Comparative analysis of intestinal microbiota composition between free-ranged captive yak populations in Nimu County.

The intestinal microbiota assumes a pivotal role in modulating host metabolism, immune responses, overall health, and additional physiological dimensions. The structural and functional characteristics of the intestinal microbiota may cause alterations within the host's body to a certain extent. The composition of the gut microbiota is associated with environmental factors, dietary habits, and other pertinent conditions. The investigation into the gut microbiota of yaks remained relatively underexplored. An examination of yak gut microbiota holds promise in elucidating the complex relationship between microbial communities and the adaptive responses of the host to its environment. In this study, yak were selected from two distinct environmental conditions: those raised in sheds (NS, n=6) and grazed in Nimu County (NF, n=6). Fecal samples were collected from the yaks and subsequently processed for analysis through 16S rDNA and ITS sequencing methodologies. The results revealed that different feeding styles result in significant differences in the Alpha diversity of fungi in the gut of yaks, while the gut microbiota of captive yaks was relatively conserved. In addition, significant differences appeared in the abundance of microorganisms in different taxa, phylum Verrucomicrobiota was significantly enriched in group NF while Firmicutes was higher in group NS. At the genus level, Akkermansia, Paenibacillus, Roseburia, Dorea, UCG_012, Anaerovorax and Marvinbryantia were enriched in group NF while Desemzia, Olsenella, Kocuria, Ornithinimicrobium and Parvibacter were higher in group NS (P<0.05 or P<0.01). There was a significant difference in the function of gut microbiota between the two groups. The observed variations are likely influenced by differences in feeding methods and environmental conditions both inside and outside the pen. The findings of this investigation offer prospective insights into enhancing the yak breeding and expansion of the yak industry.

[Analysis of saliva and intestinal microbial community in children with severe caries based on 16S rDNA high-throughput sequencing technology].

PURPOSE: The characteristics of saliva and intestinal microbial community in children with high caries and no caries were analyzed by 16S rDNA high-throughput sequencing. METHODS: Among 431 children aged 3-5 years old in Zunyi City who were investigated previously by our team, 25 children in the high caries group and the same in the caries-free group were selected for fecal and saliva samples. 16S rDNA high-throughput sequencing was used to analyze the bacterial flora structure of the samples and identify the species with different relative abundance at the species level. SPSS 18.0 software package was used for data analysis. RESULTS: The diversity of intestinal flora in the high caries group was higher than that in the caries-free group, and the difference was statistically significant(P＜0.05). The diversity of salivary flora in the high caries group was more than that in the caries-free group, with no significant difference(P＞0.05). At phylum level,there was no significant difference in intestinal and salivary flora between children with high caries and children without caries. At gene level, Blautia, [Eubacterium] hallii group and [Eubacterium] eligens group in the intestine of caries-free group were significantly higher than those of high caries group(P＜0.05), while Parasutterella and Christensenellaceae R-7 group were significantly lower than those of high caries group(P＜0.05). At gene level, Peptostreptococcus in saliva of caries-free group was significantly higher than that in high caries group(P＜0.05). Dialister, Kingella, Escherichia-Shigella and Treponema in saliva of caries-free group were significantly lower than those in high caries group(P＜0.05). CONCLUSIONS: There are significant differences in species composition of intestinal flora but no in salivary flora between children with high caries and children without caries.

Comparative analysis of gut microbiota in children with obstructive sleep apnea: assessing the efficacy of 16S rRNA gene sequencing in metabolic function prediction based on weight status.

