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Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression1–3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis4, colorectal cancer prescreening5 and therapeutic choices in melanoma6. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic7 and cardiovascular diseases8. 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. The definition of a 'healthy' microbiome is impacted by geographic regional variations.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression

Supplementary Data: contains Supplementary Methods and Materials, Supplementary Results, Supplementary Figures and Legends, References for Supplementary Data. (DOCX 3627 kb)

OBJECTIVE: To investigate the effect of intermittent fasting on metabolize and gut microbiota in obese presenium rats fed with high-fat-sugar-diet. METHODS: We fed the Wistar rats with high-fat and high-sugar diet to induce adiposity, and the rats for intermittent fasting were selected base on their body weight. The rats were subjected to fasting for 72 h every 2 weeks for 18 weeks. OGTT test was performed and fasting blood samples and fecal samples were collected for measurement of TC, TG, HDL-C and LDL-C and sequence analysis of fecal 16S rRNA V4 tags using Illumina. Gut microbial community structure was analyzed with QIIME and LEfSe. RESULTS: After the intervention, the body weight of the fasting rats was significantly lower than that in high-fat diet group (P < 0.01). OGTT results suggested impairment of sugar tolerance in the fasting group, which showed a significantly larger AUC than compared with the high-fat diet group (P < 0.05). Intermittent fasting significantly reduced blood HDL-C and LDL-C levels (P < 0.05) and partially restored liver steatosis, and improved the gut microbiota by increasing the abundance of YS2, RF32 and Helicobacteraceae and reducing Lactobacillus, Roseburia, Erysipelotrichaceae and Ralstonia. Bradyrhizobiaceae was found to be positively correlated with CHOL and HDL-C, and RF39 was inversely correlated with the weight of the rats. CONCLUSION: Intermittent fasting can decrease the body weight and blood lipid levels and restore normal gut microbiota but can cause impairment of glucose metabolism in obese presenium rats.

In the version of this article originally published, in the sentence “Applying the same approach to obesity (Fig. 2b), MetS (Fig. 2c) and fatty liver (Fig. 2d) yielded similar results,” two figure panels were cited incorrectly. The data for obesity are in Fig. 2c, and the data for MetS are in Fig. 2b. The sentence has been updated with the correct citations in the print, PDF and HTML versions of the article.

The changes in the microbial community structure during acute exacerbations of severe chronic obstructive pulmonary disease (COPD) in hospitalized patients remain largely uncharacterized. Therefore, further studies focused on the temporal dynamics and structure of sputum microbial communities during acute exacerbation of COPD (AECOPD) would still be necessary. In our study, the use of molecular microbiological techniques provided insight into both fungal and bacterial diversities in AECOPD patients during hospitalization. In particular, we examined the structure and varieties of lung microbial community in 6 patients with severe AECOPD by amplifying 16S rRNA V4 hyper-variable and internal transcribed spacer (ITS) DNA regions using barcoded primers and the Illumina sequencing platform. Sequence analysis showed 261 bacterial genera representing 20 distinct phyla, with an average number of genera per patient of >157, indicating high diversity. Acinetobacter, Prevotella, Neisseria, Rothia, Lactobacillus, Leptotrichia, Streptococcus, Veillonella, and Actinomyces were the most commonly identified genera, and the average total sequencing number per sputum sample was >10000 18S ITS sequences. The fungal population was typically dominated by Candia, Phialosimplex, Aspergillus, Penicillium, Cladosporium and Eutypella. Our findings highlight that COPD patients have personalized structures and varieties in sputum microbial community during hospitalization periods.