Soluble cello-oligosaccharide with 2-6 oligosaccharide units is a kind of oligosaccharide with various biological functions, which can promote the proliferation of intestinal probiotics such as Bifidobacteria and Lactobacillus paracei. Therefore, it has a regulatory effect on human intestinal microbiota. In this study, a Cc 01 strain was constructed by expressing cellodextrin phosphorylase (CDP) in Escherichia coli. By combining with a previously constructed COS 01 strain, a three-enzyme cascade reaction system based on strains COS 01 and Cc 01 was developed, which can convert glucose and sucrose into cello-oligosaccharide. After optimization, the final titer of soluble cello-oligosaccharides with 2-6 oligosaccharide units reached 97 g/L, with a purity of about 97%. It contained cellobiose (16.8 wt%), cellotriose (49.8 wt%), cellotetrose (16.4 wt%), cellopentaose (11.5 wt%) and cellohexose (5.5 wt%). When using inulin, xylo-oligosaccharide and fructooligosaccharide as the control substrate, the biomass (OD₆₀₀) of Lactobacillus casei (WSH 004), Lactobacillus paracei (WSH 005) and Lactobacillus acidophilus (WSH 006) on cello-oligosaccharides was about 2 folds higher than that of the control. This study demonstrated the efficient synthesis of cello-oligosaccharides by a three-enzyme cascade reaction and demonstrated that the synthesized cello-oligosaccharides was capable of promoting intestinal microbial proliferation.
Humans ; Oligosaccharides ; Biomass ; Escherichia coli/genetics* ; Gastrointestinal Microbiome ; Glucose

Microbiome is associated with a wide range of diseases. The gut microbiome is also a dynamic reflection of health status, which can be modified, thus representing great potential to exploit the mechanisms that influence human physiology. Recent years have seen a dramatic rise in gut microbiome studies, which has been enabled by the rapidly evolving high-throughput sequencing methods (i.e. 16S rRNA sequencing and shotgun sequencing). As the emerging technologies for microbiome research continue to evolve (i.e. metatranscriptomics, metabolomics, culturomics, synthetic biology), microbiome research has moved beyond phylogenetic descriptions and towards mechanistic analyses. In this review, we highlight different approaches to study the microbiome, in particular, the current limitations and future promise of these techniques. This review aims to provide clinicians with a framework for studying the microbiome, as well as to accelerate the adoption of these techniques in clinical practice.
Humans ; Gastrointestinal Microbiome ; Phylogeny ; RNA, Ribosomal, 16S/genetics* ; Health Status

Circadian Clocks/genetics* ; Reactive Oxygen Species ; Methionine ; Gastrointestinal Microbiome ; Gastrointestinal Tract ; Racemethionine ; Gene Expression

Exploring the mechanisms of maintaining microbial community structure is important to understand biofilm development or microbiota dysbiosis. In this paper, we propose a functional gene-based composition prediction (FCP) model to predict the population structure composition within a microbial community. The model predicts the community composition well in both a low-complexity community as acid mine drainage (AMD) microbiota, and a complex community as human gut microbiota. Furthermore, we define community structure shaping (CSS) genes as functional genes crucial for shaping the microbial community. We have identified CSS genes in AMD and human gut microbiota samples with FCP model and find that CSS genes change with the conditions. Compared to essential genes for microbes, CSS genes are significantly enriched in the genes involved in mobile genetic elements, cell motility, and defense mechanisms, indicating that the functions of CSS genes are focused on communication and strategies in response to the environment factors. We further find that it is the minority, rather than the majority, which contributes to maintaining community structure. Compared to health control samples, we find that some functional genes associated with metabolism of amino acids, nucleotides, and lipopolysaccharide are more likely to be CSS genes in the disease group. CSS genes may help us to understand critical cellular processes and be useful in seeking addable gene circuitries to maintain artificial self-sustainable communities. Our study suggests that functional genes are important to the assembly of microbial communities.
Gastrointestinal Microbiome ; genetics ; Genes, Microbial ; Humans ; Microbiota ; genetics ; Mining ; Models, Genetic ; Water Pollution