Background: Analyzing bacterial microbiomes consistently using next-generation sequencing (NGS) is challenging due to the diversity of synthetic platforms for 16S rRNA genes and their analytical pipelines. This study compares the efficacy of full-length (V1-V9 hypervariable regions) and partial-length (V3-V4 hypervariable regions) sequencing of synthetic 16S rRNA genes from human gut microbiomes, with a focus on childhood obesity. Methods: In this observational and comparative study, we explored the differences between these two sequencing methods in taxonomic categorization and weight status prediction among twelve children with obstructive sleep apnea. Results: The full-length NGS method by Pacbio® identified 118 genera and 248 species in the V1-V9 regions, all with a 0% unclassified rate. In contrast, the partial-length NGS method by Illumina® detected 142 genera (with a 39% unclassified rate) and 6 species (with a 99% unclassified rate) in the V3-V4 regions. These approaches showed marked differences in gut microbiome composition and functional predictions. The full-length method distinguished between obese and non-obese children using the Firmicutes/Bacteroidetes ratio, a known obesity marker (p = 0.046), whereas the partial-length method was less conclusive (p = 0.075). Additionally, out of 73 metabolic pathways identified through full-length sequencing, 35 (48%) were associated with level 1 metabolism, compared to 28 of 61 pathways (46%) identified through the partial-length method. The full-length NGS also highlighted complex associations between body mass index z-score, three bacterial species (Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912), and 17 metabolic pathways. Both sequencing techniques revealed relationships between gut microbiota composition and OSA-related parameters, with full-length sequencing offering more comprehensive insights into associated metabolic pathways than the V3-V4 technique. Conclusion: These findings highlight disparities in NGS-based assessments, emphasizing the value of full-length NGS with amplicon sequence variant analysis for clinical gut microbiome research. They underscore the importance of considering methodological differences in future meta-analyses.

Relating gut microbiome composition and life history metrics for pronghorn (Antilocapra americana) in the Red Desert, Wyoming.

Host microbial communities (hereafter, the 'microbiome') are recognized as an important aspect of host health and are gaining attention as a useful biomarker to understand the ecology and demographics of wildlife populations. Several studies indicate that the microbiome may contribute to the adaptive capacity of animals to changing environments associated with increasing habitat fragmentation and rapid climate change. To this end, we investigated the gut microbiome of pronghorn (Antilocapra americana), an iconic species in an environment that is undergoing both climatic and anthropogenic change. The bacterial composition of the pronghorn gut microbiome has yet to be described in the literature, and thus our study provides important baseline information about this species. We used 16S rRNA amplicon sequencing of fecal samples to characterize the gut microbiome of pronghorn-a facultative sagebrush (Artemisia spp.) specialist in many regions where they occur in western North America. We collected fecal pellets from 159 captured female pronghorn from four herds in the Red Desert of Wyoming during winters of 2013 and 2014. We found small, but significant differences in diversity of the gut microbiome relative to study area, capture period, and body fat measurements. In addition, we found a difference in gut microbiome composition in pronghorn across two regions separated by Interstate 80. Results indicated that the fecal microbiome may be a potential biomarker for the spatial ecology of free-ranging ungulates. The core gut microbiome of these animals-including bacteria in the phyla Firmicutes (now Bacillota) and Bacteroidota-remained relatively stable across populations and biological metrics. These findings provide a baseline for the gut microbiome of pronghorn that could potentially be used as a target in monitoring health and population structure of pronghorn relative to habitat fragmentation, climate change, and management practices.

Exploring the butyrate metabolism-related shared genes in metabolic associated steatohepatitis and ulcerative colitis.

Metabolic-associated steatohepatitis (MASH) and ulcerative colitis (UC) exhibit a complex interconnection with immune dysfunction, dysbiosis of the gut microbiota, and activation of inflammatory pathways. This study aims to identify and validate critical butyrate metabolism-related shared genes between both UC and MASH. Clinical information and gene expression profiles were sourced from the Gene Expression Omnibus (GEO) database. Shared butyrate metabolism-related differentially expressed genes (sBM-DEGs) between UC and MASH were identified via various bioinformatics methods. Functional enrichment analysis was performed, and UC patients were categorized into subtypes using the consensus clustering algorithm based on sBM-DEGs. Key genes within sBM-DEGs were screened through Random Forest, Support Vector Machines-Recursive Feature Elimination, and Light Gradient Boosting. The diagnostic efficacy of these genes was evaluated using receiver operating characteristic (ROC) analysis on independent datasets. Additionally, the expression levels of characteristic genes were validated across multiple independent datasets and human specimens. Forty-nine shared DEGs between UC and MASH were identified, with enrichment analysis highlighting significant involvement in immune, inflammatory, and metabolic pathways. The intersection of butyrate metabolism-related genes with these DEGs produced 10 sBM-DEGs. These genes facilitated the identification of molecular subtypes of UC patients using an unsupervised clustering approach. ANXA5, CD44, and SLC16A1 were pinpointed as hub genes through machine learning algorithms and feature importance rankings. ROC analysis confirmed their diagnostic efficacy in UC and MASH across various datasets. Additionally, the expression levels of these three hub genes showed significant correlations with immune cells. These findings were validated across independent datasets and human specimens, corroborating the bioinformatics analysis results. Integrated bioinformatics identified three significant biomarkers, ANXA5, CD44, and SLC16A1, as DEGs linked to butyrate metabolism. These findings offer new insights into the role of butyrate metabolism in the pathogenesis of UC and MASH, suggesting its potential as a valuable diagnostic biomarker.

