Dataset Information

Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls.

ABSTRACT: Background: The microbiota plays a critical role in the process of human carcinogenesis. Pancreatic head carcinoma (PHC)-associated tongue coating microbiome dysbiosis has not yet been clearly defined.Objective: Our aim is to reveal the bacterial composition shifts in the microbiota of the tongue coat of PHC patients.Design: The tongue coating microbiota was analyzed in 30 PHC patients and 25 healthy controls using 16S rRNA gene sequencing technology.Results: The microbiome diversity of the tongue coat in PHC patients was significantly increased, as shown by the Shannon, Simpson, inverse Simpson, Obs and incidence-based coverage estimators. Principal component analysis revealed that PHC patients were colonized by remarkably different tongue coating microbiota than healthy controls and liver cancer patients. Linear discriminant analysis effect size revealed that Leptotrichia, Fusobacterium,Rothia, Actinomyces, Corynebacterium, Atopobium, Peptostreptococcus, Catonella, Oribacterium, Filifactor, Campylobacter, Moraxella and Tannerella were overrepresented in the tongue coating of PHC patients, and Haemophilus, Porphyromonas and Paraprevotella were enriched in the tongue coating microbiota of healthy controls. Strikingly, Haemophilus, Porphyromonas, Leptotrichia and Fusobacterium could distinguish PHC patients from healthy subjects, and Streptococcus and SR1 could distinguish PHC patients from liver cancer patients. Conclusions: These findings identified the microbiota dysbiosis of the tongue coat in PHC patients, and provide insight into the association between the human microbiome and pancreatic cancer.

SUBMITTER: Lu H

PROVIDER: S-EPMC6352935 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls.

Lu Haifeng H Ren Zhigang Z Li Ang A Li Jinyou J Xu Shaoyan S Zhang Hua H Jiang Jianwen J Yang Jiezuan J Luo Qixia Q Zhou Kai K Zheng Shusen S Li Lanjuan L

Journal of oral microbiology 20190128 1

Background: The microbiota plays a critical role in the process of human carcinogenesis. Pancreatic head carcinoma (PHC)-associated tongue coating microbiome dysbiosis has not yet been clearly defined.Objective: Our aim is to reveal the bacterial composition shifts in the microbiota of the tongue coat of PHC patients.Design: The tongue coating microbiota was analyzed in 30 PHC patients and 25 healthy controls using 16S rRNA gene sequencing technology.Results: The micr ...[more]

PMID: 30728915

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Project description:Antibiotic usage is the most commonly cited risk factor for hospital-acquired Clostridium difficile infections (CDI). The increased risk is due to disruption of the indigenous microbiome and a subsequent decrease in colonization resistance by the perturbed bacterial community; however, the specific changes in the microbiome that lead to increased risk are poorly understood. We developed statistical models that incorporated microbiome data with clinical and demographic data to better understand why individuals develop CDI. The 16S rRNA genes were sequenced from the feces of 338 individuals, including cases, diarrheal controls, and nondiarrheal controls. We modeled CDI and diarrheal status using multiple clinical variables, including age, antibiotic use, antacid use, and other known risk factors using logit regression. This base model was compared to models that incorporated microbiome data, using diversity metrics, community types, or specific bacterial populations, to identify characteristics of the microbiome associated with CDI susceptibility or resistance. The addition of microbiome data significantly improved our ability to distinguish CDI status when comparing cases or diarrheal controls to nondiarrheal controls. However, only when we assigned samples to community types was it possible to differentiate cases from diarrheal controls. Several bacterial species within the Ruminococcaceae, Lachnospiraceae, Bacteroides, and Porphyromonadaceae were largely absent in cases and highly associated with nondiarrheal controls. The improved discriminatory ability of our microbiome-based models confirms the theory that factors affecting the microbiome influence CDI. IMPORTANCE The gut microbiome, composed of the trillions of bacteria residing in the gastrointestinal tract, is responsible for a number of critical functions within the host. These include digestion, immune system stimulation, and colonization resistance. The microbiome's role in colonization resistance, which is the ability to prevent and limit pathogen colonization and growth, is key for protection against Clostridium difficile infections. However, the bacteria that are important for colonization resistance have not yet been elucidated. Using statistical modeling techniques and different representations of the microbiome, we demonstrated that several community types and the loss of several bacterial populations, including Bacteroides, Lachnospiraceae, and Ruminococcaceae, are associated with CDI. Our results emphasize the importance of considering the microbiome in mediating colonization resistance and may also direct the design of future multispecies probiotic therapies.

Project description:Introduction Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches. Methods Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree. Results Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm ‘Citrus’, which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data. Discussion Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.

Dataset Information

Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls.

Publications

Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls.

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