HoPhage: an ab initio tool for identifying hosts of phage fragments from metaviromes.

SUMMARY: We present HoPhage (Host of Phage) to identify the host of a given phage fragment from metavirome data at the genus level. HoPhage integrates two modules using a deep learning algorithm and a Markov chain model, respectively. HoPhage achieves 47.90% and 82.47% mean accuracy at the genus and phylum levels for ∼1-kb long artificial phage fragments when predicting host among 50 genera, representing 7.54-20.22% and 13.55-24.31% improvement, respectively. By testing on three real virome samples, HoPhage yields 81.11% mean accuracy at the genus level within a much broader candidate host range. AVAILABILITY AND IMPLEMENTATION: HoPhage is available at http://cqb.pku.edu.cn/ZhuLab/HoPhage/data/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Global Meta-analysis of Airborne Bacterial Communities and Associations with Anthropogenic Activities.

Airborne microbiome alterations, an emerging global health concern, have been linked to anthropogenic activities in numerous studies. However, these studies have not reached a consensus. To reveal general trends, we conducted a meta-analysis using 3226 air samples from 42 studies, including 29 samples of our own. We found that samples in anthropogenic activity-related categories showed increased microbial diversity, increased relative abundance of pathogens, increased co-occurrence network complexity, and decreased positive edge proportions in the network compared with the natural environment category. Most of the above conclusions were confirmed using the samples we collected in a particular period with restricted anthropogenic activities. Additionally, unlike most previous studies, we used 15 human-production process factors to quantitatively describe anthropogenic activities. We found that microbial richness was positively correlated with fine particulate matter concentration, NH3 emissions, and agricultural land proportion and negatively correlated with the gross domestic product per capita. Airborne pathogens showed preferences for different factors, indicating potential health implications. SourceTracker analysis showed that the human body surface was a more likely source of airborne pathogens than other environments. Our results advance the understanding of relationships between anthropogenic activities and airborne bacteria and highlight the role of airborne pathogens in public health.

PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning.

SUMMARY: We present the first tool of gene prediction, PlasGUN, for plasmid metagenomic short-read data. The tool, developed based on deep learning algorithm of multiple input Convolutional Neural Network, demonstrates much better performance when tested on a benchmark dataset of artificial short reads and presents more reliable results for real plasmid metagenomic data than traditional gene prediction tools designed primarily for chromosome-derived short reads. AVAILABILITY AND IMPLEMENTATION: The PlasGUN software is available at http://cqb.pku.edu.cn/ZhuLab/PlasGUN/ or https://github.com/zhenchengfang/PlasGUN/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dynamic functional connectivity states characterize NREM sleep and wakefulness.

According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow-wave activity. We collected both eyes-closed wakefulness and sleep data from 48 healthy young volunteers with simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. EEG data were employed as the gold standard of sleep stage scoring, and DFC states were estimated based on fMRI data. The results demonstrated that DFC states of the fMRI signals consistently corresponded to wakefulness and nonrapid eye movement sleep stages independent of the number of clusters. Furthermore, the mean dwell time of these states significantly correlated with slow-wave activity. The inclusion or omission of regression of the global signal and the selection of parcellation schemes exerted minimal effects on the current findings. These results provide strong evidence that DFC states underlying fMRI signals match the fluctuations of vigilance and suggest a possible electrophysiological source of DFC states corresponding to vigilance states.

LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning.

Motivation: To characterize long non-coding RNAs (lncRNAs), both identifying and functionally annotating them are essential to be addressed. Moreover, a comprehensive construction for lncRNA annotation is desired to facilitate the research in the field. Results: We present LncADeep, a novel lncRNA identification and functional annotation tool. For lncRNA identification, LncADeep integrates intrinsic and homology features into a deep belief network and constructs models targeting both full- and partial-length transcripts. For functional annotation, LncADeep predicts a lncRNA's interacting proteins based on deep neural networks, using both sequence and structure information. Furthermore, LncADeep integrates KEGG and Reactome pathway enrichment analysis and functional module detection with the predicted interacting proteins, and provides the enriched pathways and functional modules as functional annotations for lncRNAs. Test results show that LncADeep outperforms state-of-the-art tools, both for lncRNA identification and lncRNA-protein interaction prediction, and then presents a functional interpretation. We expect that LncADeep can contribute to identifying and annotating novel lncRNAs. Availability and implementation: LncADeep is freely available for academic use at http://cqb.pku.edu.cn/ZhuLab/lncadeep/ and https://github.com/cyang235/LncADeep/. Supplementary information: Supplementary data are available at Bioinformatics online.