Familial clustering of dysbiotic oral and fecal microbiomes in juvenile dermatomyositis.

Juvenile dermatomyositis (JDM) is a rare immune-mediated disease of childhood with putative links to microbial exposures. In this multi-center, prospective, observational cohort study, we evaluated whether JDM is associated with discrete oral and gut microbiome signatures. We generated 16S rRNA sequencing data from fecal, saliva, supragingival, and subgingival plaque samples from JDM probands (n = 28). To control for genetic and environmental determinants of microbiome community structure, we also profiled microbiomes of unaffected family members (n = 27 siblings, n = 26 mothers, and n = 17 fathers). Sample type (oral-vs-fecal) and nuclear family unit were the predominant variables explaining variance in microbiome diversity, more so than having a diagnosis of JDM. The oral and gut microbiomes of JDM probands were more similar to their own unaffected siblings than they were to the microbiomes of other JDM probands. In a sibling-paired within-family analysis, several potentially immunomodulatory bacterial taxa were differentially abundant in the microbiomes of JDM probands compared to their unaffected siblings, including Faecalibacterium (gut) and Streptococcus (oral cavity). While microbiome features of JDM are often shared by unaffected family members, the loss or gain of specific fecal and oral bacteria may play a role in disease pathogenesis or be secondary to immune dysfunction in susceptible individuals.

Identification of commensal gut microbiota signatures as predictors of clinical severity and disease progression in multiple sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system and a leading cause of neurological disability in young adults. Clinical presentation and disease course are highly heterogeneous. Typically, disease progression occurs over time and is characterized by the gradual accumulation of disability. The risk of developing MS is driven by complex interactions between genetic and environmental factors, including the gut microbiome. How the commensal gut microbiota impacts disease severity and progression over time remains unknown. In a longitudinal study, disability status and associated clinical features in 58 MS patients were tracked over 4.2 ± 0.98 years, and the baseline fecal gut microbiome was characterized via 16S amplicon sequencing. Progressor status, defined as patients with an increase in Expanded Disability Status Scale (EDSS), were correlated with features of the gut microbiome to determine candidate microbiota associated with risk of MS disease progression. We found no overt differences in microbial community diversity and overall structure between MS patients exhibiting disease progression and non-progressors. However, a total of 41 bacterial species were associated with worsening disease, including a marked depletion in Akkermansia, Lachnospiraceae, and Oscillospiraceae, with an expansion of Alloprevotella, Prevotella-9, and Rhodospirillales. Analysis of the metabolic potential of the inferred metagenome from taxa associated with progression revealed enrichment in oxidative stress-inducing aerobic respiration at the expense of microbial vitamin K2 production (linked to Akkermansia), and a depletion in SCFA metabolism (linked to Oscillospiraceae). Further, as a proof of principle, statistical modeling demonstrated that microbiota composition and clinical features were sufficient to predict disease progression. Additionally, we found that constipation, a frequent gastrointestinal comorbidity among MS patients, exhibited a divergent microbial signature compared with progressor status. These results demonstrate a proof of principle for the utility of the gut microbiome for predicting disease progression in MS in a small well-defined cohort. Further, analysis of the inferred metagenome suggested that oxidative stress, vitamin K2, and SCFAs are associated with progression, warranting future functional validation and mechanistic study.