BACKGROUND: Osteoporosis (OP) has become a major public health issue, threatening the bone health of middle-aged and elderly people from all around the world. Changes in the gut microbiota (GM) are correlated with the maintenance of bone mass and bone quality. However, research results in this field remain highly controversial, and no systematic review or meta-analysis of the relationship between GM and OP has been conducted. This paper addresses this shortcoming, focusing on the difference in the GM abundance between OP patients and healthy controls based on previous 16S ribosomal RNA (rRNA) gene sequencing results, in order to provide new clinical reference information for future customized prevention and treatment options of OP. METHODS: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we comprehensively searched the databases of PubMed, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI). In addition, we applied the R programming language version 4.0.3 and Stata 15.1 software for data analysis. We also implemented the Newcastle-Ottawa Scale (NOS), funnel plot analysis, sensitivity analysis, Egger's test, and Begg's test to assess the risk of bias. RESULTS: This research ultimately considered 12 studies, which included the fecal GM data of 2033 people (604 with OP and 1429 healthy controls). In the included research papers, it was observed that the relative abundance of Lactobacillus and Ruminococcus increased in the OP group, while the relative abundance for Bacteroides of Bacteroidetes increased (except for Ireland). Meanwhile, Firmicutes, Blautia, Alistipes, Megamonas, and Anaerostipes showed reduced relative abundance in Chinese studies. In the linear discriminant analysis Effect Size (LEfSe) analysis, certain bacteria showed statistically significant results consistently across different studies. CONCLUSIONS: This observational meta-analysis revealed that changes in the GM were correlated with OP, and variations in some advantageous GM might involve regional differences.
Middle Aged ; Aged ; Humans ; Gastrointestinal Microbiome/genetics* ; RNA, Ribosomal, 16S/genetics* ; Genes, rRNA ; Osteoporosis ; Feces

The research on the relationship between gut microbiota and human health continues to be a hot topic in the field of life science. Culture independent 16S rRNA gene high-throughput sequencing is the current main research method. However, with the reduction of sequencing cost and the maturity of data analysis methods, shotgun metagenome sequencing is gradually becoming an important method for the study of gut microbiome due to its advantages of obtaining more information. With the support from the human microbiome project, 30 805 metagenome samples were sequenced in the United States. By searching NCBI PubMed and SRA databases, it was found that 72 studies collecting about 10 000 Chinese intestinal samples were used for metagenome sequencing. To date, only 56 studies were published, including 16 related to metabolic diseases, 16 related to infectious and immune diseases, and 12 related to cardiovascular and cerebrovascular diseases. The samples were mainly collected in Beijing, Guangzhou, Shanghai and other cosmopolitan cities, where great differences exist in sequencing platforms and methods. The outcome of most studies are based on correlation analysis, which has little practical value in guiding the diagnosis and treatment of clinical diseases. Standardizing sampling methods, sequencing platform and data analysis process, and carrying out multi center parallel research will contribute to data integration and comparative analysis. Moreover, insights into the functional verification and molecular mechanism by using the combination of transcriptomics, proteomics and culturomics will enable the gut microbiota research to better serve the clinical diagnosis and treatment.
China ; Gastrointestinal Microbiome/genetics* ; Humans ; Metagenome ; Microbiota ; RNA, Ribosomal, 16S/genetics* ; United States

Gut microbiota is closely related to human health, and its composition can give us health information. The large-scale population sampling is required on gut microbiome research; however, fresh feces samples are not easy to obtain, and rapid low-temperature freezing is difficult to achieve. With the development of technology, preservation solutions are widely used for sample collection, storage, and transport under normal temperature conditions. Preservation solutions can be used in large scale sample collection, wide geographical distribution, diverse on-site sampling conditions, heavy workload, and poor transportation conditions. In this study, five healthy volunteers were recruited. After collecting their fresh stool samples, effect of 5 different commercial preservation solutions was evaluated at room temperature. Samples in different preservation solutions after placing fresh stool samples at the 0, 1, 3, 7, 15, and 30 days were collected. All samples were tested by 16S rRNA V3-V4 high-throughput sequencing to analyze the influence of microbiome composition in different preservation solutions. The results show that different preservation solutions had distinct effects on the gut microbiome composition. Compared with the control, different preservation solutions had little effect on the amount of OUTs; preservation solutions A, B and C were closer to the control in the composition of the gut microbiota, but preservation solution D significantly changed the composition by increasing Actinobacteria and Firmicutes abundance. With the time, all solutions tended to reduce the diversity of the microbiota. Preservation solution E significantly reduced the diversity of the flora; on the 30th day, all five solutions changed the composition; the individual differences in the composition of the gut microbiome were the main factors affecting the similarity of each sample, and were derived from different stools donors. The same samples, no matter which storage solution and storage time, were directly closer to each other. Different storage solutions had different effects on the content of Gram-positive bacilli, Gram-positive cocci and Gram-negative bacteria. Storage solutions C and E reduced the abundance of Bifidobacterium, whereas storage solution D increased; except that preservation solution E relatively reduced the abundance of Lactobacillus, but the preservation solution A, B, C, and D were all closer to the control. Except for the greater difference in preservation solution D, preservation solution C was the closest to the control group on Streptococcus; preservation solution D reduced Ruminococcaceae UCG 003 than the control group. However, other preservation solutions were not much different from the control group; different preservation solutions increased the abundance of Escherichia-Shigella than the control group, and preservation solutions A and B increased the abundance of Klebsiella, but preservation solution C, D, and E were closer to the control group. Overall, preservation solution C performed better in stabilizing the composition of the gut microbiota. This study provides reference for standardized microbiome projects. Subsequent research can choose a targeted preservation solution and preservation time based on this study.
Bacteria/genetics* ; Feces ; Gastrointestinal Microbiome ; Humans ; RNA, Ribosomal, 16S/genetics* ; Specimen Handling

