Machine-Learning Model for Resistance/Relapse Prediction in Immune Thrombocytopenia Using Gut Microbiota and Function Signatures

Abstract Introduction Growing evidence has implicated gut microbiota in the pathogenesis of immune thrombocytopenia (ITP). In a previous research study, we found dysbiosis in the phylogenetic composition and function of gut microbiome in ITP and that corticosteroid treatment may have a strong effect on gut microbiota [Sci China Life Sci, 2020]. Corticosteroids have been widely used in the initial treatment of newly diagnosed ITP patients, but most adult patients relapse upon cessation of steroid treatment. Patients on agents in subsequent therapy may improve at any time, but which patients improve and when is unpredictable. The gut microbiome has been increasingly used in the assessment and prediction of immunomodulatory therapy in autoimmune diseases and cellular immunotherapy in cancers. Here, we provide evidence that gut microbiota and function signatures can be used to predict immune thrombocytopenia patients at high risk of relapse/resistance after corticosteroid treatment and to identify patients that are more likely to benefit from TPO-RAs in subsequent therapy. Methods Seventy-five fecal samples from 60 patients with newly diagnosed ITP (60 specimens before corticosteroid therapy and 15 specimens after corticosteroid therapy) and 41 samples from persistent/chronic ITP before and after treatment with TPO-RAs, including eltrombopag and avatrombopag were collected for deep shotgun metagenomic sequencing. To identify the microbial biomarkers related to relapse/resistance after corticosteroid treatment, we constructed a random forest classifier using machine learning to determine the risk of relapse/resistance of a training cohort of 30 patients from baseline samples and validated the classifier for 30 patients. Patients with persistent/chronic ITP were divided into responders and nonresponders according to their response to TPO-RA treatment in subsequent therapy. After identifying the microbial species and functional biomarkers related to the response to TPO-RA therapy, a random forest classifier was constructed using a training set of 20 patients and validated using a validation set of 21 patients. Results We used a metagenomic sequencing technique to investigate the differences among gut microbiota associated with relapse within 3 months of corticosteroid treatment. We observed that the diversity and composition of the microbial community in ITP patients after corticosteroid therapy (Post-C) changed significantly from the baseline (Pre-C), whereas the gut microbiota of the remission group was similar to that of the HC group, which implies that a shift in the gut microbiome could represent a return to homeostasis. To identify the microbial biomarkers related to early relapse after corticosteroid treatment, the Pre-C samples were divided into a remission group and a resistant/relapse group according to the response to corticosteroid therapy within 3 months. Nine significant associations with the microbial species and function were identified between the remission and resistant/relapse groups. A risk index built from this panel of microbes and functional pathways was used to differentiate remission from resistant/relapsed patients based on the baseline characteristics. The receiver operating characteristic (ROC) curve demonstrated that the risk index was a strong predictor of treatment response, with an area under the curve (AUC) of 0.87. Furthermore, to predict the response to TPO-RAs in subsequent therapy, the baseline gut microbiomes of responders and nonresponders before TPO-RA treatment were compared. Patients who responded to treatment exhibited an increase in Ruminococcaceae, Clostridiaceae and Bacteroides compared to nonresponders, with elevated abundance of the phosphotransferase system, tyrosine metabolism and secondary bile acid biosynthesis pathways according to KEGG analysis. Our prediction model based on the gut microbiome for TPO-RA response was robust across the cohorts and showed 89.5% and 79.2% prediction accuracy for persistent/chronic ITP patients in the training and validation sets, respectively. Conclusions The gut microbiome and function signatures based on machine learning analysis are novel potential biomarkers for predicting resistance/relapse after corticosteroid treatment and response to TPO-RAs, which may have important manifestations in the clinical. Disclosures No relevant conflicts of interest to declare.

