A Computational Framework to Quantify Host-Microbiome Interactions in Clostridioides Difficile Infection

Abstract Background: Clostridioides difficile infection (CDI) is the most common cause of healthcare–associated infection and an important cause of morbidity and mortality among hospitalized patients. A comprehensive understanding of C. difficile infection (CDI) pathogenesis is crucial for disease diagnosis, treatment and prevention. To achieve that, a quantitative study of host-microbiome interactions in CDI is a prerequisite. Yet, an effective computational framework to quantify host-microbiome interactions in CDI was lacking. Methods: Here, we characterized gut microbial compositions and abroad panel of immunological markers in a comprehensive clinical cohort of 243 well-characterized human subjects with four different C. difficile infection/colonization statuses (CDI, Asymptomatic Carriage, Non-CDI Diarrhea, and Control). Based on microbial and immunological features, we developed a computational framework to detect CDI status using random forest and symbolic classification models.Results: First, by calculating the correlations between microbial compositions and the circulating levels of host immune markers for each of the four phenotype groups, we found that the interactions between gut microbiota and host immune markers are very sensitive to the status of C. difficile colonization and infection. Second, we demonstrated that incorporating both gut microbiome and host immune marker data into random forest classifiers can better distinguish CDI from other groups than can either type of data alone. Finally, we performed symbolic classification using selected features from random forest classifiers to derive simple mathematic formulas that explicitly model the interactions between gut microbiome and host immune markers.Conclusions: Overall, this study provides an effective computational framework to quantify the role of the intricate interactions between gut microbiota and host immune markers in CDI pathogenesis. This framework may inform the design of future diagnostic and therapeutic strategies.

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Integrating Gut Microbiota and Host Immune Markers for Highly Accurate Diagnosis of Clostridioides difficile Infection

10.1101/2020.04.27.20081653 ◽

2020 ◽

Author(s):

Shanlin Ke ◽

Nira R. Pollock ◽

Xu-Wen Wang ◽

Xinhua Chen ◽

Kaitlyn Daugherty ◽

...

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Human Subjects ◽

Accurate Diagnosis ◽

Classification Models ◽

Immune Markers ◽

Immune Marker ◽

Marker Data ◽

Clostridioides Difficile ◽

Asymptomatic Carriage

AbstractExposure to Clostridioides difficile can result in asymptomatic carriage or infection with symptoms ranging from mild diarrhea to fulminant pseudomembranous colitis. A reliable diagnostic approach for C. difficile infection (CDI) remains controversial. Accurate diagnosis is paramount not only for patient management but also for epidemiology and disease research. Here, we characterized gut microbial compositions and a broad panel of innate and adaptive immunological markers in 243 well-characterized human subjects, who were divided into four phenotype groups: CDI, Asymptomatic Carriage, Non-CDI Diarrhea, and Control. We found that CDI is associated with alteration of many different aspects of the gut microbiota, including overall microbial diversity and microbial association networks. We demonstrated that incorporating both gut microbiome and host immune marker data into classification models can better distinguish CDI from other groups than can either type of data alone. Our classification models display robust diagnostic performance to differentiate CDI from Asymptomatic carriage (AUC~0.916), Non-CDI Diarrhea (AUC~0.917), or Non-CDI that combines all other three groups (AUC~0.929). Finally, we performed symbolic classification using selected features to derive simple mathematic formulas for highly accurate CDI diagnosis. Overall, this study provides evidence supporting important roles of gut microbiota and host immune markers in CDI diagnosis, which may also inform the design of future therapeutic strategies.One Sentence SummaryIncorporating both gut microbiome and host immune marker data into classification models can better distinguish CDI from other groups than can either type of data alone.

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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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Gut microbiome and Clostridioides difficile infection: a closer look at the microscopic interface

Therapeutic Advances in Gastroenterology ◽

10.1177/1756284821994736 ◽

2021 ◽

Vol 14 ◽

pp. 175628482199473

Author(s):

Kanika Sehgal ◽

Sahil Khanna

Keyword(s):