Aging progression of human gut microbiota.

BACKGROUND: Human gut microbiota are important for human health and have been regarded as a "forgotten organ", whose variation is closely linked with various factors, such as host genetics, diet, pathological conditions and external environment. The diversity of human gut microbiota has been correlated with aging, which was characterized by different abundance of bacteria in various age groups. In the literature, most of the previous studies of age-related gut microbiota changes focused on individual species in the gut community with supervised methods. Here, we aimed to examine the underlying aging progression of the human gut microbial community from an unsupervised perspective. RESULTS: We obtained raw 16S rRNA sequencing data of subjects ranging from newborns to centenarians from a previous study, and summarized the data into a relative abundance matrix of genera in all the samples. Without using the age information of samples, we applied an unsupervised algorithm to recapitulate the underlying aging progression of microbial community from hosts in different age groups and identify genera associated to this progression. Literature review of these identified genera indicated that for individuals with advanced ages, some beneficial genera are lost while some genera related with inflammation and cancer increase. CONCLUSIONS: The multivariate unsupervised analysis here revealed the existence of a continuous aging progression of human gut microbiota along with the host aging process. The identified genera associated to this aging process are meaningful for designing probiotics to maintain the gut microbiota to resemble a young age, which hopefully will lead to positive impact on human health, especially for individuals in advanced age groups.

A novel numerical model for protein sequences analysis based on spherical coordinates and multiple physicochemical properties of amino acids.

How to characterize short protein sequences to make an effective connection to their functions is an unsolved problem. Here we propose to map the physicochemical properties of each amino acid onto unit spheres so that each protein sequence can be represented quantitatively. We demonstrate the usefulness of this representation by applying it to the prediction of cell penetrating peptides. We show that its combination with traditional composition features yields the best performance across different datasets, among several methods compared. For the convenience of users, a web server has been established for automatic calculations of the proposed features at http://biophy.dzu.edu.cn/SNumD/.

MetaComp: comprehensive analysis software for comparative meta-omics including comparative metagenomics.

BACKGROUND: During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community. RESULTS: We developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function-regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples. CONCLUSION: MetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: http://cqb.pku.edu.cn/ZhuLab/MetaComp/ and https://github.com/pzhaipku/MetaComp/ .

Brain structural alterations in obese children with and without Prader-Willi Syndrome.

Prader-Willi syndrome (PWS) is a genetic imprinting disorder that is mainly characterized by hyperphagia and childhood obesity. Previous neuroimaging studies revealed that there is a significant difference in brain activation patterns between obese children with and without PWS. However, whether there are differences in the brain structure of obese children with and without PWS remains elusive. In the current study, we used T1-weighted and diffusion tensor magnetic resonance imaging to investigate alterations in the brain structure, such as the cortical volume and white matter integrity, in relation to this eating disorder in 12 children with PWS, 18 obese children without PWS (OB) and 18 healthy controls. Compared with the controls, both the PWS and OB groups exhibited alterations in cortical volume, with similar deficit patterns in 10 co-varying brain regions in the bilateral dorsolateral and medial prefrontal cortices, right anterior cingulate cortex, and bilateral temporal lobe. The white matter integrities of the above regions were then examined with an analysis method based on probabilistic tractography. The PWS group exhibited distinct changes in the reduced fractional anisotropy of white matter fibers connected to the co-varying regions, whereas the OB group did not. Our findings indicated that PWS and OB share similar gray matter alterations that are responsible for the development of eating disorders. Additionally, the distinct white matter alterations might explain the symptoms associated with food intake in PWS, including excessive hyperphagia and constant hunger. Hum Brain Mapp 38:4228-4238, 2017. © 2017 Wiley Periodicals, Inc.

Salt-induced stabilization of EIN3/EIL1 confers salinity tolerance by deterring ROS accumulation in Arabidopsis.