Delivery mode is a larger determinant of infant gut microbiome composition at 6 weeks than exposure to peripartum antibiotics.

Background. Previous research has shown that delivery mode can shape infant gut microbiome composition. However, mothers delivering by caesarean section routinely receive prophylactic antibiotics prior to delivery, resulting in antibiotic exposure to the infant via the placenta. Previously, only a small number of studies have examined the effect of delivery mode versus antibiotic exposure on the infant gut microbiome with mixed findings.Objective. We aimed to determine the effect of delivery mode compared to antibiotic use during labour and delivery on the infant and maternal gut microbiome at 6 weeks post-partum.Methodology. Twenty-five mother-infant dyads were selected from the longitudinal Queensland Family Cohort Study. The selected dyads comprised nine vaginally delivered infants without antibiotics, seven vaginally delivered infants exposed to antibiotics and nine infants born by caesarean section with routine maternal prophylactic antibiotics. Shotgun-metagenomic sequencing of DNA from stool samples collected at 6 weeks post-partum from mother and infant was used to assess microbiome composition.Results. Caesarean section infants exhibited decreases in Bacteroidetes (ANCOM-BC q<0.0001, MaAsLin 2 q=0.041), changes to several functional pathways and altered beta diversity (R 2=0.056, P=0.029), while minimal differences due to antibiotic exposure were detected. For mothers, caesarean delivery (P=0.0007) and antibiotic use (P=0.016) decreased the evenness of the gut microbiome at 6 weeks post-partum without changing beta diversity. Several taxa in the maternal microbiome were altered in association with antibiotic use, with few differentially abundant taxa associated with delivery mode.Conclusion. For infants, delivery mode appears to have a larger effect on gut microbiome composition at 6 weeks post-partum than intrapartum antibiotic exposure. For mothers, both delivery mode and intrapartum antibiotic use have a small effect on gut microbiome composition at 6 weeks post-partum.

Prediction of anastomotic insufficiency based on the mucosal microbiome prior to colorectal surgery: a proof-of-principle study.

Anastomotic leakage (AL) is a potentially life-threatening complication following colorectal cancer (CRC) resection. In this study, we aimed to unravel longitudinal changes in microbial structure before, during, and after surgery and to determine if microbial alterations may be predictive for risk assessment between sufficient anastomotic healing (AS) and AL prior surgery. We analysed the microbiota of 134 colon mucosal biopsies with 16S rRNA V1-V2 gene sequencing. Samples were collected from three location sites before, during, and after surgery, and patients received antibiotics after the initial collection and during surgery. The microbial structure showed dynamic surgery-related changes at different time points. Overall bacterial diversity and the abundance of some genera such as Faecalibacterium or Alistipes decreased over time, while the genera Enterococcus and Escherichia_Shigella increased. The distribution of taxa between AS and AL revealed significant differences in the abundance of genera such as Prevotella, Faecalibacterium and Phocaeicola. In addition to Phocaeicola, Ruminococcus2 and Blautia showed significant differences in abundance between preoperative sample types. ROC analysis of the predictive value of these genera for AL revealed an AUC of 0.802 (p = 0.0013). In summary, microbial composition was associated with postoperative outcomes, and the abundance of certain genera may be predictive of postoperative complications.

Refining the relationship between gut microbiota and common hematologic malignancies: insights from a bidirectional Mendelian randomization study.

