Dataset Information

Alterations in Skin Microbiomes of Patients With Cirrhosis.

ABSTRACT: BACKGROUND & AIMS:Patients with cirrhosis have intestinal dysbiosis and are prone to itching and skin or soft-tissue infections. The skin microbiome, and its relationship with intestinal microbiome, have not been characterized. We investigated alterations in skin microbiota of patients with cirrhosis and their association with intestinal microbiota and modulators of itch. METHODS:We collected skin swabs at 7 sites and blood and stool samples from 20 healthy individuals (control subjects; mean age, 59 years) and 50 patients with cirrhosis (mean age, 61 years; mean model for end-stage disease score, 12; 20 with decompensation). Skin and stool samples were analyzed by 16s rRNA sequencing and serum samples were analyzed by liquid chromatography and mass spectrometry for levels of bile acids (BAs) and by an ELISA for autotaxin (an itch modulator). Participants were analyzed by the visual analog itch scale (VAS, 0-10,10 = maximum intensity). Data were compared between groups (cirrhosis vs control subjects, with vs without decompensation, VAS 5 or higher vs less than 5). Correlation networks between serum levels of BAs and skin microbiomes were compared between patients with cirrhosis with vs without itching. RESULTS:The composition of microbiomes at all skin sites differed between control subjects and patients with cirrhosis and between patients with compensated vs decompensated cirrhosis. Skin microbiomes of patients with cirrhosis (especially those with decompensation) contained a higher relative abundance of Gammaproteobacteria, Streptococaceae, and Staphylococcaceae, and fecal microbiomes contained a higher relative abundance of Gammaproteobacteria, than control subjects. These bacterial taxa were associated with serum levels of autotaxin and BAs, which were higher in patients with VAS scores ?5. Based on network statistics, microbial and BA interactions at all sites were more complex in patients with greater levels of itching in the shin, the most common site of itch. CONCLUSIONS:We identified alterations in skin microbiome of patients with cirrhosis (in Gammaproteobacteria, Streptococcaceae, and Staphylococcaceae)-especially in patients with decompensation; fecal microbiomes of patients with cirrhosis had a higher relative abundance of Gammaproteobacteria than control subjects. These specific microbial taxa are associated with itching intensity and itch modulators, such as serum levels of BAs and autotaxin.

SUBMITTER: Bajaj JS

PROVIDER: S-EPMC6754819 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Alterations in Skin Microbiomes of Patients With Cirrhosis.

Bajaj Jasmohan S JS Fagan Andrew A Sikaroodi Masoumeh M Kakiyama Genta G Takei Hajme H Degefu Yordanos Y Pandak William M WM Hylemon Phillip B PB Fuchs Michael M John Binu B Heuman Douglas M DM Gavis Edith E Nittono Hiroshi H Patil Rohan R Gillevet Patrick M PM

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 20190321 12

<h4>Background & aims</h4>Patients with cirrhosis have intestinal dysbiosis and are prone to itching and skin or soft-tissue infections. The skin microbiome, and its relationship with intestinal microbiome, have not been characterized. We investigated alterations in skin microbiota of patients with cirrhosis and their association with intestinal microbiota and modulators of itch.<h4>Methods</h4>We collected skin swabs at 7 sites and blood and stool samples from 20 healthy individuals (control su ...[more]

PMID: 30905718

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Project description:BackgroundHuman gut microbiota plays an important role in the pathogenesis of cirrhosis complications. Although the phylogenetic diversity of intestinal microbiota in patients with liver cirrhosis has been examined in several studies, little is known about their functional composition and structure.ResultsTo characterize the functional gene diversity of the gut microbiome in cirrhotic patients, we recruited a total of 42 individuals, 12 alcoholic cirrhosis patients, 18 hepatitis B virus (HBV)-related cirrhosis patients, and 12 normal controls. We determined the functional structure of these samples using a specific functional gene array, which is a combination of GeoChip for monitoring biogeochemical processes and HuMiChip specifically designed for analyzing human microbiomes. Our experimental data showed that the microbial community functional composition and structure were dramatically distinctive in the alcoholic cirrhosis. Various microbial functional genes involved in organic remediation, stress response, antibiotic resistance, metal resistance, and virulence were highly enriched in the alcoholic cirrhosis group compared to the control group and HBV-related cirrhosis group. Cirrhosis may have distinct influences on metabolic potential of fecal microbial communities. The abundance of functional genes relevant to nutrient metabolism, including amino acid metabolism, lipid metabolism, nucleotide metabolism, and isoprenoid biosynthesis, were significantly decreased in both alcoholic cirrhosis group and HBV-related cirrhosis group. Significant correlations were observed between functional gene abundances and Child-Pugh scores, such as those encoding aspartate-ammonia ligase, transaldolase, adenylosuccinate synthetase and IMP dehydrogenase.ConclusionsFunctional gene array was utilized to study the gut microbiome in alcoholic and HBV-related cirrhosis patients and controls in this study. Our array data indicated that the functional composition of fecal microbiomes was heavily influenced by cirrhosis, especially by alcoholic cirrhosis. This study provides new insights into the functional potentials and activity of gut microbiota in cirrhotic patients with different etiologies.

Project description:The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification.IMPORTANCE A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.

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Dataset Information

Alterations in Skin Microbiomes of Patients With Cirrhosis.

Publications

Alterations in Skin Microbiomes of Patients With Cirrhosis.

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