Alessandra Palladini

The Role of Antibiotics in Gut Microbiota Modulation: The Eubiotic Effects of Rifaximin

Antibiotics are mainly used in clinical practice for their activity against pathogens, but they also alter the composition of commensal gut microbial community. Rifaximin is a non-absorbable antibiotic with additional effects on the gut microbiota about which very little is known. It is still not clear to what extent rifaximin can be able to modulate gut microbiota composition and diversity in different clinical settings. Studies based on culture-dependent techniques revealed that rifaximin treatment promotes the growth of beneficial bacteria, such as Bifidobacteria and Lactobacilli. Accordingly, our metagenomic analysis carried out on patients with different gastrointestinal and liver diseases highlighted a significant increase in Lactobacilli after rifaximin treatment, persisting in the short time period. This result was independent of the disease background and was not accompanied by a significant alteration of the overall gut microbial ecology. This suggests that rifaximin can exert important eubiotic effects independently of the original disease, producing a favorable gut microbiota perturbation without changing its overall composition and diversity.

Effects of Proton Pump Inhibitors on the Gastric Mucosa-Associated Microbiota in Dyspeptic Patients

ABSTRACT Besides being part of anti-Helicobacter pylori treatment regimens, proton pump inhibitors (PPIs) are increasingly being used to treat dyspepsia. However, little is known about the effects of PPIs on the human gastric microbiota, especially those related to H. pylori infection. The goal of this study was to characterize the stomach microbial communities in patients with dyspepsia and to investigate their relationships with PPI use and H. pylori status. Using 16S rRNA gene pyrosequencing, we analyzed the mucosa-associated microbial populations of 24 patients, of whom 12 were treated with the PPI omeprazole and 9 (5 treated and 4 untreated) were positive for H. pylori infection. The Proteobacteria, Firmicutes, Bacteroidetes, Fusobacteria, and Actinobacteria phyla accounted for 98% of all of the sequences, with Helicobacter, Streptococcus, and Prevotella ranking among the 10 most abundant genera. H. pylori infection or PPI treatment did not significantly influence gastric microbial species composition in dyspeptic patients. Principal-coordinate analysis of weighted UniFrac distances in these communities revealed clear but significant separation according to H. pylori status only. However, in PPI-treated patients, Firmicutes, particularly Streptococcaceae, were significantly increased in relative abundance compared to those in untreated patients. Consistently, Streptococcus was also found to significantly increase in relation to PPI treatment, and this increase seemed to occur independently of H. pylori infection. Our results suggest that Streptococcus may be a key indicator of PPI-induced gastric microbial composition changes in dyspeptic patients. Whether the gastric microbiota alteration contributes to dyspepsia needs further investigation. IMPORTANCE Although PPIs have become a popular treatment choice, a growing number of dyspeptic patients may be treated unnecessarily. We found that patients treated with omeprazole showed gastric microbial communities that were different from those of untreated patients. These differences regarded the abundances of specific taxa. By understanding the relationships between PPIs and members of the gastric microbiota, it will be possible to envisage new strategies for better managing patients with dyspepsia.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Mo1257 The Microbial Basis of Rifaximin Efficacy in Cirrhotic Patients With Overt Hepatic Encephalopathy: Preliminary Results From an Italian Cohort

G A A b st ra ct s (Clostridiaceae, Ruminococcaceae, Veillonellaceae, Lachnospiraceae, Clostridiaceae, Peptostreptococcaceae, Peptococcaceae) were the most abundant in the initial phases of the disease (62.3% in Child A patients p=0.06). Patients with at least one clinical sign of decompensation lost the relative abundance of Clostridia (abundance 62.3% in compensated patients vs 20.8% in decompensated ones vs 35.4% in healthy controls p=0.04). CONCLUSION: Liver cirrhosis is a fascinating model of gut microbiota modifications, that are connected with the progression of liver impairment.