Background: The relationship between gut microbiota and hematologic malignancies has attracted considerable attention. As research progresses, it has become increasingly clear that the composition of gut microbiota may influence the onset and progression of hematologic malignancies. However, our understanding of this association remains limited. Methods: In our study, we classified gut microbiota into five groups based on information at the phylum, class, order, family, and genus levels. Subsequently, we obtained data related to common hematologic malignancies from the IEU Open GWAS project. We then employed a bidirectional Mendelian Randomization (MR) approach to determine whether there is a causal relationship between gut microbiota and hematologic malignancies. Additionally, we conducted bidirectional MR analyses to ascertain the directionality of this causal relationship. Results: Through forward and reverse MR analyses, we found the risk of lymphoid leukemia was significantly associated with the abundance of phylum Cyanobacteria, order Methanobacteriales, class Methanobacteria, family Peptococcaceae, family Methanobacteriaceae, and genera Lachnospiraceae UCG010, Methanobrevibacter, Eubacterium brachy group, and Butyrivibrio. The risk of myeloid leukemia was significantly associated with the abundance of phylum Actinobacteria, phylum Firmicutes, order Bifidobacteriales, order Clostridiales, class Actinobacteria, class Gammaproteobacteria, class Clostridia, family Bifidobacteriaceae, and genera Fusicatenibacter, Eubacterium hallii group, Blautia, Collinsella, Ruminococcus gauvreauii group, and Bifidobacterium. The risk of Hodgkin lymphoma was significantly associated with the abundance of family Clostridiales vadinBB60 group, genus Peptococcus, and genus Ruminococcaceae UCG010. The risk of malignant plasma cell tumor was significantly associated with the abundance of genera Romboutsia and Eubacterium rectale group. The risk of diffuse large B-cell lymphoma was significantly associated with the abundance of genera Erysipelatoclostridium and Eubacterium coprostanoligenes group. The risk of mature T/NK cell lymphomas was significantly associated with the abundance of phylum Verrucomicrobia, genus Ruminococcaceae UCG013, genus Lachnoclostridium, and genus Eubacterium rectale group. Lastly, the risk of myeloproliferative neoplasms was significantly associated with the abundance of genus Coprococcus 3 and Eubacterium hallii group. Conclusion: Our study provided new evidence for the causal relationship between gut microbiota and hematologic malignancies, offering novel insights and approaches for the prevention and treatment of these tumors.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

Human gastric microbiota analysis of refractory H. pylori infection.

H. pylori infection is gaining increasing attention, but detailed investigations into its impact on gastric microbiota remain limited. We collected gastric mucosa samples from 47 individuals divided into three groups: 1. Group HP: patients with initial positive H. pylori infection (25 cases); 2. Group ck: H. pylori-negative patients (14 cases); 3. Group DiffHP: patients with refractory H. pylori infection (8 cases). The samples were analyzed using 16S rDNA sequencing and functional prediction with PICRUSt. Group HP showed differences in flora distribution and function compared to Group ck, while Group DiffHP overlapped with Group HP. The abundances of Aeromonas piscicola, Shewanella algae, Vibrio plantisponsor, Aeromonas caviae, Serratia marcescens, Vibrio parahaemolyticus, Microbacterium lacticum, and Prevotella nigrescens were significantly reduced in both Group DiffHP and Group HP compared to Group ck. Vibrio shilonii was reduced only in Group DiffHP compared to Group ck, while Clostridium perfringens and Paracoccus marinus were increased only in Group DiffHP. LEfSe analysis revealed that Clostridium perfringens and Paracoccus marinus were enriched, whereas Vibrio shilonii was reduced in Group DiffHP compared to Group ck at the species level. In individuals with refractory H. pylori infection, the gastric microbiota exhibited enrichment in various human diseases, organic systems, and metabolic pathways (amino acid metabolism, carbohydrate metabolism, transcription, replication and repair, cell cycle pathways, and apoptosis). Patients with multiple failed H. pylori eradication exhibited significant changes in the gastric microbiota. An increase in Clostridium perfringens and Paracoccus marinus and a decrease in Vibrio shilonii appears to be characteristic of refractory H. pylori infection.

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets.

BACKGROUND: Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration. RESULTS: In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case-control relationships. CONCLUSIONS: CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn's disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract.

Association between gut microbiota and malignant cardiac tumors: A two-sample Mendelian randomization study.