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Berberine May Correct Corticosteroid Resistance Induced By Gut Microbiota Dysbiosis in Immune Thrombocytopenia

Blood ◽

10.1182/blood-2018-99-113983 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3769-3769 ◽

Cited By ~ 1

Author(s):

Ya-Nan Wang ◽

Xiao Liu ◽

Yun He ◽

Qian-Ming Wang ◽

Xiao-Lu Zhu ◽

...

Keyword(s):

Gut Microbiota ◽

Corticosteroid Therapy ◽

Gut Microbiome ◽

Immune Thrombocytopenia ◽

Shotgun Sequencing ◽

Healthy Controls ◽

Sequencing Data ◽

Corticosteroid Resistance ◽

Gut Microbiota Dysbiosis ◽

Microbiota Structure

Abstract Introduction Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by abnormal immune response. Though many therapies have been used, corticosteroid-resistance remains to be a challenge clinically. Extensive research has improved our understanding of ITP, showing that environmental factors affect the disease profile, such as Helicobacter pylori being proven to be associated with thrombocytopenia. Though evidence that the gut microbiota contributes to the development of auto-immune disorders is accumulating, there is no information available on relationship between gut microbiome and ITP. Berberine(BBR), a traditional compound isolated from a Chinese herb, has been widely used as a nonprescription drug to treat diarrhea. Recently, BBR has been reported to modulate microbiota structures, which contributes to improving metabolic disorders. Here, we hypothesized that BBR might modulate gut microbiota to treat ITP. Methods In order to investigate the relationship between gut microbiome and ITP, we performed deep shotgun sequencing on 253 fecal samples totally from consecutive ITP patients and healthy controls. Metagenome-wide association study (MWAS) was conducted, and clinical characteristics of patients were collected to analyze the correlation with gut microbiome (Nan Qin, et al. Nature. 2012). Certainly, a clinical cohort study was performed to assess the efficacy of BBR in corticosteroid-resistant ITP patients. To better characterize the role of gut microbiota in the development of ITP and to verify the modulating effect of BBR on gut microbiota structure, we performed colonization of mice with specific bacterium and established active murine models (immunized-splenocyte engraftment) of BBR treatment. Result We integrated the sequencing data into an existing gut microbial reference-gene catalog and identified 35275 genes that differ in abundance between ITP patients and healthy controls. We then clustered the genes into metagenomic species (MGS) and finally identified 15 MGS which were significantly different in both discovery cohort and validation cohort. Dysbiosis was detected in the gut microbiome of ITP patients, as both phylogenetic analysis and MGS annotation indicated that Lachnospiraceae bacterium, Clostridium asparagiforme were over-represented while Bacteroides spp was depleted in ITP patients comparing with healthy controls. Functionally, KEGG annotation showed that the most enriched orthologs in ITP patients were related to membrane transport. Moreover, the biosynthesis of microbe-associated molecular patterns (MAMPs) such as lipopolysaccharides (LPS), peptidoglycan biosynthesis and flagellin were highly abundant in patients. Gene biomarkers and cluster markers based on gut microbiome were established to identify ITP patients and were validated in an independent cohort. The alterations of gut microbiome in ITP patients are partly reversed after treatment. Furthermore, L. bacterium shows more abundant in corticosteroid-resistant ITP comparing with newly diagnosed ITP. Specifically, BBR treatment could improve the microbial dysbiosis of corticosteroid-resistant ITP patients, the complete response (CR), response(R), and overall response (OR) rates being 26.3%, 47.4% and 73.7%, respectively. Targeted QPCR assay determined that L. bacterium accumulated in corticosteroid-resistant ITP, consistent with the result of shotgun sequencing data. Gavage with L. bacterium results in significantly alterations of gut microbiota structure in mice comparing with mice without bacterial administration or those receiving Clostridium asparagiforme administration. Moreover, colonization of L. bacterium caused more severe thrombocytopenia and impaired the response to corticosteroid therapy in active ITP model. Intriguingly, BBR treatment, but not any other antibiotics, could reverse the effect of L. bacterium colonization on gut microbiota structure and enhance the response to corticosteroid therapy. Conclusion Our findings demonstrate that the gut microbiome alters in ITP and is partly normalized after treatment. Gut microbiota dysbiosis may contribute to the development of corticosteroid-resistant ITP. BBR may correct corticosteroid-resistance by modulating the gut microbiota structure, thus being a novel potential second-line candidate to treat ITP. Disclosures No relevant conflicts of interest to declare.

