Integrated Human Skin Bacteria Genome Catalog Reveals Extensive Unexplored Habitat-Specific Microbiome Diversity and Function.

The skin is the largest organ in the human body. Various skin environments on its surface constitutes a complex ecosystem. One of the characteristics of the skin micro-ecosystem is low biomass, which greatly limits a comprehensive identification of the microbial species through sequencing. In this study, deep-shotgun sequencing (average 21.5 Gigabyte (Gb)) from 450 facial samples and publicly available skin metagenomic datasets of 2069 samples to assemble a Unified Human Skin Genome (UHSG) catalog is integrated. The UHSG encompasses 813 prokaryotic species derived from 5779 metagenome-assembled genomes, among which 470 are novel species covering 20 phyla with 1385 novel assembled genomes. Based on the UHSG, the core functions of the skin microbiome are described and the differences in amino acid metabolism, carbohydrate metabolism, and drug resistance functions among different phyla are identified. Furthermore, analysis of secondary metabolites of the near-complete genomes further find 1220 putative novel secondary metabolites, several of which are found in previously unknown genomes. Single nucleotide variant (SNV) reveals a possible skin protection mechanism: the negative selection process of the skin environment to conditional pathogens. UHSG offers a convenient reference database that will facilitate a more in-depth understanding of the role of skin microorganisms in the skin.

Distinct biological ages of organs and systems identified from a multi-omics study.

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.

Over 50,000 Metagenomically Assembled Draft Genomes for the Human Oral Microbiome Reveal New Taxa.

The oral cavity of each person is home to hundreds of bacterial species. While taxa for oral diseases have been studied using culture-based characterization as well as amplicon sequencing, metagenomic and genomic information remains scarce compared to the fecal microbiome. Here, using metagenomic shotgun data for 3346 oral metagenomic samples together with 808 published samples, we obtain 56,213 metagenome-assembled genomes (MAGs), and more than 64% of the 3589 species-level genome bins (SGBs) contain no publicly available genomes. The resulting genome collection is representative of samples around the world and contains many genomes from candidate phyla radiation (CPR) that lack monoculture. Also, it enables the discovery of new taxa such as a genus Candidatus Bgiplasma within the family Acholeplasmataceae. Large-scale metagenomic data from massive samples also allow the assembly of strains from important oral taxa such as Porphyromonas and Neisseria. The oral microbes encode genes that could potentially metabolize drugs. Apart from these findings, a strongly male-enriched Campylobacter species was identified. Oral samples would be more user-friendly collected than fecal samples and have the potential for disease diagnosis. Thus, these data lay down a genomic framework for future inquiries of the human oral microbiome.

Life History Recorded in the Vagino-cervical Microbiome Along with Multi-omes.

The vagina contains at least a billion microbial cells, dominated by lactobacilli. Here we perform metagenomic shotgun sequencing on cervical and fecal samples from a cohort of 516 Chinese women of reproductive age, as well as cervical, fecal, and salivary samples from a second cohort of 632 women. Factors such as pregnancyhistory, delivery history, cesarean section, and breastfeeding were all more important than menstrual cycle in shaping the microbiome, and such information would be necessary before trying to interpret differences between vagino-cervical microbiome data. Greater proportion of Bifidobacterium breve was seen with older age at sexual debut. The relative abundance of lactobacilli especially Lactobacillus crispatus was negatively associated with pregnancy history. Potential markers for lack of menstrual regularity, heavy flow, dysmenorrhea, and contraceptives were also identified. Lactobacilli were rare during breastfeeding or post-menopause. Other features such as mood fluctuations and facial speckles could potentially be predicted from the vagino-cervical microbiome. Gut and salivary microbiomes, plasma vitamins, metals, amino acids, and hormones showed associations with the vagino-cervical microbiome. Our results offer an unprecedented glimpse into the microbiota of the female reproductive tract and call for international collaborations to better understand its long-term health impact other than in the settings of infection or pre-term birth.

Gut Microbiota Perturbation in IgA Deficiency Is Influenced by IgA-Autoantibody Status.

