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.

Cryptosporidium spp. are zoonotic protozoan parasites with a global prevalence, with both gastrointestinal and pulmonary involvement. Though symptoms can often be relatively mild, they can become severe and even fatal in children under five, the elderly, and in immunocompromised individuals, making cryptosporidiosis a leading cause of morbidity and mortality in fragile populations. Furthermore, there is an urgent clinical need for alternative therapies against cryptosporidiosis, as currently available FDA-approved treatments are ineffective in the immunocompromised. Recent evidence in animal models suggests that the gut microbiota (GM) can influence both host and parasite biology to influence the course of Cryptosporidium infection. Here, we present GM profiles in five cases of cryptosporidiosis, associated with varying underlying pathologies. We found that moderate–severe cryptosporidiosis was characterized by a reduction in alpha-diversity and an enrichment of Enterococcus spp., while decreases in Bifidobacterium, Gemmiger, and Blautia were detectable in the milder manifestations of the disease. Our results suggest that severe cryptosporidiosis is associated with a stronger change on the GM than is age or underlying pathology. Together with previously published studies in animal models, we believe that these results suggest that the GM could be a potential therapeutic target for human patients as well, particularly in the immunocompromised for whom anti-Cryptosporidium treatment remains largely ineffective.