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mSystems ; 4(5)2019 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-31594827


Gut microbiota play important roles in host metabolism, especially in diabetes. However, why different diets lead to similar diabetic states despite being associated with different microbiota is not clear. Mice were fed two high-energy diets (HED) with the same energy density but different fat-to-sugar ratios to determine the associations between the microbiota and early-stage metabolic syndrome. The two diets resulted in different microbiota but similar diabetic states. Interestingly, the microbial gene profiles were not significantly different, and many common metabolites were identified, including l-aspartic acid, cholestan-3-ol (5ß, 3α), and campesterol, which have been associated with lipogenesis and inflammation. Our study suggests that different metabolic-syndrome-inducing diets may result in different microbiota but similar microbiomes and metabolomes. This suggests that the metagenome and metabolome are crucial for the prognosis and pathogenesis of obesity and metabolic syndrome.IMPORTANCE Various types of diet can lead to type 2 diabetes. The gut microbiota in type 2 diabetic patients are also different. So, two questions arise: whether there are any commonalities between gut microbiota induced by different pro-obese diets and whether these commonalities lead to disease. Here we found that high-energy diets with two different fat-to-sugar ratios can both cause obesity and prediabetes but enrich different gut microbiota. Still, these different gut microbiota have similar genetic and metabolite compositions. The microbial metabolites in common between the diets modulate lipid accumulation and macrophage inflammation in vivo and in vitro This work suggests that studies that only use 16S rRNA amplicon sequencing to determine how the microbes respond to diet and associate with diabetic state are missing vital information.

Sci Total Environ ; 688: 867-879, 2019 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-31255824


Freshwater lakes are threatened by harmful blooms characterized by Cyanobacterial Aggregates (CAs) that are normally aggregated with extracellular polysaccharides released by cyanobacteria to form a phycosphere. It is possible that mutualistic relationships exist between bacteria and cyanobacteria in these CAs wherein bacterial products supplement cyanobacterial growth, and cyanobacterial exudates, in turn, serve as substrates for bacteria, thus augmenting the stability of each constituent. However, little is known about the exact interaction between cyanobacteria and their attached bacteria in CAs. Therefore, in this study, we collected 26 CA samples from Lake Taihu, a large freshwater lake in China from March of 2015 to February of 2016. We then sequenced both the V4 regions of 16S rRNA genes and full metagenomes, resulting in 610 Mb of 16S rRNA gene data and 198.98 Gb of high-quality metagenomic data. We observed that two cyanobacteria genera (Microcystis and Dolichospermum) alternately dominated CAs along the sampling time and specific bacterial genera attached to different cyanobacteria genera dominated CAs. More specifically, Dolichospermum dominates CAs when water temperature is low and total nitrogen is high, while Microcystis dominates CAs when water temperature is high and total nitrogen is low. Moreover, we found specific bacterial genera attached to different cyanobacteria genera dominated CAs. The cyanobacteria-bacteria related pairs Dolichospermum-Burkholderia and Microcystis-Hyphomonas were detected by ecological networks construction. Bacterial communities in CAs were found to be more correlated with the cyanobacterial community (Mantel's r = 0.76, P = 0.001) than with environmental factors (Mantel's r = 0.27, P = 0.017). A potential codependent nitrogen-cycling pathway between cyanobacteria and their attached bacteria was constructed, indicating their functional link. Overall, these results demonstrated that mutualistic relationships do, indeed, exist between cyanobacteria and bacteria in CAs at both taxonomic and gene levels, providing biological clues potentially leading to the control of blooms by interventional strategies to disrupt bacteria-cyanobacteria relationships and co-pathways.

Cianobactérias/fisiologia , China , Monitoramento Ambiental , Lagos , Nitrogênio , RNA Ribossômico 16S , Estações do Ano
Sci Total Environ ; 669: 29-40, 2019 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-30877958


In aquatic ecosystems, both phytoplankton and bacteria play pivotal roles. Based on 16S rRNA gene sequencing, considerable research focused on phytoplankton colony attached and free-living bacteria has revealed the close relationship between them, and indicated that the entire bacterial community mediates crucial biogeochemical processes in aquatic ecosystems. However, our understanding of their distribution patterns and response to environmental factors remains poor. Besides, picocyanobacteria, which were omitted from attached bacteria analysis, were reported to be important in cyanobacterial blooms. To explore the spatiotemporal variation of the entire bacterial community with their driving environmental factors and detect the relationships among them, we collected 61 water samples spanning one year and the entire Lake Taihu regions for surveying the entire bacterial community. Our results indicated: 1) seasonal variation of the bacterial community composition was stronger than spatial variation due to the clearly seasonal variation of Microcystis, Synechococcus (pico-cyanobacteria) and other bacteria (Actinomycetales, Pirellulaceae and Sphingobacteriaceae); 2) the spatial distribution of the bacterial community showed that different phyla were dominant in different regions; 3) the bacterial co-occurrence networks varied seasonally and were dominated by Microcystis, ACK-M1, Chthoniobacteraceae, Synechococcus, Pirellulaceae and Pelagibacteraceae; 4) phytoplankton density, chlorophyll a, water temperature and total nitrogen were the major factors that drove the spatiotemporal variation of bacterial community composition. This study revealed the seasonal succession and spatial distribution of the entire bacterial community in Lake Taihu, providing new insights into the relationship between water bloom-forming cyanobacterial species and other bacteria, and their response to environmental factors in eutrophic freshwater ecosystem.

Cianobactérias/fisiologia , Eutrofização , Lagos/microbiologia , Microbiota , Fitoplâncton/fisiologia , China , RNA Bacteriano/análise , RNA Ribossômico 16S/análise , Estações do Ano , Análise de Sequência de RNA
Sci Total Environ ; 618: 1254-1267, 2018 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-29089134


Massive partial sequencing of 16S rRNA genes has become the predominant tool used for studying microbial ecology. However, determining which hypervariable regions and primer sets should be used for screening microbial communities requires extensive investigation if controversial results are to be avoided. Here, the performances of different variable regions of the 16S rRNA gene on bacterial diversity studies were evaluated in silico with respect to the SILVA non-redundant reference database (SILVA SSU Ref 123NR), and subsequently verified using samples from Lake Taihu in China, a eutrophic lake. We found that the bacterial community composition results were strongly impacted by the different V regions. The results show that V1-V2 and V1-V3 regions were the most reliable regions in the full-length 16S rRNA sequences, while most V3 to V6 regions (including V3, V4, V3-V4, V5, V4-V5, V6, V3-V6, V4-V6, and V5-V6) were more closely aligned with the SILVA SSU Ref 123NR database. Overall, V4 was the most prominent V region for achieving good domain specificity, higher coverage and a broader spectrum in the Bacteria domain, as confirmed by the validation experiments. S-D-Bact-0564-a-S-15/S-D-Bact-0785-b-A-18 is, therefore, a promising primer set for surveying bacterial diversity in eutrophic lakes.

Bactérias/genética , Monitoramento Ambiental , Eutrofização , Lagos/microbiologia , Bactérias/classificação , China , Genes Bacterianos , Variação Genética , RNA Ribossômico 16S , Análise de Sequência de DNA