Gut Microbes最新文献

We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.

Aberration of the "gut-liver axis" contributes to the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Here, we use multi-omics to analyze the gut microbiota composition and metabolic profile of patients with type-2 diabetes mellitus (T2DM). T2DM patients were screened for liver disease by blood tests, ultrasound, and liver stiffness measurements. Stool microbiota was analyzed by 16S rRNA gene sequencing; metabolomic profiling by Nuclear Magnetic Resonance spectroscopy and Ultra-High Performance-Mass Spectrometry. Microbiome and metabolic signatures were analyzed in the whole cohort and in matched subsets to identify signatures specific for steatosis (MASLD±) or fibrosis (Fibrosis±). Gut permeability was assessed in-vitro using monolayers of MDCK cells and trans-epithelial electric resistance (TEER). Cytokine profile was assessed in serum and stools.Overall, 285 patients were enrolled: 255 serum, 252 urine and 97 stool samples were analyzed. Anaeroplasma and Escherichia/Shigella ASVs were higher, while Butyricicoccus ASVs were lower in those with normal liver. In MASLD±, Butyricicoccus ASV was significantly higher in those with steatosis. In the Fibrosis±, Butyricicoccus ASV was significantly lower in those with fibrosis. Glycochenodeoxycholic acid-3-sulfate (G-UDCA-3S) appeared to be higher in MASLD with fibrosis. Fecal water from patients with MASLD and fibrosis caused the greatest drop in the TEER vs those with normal liver; this was reversed with protease inhibitors. Finally, fecal IL-13 was lower in MASLD with fibrosis. We identified microbiome signatures which were specific for steatosis and fibrosis and independent of other metabolic risk factors. Moreover, we conclude that protease-related gut permeability plays a role in those MASLD patients with fibrosis, and that disease progression is linked to a gut-liver axis which is at least partially independent of T2DM.

The gut microbiota (GM) and its metabolites affect the host nervous system and are involved in the pathogeneses of various neurological diseases. However, the specific GM alterations under pathogenetic pressure and their contributions to the "microbiota - metabolite - brain axis" in Alzheimer's disease (AD) remain unclear. Here, we investigated the GM and the fecal, serum, cortical metabolomes in APP/PS1 and wild-type (WT) mice, revealing distinct hub bacteria in AD mice within scale-free GM networks shared by both groups. Moreover, we identified diverse peripheral - central metabolic landscapes between AD and WT mice that featured bile acids (e.g. deoxycholic and isodeoxycholic acid) and unsaturated fatty acids (e.g. 11Z-eicosenoic and palmitoleic acid). Machine-learning models revealed the relationships between the differential/hub bacteria and these metabolic signatures from the periphery to the brain. Notably, AD-enriched Dubosiella affected AD occurrence via cortical palmitoleic acid and vice versa. Considering the transgenic background of the AD mice, we propose that Dubosiella enrichment impedes AD progression via the synthesis of palmitoleic acid, which has protective properties against inflammation and metabolic disorders. We identified another association involving fecal deoxycholic acid-mediated interactions between the AD hub bacteria Erysipelatoclostridium and AD occurrence, which was corroborated by the correlation between deoxycholate levels and cognitive scores in humans. Overall, this study elucidated the GM network alterations, contributions of the GM to peripheral - central metabolic landscapes, and mediatory roles of metabolites between the GM and AD occurrence, thus revealing the critical roles of bacteria in AD pathogenesis and gut - brain communications under pathogenetic pressure.

Clostridioides difficile (C. difficile), a gram-positive anaerobic and spore-forming bacterium, is the leading cause of nosocomial antibiotic-associated diarrhea in adults which is characterized by high levels of recurrence and mortality. Surface (S)-layer Protein A (SlpA), the most abundantly expressed protein on the bacterial surface, plays a crucial role in the early stages of infection although the nature of its involvement in C. difficile physiology is yet to be fully understood. Anti-S-layer antibodies have been identified in the sera of convalescent patients and have been correlated with improved outcomes of C. difficile infection (CDI). However, the precise mechanisms by which anti-S-layer antibodies confer protection to the host remain unknown. In this study, we report the first monoclonal antibodies (mAbs) targeting the S-layer of reference strain 630. Characterization of these mAbs unraveled important roles for the S-layer protein in growth, toxin secretion, and biofilm formation by C. difficile, with differential and even opposite effects of various anti-SlpA mAbs on these functions. Moreover, one anti-SlpA mAb impaired C. difficile growth and conferred sensitivity to lysozyme-induced lysis. The results of this study show that anti-S-layer antibody responses can be beneficial or harmful for the course of CDI and provide important insights for the development of adequate S-layer-targeting therapeutics.

