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Abstract Background Recently, there has been increasing interest in the possible role of the gut microbiota (GM) in the onset of migraine. Our aim was to verify whether bacterial populations associated with intestinal dysbiosis are found in pediatric patients with migraine. We looked for which metabolic pathways, these bacteria were involved and whether they might be associated with gut inflammation and increased intestinal permeability. Methods Patients aged between 6 and 17 years were recruited. The GM profiling was performed by the 16S rRNA metataxonomics of faecal samples from 98 patients with migraine and 98 healthy subjects. Alpha and beta diversity analyses and multivariate and univariate analyses were applied to compare the gut microbiota profiles between the two group. To predict functional metabolic pathways, we used phylogenetic analysis of communities. The level of indican in urine was analyzed to investigate the presence of metabolic dysbiosis. To assess gut inflammation, increased intestinal permeability and the mucosal immune activation, we measured the plasmatic levels of lipopolysaccharide, occludin and IgA, respectively. Results The α-diversity analysis revealed a significant increase of bacterial richness in the migraine group. The β-diversity analysis showed significant differences between the two groups indicating gut dysbiosis in patients with migraine. Thirty-seven metabolic pathways were increased in the migraine group, which includes changes in tryptophan and phenylalanine metabolism. The presence of metabolic dysbiosis was confirmed by the increased level of indican in urine. Increased levels of plasmatic occludin and IgA indicated the presence of intestinal permeability and mucosal immune activation. The plasmatic LPS levels showed a low intestinal inflammation in patients with migraine. Conclusions Pediatric patients with migraine present GM profiles different from healthy subjects, associated with metabolic pathways important in migraine.

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.

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.

BackgroundUlcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches.MethodsThe 16S rRNA- and ITS2-based metataxonomics and gas chromatography–mass spectrometry/solid phase microextraction (GC–MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis.ResultsIn the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions.ConclusionIn this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.

Abstract Williams–Beuren syndrome (WBS) is a multisystem genetic disease caused by the deletion of a region of 1.5–1.8 Mb on chromosome 7q11.23. The elastin gene seems to account for several comorbidities and distinct clinical features such including cardiovascular disease, connective tissue abnormalities, growth retardation, and gastrointestinal (GI) symptoms. Increasing evidence points to alterations in gut microbiota composition as a primary or secondary cause of some GI or extra-intestinal characteristics. In this study, we performed the first exploratory analysis of gut microbiota in WBS patients compared to healthy subjects (CTRLs) using 16S rRNA amplicon sequencing, by investigating the gut dysbiosis in relation to diseases and comorbidities. We found that patients with WBS have significant dysbiosis compared to age-matched CTRLs, characterized by an increase in proinflammatory bacteria such as Pseudomonas, Gluconacetobacter and Eggerthella, and a reduction of anti-inflammatory bacteria including Akkermansia and Bifidobacterium. Microbial biomarkers associated with weight gain, GI symptoms and hypertension were identified. Gut microbiota profiling could represent a new tool that characterise intestinal dysbiosis to complement the clinical management of these patients. In particular, the administration of microbial-based treatments, alongside traditional therapies, could help in reducing or preventing the burden of these symptoms and improve the quality of life of these patients.

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.

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.

Deserts represent extreme habitats where photosynthetic life is restricted to the lithic niche. The ability of rock-inhabiting cyanobacteria to modify their photosynthetic apparatus and harvest far-red light (near-infrared) was investigated in 10 strains of the genus Chroococcidiopsis, previously isolated from diverse endolithic and hypolithic desert communities. The analysis of their growth capacity, photosynthetic pigments, and apcE2-gene presence revealed that only Chroococcidiopsis sp. CCMEE 010 was capable of far-red light photoacclimation (FaRLiP). A total of 15 FaRLiP genes were identified, encoding paralogous subunits of photosystem I, photosystem II, and the phycobilisome, along with three regulatory elements. CCMEE 010 is unique among known FaRLiP strains by undergoing this acclimation process with a significantly reduced cluster, which lacks major photosystem I paralogs psaA and psaB. The identification of an endolithic, extremotolerant cyanobacterium capable of FaRLiP not only contributes to our appreciation of this phenotype’s distribution in nature but also has implications for the possibility of oxygenic photosynthesis on exoplanets.