Gut Microbiota ◽

Proton Pump Inhibitors ◽

Proton Pump ◽

Gut Microbiome ◽

Gastrointestinal Diseases ◽

Clostridioides Difficile ◽

Number Of Factors ◽

Clostridioides Difficile Infection ◽

Microbiota Structure

The pathogenesis of Clostridioides difficile infection (CDI) was recognized with its link to the use of antimicrobials. Antimicrobials significantly alter gut microbiota structure and composition, which led to the discovery of the association of this gut perturbation with the development of CDI. A number of factors implicated in its pathogenesis, such as advancing age, proton-pump inhibitors, and gastrointestinal diseases, are linked to gut microbiota perturbations. In an effort to better understand CDI, a multitude of studies have tried to ascertain protective and predictive microbial footprints linked with CDI. It has further been realized that CDI in itself can alter the gut microbiome. Its spore-forming capability poses as an impediment in the management of the infection and contributes to its recurrence. Antibiotic therapies used for its management have also been linked to gut microbiota changes, making its treatment a little more challenging. In an effort to exploit and utilize this association, gut microbial restoration therapies, particularly in the form of fecal microbial transplant, are increasingly being put to use and are proving to be beneficial. In this review, we summarize the association of the gut microbiome and microbial perturbation with initial and recurrent CDI.

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DETERMINING RELATIVE IMPORTANCE OF HUMAN GUT MICROBIOTA, AGE, GENDER AND LIFESTYLE PATTERN AS A PREDICTOR FOR BMI USING LOGMPIE DATA

PLANT ARCHIVES ◽

10.51470/plantarchives.2021.v21.no2.054 ◽

2021 ◽

Vol 21 (2) ◽

Author(s):

Komal Jani ◽

Shelly Gupta

Keyword(s):

Physical Activity ◽

Body Mass Index ◽

Random Forest ◽

Gut Microbiota ◽

Relative Abundance ◽

Gut Microbiome ◽

Relative Importance ◽

Human Gut ◽

Human Gut Microbiota ◽

Lifestyle Pattern

We use the ‘Relative Abundance Table’ and ‘LogMPIE Study Metadata’ from the “Landscape of Gut Microbiome - Pan-India Exploration”, or LogMPIE dataset to find out the relative importance of human gut microbiota abundance (specifically genus), age, gender, and lifestyle pattern as a predictor for BMI (Body Mass Index). The LogMPIE data is taken from 1004 subjects and 993 unique microorganisms are reported along with BMI, age, and physical activity. We use Random Forest Regressor to find out the relative importance of the above-mentioned features (microorganism genus abundance, age, gender, and lifestyle pattern) in predicting the BMI of a subject. The objective here is not the prediction of BMI using the features but to find out the relative importance of these features as much as these affect the BMI.

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Anti-Acid Drug Treatment Induces Changes in the Gut Microbiome Composition of Hemodialysis Patients

Microorganisms ◽

10.3390/microorganisms9020286 ◽

2021 ◽

Vol 9 (2) ◽

pp. 286

Author(s):

Yi-Ting Lin ◽

Ting-Yun Lin ◽

Szu-Chun Hung ◽

Po-Yu Liu ◽

Ping-Hsun Wu ◽

...

Keyword(s):

Random Forest ◽

Gut Microbiota ◽

Gut Microbiome ◽

Hemodialysis Patients ◽

Microbiota Composition ◽

Microbial Composition ◽

Linear Discriminant ◽

H2 Blocker ◽

Α Diversity ◽

16S Rna

Anti-acid drugs, proton pump inhibitor (PPI) and histamine-2 blocker (H2-blocker), are commonly prescribed to treat gastrointestinal disorders. These anti-acid drugs alter gut microbiota in the general population, but their effects are not known in hemodialysis patients. Hence, we investigated the microbiota composition in hemodialysis patients treated with PPIs or H2-blocker. Among 193 hemodialysis patients, we identified 32 H2-blocker users, 23 PPI users, and 138 no anti-acid drug subjects. Fecal samples were obtained to analyze the gut microbiome using 16S RNA amplicon sequencing. Differences in the microbial composition of the H2-blocker users, PPI users, and controls were assessed using linear discriminant analysis effect size and the random forest algorithm. The species richness or evenness (α-diversity) was similar among the three groups, whereas the inter-individual diversity (β-diversity) was different between H2-blocker users, PPI users, and controls. Hemodialysis patients treated with H2-blocker and PPIs had a higher microbial dysbiosis index than the controls, with a significant increase in the genera Provetella 2, Phascolarctobacterium, Christensenellaceae R-7 group, and Eubacterium oxidoreducens group in H2-blocker users, and Streptococcus and Veillonella in PPI users. In addition, compared to the H2-blocker users, there was a significant enrichment of the genera Streptococcus in PPI users, as confirmed by the random forest analysis and the confounder-adjusted regression model. In conclusion, PPIs significantly changed the gut microbiota composition in hemodialysis patients compared to H2-blocker users or controls. Importantly, the Streptococcus genus was significantly increased in PPI treatment. These findings caution against the overuse of PPIs.

