Gut microbiota signatures in cystic fibrosis: Loss of host CFTR function drives the microbiota enterophenotype

Abstract

BackgroundCystic fibrosis (CF) is a disorder affecting the respiratory, digestive, reproductive systems and sweat glands. This lethal hereditary disease has known or suspected links to the dysbiosis gut microbiota. High-throughput meta-omics-based approaches may assist in unveiling this complex network of symbiosis modifications.ObjectivesThe aim of this study was to provide a predictive and functional model of the gut microbiota enterophenotype of pediatric patients affected by CF under clinical stability.MethodsThirty-one fecal samples were collected from CF patients and healthy children (HC) (age range, 1–6 years) and analysed using targeted-metagenomics and metabolomics to characterize the ecology and metabolism of CF-linked gut microbiota. The multidimensional data were low fused and processed by chemometric classification analysis.ResultsThe fused metagenomics and metabolomics based gut microbiota profile was characterized by a high abundance of Propionibacterium, Staphylococcus and Clostridiaceae, including Clostridium difficile, and a low abundance of Eggerthella, Eubacterium, Ruminococcus, Dorea, Faecalibacterium prausnitzii, and Lachnospiraceae, associated with overexpression of 4-aminobutyrate (GABA), choline, ethanol, propylbutyrate, and pyridine and low levels of sarcosine, 4-methylphenol, uracil, glucose, acetate, phenol, benzaldehyde, and methylacetate. The CF gut microbiota pattern revealed an enterophenotype intrinsically linked to disease, regardless of age, and with dysbiosis uninduced by reduced pancreatic function and only partially related to oral antibiotic administration or lung colonization/infection.ConclusionsAll together, the results obtained suggest that the gut microbiota enterophenotypes of CF, together with endogenous and bacterial CF biomarkers, are direct expression of functional alterations at the intestinal level. Hence, it’s possible to infer that CFTR impairment causes the gut ecosystem imbalance.This new understanding of CF host-gut microbiota interactions may be helpful to rationalize novel clinical interventions to improve the affected children’s nutritional status and intestinal function.

The Cross-Talk Between Gut Microbiota and Lungs in Common Lung Diseases

“…Furthermore, the intestinal inflammation in patients with cystic fibrosis might be related to alterations of the composition of the gut microbiota (de Freitas et al, 2018). It has been demonstrated that the gut microbiota pattern is caused by cystic fibrosis transmembrane conductance regulator (CFTR) function impairment (De Lisle, 2016;Vernocchi et al, 2018). The interaction of cystic fibrosis host-gut microbiota provides new insights to the treatments or interventions to enhance intestinal function and nutritional status of children suffering from cystic fibrosis.…”

Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

“…Fecal volatile organic compounds (VOCs) from 11 NSCLC patients were detected according to Vernocchi et al [29] by using the carboxen-polydimethylsiloxane coated fiber (CAR-PDMS) (85 μm) and the manual SPME holder (Supelco Inc., Bellefonte, PA, USA) according to Vernocchi et al [29]. The SPME fiber was exposed to each sample for 45 min.…”

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

“…Indeed, big data are usually produced by considering an order of tens or hundreds of samples, but this point (i.e., the number of samples) can be established for each produced chemometric model using different multivariate statistical methods (e.g., Hotelling T2, number of misclassifications (NMC), area under the receiver operating characteristics curves (AUROC), and discriminant Q2; in the case of the latter, in contrast to NMC and AUROC, PLS-DA models with low complexity compared to PLS models are preferred). Therefore, each integrated model based on omics-derived data needs to be validated for each sample set under analysis [ 92 , 93 , 94 , 95 ].…”

“…In this way, the integrated generated model can be validated for each type of sample set (e.g., row data from GC-MS, LC-MS, 1 H-NMR, etc.) and for each patient and/or reference subject dataset (e.g., phenomics data) [ 92 , 93 , 94 , 95 ].…”