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Role of Biological Sex in the Cardiovascular-Gut Microbiome Axis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.759735 ◽

2022 ◽

Vol 8 ◽

Author(s):

Shuangyue Li ◽

Georgios Kararigas

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Microbial Composition ◽

Biological Sex ◽

Technological Advances ◽

Host Health ◽

Health And Disease ◽

And Function ◽

Insight Into

There has been a recent, unprecedented interest in the role of gut microbiota in host health and disease. Technological advances have dramatically expanded our knowledge of the gut microbiome. Increasing evidence has indicated a strong link between gut microbiota and the development of cardiovascular diseases (CVD). In the present article, we discuss the contribution of gut microbiota in the development and progression of CVD. We further discuss how the gut microbiome may differ between the sexes and how it may be influenced by sex hormones. We put forward that regulation of microbial composition and function by sex might lead to sex-biased disease susceptibility, thereby offering a mechanistic insight into sex differences in CVD. A better understanding of this could identify novel targets, ultimately contributing to the development of innovative preventive, diagnostic and therapeutic strategies for men and women.

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Identification of gut microbiome markers for schizophrenia delineates a potential role of Streptococcus

10.1101/774265 ◽

2019 ◽

Cited By ~ 1

Author(s):

Feng Zhu ◽

Yanmei Ju ◽

Wei Wang ◽

Qi Wang ◽

Ruijin Guo ◽

...

Keyword(s):

Gut Microbiota ◽

Validation Cohort ◽

Bacterial Species ◽

Metagenomic Sequencing ◽

Operating Characteristics ◽

Discovery Cohort ◽

Peripheral Tissues ◽

Functional Changes ◽

Microbial Biomarkers

AbstractEmerging evidence has linked the gut microbiota to schizophrenia. However, the functional changes in the gut microbiota and the biological role of individual bacterial species in schizophrenia have not been explored systematically. Here, we characterized the gut microbiota in schizophrenia using shotgun metagenomic sequencing of feces from a discovery cohort of 90 drug-free patients and 81 controls, as well as a validation cohort of 45 patients taking antipsychotics and 45 controls. We screened 83 schizophrenia-associated bacterial species and constructed a classifier comprising 26 microbial biomarkers that distinguished patients from controls with a 0.896 area under the receiver operating characteristics curve (AUC) in the discovery cohort and 0.765 AUC in the validation cohort. Our analysis of fecal metagenomes revealed that schizophrenia-associated gut–brain modules included short-chain fatty acids synthesis, tryptophan metabolism, and synthesis/degradation of neurotransmitters including glutamate, γ-aminobutyric acid, and nitric oxide. The schizophrenia-enriched gut bacterial species include several oral cavity-resident microbes, such as Streptococcus vestibularis. We transplanted Streptococcus vestibularis into the gut of the mice with antibiotic-induced microbiota depletion to explore its functional role. We observed that this microbe transiently inhabited the mouse gut and this was followed by hyperactivity and deficit in social behaviors, accompanied with altered neurotransmitter levels in peripheral tissues. In conclusion, our study identified 26 schizophrenia-associated bacterial species representing potential microbial targets for future treatment, as well as gut–brain modules, some of which may give rise to new microbial metabolites involved in the development of schizophrenia.

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Yoghurt Consumption is Associated With Transient Changes in the Composition of the Human Gut Microbiome

10.21203/rs.3.rs-99718/v1 ◽

2020 ◽

Author(s):

Caroline Ivanne Le Roy ◽

Alexander Kurilshikov ◽

Emily Leeming ◽

Alessia Visconti ◽

Ruth Bowyer ◽

...