Ethylene has been regarded as a stress hormone to regulate myriad stress responses. Salinity stress is one of the most serious abiotic stresses limiting plant growth and development. But how ethylene signaling is involved in plant response to salt stress is poorly understood. Here we showed that Arabidopsis plants pretreated with ethylene exhibited enhanced tolerance to salt stress. Gain- and loss-of-function studies demonstrated that EIN3 (ETHYLENE INSENSITIVE 3) and EIL1 (EIN3-LIKE 1), two ethylene-activated transcription factors, are necessary and sufficient for the enhanced salt tolerance. High salinity induced the accumulation of EIN3/EIL1 proteins by promoting the proteasomal degradation of two EIN3/EIL1-targeting F-box proteins, EBF1 and EBF2, in an EIN2-independent manner. Whole-genome transcriptome analysis identified a list of SIED (Salt-Induced and EIN3/EIL1-Dependent) genes that participate in salt stress responses, including several genes encoding reactive oxygen species (ROS) scavengers. We performed a genetic screen for ein3 eil1-like salt-hypersensitive mutants and identified 5 EIN3 direct target genes including a previously unknown gene, SIED1 (At5g22270), which encodes a 93-amino acid polypeptide involved in ROS dismissal. We also found that activation of EIN3 increased peroxidase (POD) activity through the direct transcriptional regulation of PODs expression. Accordingly, ethylene pretreatment or EIN3 activation was able to preclude excess ROS accumulation and increased tolerance to salt stress. Taken together, our study provides new insights into the molecular action of ethylene signaling to enhance plant salt tolerance, and elucidates the transcriptional network of EIN3 in salt stress response.

Identifying micro-inversions using high-throughput sequencing reads.

BACKGROUND: The identification of inversions of DNA segments shorter than read length (e.g., 100 bp), defined as micro-inversions (MIs), remains challenging for next-generation sequencing reads. It is acknowledged that MIs are important genomic variation and may play roles in causing genetic disease. However, current alignment methods are generally insensitive to detect MIs. Here we develop a novel tool, MID (Micro-Inversion Detector), to identify MIs in human genomes using next-generation sequencing reads. RESULTS: The algorithm of MID is designed based on a dynamic programming path-finding approach. What makes MID different from other variant detection tools is that MID can handle small MIs and multiple breakpoints within an unmapped read. Moreover, MID improves reliability in low coverage data by integrating multiple samples. Our evaluation demonstrated that MID outperforms Gustaf, which can currently detect inversions from 30 bp to 500 bp. CONCLUSIONS: To our knowledge, MID is the first method that can efficiently and reliably identify MIs from unmapped short next-generation sequencing reads. MID is reliable on low coverage data, which is suitable for large-scale projects such as the 1000 Genomes Project (1KGP). MID identified previously unknown MIs from the 1KGP that overlap with genes and regulatory elements in the human genome. We also identified MIs in cancer cell lines from Cancer Cell Line Encyclopedia (CCLE). Therefore our tool is expected to be useful to improve the study of MIs as a type of genetic variant in the human genome. The source code can be downloaded from: http://cqb.pku.edu.cn/ZhuLab/MID .

Similar Fecal Microbiota Signatures in Patients With Diarrhea-Predominant Irritable Bowel Syndrome and Patients With Depression.

BACKGROUND & AIMS: Patients with irritable bowel syndrome (IBS) often have psychiatric comorbidities. Alterations in the intestinal microbiota have been associated with IBS and depression, but it is not clear if there is a microbial relationship between these disorders. We studied the profiles of fecal microbiota samples from patients with IBS, depression, or comorbidities of IBS and depression; we determined the relationships among these profiles and clinical and pathophysiological features of these disorders. METHODS: We used 454 pyrosequencing to analyze fecal microbiota samples from 100 subjects (40 with diarrhea-predominant IBS [IBS-D], 15 with depression, 25 with comorbidities of IBS and depression, and 20 healthy individuals [controls]), recruited at Peking University. Abdominal and psychological symptoms were evaluated with validated questionnaires. Visceral sensitivity was evaluated using a barostat. Colonic mucosal inflammation was assayed by immunohistochemical analyses of sigmoid tissue biopsy specimens. RESULTS: Fecal microbiota signatures were similar between patients with IBS-D and depression in that they were less diverse than samples from controls and had similar abundances of alterations. They were characterized by high proportions of Bacteroides (type I), Prevotella (type II), or nondominant microbiota (type III). Most patients with IBS-D or depression had type I or type II profiles (IBS-D had 85% type I and type II profiles, depression had 80% type I and type II profiles). Colon tissues from patients with type I or type II profiles had higher levels of inflammatory markers than colon tissues from patients with type III profiles. The level of colon inflammation correlated with the severity of IBS symptoms. CONCLUSIONS: Patients with IBS-D and depression have similar alterations in fecal microbiota; these might be related to the pathogenesis of these disorders. We identified 3 microbial profiles in patients that could indicate different subtypes of IBS and depression or be used as diagnostic biomarkers.

