Many recent studies have shown that the gut microbiome plays important roles in human physiology and pathology.Also,microbiome-based therapies have been used to improve health status and treat diseases.In addition,agi...Many recent studies have shown that the gut microbiome plays important roles in human physiology and pathology.Also,microbiome-based therapies have been used to improve health status and treat diseases.In addition,aging and neurodegenerative diseases,including Alzheimer’s disease and Parkinson’s disease,have become topics of intense interest in biomedical research.Several researchers have explored the links between these topics to study the potential pathogenic or therapeutic effects of intestinal microbiota in disease.But the exact relationship between neurodegenerative diseases and gut microbiota remains unclear.As technology advances,new techniques for studying the microbiome will be developed and refined,and the relationship between diseases and gut microbiota will be revealed.This article summarizes the known interactions between the gut microbiome and neurodegenerative diseases,highlighting assay techniques for the gut microbiome,and we also discuss the potential therapeutic role of microbiome-based therapies in diseases.展开更多
Research on microecology has been carried out with broad perspectives in recent decades,which has enabled a better understanding of the gut microbiota and its roles in human health and disease.It is of great significa...Research on microecology has been carried out with broad perspectives in recent decades,which has enabled a better understanding of the gut microbiota and its roles in human health and disease.It is of great significance to routinely acquire the status of the human gut microbiota;however,there is no method to evaluate the gut microbiome through small amounts of fecal microbes.In this study,we found ten predominant groups of gut bacteria that characterized the whole microbiome in the human gut from a large-sample Chinese cohort,constructed a real-time quantitative polymerase chain reaction(qPCR)method and developed a set of analytical approaches to detect these ten groups of predominant gut bacterial species with great maneuverability,efficiency,and quantitative features.Reference ranges for the ten predominant gut bacterial groups were established,and we found that the concentration and pairwise ratios of the ten predominant gut bacterial groups varied with age,indicating gut microbial dysbiosis.By comparing the detection results of liver cirrhosis(LC)patients with those of healthy control subjects,differences were then analyzed,and a classification model for the two groups was built by machine learning.Among the six established classification models,the model established by using the random forest algorithm achieved the highest area under the curve(AUC)value and sensitivity for predicting LC.This research enables easy,rapid,stable,and reliable testing and evaluation of the balance of the gut microbiota in the human body,which may contribute to clinical work.展开更多
In-situ oral delivery of therapeutic antibodies,like monoclonal antibody,for chronic inflammation treatment is the most convenient approach compared with other administration routes.Moreover,the abundant links between...In-situ oral delivery of therapeutic antibodies,like monoclonal antibody,for chronic inflammation treatment is the most convenient approach compared with other administration routes.Moreover,the abundant links between the gut microbiota and colonic inflammation indicate that the synergistic or antagonistic effect of gut microbiota to colonic inflammation.However,the antibody activity would be significantly affected while transferring through the gastrointestinal tract due to hostile conditions.Moreover,these antibodies have short serum half-lives,thus,require to be frequently administered with high doses to be effective,leading to low patient tolerance.Here,we develop a strategy utilizing thin shell hydrogel microcapsule fabricated by microfluidic technique as the oral delivering carrier.By encapsulating antibodies in these microcapsules,antibodies survive in the hostile gastrointestinal environment and rapidly release into the small intestine through oral administration route,achieving the same therapeutic effect as the intravenous injection evaluated by a colonic inflammation disease model.Moreover,the abundance of some intestinal microorganisms as the indication of the improvement of inflammation has remarkably altered after in-situ antibody-laden microcapsules delivery,implying the restoration of micro-ecology of the intestine.These findings prove our microcapsules are exploited as an efficient oral delivery agent for antibodies with programmable function in clinical application.展开更多