OBJECTIVES: Intracerebral hemorrhage (ICH) has the highest mortality and disability rates among various subtypes of stroke. Previous studies have shown that the gut microbiome (GM) is closely related to the risk factors and pathological basis of ICH. This study aims to explore the causal effect of GM on ICH and the potential mechanisms. METHODS: Genome wide association study (GWAS) data on GM and ICH were obtained from Microbiome Genome and International Stroke Genetics Consortium. Based on the GWAS data, we first performed Mendelian randomization (MR) analysis to evaluate the causal association between GM and ICH. Then, a conditional false discovery rate (cFDR) method was conducted to identify the pleiotropic variants. RESULTS: MR analysis showed that Pasteurellales, Pasteurellaceae, and Haemophilus were negatively correlated with the risk of ICH, whileVerrucomicrobiae, Verrucomicrobiales, Verrucomicrobiaceae, Akkermansia, Holdemanella, and LachnospiraceaeUCG010 were positively correlated with ICH. By applying the cFDR method, 3 pleiotropic loci (rs331083, rs4315115, and rs12553325) were found to be associated with both GM and ICH. CONCLUSIONS There is a causal association and pleiotropic variants between GM and ICH.
Humans ; Genome-Wide Association Study ; Gastrointestinal Microbiome/genetics* ; Genetic Predisposition to Disease ; Cerebral Hemorrhage/genetics* ; Stroke

OBJECTIVE: To investigate the gut microbiota in newly diagnosed IgA nephropathy patients with chronic kidney disease (CKD) stages 1-2 and the association between the gut microbiota and the clinical risk factors of IgA nephropathy. METHODS: Fresh fecal samples were collected from nineteen newly diagnosed IgA nephropathy patients with CKD stages 1-2 and fifteen age- and sex-matched healthy controls. Fecal bacterial DNA was extracted and microbiota composition were characterized using 16S ribosomal RNA (16S rRNA) high-throughput sequencing for the V3-V4 region. The Illumina Miseq platform was used to analyze the results of 16S rRNA high-throughput sequencing of fecal flora. At the same time, the clinical risk factors of IgA nephropathy patients were collected to investigate the association between the gut microbiota and the clinical risk factors. RESULTS: (1) At the phylum level, the abundance of Bacteroidetes was significantly reduced (P=0.046), and the abundance of Actinobacteria was significantly increased (P=0.001). At the genus level, the abundance of Escherichia-Shigella, Bifidobacte-rium, Dorea and others were significantly increased (P < 0.05). The abundance of Lachnospira, Coprococcus_2 and Sutterella was significantly reduced (P < 0.05). (2) There was no significant difference in the abundance of gut microbiota between the newly diagnosed IgA nephropathy patients and the healthy control group (P>0.05), but there were differences in the structure of the gut microbiota between the two groups. The results of LEfSe analysis showed that there were 16 differential bacteria in the newly diagnosed IgA nephropathy patients and healthy controls. Among them, the abundance of the newly diagnosed IgA nephropathy patients was increased in Enterobacteriales, Actinobacteria, Escherichia-Shigella, etc. The healthy control group was increased in Bacteroidetes and Lachnospira. (3) The result of redundancy analysis (RDA) showed that Bifidobacterium was positively correlated with serum IgA levels, 24-hour urinary protein levels and the presence of hypertension. Lachnoclostridium was positively correlated with the presence of hypertension. Escherichia-Shigella was positively correlated with urine red blood cells account. Bifidobacterium was positively correlated with the proliferation of capillaries. Faecalibacterium was positively correlated with cell/fibrocytic crescents. Ruminococcus_2 was positively correlated with mesangial cell proliferation, glomerular segmental sclerosis and renal tubular atrophy/interstitial fibrosis. CONCLUSION The gut microbiota in the newly diagnosed IgA nephropathy patients with CKD stages 1-2 is different from that of the healthy controls. Most importantly, some gut bacteria are related to the clinical risk factors of IgA nephropathy. Further research is needed to understand the potential role of these bacteria in IgA nephropathy.
Humans ; Gastrointestinal Microbiome ; RNA, Ribosomal, 16S/genetics* ; Glomerulonephritis, IGA ; Bacteria/genetics* ; Risk Factors ; Renal Insufficiency, Chronic

Altitude ; Animals ; Bacteria ; classification ; genetics ; Feces ; microbiology ; Gastrointestinal Microbiome ; Hypoxia ; microbiology ; Male ; Rats, Sprague-Dawley