Keyword(s):

16S Rrna ◽

Gut Microbiota ◽

Visceral Fat ◽

Gut Microbiome ◽

Body Weight Gain ◽

Healthy Eating Index ◽

Rrna Gene ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Shotgun Metagenomic Sequencing

Abstract Background: Yoghurt contains live bacteria that could contribute via modulation of the gut microbiota to its reported beneficial effects such as reduced body weight gain and lower incidence of type 2 diabetes. To date, the association between yoghurt consumption and the composition of the gut microbiota is underexplored. Here we used clinical variables, metabolomics, 16S rRNA and shotgun metagenomic sequencing data collected on over 1000 predominantly female UK twins to define the link between the gut microbiota and yoghurt-associated health benefits. Results: According to food frequency questionnaires (FFQ), 73% of subjects consumed yoghurt. Consumers presented a healthier diet pattern (healthy eating index: beta = 2.17±0.34; P = 2.72x10-10) and improved metabolic health characterised by reduced visceral fat (beta = -28.18±11.71 g; P = 0.01). According to 16S rRNA gene analyses and whole shotgun metagenomic sequencing approach consistent taxonomic variations were observed with yoghurt consumption. More specifically, we identified higher abundance of species used as yoghurt starters Streptococcus thermophilus (beta = 0.41±0.051; P = 6.14x10-12) and sometimes added Bifidobacterium animalis subsp. lactis (beta = 0.30±0.052; P = 1.49x10-8) in the gut of yoghurt consumers. Replication in 1103 volunteers from the LifeLines-DEEP cohort confirmed the increase of S. thermophilus among yoghurt consumers. Using food records collected the day prior to faecal sampling we showed that increase in these two yoghurt bacteria could be transient. Metabolomics analysis revealed that B. animalis subsp. lactis was associated with 13 faecal metabolites including a 3-hydroxyoctanoic acid, known to be involved in the regulation of gut inflammation.Conclusions: Yoghurt consumption is associated with reduced visceral fat mass and changes in gut microbiome including transient increase of yoghurt-contained species (i.e. S. thermophilus and B. lactis).

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Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease

Hypertension ◽

10.1161/hypertensionaha.120.15885 ◽

2020 ◽

Vol 76 (5) ◽

pp. 1555-1562

Author(s):

Sachin Aryal ◽

Ahmad Alimadadi ◽

Ishan Manandhar ◽

Bina Joe ◽

Xi Cheng

Keyword(s):

Machine Learning ◽

Random Forest ◽

Gut Microbiota ◽

Receiver Operating Characteristic ◽

Gut Microbiome ◽

Operating Characteristic ◽

Diagnostic Screening ◽

Testing Area ◽

Receiver Operating Characteristic Curves ◽

Receiver Operating

Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome–based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome–based ML approach for diagnostic screening of CVD.

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Liver-specific knockdown of ANGPTL8 alters the structure of the gut microbiota in mice

Annals of Microbiology ◽

10.1186/s13213-020-01599-0 ◽

2020 ◽

Vol 70 (1) ◽

Author(s):

Yinlong Cheng ◽

Yining Li ◽

Yonghong Xiong ◽

Yixin Zou ◽

Siyu Chen ◽

...