This study explores the potential of using the gut microbiota as a biomarker for liver disease classification by constructingmachine learning classifiers.The classifiers were designed based on the abundance of 10 domi...This study explores the potential of using the gut microbiota as a biomarker for liver disease classification by constructingmachine learning classifiers.The classifiers were designed based on the abundance of 10 dominant bacterial taxa to categorize different liver disease scoring systems,including ChildPugh score,model for end-stage liver disease(MELD),albumin-bilirubin(ALBI),fibrosis 4 score(FIB-4)and aspartate aminotransferase-to-platelet ratio index(APRI).Significant variations in gutmicrobiota composition were observed across various stages of liver disease.For example,the relative abundances of Enterococcus,Lactobacillus and Eubacterium rectale exhibited notable differences between cirrhotic patients with ChildPugh grades A and B,between thosewith grades A and C,and between patients with MELD scores of 615 and those with MELD scores of 1540.In terms of the FIB-4 index,Enterococcus,Lactobacillus,Clostridium leptum,E.rectale and Faecalibacterium prausnitzii differed significantly across the low-,medium-and high-fibrosis groups.Analysis of the ALBI score revealed significant differences in the abundances of Enterococcus,Lactobacillus,Bacteroides,C.leptum,E.rectale and F.prausnitzii between grade 1 and grades 23.We constructed classifiers using machine learning algorithms based on the content of 10 dominant gut bacteria to classify the grading of different scoring systems.The highest area under the receiver operating characteristic values reported were for CP_XGBoost(0.7090 for ChildPugh),MELD_SVM_UP(0.6346 for MELD),ALBI_XGBoost_SMOTE(0.7298 for ALBI),FIB4_SVM_UP(0.5873 for FIB-4)and APRI_SVM_UP(0.6826 for APRI).These findings highlight the potential of integrating gut microbiota analysis into existing liver disease scoring frameworks to increase diagnostic accuracy and improve patient care.展开更多
基金by a National Key Science and Technology Project of China(2018YFC2000500,03)the National Natural Science Foundation of China(81790631 and 81703430)the CAMS Innovation Fund for Medical Sciences(2019-I2M-5-045)。
文摘Many recent studies have shown that the gut microbiome plays important roles in human physiology and pathology.Also,microbiome-based therapies have been used to improve health status and treat diseases.In addition,aging and neurodegenerative diseases,including Alzheimer’s disease and Parkinson’s disease,have become topics of intense interest in biomedical research.Several researchers have explored the links between these topics to study the potential pathogenic or therapeutic effects of intestinal microbiota in disease.But the exact relationship between neurodegenerative diseases and gut microbiota remains unclear.As technology advances,new techniques for studying the microbiome will be developed and refined,and the relationship between diseases and gut microbiota will be revealed.This article summarizes the known interactions between the gut microbiome and neurodegenerative diseases,highlighting assay techniques for the gut microbiome,and we also discuss the potential therapeutic role of microbiome-based therapies in diseases.
基金supported by the National Key Research and Development Program of China(2018YFC2000500)the Fundamental Research Funds for the Central Universities(2022ZFJH003)+3 种基金the Independent Task of State Key Laboratory for Diagnosis and Treatment of Infectious Diseases(2022zz22)the National Natural Science Foundation of China(81703430,32170058,and 82200994)the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(2019-I2M-5-045)the Research Project of Jinan Microecological Biomedicine Shandong Laboratory(JNL-2022051B)。
文摘Research on microecology has been carried out with broad perspectives in recent decades,which has enabled a better understanding of the gut microbiota and its roles in human health and disease.It is of great significance to routinely acquire the status of the human gut microbiota;however,there is no method to evaluate the gut microbiome through small amounts of fecal microbes.In this study,we found ten predominant groups of gut bacteria that characterized the whole microbiome in the human gut from a large-sample Chinese cohort,constructed a real-time quantitative polymerase chain reaction(qPCR)method and developed a set of analytical approaches to detect these ten groups of predominant gut bacterial species with great maneuverability,efficiency,and quantitative features.Reference ranges for the ten predominant gut bacterial groups were established,and we found that the concentration and pairwise ratios of the ten predominant gut bacterial groups varied with age,indicating gut microbial dysbiosis.By comparing the detection results of liver cirrhosis(LC)patients with those of healthy control subjects,differences were then analyzed,and a classification model for the two groups was built by machine learning.Among the six established classification models,the model established by using the random forest algorithm achieved the highest area under the curve(AUC)value and sensitivity for predicting LC.This research enables easy,rapid,stable,and reliable testing and evaluation of the balance of the gut microbiota in the human body,which may contribute to clinical work.
