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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.

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.

Clostridioides difficile infection (CDI) is generally treated with vancomycin, metronidazole or fidaxomicin, although fecal microbiota transplantation (FMT) represents a promising therapeutic option for antibiotic-resistant recurrent C. difficile infections (rCDIs) in adults. In pediatric cystic fibrosis (CF) patients, CDIs are generally asymptomatic and respond to treatment. Here, we present the case of an 8-year-old female, initially diagnosed as “CFTR-related metabolic syndrome/cystic fibrosis screen positive, inconclusive diagnosis” (CMRS/CFSPID), who then progressed to CF at 12 months. In the absence of CF-related symptoms, she presented multiple and disabling episodes of bloody diarrhoea with positive tests for C. difficile antigen and A/B toxin. After conventional treatments failed and several CDI relapses, FMT was proposed. Donor screening and GM donor–receiver matching identified her mother as a donor. Metataxonomy and targeted metabolomics provided, through a pre- and post-FMT time course, gut microbiota (GM) profiling to assess GM engraftment. At first, the GM map revealed severe dysbiosis, with a prevalence of Bacteroidetes and Proteobacteria (i.e., Klebsiella spp., Escherichia coli), a reduction in Firmicutes, a GM nearly entirely composed of Enterococcaceae (i.e., Enterococcus) and an almost complete depletion of Verrucomicrobia and Actinobacteria, mostly represented by Veillonella dispar. Post FMT, an increment in Bifidobacterium spp. and Collinsella spp. with a decrease in V. dispar restored intestinal eubiosis. Consistently, four weeks after FMT treatment, the child’s gut symptoms cleared, without CDI recurrence.

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.

Fosetyl-aluminum is a synthetic fungicide administered to plants especially to prevent diseases caused by the members of the Peronosporales and several Phytophthora species. Herein, we present a selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to analyze residues of fosetyl-A1 in air particulate matter. This study was performed in perspective of an exposure assessment of this substance of health concern in environments where high levels of fosetly-Al, relatively to airborne particulate matter, can be found after spraying it. The cleanup procedure of the analyte, from sampled filters of atmospheric particulate matter, was optimized using a Strata X solid-phase extraction cartridge, after accelerated extraction by using water. The chromatographic separation was achieved using a polymeric column based on hydrophilic interaction in step elution with water/acetonitrile, whereas the mass spectrometric detection was performed in negative electrospray ionization. The proposed method resulted to be a simple, fast, and suitable method for confirmation purposes.

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. [ABSTRACT FROM AUTHOR]

Clostridioides difficile infection (CDI) is generally treated with vancomycin, metronidazole or fidaxomicin, although fecal microbiota transplantation (FMT) represents a promising therapeutic option for antibiotic-resistant recurrent C. difficile infections (rCDIs) in adults. In pediatric cystic fibrosis (CF) patients, CDIs are generally asymptomatic and respond to treatment. Here, we present the case of an 8-year-old female, initially diagnosed as "CFTR-related metabolic syndrome/cystic fibrosis screen positive, inconclusive diagnosis" (CMRS/CFSPID), who then progressed to CF at 12 months. In the absence of CF-related symptoms, she presented multiple and disabling episodes of bloody diarrhoea with positive tests for C. difficile antigen and A/B toxin. After conventional treatments failed and several CDI relapses, FMT was proposed. Donor screening and GM donor–receiver matching identified her mother as a donor. Metataxonomy and targeted metabolomics provided, through a pre- and post-FMT time course, gut microbiota (GM) profiling to assess GM engraftment. At first, the GM map revealed severe dysbiosis, with a prevalence of Bacteroidetes and Proteobacteria (i.e., Klebsiella spp., Escherichia coli), a reduction in Firmicutes, a GM nearly entirely composed of Enterococcaceae (i.e., Enterococcus) and an almost complete depletion of Verrucomicrobia and Actinobacteria, mostly represented by Veillonella dispar. Post FMT, an increment in Bifidobacterium spp. and Collinsella spp. with a decrease in V. dispar restored intestinal eubiosis. Consistently, four weeks after FMT treatment, the child's gut symptoms cleared, without CDI recurrence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata. Methods: Fecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC–MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns. Results: The GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors. Conclusion: Our results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors. [ABSTRACT FROM AUTHOR]

Mycotoxins are chemical contaminants that are invisible, tasteless, chemically stable and survive food processing. Contamination along the agri-food chain is difficult to control since their production and spreading are due to numerous factors including temperature, relative humidity, insect infestation, and susceptibility of the host plant. This is a pilot study which aims at assessing the contamination level of deoxynivalenol (DON), and its plant metabolites (3AcDON, 15 AcDON, DON 3G), nivalenol, T-2 and HT-2 toxins, and ochratoxin A in thirty-seven traditional varieties of Ethiopian durum wheat, and aflatoxins B1, B2, G1, and G2 in thirty-one varieties of Ugandan groundnuts grown in non-intensive rainfed production systems. Results indicate absence of mycotoxin contamination in all durum wheat samples and negligible levels of contamination (below the maximum levels tolerated by international standards) in groundnut samples. Further studies are required to assess if non-intensive production systems and varieties have a role in preventing and/or reducing mycotoxin contamination of the crops. [ABSTRACT FROM AUTHOR]