16S rRNA gene-based metagenomic analysis reveals differences in bacteria-derived extracellular vesicles in the urine of pregnant and non-pregnant women

Direct MALDI-TOF MS Identification of Bacterial Mixtures

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is now widely used to characterize bacterial samples for clinical diagnosis, food safety control, environmental monitoring, and so on. However, existing standard approaches are only applied to analyze single colonies purified by plate culture, which limits the approaches to cultivable bacteria and makes the whole approaches time-consuming. In this work, we propose a new framework to analyze MALDI-TOF spectra of bacterial mixtures and to directly characterize each component without purification procedures. The framework is a combination of a synthetic mixture model based on a non-negative linear combination of candidate reference spectra and a statistical assessment by in silico generated spectra via a jackknife resampling. Ninety-seven model bacterial mixture samples and 8 cocultured blind-coded bacterial mixture samples, containing up to 6 strains in varied ratios in each sample, together with a reference database containing the mass spectra of 1081 strains, were used to validate the framework. High sensitivity (>80%, with error rate <5%) was achieved for balanced binary and ternary mixtures. The sensitivity was >60% for balanced quaternary and pentabasic mixtures, and 48%-71% for asymmetric situation, with error rate <5%. The work can facilitate rapid and reliable characterization of bacterial mixtures without purification procedures, which is of practical value in clinical diagnosis, food safety control, environmental monitoring, and so on. The framework can be further applied to many other spectroscopy-based analytics to interpret spectra from mixed samples.

Ursodeoxycholic acid improves liver function via phenylalanine/tyrosine pathway and microbiome remodelling in patients with liver dysfunction

Ursodeoxycholic acid (UDCA) is a metabolic by-product of intestinal bacteria, showing hepatoprotective effects. However, its underlying molecular mechanisms remain unclear. The purpose of this study was to elucidate the action mechanisms underlying the protective effects of UDCA and vitamin E against liver dysfunction using metabolomics and metagenomic analysis. In this study, we analysed blood and urine samples from patients with obesity and liver dysfunction. Nine patients were randomly assigned to receive UDCA (300 mg twice daily), and 10 subjects received vitamin E (400 IU twice daily) for 8 weeks. UDCA significantly improved the liver function scores after 4 weeks of treatment and effectively reduced hepatic deoxycholic acid and serum microRNA-122 levels. To better understand its protective mechanism, a global metabolomics study was conducted, and we found that UDCA regulated uremic toxins (hippuric acid, p-cresol sulphate, and indole-derived metabolites), antioxidants (ascorbate sulphate and N-acetyl-L-cysteine), and the phenylalanine/tyrosine pathway. Furthermore, microbiome involvement, particularly of Lactobacillus and Bifidobacterium, was demonstrated through metagenomic analysis of bacteria-derived extracellular vesicles. Meanwhile, vitamin E treatment did not result in such alterations, except that it reduced uremic toxins and liver dysfunction. Our findings suggested that both treatments were effective in improving liver function, albeit via different mechanisms.

Gram-Positive Bacterial Extracellular Vesicles and Their Impact on Health and Disease

During recent years it has become increasingly clear that the release of extracellular vesicles (EVs) is a feature inherent to all cellular life forms. These lipid bilayer-enclosed particles are secreted by members of all domains of life: Eukarya, Bacteria and Archaea, being similar in size, general composition, and potency as a functional entity. Noticeably, the recent discovery of EVs derived from bacteria belonging to the Gram-positive phyla Actinobacteria and Firmicutes has added a new layer of complexity to our understanding of bacterial physiology, host interactions, and pathogenesis. Being nano-sized structures, Gram-positive EVs carry a large diversity of cargo compounds, including nucleic acids, viral particles, enzymes, and effector proteins. The diversity in cargo molecules may point to roles of EVs in bacterial competition, survival, material exchange, host immune evasion and modulation, as well as infection and invasion. Consequently, the impact of Gram-positive EVs on health and disease are being revealed gradually. These findings have opened up new leads for the development of medical advances, including strategies for vaccination and anti-bacterial treatment. The rapidly advancing research into Gram-positive EVs is currently in a crucial phase, therefore this review aims to give an overview of the groundwork that has been laid at present and to discuss implications and future challenges of this new research field.