BACKGROUND & AIMS: IgA exerts its primary function at mucosal surfaces, where it binds microbial antigens to regulate bacterial growth and epithelial attachment. One third of individuals with IgA deficiency (IgAD) suffers from recurrent mucosal infections, possibly related to an altered microbiota. We aimed to delineate the impact of IgAD and the IgA-autoantibody status on the composition and functional capacity of the gut microbiota. METHODS: We performed a paired, lifestyle-balanced analysis of the effect of IgA on the gut microbiota composition and functionality based on fecal samples from individuals with IgAD and IgA-sufficient household members (n = 100), involving quantitative shotgun metagenomics, species-centric functional annotation of gut bacteria, and strain-level analyses. We supplemented the data set with 32 individuals with IgAD and examined the influence of IgA-autoantibody status on the composition and functionality of the gut microbiota. RESULTS: The gut microbiota of individuals with IgAD exhibited decreased richness and diversity and was enriched for bacterial species encoding pathogen-related functions including multidrug and antimicrobial peptide resistance, virulence factors, and type III and VI secretion systems. These functional changes were largely attributed to Escherichia coli but were independent of E coli strain variations and most prominent in individuals with IgAD with IgA-specific autoreactive antibodies. CONCLUSIONS: The microbiota of individuals with IgAD is enriched for species holding increased proinflammatory potential, thereby potentially decreasing the resistance to gut barrier-perturbing events. This phenotype is especially pronounced in individuals with IgAD with IgA-specific autoreactive antibodies, thus warranting a screening for IgA-specific autoreactive antibodies in IgAD to identify patients with IgAD with increased risk for gastrointestinal implications.

The Baseline Gut Microbiota Directs Dieting-Induced Weight Loss Trajectories.

BACKGROUND & AIMS: Elucidating key factors affecting personal responses to food is the first step toward implementing personalized nutrition strategies in for example weight loss programs. Here, we aimed to identify factors of importance for individual weight loss trajectories in a natural setting where participants were provided dietary advice but otherwise asked to self-manage the daily caloric intake and data reporting. METHODS: A 6-month weight-reduction program with longitudinal collection of dietary, physical activity, body weight, and fecal microbiome data as well as single-nucleotide polymorphism genotypes in 83 participants was conducted, followed by integration of the high-dimensional data to define the most determining factors for weight loss in a dietician-guided, smartphone-assisted dieting program. RESULTS: The baseline gut microbiota was found to outperform other factors as a predieting predictor of individual weight loss trajectories. Weight loss was also linked to the magnitude of changes in abundances of certain bacterial species during dieting. Ruminococcus gnavus (MGS0160) was significantly enriched in obese individuals and decreased during weight loss. Akkermansia muciniphila (MGS0120) and Alistipes obesi (MGS0342) were significantly enriched in lean individuals, and their abundance increased during dieting. Finally, Blautia wexlerae (MGS0575) and Bacteroides dorei (MGS0187) were the strongest predictors for weight loss when present in high abundance at baseline. CONCLUSION: Altogether, the baseline gut microbiota was found to excel as a central personal factor in capturing the relationship between dietary factors and weight loss among individuals on a dieting program.

Effect of Caloric Restriction on BMI, Gut Microbiota, and Blood Amino Acid Levels in Non-Obese Adults.

Adequate calorie restriction (CR) as a healthy lifestyle is recommended not only for people with metabolic disorders but also for healthy adults. Previous studies have mainly focused on the beneficial metabolic effects of CR on obese subjects, while its effects on non-obese subjects are still scarce. Here, we conducted a three-week non-controlled CR intervention in 41 subjects, with approximately 40% fewer calories than the recommended daily energy intake. We measured BMI, and applied targeted metabolic profiling on fasting blood samples and shotgun metagenomic sequencing on fecal samples, before and after intervention. Subjects were stratified into two enterotypes according to their baseline microbial composition, including 28 enterotype Bacteroides (ETB) subjects and 13 enterotype Prevotella (ETP) subjects. CR decreased BMI in most subjects, and ETP subjects exhibited a significantly higher BMI loss ratio than the ETB subjects. Additionally, CR induced limited changes in gut microbial composition but substantial microbial-independent changes in blood AAs, including a significant increase in 3-methylhistidine, a biomarker of the skeletal muscle protein turnover. Finally, baseline abundances of seven microbial species, rather than baseline AA levels, could well predict CR-induced BMI loss. This non-controlled intervention study revealed associations between baseline gut microbiota and CR-induced BMI loss and provided evidence to accelerate the application of microbiome stratification in future personalized nutrition intervention.

Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children.