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Shannon Index ◽

Microbiota Composition ◽

Functional Prediction ◽

Adeno Associated Virus ◽

Virus Serotype ◽

Significant Difference ◽

And Function ◽

Gut Microbiota Composition

Abstract Purpose To investigate the effect of liver-specific knockdown of ANGPTL8 on the structure of the gut microbiota. Methods We constructed mice with liver-specific ANGPTL8 knockdown by using an adeno-associated virus serotype 8 (AAV8) system harbouring an ANGPTL8 shRNA. We analysed the structure and function of the gut microbiome through pyrosequencing and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional prediction. Results Compared with controls, ANGPTL8 shRNA reduced the Simpson index and Shannon index (p < 0.01) of the gut microbiota in mice. At the phylum level, the sh-ANGPTL8 group showed a healthier gut microbiota composition than controls (Bacteroidetes: controls 67.52%, sh-ANGPTL8 80.75%; Firmicutes: controls 10.96%, sh-ANGPTL8 8.58%; Proteobacteria: controls 9.29%, sh-ANGPTL8 0.98%; F/B ratio: controls 0.16, sh-ANGPTL8 0.11). PCoA and UPGMA analysis revealed a significant difference in microbiota composition, while KEGG analysis revealed a significant difference in microbiota function between controls and the sh-ANGPTL8 group. Conclusion Our results revealed that inhibition of ANGPTL8 signalling altered the structure of the gut microbiome, which might further affect the metabolism of mice. We have thus identified ANGPTL8 as a novel hepatogenic hormone potentially involving the liver-gut axis and regulating the structure of the gut microbiota.

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Metformin Affects Gut Microbiome Composition and Function and Circulating Short-Chain Fatty Acids: A Randomized Trial

10.2337/figshare.14340419 ◽

2021 ◽

Author(s):

Noel T. Mueller ◽

Moira K. Differding ◽

Mingyu Zhang ◽

Nisa Maruthar ◽

Stephen P Juraschek ◽

...

Keyword(s):

Weight Loss ◽

Gut Microbiota ◽

Randomized Trial ◽

Short Chain Fatty Acids ◽

Metagenomic Sequencing ◽

Microbiota Composition ◽

Behavioral Weight Loss ◽

Microbiome Composition ◽

Weight Loss Group ◽

And Function

Objective: To determine the longer-term effects of metformin and behavioral weight loss on gut microbiota and SCFAs. Methods: We conducted a parallel-arm, randomized trial. We enrolled overweight/obese adults who had been treated for solid tumors but had no ongoing cancer treatment and randomized them (n=121) to: 1) metformin (up to 2000mg), 2) coach-directed behavioral weight loss, or 3) self-directed care (control) for 12 months. We collected stool and serum at baseline (n=114), 6 months (n=109) and 12 months (n=105). From stool, we extracted microbial DNA and conducted amplicon and metagenomic sequencing. We measured SCFAs and other biochemical parameters from fasting serum. Results: Of the 121 participants, 79% were female, 46% were black, and the mean age was 60y. Only metformin intervention significantly altered microbiota composition. Compared to control, metformin increased E. Coli and Ruminococcus torques and decreased Intestinibacter Bartletti at both 6 and 12 months, and decreased the genus Roseburia (genus), including R. faecis and R. intestinalis, at 12 months. Effects were similar when comparing metformin to the behavioral weight loss group. Metformin also altered 62 metagenomic functional pathways and increased butyrate, acetate, and valerate at 6 months. Behavioral weight loss vs. control did not significantly alter microbiota composition, but did increase acetate at 6 months. Increases in acetate were associated with decreases in fasting insulin. Conclusions: Metformin, but not behavioral weight loss, impacted gut microbiota composition and function at 6 months and 12 months. Both metformin and behavioral weight loss altered 6-month SCFAs, including increasing acetate which correlated with improved insulin sensitivity.

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Dysbiosis of Gut Microbiota with Increased Trimethylamine N-oxide Level in Patients with Large Artery Atherosclerotic and Cardioembolic Strokes

10.21203/rs.3.rs-20073/v1 ◽

2020 ◽

Author(s):

Dong-Juan Xu ◽

Kai Cheng Wang ◽

Lin-Bo Yuan ◽

Qiong-Qiong Lin ◽

Hong-Fei Li ◽

...