Metagenome Analysis of Bodily Microbiota in a Mouse Model of Alzheimer Disease Using Bacteria-derived Membrane Vesicles in Blood

Emerging evidence has suggested that the gut microbiota contribute to brain dysfunction, including pathological symptoms of Alzheimer disease (AD). Microbiota secrete membrane vesicles, also called extracellular vesicles (EVs), which contain bacterial genomic DNA fragments and other molecules and are distributed throughout the host body, including blood. In the present study, we investigated whether bacteria-derived EVs in blood are useful for metagenome analysis in an AD mouse model. Sequence readings of variable regions of 16S rRNA genes prepared from blood EVs in Tg-APP/PS1 mice allowed us to identify over 3,200 operational taxonomic units corresponding to gut microbiota reported in previous studies. Further analysis revealed a distinctive microbiota landscape in Tg-APP/PS1 mice, with a dramatic alteration in specific microbiota at all taxonomy levels examined. Specifically, at the phylum level, the occupancy of p_Firmicutes increased, while the occupancy of p_Proteobacteria and p_Bacteroidetes moderately decreased in Tg-APP/PS1 mice. At the genus level, the occupancy of g_Aerococcus, g_Jeotgalicoccus, g_Blautia, g_Pseudomonas and unclassified members of f_Clostridiale and f_Ruminococcaceae increased, while the occupancy of g_Lactobacillus, unclassified members of f_S24-7, and g_Corynebacterium decreased in Tg-APP/PS1 mice. A number of genus members were detected in Tg-APP/PS1 mice, but not in wild-type mice, while other genus members were detected in wild-type mice, but lost in Tg-APP/PS1 mice. The results of the present study suggest that the bodily microbiota profile is altered in Tg-APP/PS1 mice, and that blood EVs are useful for the metagenome analysis of bodily microbiota in AD.

Rapid Assessment of Microbiota Changes in Individuals with Autism Spectrum Disorder Using Bacteria-derived Membrane Vesicles in Urine

Individuals with autism spectrum disorder (ASD) have altered gut microbiota, which appears to regulate ASD symptoms via gut microbiota-brain interactions. Rapid assessment of gut microbiota profiles in ASD individuals in varying physiological contexts is important to understanding the role of the microbiota in regulating ASD symptoms. Microbiomes secrete extracellular membrane vesicles (EVs) to communicate with host cells and secreted EVs are widely distributed throughout the body including the blood and urine. In the present study, we investigated whether bacteria-derived EVs in urine are useful for the metagenome analysis of microbiota in ASD individuals. To address this, bacterial DNA was isolated from bacteria-derived EVs in the urine of ASD individuals. Subsequent metagenome analysis indicated markedly altered microbiota profiles at the levels of the phylum, class, order, family, and genus in ASD individuals relative to control subjects. Microbiota identified from urine EVs included gut microbiota reported in previous studies and their up- and down-regulation in ASD individuals were partially consistent with microbiota profiles previously assessed from ASD fecal samples. However, overall microbiota profiles identified in the present study represented a distinctive microbiota landscape for ASD. Particularly, the occupancy of g_Pseudomonas, g_Sphingomonas, g_Agrobacterium, g_Achromobacter, and g_Roseateles decreased in ASD, whereas g_Streptococcus, g_Akkermansia, g_Rhodococcus, and g_Halomonas increased. These results demonstrate distinctively altered gut microbiota profiles in ASD, and validate the utilization of urine EVs for the rapid assessment of microbiota in ASD.

Bacterial Diversity and Community Structure of Supragingival Plaques in Adults with Dental Health or Caries Revealed by 16S Pyrosequencing

Dental caries has a polymicrobial etiology within the complex oral microbial ecosystem. However, the overall diversity and structure of supragingival plaque microbiota in adult dental health and caries are not well understood. Here, 160 supragingival plaque samples from patients with dental health and different severities of dental caries were collected for bacterial genomic DNA extraction, pyrosequencing by amplification of the 16S rDNA V1–V3 hypervariable regions, and bioinformatic analysis. High-quality sequences (2,261,700) clustered into 10,365 operational taxonomic units (OTUs; 97% identity), representing 453 independent species belonging to 122 genera, 66 families, 34 orders, 21 classes, and 12 phyla. All groups shared 7522 OTUs, indicating the presence of a core plaque microbiome. α diversity analysis showed that the microbial diversity in healthy plaques exceeded that of dental caries, with the diversity decreasing gradually with the severity of caries. The dominant phyla of plaque microbiota included Bacteroidetes, Actinobacteria, Proteobacteria, Firmicutes, Fusobacteria, and TM7. The dominant genera included Capnocytophaga, Prevotella, Actinomyces, Corynebacterium, Neisseria, Streptococcus, Rothia, and Leptotrichia. β diversity analysis showed that the plaque microbial community structure was similar in all groups. Using LEfSe analysis, 25 differentially abundant taxa were identified as potential biomarkers. Key genera (27) that potentially contributed to the differential distributions of plaque microbiota between groups were identified by PLS-DA analysis. Finally, co-occurrence network analysis and function predictions were performed. Treatment strategies directed toward modulating microbial interactions and their functional output should be further developed.