Ischemic stroke (IS) can be caused by perturbations of the gut–brain axis. An imbalance in the gut microbiota (GM), or dysbiosis, may be linked to several IS risk factors and can influence the brain through the production of different metabolites, such as short-chain fatty acids (SCFAs), indole and derivatives. This study examines ecological changes in the GM and its metabolic activities after stroke. Fecal samples of 10 IS patients were compared to 21 healthy controls (CTRLs). GM ecological profiles were generated via 16S rRNA taxonomy as functional profiles using metabolomics analysis performed with a gas chromatograph coupled to a mass spectrometer (GC-MS). Additionally fecal zonulin, a marker of gut permeability, was measured using an enzyme-linked immuno assay (ELISA). Data were analyzed using univariate and multivariate statistical analyses and correlated with clinical features and biochemical variables using correlation and nonparametric tests. Metabolomic analyses, carried out on a subject subgroup, revealed a high concentration of fecal metabolites, such as SCFAs, in the GM of IS patients, which was corroborated by the enrichment of SCFA-producing bacterial genera such as Bacteroides, Christensellaceae, Alistipes and Akkermansia. Conversely, indole and 3-methyl indole (skatole) decreased compared to a subset of six CTRLs. This study illustrates how IS might affect the gut microbial milieu and may suggest potential microbial and metabolic biomarkers of IS. Expanded populations of Akkermansia and enrichment of acetic acid could be considered potential disease phenotype signatures.

Recent evidence indicates that the gut microbiota (GM) has a significant impact on the inflammatory bowel disease (IBD) progression. Our aim was to investigate the GM profiles, the Microbial Dysbiosis Index (MDI) and the intestinal microbiota-associated markers in relation to IBD clinical characteristics and disease state. We performed 16S rRNA metataxonomy on both stools and ileal biopsies, metabolic dysbiosis tests on urine and intestinal permeability and mucosal immunity activation tests on the stools of 35 IBD paediatric patients. On the GM profile, we assigned the MDI to each patient. In the statistical analyses, the MDI was correlated with clinical parameters and intestinal microbial-associated markers. In IBD patients with high MDI, Gemellaceae and Enterobacteriaceae were increased in stools, and Fusobacterium, Haemophilus and Veillonella were increased in ileal biopsies. Ruminococcaceae and WAL_1855D were enriched in active disease condition; the last one was also positively correlated to MDI. Furthermore, the MDI results correlated with PUCAI and Matts scores in ulcerative colitis patients (UC). Finally, in our patients, we detected metabolic dysbiosis, intestinal permeability and mucosal immunity activation. In conclusion, the MDI showed a strong association with both severity and activity of IBD and a positive correlation with clinical scores, especially in UC. Thus, this evidence could be a useful tool for the diagnosis and prognosis of IBD.

Background: To date, there have been numerous metataxonomic studies on gut microbiota (GM) profiling based on the analyses of data from public repositories. However, differences in study population and wet and dry pipelines have produced discordant results. Herein, we propose a biostatistical approach to remove these batch effects for the GM characterization in the case of autism spectrum disorders (ASDs). Methods: An original dataset of GM profiles from patients with ASD was ecologically characterized and compared with GM public digital profiles of age-matched neurotypical controls (NCs). Also, GM data from seven case–control studies on ASD were retrieved from the NCBI platform and exploited for analysis. Hence, on each dataset, conditional quantile regression (CQR) was performed to reduce the batch effects originating from both technical and geographical confounders affecting the GM-related data. This method was further applied to the whole dataset matrix, obtained by merging all datasets. The ASD GM markers were identified by the random forest (RF) model. Results: We observed a different GM profile in patients with ASD compared with NC subjects. Moreover, a significant reduction of technical- and geographical-dependent batch effects in all datasets was achieved. We identified Bacteroides_H, Faecalibacterium, Gemmiger_A_73129, Blautia_A_141781, Bifidobacterium_388775, and Phocaeicola_A_858004 as robust GM bacterial biomarkers of ASD. Finally, our validation approach provided evidence of the validity of the QCR method, showing high values of accuracy, specificity, sensitivity, and AUC-ROC. Conclusions: Herein, we proposed an updated biostatistical approach to reduce the technical and geographical batch effects that may negatively affect the description of bacterial composition in microbiota studies.