基金support from the National Key Science and Technology Project of China(grant number 2018YFC2000500,03)National Natural Science Foundation of China 81703430 and 81803449,CAMS Innovation Fund for Medical Sciences(grant number 2019-I2M-5-045)the Natural Science Foundation of Zhejiang Province(LYY20H300003).
文摘In-situ oral delivery of therapeutic antibodies,like monoclonal antibody,for chronic inflammation treatment is the most convenient approach compared with other administration routes.Moreover,the abundant links between the gut microbiota and colonic inflammation indicate that the synergistic or antagonistic effect of gut microbiota to colonic inflammation.However,the antibody activity would be significantly affected while transferring through the gastrointestinal tract due to hostile conditions.Moreover,these antibodies have short serum half-lives,thus,require to be frequently administered with high doses to be effective,leading to low patient tolerance.Here,we develop a strategy utilizing thin shell hydrogel microcapsule fabricated by microfluidic technique as the oral delivering carrier.By encapsulating antibodies in these microcapsules,antibodies survive in the hostile gastrointestinal environment and rapidly release into the small intestine through oral administration route,achieving the same therapeutic effect as the intravenous injection evaluated by a colonic inflammation disease model.Moreover,the abundance of some intestinal microorganisms as the indication of the improvement of inflammation has remarkably altered after in-situ antibody-laden microcapsules delivery,implying the restoration of micro-ecology of the intestine.These findings prove our microcapsules are exploited as an efficient oral delivery agent for antibodies with programmable function in clinical application.
基金supported by the Fundamental Research Funds for the Central Universities(2025ZFJH03)the National Key R&D Program of China(2023YFC2506000)+3 种基金the Medical Science and Technology Development Foundation,Nanjing Department of Health(YKK24177)the Independent Task of State Key Laboratory for Diagnosis and Treatment of Infectious Diseases(zz202411)Innovation Center for Infectious Disease of Jiangsu Province(No.CXZX202232)supported by the 333 Project of Jiangsu Province and the Nanjing Infectious Disease Clinical Medical Center.
文摘This study explores the potential of using the gut microbiota as a biomarker for liver disease classification by constructingmachine learning classifiers.The classifiers were designed based on the abundance of 10 dominant bacterial taxa to categorize different liver disease scoring systems,including ChildPugh score,model for end-stage liver disease(MELD),albumin-bilirubin(ALBI),fibrosis 4 score(FIB-4)and aspartate aminotransferase-to-platelet ratio index(APRI).Significant variations in gutmicrobiota composition were observed across various stages of liver disease.For example,the relative abundances of Enterococcus,Lactobacillus and Eubacterium rectale exhibited notable differences between cirrhotic patients with ChildPugh grades A and B,between thosewith grades A and C,and between patients with MELD scores of 615 and those with MELD scores of 1540.In terms of the FIB-4 index,Enterococcus,Lactobacillus,Clostridium leptum,E.rectale and Faecalibacterium prausnitzii differed significantly across the low-,medium-and high-fibrosis groups.Analysis of the ALBI score revealed significant differences in the abundances of Enterococcus,Lactobacillus,Bacteroides,C.leptum,E.rectale and F.prausnitzii between grade 1 and grades 23.We constructed classifiers using machine learning algorithms based on the content of 10 dominant gut bacteria to classify the grading of different scoring systems.The highest area under the receiver operating characteristic values reported were for CP_XGBoost(0.7090 for ChildPugh),MELD_SVM_UP(0.6346 for MELD),ALBI_XGBoost_SMOTE(0.7298 for ALBI),FIB4_SVM_UP(0.5873 for FIB-4)and APRI_SVM_UP(0.6826 for APRI).These findings highlight the potential of integrating gut microbiota analysis into existing liver disease scoring frameworks to increase diagnostic accuracy and improve patient care.