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Control Group ◽

Large Artery ◽

Microbiome Composition ◽

Plaque Area ◽

Liquid Chromatography Tandem Mass ◽

And Function ◽

Dietary Choline ◽

Asymptomatic Control

Abstract Background — With the establishment of the concept of the gut–brain axis, increasing evidence has shown that the gut microbiome plays an important role in the pathogenesis of cardiovascular diseases. Gut bacteria can transform dietary choline, L-carnitine, and trimethylamine N -oxide (TMAO) into trimethylamine, which can be oxidized into TMAO again in the liver and participate in atherogenesis. However, only few studies have described alterations in the gut microbiota composition and function in cardioembolic (CE) and large artery atherosclerotic (LAA) strokes. Methods and Results — A case–control study was performed on patients with LAA and CE strokes. TMAO was determined via liquid chromatography tandem mass spectrometry. Gut microbiome was profiled through Illumina sequencing of the 16S ribosomal RNA gene (V4–V5 regions). The TMAO levels in the plasma of patients with LAA and CE strokes were significantly increased (TMAO: LAA stroke, 2931±456.4 ng/mL vs. CE stroke, 4220±577.6 ng/mL vs. control, 1663±117.8 ng/mL; P < 0.05). The TMAO level in patients with LAA stroke was positively correlated with the carotid plaque area (rho = 0.333, 95% confidence interval = 0.08 to 0.55, and P = 0.0093). The composition and function of gut microbiomes in the LAA and CE stroke groups were significantly different from those of the asymptomatic control. In addition to the significantly increased α and β diversities, the gut microbiome composition and function showed that the LAA group had more microorganisms than the asymptomatic control group; such microorganisms convert dietary source choline, TMAO to TMA. Parabacteroides and Streptococcus exhibited the strongest association with LAA and CE strokes. Conclusions — This study established the compositional and functional alterations of gut microbiomes in patients with LAA and CE strokes and the relationship between plasma TMAO and gut microbiota. The findings suggest the potential of using gut microbiota as a biomarker for patients with LAA and CE strokes.

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Diversity and Function of Wolf Spider Gut Microbiota Revealed by Shotgun Metagenomics

Frontiers in Microbiology ◽

10.3389/fmicb.2021.758794 ◽

2021 ◽

Vol 12 ◽

Author(s):

Runbiao Wu ◽

Luyu Wang ◽

Jianping Xie ◽

Zhisheng Zhang

Keyword(s):

Gut Microbiota ◽

Wolf Spider ◽

Metagenomic Sequencing ◽

Wolf Spiders ◽

Shotgun Metagenomics ◽

Microbial Genomes ◽

Shotgun Metagenomic Sequencing ◽

Crucial Component ◽

Genes Encoding ◽

And Function

Wolf spiders (Lycosidae) are crucial component of integrated pest management programs and the characteristics of their gut microbiota are known to play important roles in improving fitness and survival of the host. However, there are only few studies of the gut microbiota among closely related species of wolf spider. Whether wolf spiders gut microbiota vary with habitats remains unknown. Here, we used shotgun metagenomic sequencing to compare the gut microbiota of two wolf spider species, Pardosa agraria and P. laura from farmland and woodland ecosystems, respectively. The results show that the gut microbiota of Pardosa spiders is similar in richness and abundance. Approximately 27.3% of the gut microbiota of P. agraria comprises Proteobacteria, and approximately 34.5% of the gut microbiota of P. laura comprises Firmicutes. We assembled microbial genomes and found that the gut microbiota of P. laura are enriched in genes for carbohydrate metabolism. In contrast, those of P. agraria showed a higher proportion of genes encoding acetyltransferase, an enzyme involved in resistance to antibiotics. We reconstructed three high-quality and species-level microbial genomes: Vulcaniibacterium thermophilum, Anoxybacillus flavithermus and an unknown bacterium belonging to the family Simkaniaceae. Our results contribute to an understanding of the diversity and function of gut microbiota in closely related spiders.

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