InteMAP: Integrated metagenomic assembly pipeline for NGS short reads.

BACKGROUND: Next-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed. RESULTS: We conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others. CONCLUSIONS: We developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.

Horizontal gene transfer in an acid mine drainage microbial community.

BACKGROUND: Horizontal gene transfer (HGT) has been widely identified in complete prokaryotic genomes. However, the roles of HGT among members of a microbial community and in evolution remain largely unknown. With the emergence of metagenomics, it is nontrivial to investigate such horizontal flow of genetic materials among members in a microbial community from the natural environment. Because of the lack of suitable methods for metagenomics gene transfer detection, microorganisms from a low-complexity community acid mine drainage (AMD) with near-complete genomes were used to detect possible gene transfer events and suggest the biological significance. RESULTS: Using the annotation of coding regions by the current tools, a phylogenetic approach, and an approximately unbiased test, we found that HGTs in AMD organisms are not rare, and we predicted 119 putative transferred genes. Among them, 14 HGT events were determined to be transfer events among the AMD members. Further analysis of the 14 transferred genes revealed that the HGT events affected the functional evolution of archaea or bacteria in AMD, and it probably shaped the community structure, such as the dominance of G-plasma in archaea in AMD through HGT. CONCLUSIONS: Our study provides a novel insight into HGT events among microorganisms in natural communities. The interconnectedness between HGT and community evolution is essential to understand microbial community formation and development.

IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning.

BACKGROUND: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses. FINDINGS: We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals. CONCLUSIONS: IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.

Multiomics Analyses With Stool-Type Stratification in Patient Cohorts and Blautia Identification as a Potential Bacterial Modulator in Type 2 Diabetes Mellitus.

Heterogeneity in host and gut microbiota hampers microbial precision intervention of type 2 diabetes mellitus (T2DM). Here, we investigated novel features for patient stratification and bacterial modulators for intervention, using cross-sectional patient cohorts and animal experiments. We collected stool, blood, and urine samples from 103 patients with recent-onset T2DM and 25 healthy control subjects (HCs), performed gut microbial composition and metabolite profiling, and combined it with host transcriptome, metabolome, cytokine, and clinical data. Stool type (dry or loose stool), a feature of the stool microenvironment recently explored in microbiome studies, was used for stratification of patients with T2DM as it explained most of the variation in the multiomics data set among all clinical parameters in our covariate analysis. T2DM with dry stool (DM-DS) and loose stool (DM-LS) were clearly differentiated from HC and each other by LightGBM models, optimal among multiple machine learning models. Compared with DM-DS, DM-LS exhibited discordant gut microbial taxonomic and functional profiles, severe host metabolic disorder, and excessive insulin secretion. Further cross-measurement association analysis linked the differential microbial profiles, in particular Blautia abundances, to T2DM phenotypes in our stratified multiomics data set. Notably, oral supplementation of Blautia to T2DM mice induced inhibitory effects on lipid accumulation, weight gain, and blood glucose elevation with simultaneous modulation of gut bacterial composition, revealing the therapeutic potential of Blautia. Our study highlights the clinical implications of stool microenvironment stratification and Blautia supplementation in T2DM, offering promising prospects for microbial precision treatment of metabolic diseases.

Gene prediction in metagenomic fragments based on the SVM algorithm.