BACKGROUND: The gut microbiota evolves from birth and is in early life influenced by events such as birth mode, type of infant feeding, and maternal and infant antibiotics use. However, we still have a gap in our understanding of gut microbiota development in older children, and to what extent early events and pre-school lifestyle modulate the composition of the gut microbiota, and how this impinges on whole body metabolic regulation in school-age children. RESULTS: Taking advantage of the KOALA Birth Cohort Study, a long-term prospective birth cohort in the Netherlands with extensive collection of high-quality host metadata, we applied shotgun metagenomics sequencing and systematically investigated the gut microbiota of children at 6-9 years of age. We demonstrated an overall adult-like gut microbiota in the 281 Dutch school-age children and identified 3 enterotypes dominated by the genera Bacteroides, Prevotella, and Bifidobacterium, respectively. Importantly, we found that breastfeeding duration in early life and pre-school dietary lifestyle correlated with the composition and functional competences of the gut microbiota in the children at school age. The correlations between pre-school dietary lifestyle and metabolic phenotypes exhibited a striking enterotype dependency. Thus, an inverse correlation between high dietary fiber consumption and low plasma insulin levels was only observed in individuals with the Bacteroides and Prevotella enterotypes, but not in Bifidobacterium enterotype individuals in whom the gut microbiota displayed overall lower microbial gene richness, alpha-diversity, functional potential for complex carbohydrate fermentation, and butyrate and succinate production. High total fat consumption and elevated plasma free fatty acid levels in the Bifidobacterium enterotype are associated with the co-occurrence of Streptococcus. CONCLUSIONS: Our work highlights the persistent effects of breastfeeding duration and pre-school dietary lifestyle in affecting the gut microbiota in school-age children and reveals distinct compositional and functional potential in children according to enterotypes. The findings underscore enterotype-specific links between the host metabolic phenotypes and dietary patterns, emphasizing the importance of microbiome-based stratification when investigating metabolic responses to diets. Future diet intervention studies are clearly warranted to examine gut microbe-diet-host relationships to promote knowledge-based recommendations in relation to improving metabolic health in children.

Impact of a 3-Months Vegetarian Diet on the Gut Microbiota and Immune Repertoire.

The dietary pattern can influence the immune system directly, but may also modulate it indirectly by regulating the gut microbiota. Here, we investigated the effect of a 3-months lacto-ovo-vegetarian diet on the diversity of gut microbiota and the immune system in healthy omnivorous volunteers, using high-throughput sequencing technologies. The short-term vegetarian diet did not have any major effect on the diversity of the immune system and the overall composition of the metagenome. The prevalence of bacterial genera/species with known beneficial effects on the intestine, including butyrate-producers and probiotic species and the balance of autoimmune-related variable genes/families were, however, altered in the short-term vegetarians. A number of bacterial species that are associated with the expression level of IgA, a key immunoglobulin class that protects the gastrointestinal mucosal system, were also identified. Furthermore, a lower diversity of T-cell repertoire and expression level of IgE, as well as a reduced abundance of inflammation-related genes in the gut microbiota were potentially associated with a control group with long-term vegetarians. Thus, the composition and duration of the diet may have an impact on the balance of pro-/anti-inflammatory factors in the gut microbiota and immune system.

NeSSM: a Next-generation Sequencing Simulator for Metagenomics.

BACKGROUND: Metagenomics can reveal the vast majority of microbes that have been missed by traditional cultivation-based methods. Due to its extremely wide range of application areas, fast metagenome sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of metagenomics analysis tools. RESULTS: We present here a customizable metagenome simulation system: NeSSM (Next-generation Sequencing Simulator for Metagenomics). Combining complete genomes currently available, a community composition table, and sequencing parameters, it can simulate metagenome sequencing better than existing systems. Sequencing error models based on the explicit distribution of errors at each base and sequencing coverage bias are incorporated in the simulation. In order to improve the fidelity of simulation, tools are provided by NeSSM to estimate the sequencing error models, sequencing coverage bias and the community composition directly from existing metagenome sequencing data. Currently, NeSSM supports single-end and pair-end sequencing for both 454 and Illumina platforms. In addition, a GPU (graphics processing units) version of NeSSM is also developed to accelerate the simulation. By comparing the simulated sequencing data from NeSSM with experimental metagenome sequencing data, we have demonstrated that NeSSM performs better in many aspects than existing popular metagenome simulators, such as MetaSim, GemSIM and Grinder. The GPU version of NeSSM is more than one-order of magnitude faster than MetaSim. CONCLUSIONS: NeSSM is a fast simulation system for high-throughput metagenome sequencing. It can be helpful to develop tools and evaluate strategies for metagenomics analysis and it's freely available for academic users at http://cbb.sjtu.edu.cn/~ccwei/pub/software/NeSSM.php.