BACKGROUND: Recent studies provide compelling evidence linking the gut microbiota to most cancers. Nevertheless, further research is required to establish a definitive causal relationship between the gut microbiota and malignant cardiac tumors. METHODS: The genome-wide association studies (GWAS) data on the human gut Microbiota, included in the IEU Open GWAS project, was initially collected by the MiBioGen consortium. It encompasses 14,306 individuals and comprises a total of 5,665,279 SNPs. Similarly, the GWAS data on malignant cardiac tumors, also sourced from the IEU Open GWAS project, was initially stored in the finnGen database, including 16,380,303 SNPs observed within a cohort of 174,108 individuals within the European population. Utilizing a two-sample Mendelian randomization (MR) methodology, we examined whether there exists a causal association between the gut microbiota and cardiac tumors. Additionally, to bolster the credibility and robustness of the identified causal relationships, we conducted an extensive array of sensitivity analyses, encompassing Cochran's Q test, MR-PRESSO tests, MR-Egger interpret test, directionality test and leave-one-out analysis. RESULTS: Our analysis unveiled seven distinct causal associations between genetic susceptibility in the gut microbiota and the incidence of malignant cardiac tumors. Among these, the Family Rikenellaceae, genus Eubacterium brachy group, and genus Ruminococcaceae UCG009 exhibited an elevated risk of cardiac tumors, while the phylum Verrucomicrobia, genus Lactobacillus, genus Ruminiclostridium5, and an unknown genus id.1868 were genetically linked to a reduced risk of cardiac tumors. The causal relationship between these two bacteria, belonging to the phylum Verrucomicrobia (OR = 0.178, 95% CI: 0.052-0.614, p = 0.006) and the genus Ruminococcaceae UCG009 (OR = 3.071, 95% CI: 1.236-7.627, p = 0.016), and cardiac tumors was further validated through sensitivity analyses, reinforcing the robustness and reliability of the observed associations. CONCLUSION: Our MR analysis confirms that the phylum Verrucomicrobia displays significant protection against cardiac tumor, and the genus Ruminococcaceae UCG009 leads to an increasing risk of cardiac tumor.

Impact of captivity and natural habitats on gut microbiome in Epinephelus akaara across seasons.

BACKGROUND: The gut microbiota significantly influences the health and growth of red-spotted grouper (Epinephelus akaara), a well-known commercial marine fish from Fujian Province in southern China. However, variations in survival strategies and seasons can impact the stability of gut microbiota data, rendering it inaccurate in reflecting the state of gut microbiota. Which impedes the effective enhancement of aquaculture health through a nuanced understanding of gut microbiota. Inspired by this, we conducted a comprehensive analysis of the gut microbiota of wild and captive E. akaara in four seasons. RESULTS: Seventy-two E. akaara samples were collected from wild and captive populations in Dongshan city, during four different seasons. Four sections of the gut were collected to obtain comprehensive information on the gut microbial composition and sequenced using 16S rRNA next-generation Illumina MiSeq. We observed the highest gut microbial diversity in both captive and wild E. akaara during the winter season, and identified strong correlations with water temperature using Mantel analysis. Compared to wild E. akaara, we found a more complex microbial network in captive E. akaara, as evidenced by increased abundance of Bacillaceae, Moraxellaceae and Enterobacteriaceae. In contrast, Vibrionaceae, Clostridiaceae, Flavobacteriaceae and Rhodobacteraceae were found to be more active in wild E. akaara. However, some core microorganisms, such as Firmicutes and Photobacterium, showed similar distribution patterns in both wild and captive groups. Moreover, we found the common community composition and distribution characteristics of top 10 core microbes from foregut to hindgut in E. akaara. CONCLUSIONS: Collectively, the study provides relatively more comprehensive description of the gut microbiota in E. akaara, taking into account survival strategies and temporal dimensions, which yields valuable insights into the gut microbiota of E. akaara and provides a valuable reference to its aquaculture.

Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer.