BACKGROUND: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms from various environments, such as human body, without isolation and cultivation. Accurately identifying genes from metagenomic fragments is one of the most fundamental issues. RESULTS: In this article, we present a novel gene prediction method named MetaGUN for metagenomic fragments based on a machine learning approach of SVM. It implements in a three-stage strategy to predict genes. Firstly, it classifies input fragments into phylogenetic groups by a k-mer based sequence binning method. Then, protein-coding sequences are identified for each group independently with SVM classifiers that integrate entropy density profiles (EDP) of codon usage, translation initiation site (TIS) scores and open reading frame (ORF) length as input patterns. Finally, the TISs are adjusted by employing a modified version of MetaTISA. To identify protein-coding sequences, MetaGun builds the universal module and the novel module. The former is based on a set of representative species, while the latter is designed to find potential functionary DNA sequences with conserved domains. CONCLUSIONS: Comparisons on artificial shotgun fragments with multiple current metagenomic gene finders show that MetaGUN predicts better results on both 3' and 5' ends of genes with fragments of various lengths. Especially, it makes the most reliable predictions among these methods. As an application, MetaGUN was used to predict genes for two samples of human gut microbiome. It identifies thousands of additional genes with significant evidences. Further analysis indicates that MetaGUN tends to predict more potential novel genes than other current metagenomic gene finders.

A de novo metagenomic assembly program for shotgun DNA reads.

MOTIVATION: A high-quality assembly of reads generated from shotgun sequencing is a substantial step in metagenome projects. Although traditional assemblers have been employed in initial analysis of metagenomes, they cannot surmount the challenges created by the features of metagenomic data. RESULT: We present a de novo assembly approach and its implementation named MAP (metagenomic assembly program). Based on an improved overlap/layout/consensus (OLC) strategy incorporated with several special algorithms, MAP uses the mate pair information, resulting in being more applicable to shotgun DNA reads (recommended as >200 bp) currently widely used in metagenome projects. Results of extensive tests on simulated data show that MAP can be superior to both Celera and Phrap for typical longer reads by Sanger sequencing, as well as has an evident advantage over Celera, Newbler and the newest Genovo, for typical shorter reads by 454 sequencing. AVAILABILITY AND IMPLEMENTATION: The source code of MAP is distributed as open source under the GNU GPL license, the MAP program and all simulated datasets can be freely available at http://bioinfo.ctb.pku.edu.cn/MAP/

Integrated Traditional Chinese Medicine Improves Functional Outcome in Acute Ischemic Stroke: From Clinic to Mechanism Exploration With Gut Microbiota.

As a life-threatening disease, stroke is the leading cause of death and also induces adult disability worldwide. To investigate the efficacy of the integrated traditional Chinese medicine (ITCM) on the therapeutic effects of acute ischemic stroke (AIS) patients, we enrolled 26 patients in the ITCM [Tanhuo decoction (THD) + Western medicine (WM)] group and 23 in the WM group. Thirty healthy people were also included in the healthy control (HC) group. ITCM achieved better functional outcomes than WM, including significant reduction of the phlegm-heat syndrome and neurological impairment, and improvement of ability. These facts were observed in different pretreatment gut enterotypes. In this paper, we collected the stool samples of all participants and analyzed the 16S rRNA sequence data of the gut microbiota. We identified two enterotypes (Type-A and Type-B) of the gut microbial community in AIS samples before treatment. Compared to Type-B, Type-A was characterized by a high proportion of Bacteroides, relatively high diversity, and severe functional damage. In the ITCM treatment group, we observed better clinical efficacy and positive alterations in microbial diversity and beneficial bacterial abundance, and the effect of approaching healthy people's gut microbiota, regardless of gut enterotypes identified in pretreatment. Furthermore, we detected several gut microbiota as potential therapeutic targets of ITCM treatment by analyzing the correlations between bacterial abundance alterations and functional outcomes, where Dorea with the strongest correlation was known to produce anti-inflammatory metabolite and negatively linked to trimethylamine-N-oxide (TMAO), a biomarker of AIS. This study analyzed clinical and gut microbial data and revealed the possibility of a broad application independent of the enterotypes, as well as the therapeutic targets of the ITCM in treating AIS patients with phlegm-heat syndrome.