Diet-induced metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent metabolic disorder with limited effective interventions available. A novel approach to address this issue is through gut microbiota-based therapy. In our study, we utilized multi-omics analysis to identify Phocaeicola vulgatus (P. vulgatus) as a potential probiotic for the treatment of MASLD. Our findings from murine models clearly illustrate that the supplementation of P. vulgatus mitigates the development of MASLD. This beneficial effect is partly attributed to the metabolite 3-Hydroxyphenylacetic acid (3-HPAA) produced by P. vulgatus, which reduces the acetylation levels of H3K27 and downregulates the transcription of Squalene Epoxidase (SQLE), a rate-limiting enzyme in steroid biosynthesis that promotes lipid accumulation in liver cells. This study underscores the significant role of P. vulgatus in the development of MASLD and the critical importance of its metabolite 3-HPAA in regulating lipid homeostasis. These findings offer a promising avenue for early intervention therapy in the context of MASLD.

Chronic pain is commonly linked with diminished working memory. This study explores the impact of the anesthetic (S)-ketamine on spatial working memory in a chronic constriction injury (CCI) mouse model, focusing on gut microbiome. We found that multiple doses of (S)-ketamine, unlike a single dose, counteracted the reduced spontaneous alteration percentage (%SA) in the Y-maze spatial working memory test, without affecting mechanical or thermal pain sensitivity. Additionally, repeated (S)-ketamine treatments improved the abnormal composition of the gut microbiome (β-diversity), as indicated by fecal 16S rRNA analysis, and increased levels of butyrate, a key gut - brain axis mediator. Protein analysis showed that these treatments also corrected the upregulated histone deacetylase 2 (HDAC2) and downregulated brain-derived neurotrophic factor (BDNF) in the hippocampi of CCI mice. Remarkably, fecal microbiota transplantation from mice treated repeatedly with (S)-ketamine to CCI mice restored %SA and hippocampal BDNF levels in CCI mice. Butyrate supplementation alone also improved %SA, BDNF, and HDAC2 levels in CCI mice. Furthermore, the TrkB receptor antagonist ANA-12 negated the beneficial effects of repeated (S)-ketamine on spatial working memory impairment in CCI mice. These results indicate that repeated (S)-ketamine administration ameliorates spatial working memory impairment in CCI mice, mediated by a gut microbiota - brain axis, primarily through the enhancement of hippocampal BDNF - TrkB signaling by butyrate.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor prognosis. This is due to the fact that most cases are only diagnosed at an advanced and palliative disease stage, and there is a high incidence of therapy resistance. Despite ongoing efforts, to date, the mechanisms underlying PDAC oncogenesis and its poor responses to treatment are still largely unclear. As the study of the microbiome in cancer progresses, growing evidence suggests that bacteria or fungi might be key players both in PDAC oncogenesis as well as in its resistance to chemo- and immunotherapy, for instance through modulation of the tumor microenvironment and reshaping of the host immune response. Here, we review how the microbiota exerts these effects directly or indirectly via microbial-derived metabolites. Finally, we further discuss the potential of modulating the microbiota composition as a therapy in PDAC.

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

Rapid and accurate clinical staging of pediatric patients with inflammatory bowel disease (IBD) is crucial to determine the appropriate therapeutic approach. This study aimed to identify effective, convenient biomarkers for staging IBD in pediatric patients. We recruited cohorts of pediatric patients with varying severities of IBD to compare the features of the intestinal microbiota and metabolites between the active and remitting disease stages. Metabolites with potential for staging were targeted for further assessment in both patients and colitis model mice. The performance of these markers was determined using machine learning and was validated in a separate patient cohort. Pediatric patients with IBD exhibited distinct gut microbiota structures at different stages of disease activity. The enterotypes of patients with remitting and active disease were Bacteroides-dominant and Escherichia-Shigella-dominant, respectively. The bile secretion pathway showed the most significant differences between the two stages. Fecal and serum bile acid (BA) levels were strongly related to disease activity in both children and mice. The ratio of primary BAs to secondary BAs in serum was developed as a novel comprehensive index, showing excellent diagnostic performance in stratifying IBD activity (0.84 area under the receiver operating characteristic curve in the primary cohort; 77% accuracy in the validation cohort). In conclusion, we report profound insights into the interactions between the gut microbiota and metabolites in pediatric IBD. Serum BAs have potential as biomarkers for classifying disease activity, and may facilitate the personalization of treatment for IBD.

In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point or critical state in a reliable manner, thereby providing accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states. To address this problem, we proposed a new method - microbiome network flow entropy (mNFE) based on a single sample from each individual - for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identified some non-differential 'dark species' play important roles during the T1D progression. mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach not only for T1D pre-disease diagnosis or preventative treatment but also for preventative medicine of other diseases by gut microbiome.