efficiency in training [area under the curve (AUC) = 0.9527; 95% CI, 0.9170–0.9883], validation cohort (AUC = 0.9825; 95% CI, 0.9606–1), and testing cohort (AUC = 0.9627; 95% CI, 0.9263–0.9991). CONCLUSIONS In summary, this study revealed abundant exLRs in human plasma and identified diverse specific markers potentially useful for cancer diagnosis.