{"title":"自闭症谱系障碍中人类肠道微生物组的弹性:使用刚度网络分析测量。","authors":"Hongju Daisy Chen, Bin Yi, Zhanshan Sam Ma","doi":"10.1128/spectrum.01078-24","DOIUrl":null,"url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) affects an estimated 1%-2% of children worldwide, but its specific etiology remains unclear. In recent years, the gut microbiome's role in ASD pathogenesis has garnered increasing attention. However, the exact relationship between microbiota and ASD-such as which microbial species significantly impact disease onset and progression-remains unresolved, and effective methods to measure microbial interactions are still lacking. In this study, we introduce an innovative stiffness network analysis (SNA) method to quantify changes in microbial network structure and identify disease-specific microbial bacteria theoretically. The SNA method was applied to reanalyze eight ASD gut microbiome data sets, encompassing 898 ASD samples and 467 healthy control (HC) samples from 16S-rRNA sequencing data. Key findings include the following: (i) an \"allies\" biomarker subgroup consisting of <i>Bacteroides plebeius</i>, <i>Sutterella</i>, <i>Lachnospira</i>, and <i>Prevotella copri</i> was identified; (ii) a profile monitoring score of 0.72 for the biomarker subgroup, indicating significant relationship changes between HC and ASD states, and (iii) a P/N ratio of biomarker subgroup in ASD-associated gut bacteria that was three times higher than that of HC microbiomes. Additionally, we discuss the non-monotonic relationship alterations within microbial sub-communities in the ASD gut microbiome.IMPORTANCEIt is crucial to assess alterations in network structure in different biological states in order to promote health. The stiffness network allows for the exploration of species interactions and the measurement of resilience in complex microbial networks. The objective of this study was to develop a stiffness network analysis (SNA) method for evaluating the contribution of microbial bacteria in differentiating disease samples from healthy control samples by examining changes in network stiffness parameters. Furthermore, the SNA method was employed on both simulated and real autism spectrum disorder gut microbiome data sets to identify potential microbial biomarker subgroups, with a particular focus on the relationship alterations within microbial networks.</p>","PeriodicalId":18670,"journal":{"name":"Microbiology spectrum","volume":" ","pages":"e0107824"},"PeriodicalIF":3.8000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878074/pdf/","citationCount":"0","resultStr":"{\"title\":\"Resilience of human gut microbiomes in autism spectrum disorder: measured using stiffness network analysis.\",\"authors\":\"Hongju Daisy Chen, Bin Yi, Zhanshan Sam Ma\",\"doi\":\"10.1128/spectrum.01078-24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Autism spectrum disorder (ASD) affects an estimated 1%-2% of children worldwide, but its specific etiology remains unclear. In recent years, the gut microbiome's role in ASD pathogenesis has garnered increasing attention. However, the exact relationship between microbiota and ASD-such as which microbial species significantly impact disease onset and progression-remains unresolved, and effective methods to measure microbial interactions are still lacking. In this study, we introduce an innovative stiffness network analysis (SNA) method to quantify changes in microbial network structure and identify disease-specific microbial bacteria theoretically. The SNA method was applied to reanalyze eight ASD gut microbiome data sets, encompassing 898 ASD samples and 467 healthy control (HC) samples from 16S-rRNA sequencing data. Key findings include the following: (i) an \\\"allies\\\" biomarker subgroup consisting of <i>Bacteroides plebeius</i>, <i>Sutterella</i>, <i>Lachnospira</i>, and <i>Prevotella copri</i> was identified; (ii) a profile monitoring score of 0.72 for the biomarker subgroup, indicating significant relationship changes between HC and ASD states, and (iii) a P/N ratio of biomarker subgroup in ASD-associated gut bacteria that was three times higher than that of HC microbiomes. Additionally, we discuss the non-monotonic relationship alterations within microbial sub-communities in the ASD gut microbiome.IMPORTANCEIt is crucial to assess alterations in network structure in different biological states in order to promote health. The stiffness network allows for the exploration of species interactions and the measurement of resilience in complex microbial networks. The objective of this study was to develop a stiffness network analysis (SNA) method for evaluating the contribution of microbial bacteria in differentiating disease samples from healthy control samples by examining changes in network stiffness parameters. Furthermore, the SNA method was employed on both simulated and real autism spectrum disorder gut microbiome data sets to identify potential microbial biomarker subgroups, with a particular focus on the relationship alterations within microbial networks.</p>\",\"PeriodicalId\":18670,\"journal\":{\"name\":\"Microbiology spectrum\",\"volume\":\" \",\"pages\":\"e0107824\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11878074/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Microbiology spectrum\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1128/spectrum.01078-24\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbiology spectrum","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1128/spectrum.01078-24","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/4 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
Resilience of human gut microbiomes in autism spectrum disorder: measured using stiffness network analysis.
Autism spectrum disorder (ASD) affects an estimated 1%-2% of children worldwide, but its specific etiology remains unclear. In recent years, the gut microbiome's role in ASD pathogenesis has garnered increasing attention. However, the exact relationship between microbiota and ASD-such as which microbial species significantly impact disease onset and progression-remains unresolved, and effective methods to measure microbial interactions are still lacking. In this study, we introduce an innovative stiffness network analysis (SNA) method to quantify changes in microbial network structure and identify disease-specific microbial bacteria theoretically. The SNA method was applied to reanalyze eight ASD gut microbiome data sets, encompassing 898 ASD samples and 467 healthy control (HC) samples from 16S-rRNA sequencing data. Key findings include the following: (i) an "allies" biomarker subgroup consisting of Bacteroides plebeius, Sutterella, Lachnospira, and Prevotella copri was identified; (ii) a profile monitoring score of 0.72 for the biomarker subgroup, indicating significant relationship changes between HC and ASD states, and (iii) a P/N ratio of biomarker subgroup in ASD-associated gut bacteria that was three times higher than that of HC microbiomes. Additionally, we discuss the non-monotonic relationship alterations within microbial sub-communities in the ASD gut microbiome.IMPORTANCEIt is crucial to assess alterations in network structure in different biological states in order to promote health. The stiffness network allows for the exploration of species interactions and the measurement of resilience in complex microbial networks. The objective of this study was to develop a stiffness network analysis (SNA) method for evaluating the contribution of microbial bacteria in differentiating disease samples from healthy control samples by examining changes in network stiffness parameters. Furthermore, the SNA method was employed on both simulated and real autism spectrum disorder gut microbiome data sets to identify potential microbial biomarker subgroups, with a particular focus on the relationship alterations within microbial networks.
期刊介绍:
Microbiology Spectrum publishes commissioned review articles on topics in microbiology representing ten content areas: Archaea; Food Microbiology; Bacterial Genetics, Cell Biology, and Physiology; Clinical Microbiology; Environmental Microbiology and Ecology; Eukaryotic Microbes; Genomics, Computational, and Synthetic Microbiology; Immunology; Pathogenesis; and Virology. Reviews are interrelated, with each review linking to other related content. A large board of Microbiology Spectrum editors aids in the development of topics for potential reviews and in the identification of an editor, or editors, who shepherd each collection.