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Gut microbiota features associated with Clostridioides difficile colonization in dairy calves

10.1101/2021.05.11.443551 ◽

2021 ◽

Author(s):

Laurel E. Redding ◽

Alexander S. Berry ◽

Nagaraju Indugu ◽

Elizabeth Huang ◽

Daniel P. Beiting ◽

...

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Beta Diversity ◽

Diarrheal Disease ◽

Alpha Diversity ◽

Opportunistic Pathogen ◽

Fecal Microbiota ◽

Dairy Calves ◽

Clostridioides Difficile ◽

Significant Difference

Diarrheal disease, a major cause of morbidity and mortality in dairy calves, is strongly associated with the health and composition of the gut microbiome. Clostridioides difficile is an opportunistic pathogen that proliferates and can produce enterotoxins when the host experiences gut dysbiosis. However, even asymptomatic colonization with C. difficile can be associated with differing degrees of microbiome disruption in a range of species, including people, swine, and dogs. Little is known about the interaction between C. difficile and the gut microbiome in dairy calves. In this study, we sought to define microbial features associated with C. difficile colonization in pre-weaned dairy calves less than 2 weeks of age. We characterized the fecal microbiota of 80 calves from 23 different farms using 16S rRNA sequencing and compared the microbiota of C. difficile -positive (n=24) and C. difficile -negative calves (n=56). Farm appeared to be the greatest source of variability in the gut microbiota. When controlling for calf age, diet, and farm location, there was no significant difference in Shannon alpha diversity ( P = 0.50) or in weighted UniFrac beta diversity (P=0.19) between C. difficile -positive and –negative calves. However, there was a significant difference in beta diversity as assessed using Bray-Curtiss diversity ( P =0.0077), and C. difficile -positive calves had significantly increased levels of Ruminococcus (gnavus group) ( Adj. P =0.052), Lachnoclostridium ( Adj. P =0.060), Butyricicoccus ( Adj. P =0.060), and Clostridium sensu stricto 2 compared to C. difficile -negative calves. Additionally, C. difficile -positive calves had fewer microbial co-occurrences than C. difficile –negative calves, indicating reduced bacterial synergies. Thus, while C. difficile colonization alone is not associated with dysbiosis and is therefore unlikely to result in an increased likelihood of diarrhea in dairy calves, it may be associated with a more disrupted microbiota.

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Disordered intestinal microbes are associated with the activity of Systemic Lupus Erythematosus

Clinical Science ◽

10.1042/cs20180841 ◽

2019 ◽

Vol 133 (7) ◽

pp. 821-838 ◽

Cited By ~ 20

Author(s):

Yao Li ◽

Hai-Fang Wang ◽

Xin Li ◽

Hai-Xia Li ◽

Qiong Zhang ◽

...

Keyword(s):

Systemic Lupus Erythematosus ◽

Random Forest ◽

Gut Microbiota ◽

Disease Activity ◽

Lupus Erythematosus ◽

Gut Microbiome ◽

Metabolic Pathways ◽

Systemic Lupus ◽

Forest Model ◽

Active Patients

Abstract Intestinal dysbiosis is implicated in Systemic Lupus Erythematosus (SLE). However, the evidence of gut microbiome changes in SLE is limited, and the association of changed gut microbiome with the activity of SLE, as well as its functional relevance with SLE still remains unknown. Here, we sequenced 16S rRNA amplicon on fecal samples from 40 SLE patients (19 active patients, 21 remissive patients), 20 disease controls (Rheumatoid Arthritis (RA) patients), and 22 healthy controls (HCs), and investigated the association of functional categories with taxonomic composition by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). We demonstrated SLE patients, particularly the active patients, had significant dysbiosis in gut microbiota with reduced bacterial diversity and biased community constitutions. Amongst the disordered microbiota, the genera Streptococcus, Campylobacter, Veillonella, the species anginosus and dispar, were positively correlated with lupus activity, while the genus Bifidobacterium was negatively associated with the disease activity. PICRUSt analysis showed metabolic pathways were different between SLE and HCs, and also between active and remissive SLE patients. Moreover, we revealed that a random forest model could distinguish SLE from RA and HCs (area under the curve (AUC) = 0.792), and another random forest model could well predict the activity of SLE patients (AUC = 0.811). In summary, SLE patients, especially the active patients, show an apparent dysbiosis in gut microbiota and its related metabolic pathways. Amongst the disordered microflora, four genera and two species are associated with lupus activity. Furthermore, the random forest models are able to diagnose SLE and predict disease activity.