Background: The pathogenesis of many syncopal episodes remains unexplained. Intestinal dysbiosis could be involved in the pathophysiological mechanisms of syncope due to its connection with the central nervous system via the microbiota–gut–brain axis. This pilot study aimed to explore the specific cardiometabolic risk factors and gut microbiota in unexplained syncope (US), compared to other types of syncope, to assess their similarity or verify their different origins. Methods: We studied 86 participants with syncope, who were divided into four groups: an orthostatic syncope group (OH, n = 24), a neuromediated syncope group (NMS, n = 26), a cardiological syncope group (CS, n = 9), and an unexplained syncope group (US, n = 27). We evaluated the anthropometric, clinical, and metabolic characteristics of the four groups; the α- and β-diversity; and the differences in the abundance of the microbial taxa. Results: The US group had a lower incidence of systolic hypertension at the first visit and a lower frequency of patients with nocturnal hypertension than the CS group. Compared to the OH and NMS groups, the US group had a higher incidence of carotid plaques and greater carotid intima–media thickness, respectively. The microbiota differed significantly between the US and CS groups, but not between the US group and the OH or NMS group. Conclusions: We observed significant differences in the gut microbiota between CS and US. Future studies are necessary to evaluate the involvement of the gut microbiota in the complex pathogenesis of syncope and whether its analysis could support the interpretation of the pathophysiological mechasnisms underlying some episodes classifiable as US.

High-throughput 16S rRNA metagenomic sequencing has advanced our understanding of the gut microbiome, but its reliability depends on upstream processes such as DNA extraction and bacterial library preparation. In this study, we evaluated the impact of three different DNA extraction methods (a manual method with an ad hoc-designed pre-extraction phase (PE-QIA), and two automated magnetic bead-based methods (T180H and TAT132H)) and two bacterial library preparation protocols (home brew and VeriFi) on the 16S rRNA-based metagenomic profiling of faecal samples. T180H and TAT132H produced significantly higher DNA concentrations than PE-QIA, whereas TAT132H yielded DNA of lower purity compared to the others. In the taxonomic analysis, PE-QIA provided a balanced recovery of Gram-positive and Gram-negative bacteria, TAT132H was enriched in Gram-positive taxa, and T180H was enriched in Gram-negative taxa. An analysis of Microbial Community Standard (MOCK) samples showed that PE-QIA and T180H were more accurate than TAT132H. Finally, the VeriFi method yielded higher amplicon concentrations and sequence counts than the home brew protocol, despite the high level of chimeras. In conclusion, a robust performance in terms of DNA yield, purity, and taxonomic representation was obtained by PE-QIA and T180H. Furthermore, it was found that the impact of PCR-based steps on gut microbiota profiling can be minimized by an accurate bioinformatic pipeline.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Investigations of gut microbiota (GM) play an important role in deciphering disease severity and symptoms. Overall, we stratified 70 ASD patients by neuropsychological assessment, based on Calibrated Severity Scores (CSSs) of the Autism Diagnostic Observation Schedule-Second edition (ADOS-2), Child Behavior Checklist (CBCL) and intelligent quotient/developmental quotient (IQ/DQ) parameters. Hence, metataxonomy and PICRUSt-based KEGG predictions of fecal GM were assessed for each clinical subset. Here, 60% of ASD patients showed mild to moderate autism, while the remaining 40% showed severe symptoms; 23% showed no clinical symptoms, 21% had a risk of behavior problems and 56% had clinical symptoms based on the CBCL, which assesses internalizing problems; further, 52% had no clinical symptoms, 21% showed risk, and 26% had clinical symptoms classified by CBCL externalizing problems. Considering the total CBCL index, 34% showed no clinical symptoms, 13% showed risk, and 52% had clinical symptoms. Here, 70% of ASD patients showed cognitive impairment/developmental delay (CI/DD). The GM of ASDs with severe autism was characterized by an increase in Veillonella, a decrease in Monoglobus pectinilyticus and a higher microbial dysbiosis index (MDI) when compared to mild-moderate ASDs. Patients at risk for behavior problems and showing clinical symptoms were characterized by a GM with an increase of Clostridium, Eggerthella, Blautia, Intestinibacter, Coprococcus, Ruminococcus, Onthenecus and Bariatricus, respectively. Peptidoglycan biosynthesis and biofilm formation KEGGs characterized patients with clinical symptoms, while potential microbiota-activated PPAR-γ-signaling was seen in CI/DD patients. This evidence derived from GM profiling may be used to further improve ASD understanding, leasing to a better comprehension of the neurological phenotype.