BACKGROUND & AIMS: Gut microbiota is closely related to the occurrence and development of colorectal cancer (CRC). However, the differences in bacterial co-abundance groups (CAGs) between tumor tissue (TT) and normal tissue (NT), as well as their associations with clinical features, are needed to be clarified. METHODS: Bacterial 16 S rRNA sequencing was performed by using TT samples and NT samples of 251 patients with colorectal cancer. Microbial diversity, taxonomic characteristics, microbial composition, and functional pathways were compared between TT and NT. Hierarchical clustering was used to construct CAGs. RESULTS: Four CAGs were grouped in the hierarchical cluster analysis. CAG 2, which was mainly comprised of pathogenic bacteria, was significantly enriched in TT samples (2.27% in TT vs. 0.78% in NT, p < 0.0001). CAG 4, which was mainly comprised of non-pathogenic bacteria, was significantly enriched in NT samples (0.62% in TT vs. 0.79% in NT, p = 0.0004). In addition, CAG 2 was also significantly associated with tumor microsatellite instability (13.2% in unstable vs. 2.0% in stable, p = 0.016), and CAG 4 was positively correlated with the level of CA199 (r = 0.17, p = 0.009). CONCLUSIONS: Our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition.

Intestinal microbiota and tuberculosis: Insights from Mendelian randomization.

Respiratory tuberculosis (RTB), a global health concern affecting millions of people, has been observationally linked to the gut microbiota, but the depth and nature of this association remain elusive. Despite these findings, the underlying causal relationship is still uncertain. Consequently, we used the Mendelian randomization (MR) method to further investigate this potential causal connection. We sourced data on the gut microbiota from a comprehensive genome-wide association study (GWAS) conducted by the MiBioGen Consortium (7686 cases, and 115,893 controls). For RTB, we procured 2 distinct datasets, labeled the Fingen R9 TBC RESP and Fingen R9 AB1 RESP, from the Finnish Genetic Consortium. To decipher the potential relationship between the gut microbiota and RTB, we employed MR on both datasets. Our primary mode of analysis was the inverse variance weighting (IVW) method. To ensure robustness and mitigate potential confounders, we meticulously evaluated the heterogeneity and potential pleiotropy of the outcomes. In the TBC RESP (RTB1) dataset related to the gut microbiota, the IVW methodology revealed 7 microbial taxa that were significantly associated with RTB. In a parallel vein, the AB1 RESP (RTB2) dataset highlighted 4 microbial taxa with notable links. Notably, Lachnospiraceae UCG010 was consistently identified across both datasets. This correlation was especially evident in the data segments designated Fingen R9 TBC RESP (OR = 1.799, 95% CI = 1.243-2.604) and Finngen R9 AB1 RESP (OR = 2.131, 95% CI = 1.088-4.172). Our study identified a causal relationship between particular gut microbiota and RTB at the level of prediction based on genetics. This discovery sheds new light on the mechanisms of RTB development, which are mediated by the gut microbiota.

Metagenomic comparison of intestinal microbiota between normal and liver fibrotic rhesus macaques (Macaca mulatta).

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis.

Gut microbiome and major depressive disorder: insights from two-sample Mendelian randomization.

BACKGROUND: Existing evidence suggests that alterations in the gut microbiome are closely associated with major depressive disorder (MDD). We aimed to reveal the causal relationships between MDD and various microbial taxa in the gut. METHODS: We used the two-sample Mendelian randomization (TSMR) to explore the bidirectional causal effects between gut microbiota and MDD. The genome-wide association studies summary results of gut microbiota were obtained from two large consortia, the MibioGen consortium and the Dutch Microbiome Project, which we analyzed separately. RESULTS: Our TSMR analysis identified 10 gut bacterial taxa that were protective against MDD, including phylum Actinobacteria, order Clostridiales, and family Bifidobacteriaceae (OR: 0.96 ∼ 0.98). Ten taxa were associated with an increased risk of MDD, including phyla Firmicutes and Proteobacteria, class Actinobacteria, and genus Alistipes (OR: 1.01 ∼ 1.09). On the other hand, MDD may decrease the abundance of 12 taxa, including phyla Actinobacteria and Firmicutes, families Bifidobacteriaceae and Defluviitaleaceae (OR: 0.63 ∼ 0.88). MDD may increase the abundance of 8 taxa, including phylum Bacteroidetes, genera Parabacteroides, and Bacteroides (OR: 1.12 ∼ 1.43). CONCLUSIONS: Our study supports that there are mutual causal relationships between certain gut microbiota and the development of MDD suggesting that gut microbiota may be targeted in the treatment of MDD.