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Integrating gut microbiome and host immune markers to understand the pathogenesis of Clostridioides difficile infection

Gut Microbes ◽

10.1080/19490976.2021.1935186 ◽

2021 ◽

Vol 13 (1) ◽

pp. 1-18

Author(s):

Shanlin Ke ◽

Nira R. Pollock ◽

Xu-Wen Wang ◽

Xinhua Chen ◽

Kaitlyn Daugherty ◽

...

Keyword(s):

Gut Microbiome ◽

Immune Markers ◽

Clostridioides Difficile ◽

Clostridioides Difficile Infection

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Gut microbiome-Mediterranean diet interactions in improving host health

F1000Research ◽

10.12688/f1000research.18992.1 ◽

2019 ◽

Vol 8 ◽

pp. 699 ◽

Cited By ~ 4

Author(s):

Ravinder Nagpal ◽

Carol A. Shively ◽

Thomas C. Register ◽

Suzanne Craft ◽

Hariom Yadav

Keyword(s):

Gut Microbiota ◽

Health Effects ◽

Mediterranean Diet ◽

Gut Microbiome ◽

Unsaturated Fatty Acids ◽

Metabolic Diseases ◽

Human Subjects ◽

Wide Range ◽

Host Health ◽

The Mediterranean

The gut microbiota plays a fundamental role in host health and disease. Host diet is one of the most significant modulators of the gut microbial community and its metabolic activities. Evidence demonstrates that dietary patterns such as the ‘Western diet’ and perturbations in gut microbiome (dysbiosis) have strong associations with a wide range of human diseases, including obesity, metabolic syndrome, type-2 diabetes and cardiovascular diseases. However, consumption of Mediterranean-style diets is considered healthy and associated with the prevention of cardiovascular and metabolic diseases, colorectal cancers and many other diseases. Such beneficial effects of the Mediterranean diet might be attributed to high proportion of fibers, mono- and poly-unsaturated fatty acids, antioxidants and polyphenols. Concurrent literature has demonstrated beneficial modulation of the gut microbiome following a Mediterranean-style diet in humans as well as in experimental animal models such as rodents. We recently demonstrated similar positive changes in the gut microbiome of non-human primates consuming a Mediterranean-style diet for long term (30 months). Therefore, it is rational to speculate that this positive modulation of the gut microbiome diversity, composition and function is one of the main factors intermediating the health effects of Mediterranean diet on the host. The present perspective discusses the evidences that the Mediterranean diet induces gut microbiome modulation in rodents, non-human primates and human subjects, and discusses the potential role of gut microbiota and microbial metabolites as one of the fundamental catalysts intermediating various beneficial health effects of Mediterranean diet on the host.

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Significance of the Gut Microbiome for Viral Diarrheal and Extra-Intestinal Diseases

Viruses ◽

10.3390/v13081601 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1601

Author(s):

Ulrich Desselberger

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Neurological Diseases ◽

Bacterial Species ◽

Future Research ◽

Clostridioides Difficile ◽

Host Diet ◽

Enteric Infections ◽

Childhood Vaccines ◽

Definition Of

The composition of the mammalian gut microbiome is very important for the health and disease of the host. Significant correlations of particular gut microbiota with host immune responsiveness and various infectious and noninfectious host conditions, such as chronic enteric infections, type 2 diabetes, obesity, asthma, and neurological diseases, have been uncovered. Recently, research has moved on to exploring the causalities of such relationships. The metabolites of gut microbiota and those of the host are considered in a ‘holobiontic’ way. It turns out that the host’s diet is a major determinant of the composition of the gut microbiome and its metabolites. Animal models of bacterial and viral intestinal infections have been developed to explore the interrelationships of diet, gut microbiome, and health/disease phenotypes of the host. Dietary fibers can act as prebiotics, and certain bacterial species support the host’s wellbeing as probiotics. In cases of Clostridioides difficile-associated antibiotic-resistant chronic diarrhea, transplantation of fecal microbiomes has sometimes cured the disease. Future research will concentrate on the definition of microbial/host/diet interrelationships which will inform rationales for improving host conditions, in particular in relation to optimization of immune responses to childhood vaccines.

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