Buty and the beast: the complex role of butyrate in Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disease which is often associated with gastrointestinal (GI) dysfunction. The GI tract is home to a wide range of microorganisms, among which bacteria, that can influence the host through various mechanisms. Products produced by these bacteria can act in the gut but can also exert effects in the brain via what is now well established to be the microbiota-gut-brain axis. In those with PD the gut-bacteria composition is often found to be different to that of non-PD individuals. In addition to compositional changes, the metabolic activity of the gut-microbiota is also changed in PD. Specifically, it is often reported that key producers of short chain fatty acids (SCFAs) as well as the concentration of SCFAs themselves are altered in the stool and blood of those with PD. These SCFAs, among which butyrate, are essential nutrients for the host and are a major energy source for epithelial cells of the GI tract. Additionally, butyrate plays a key role in regulating various host responses particularly in relation to inflammation. Studies have demonstrated that a reduction in butyrate levels can have a critical role in the onset and progression of PD. Furthermore, it has been shown that restoring butyrate levels in those with PD through methods such as probiotics, prebiotics, sodium butyrate supplementation, and fecal transplantation can have a beneficial effect on both motor and non-motor outcomes of the disease. This review presents an overview of evidence for the altered gut-bacteria composition and corresponding metabolite production in those with PD, with a particular focus on the SCFA butyrate. In addition to presenting current studies regarding SCFA in clinical and preclinical reports, evidence for the possibility to target butyrate production using microbiome based approaches in a therapeutic context is discussed.

Exhaled Volatile Organic Compounds for Asthma Control Classification in Children with Moderate to Severe Asthma: Results from the SysPharmPediA Study.

RATIONALE: Early identification of children with poorly controlled asthma is imperative for optimizing treatment strategies. The analysis of exhaled volatile organic compounds (VOCs) is an emerging approach to identify prognostic and diagnostic biomarkers in pediatric asthma. OBJECTIVES: To assess the accuracy of gas chromatography-mass spectrometry based exhaled metabolite analysis to differentiate between controlled and uncontrolled pediatric asthma. METHODS: This study encompassed a discovery (SysPharmPediA) and validation phase (U-BIOPRED, PANDA). Firstly, exhaled VOCs that discriminated asthma control levels were identified. Subsequently, outcomes were validated in two independent cohorts. Patients were classified as controlled or uncontrolled, based on asthma control test scores and number of severe attacks in the past year. Additionally, potential of VOCs in predicting two or more future severe asthma attacks in SysPharmPediA was evaluated. MEASUREMENTS AND MAIN RESULTS: Complete data were available for 196 children (SysPharmPediA=100, U-BIOPRED=49, PANDA=47). In SysPharmPediA, after randomly splitting the population into training (n=51) and test sets (n=49), three compounds (acetophenone, ethylbenzene, and styrene) distinguished between uncontrolled and controlled asthmatics. The area under the receiver operating characteristic curve (AUROCC) for training and test sets were respectively: 0.83 (95% CI: 0.65-1.00) and 0.77 (95% CI: 0.58-0.96). Combinations of these VOCs resulted in AUROCCs of 0.74 ±0.06 (UBIOPRED) and 0.68 ±0.05 (PANDA). Attacks prediction tests, resulted in AUROCCs of 0.71 (95% CI 0.51-0.91) and 0.71 (95% CI 0.52-0.90) for training and test sets. CONCLUSIONS: Exhaled metabolites analysis might enable asthma control classification in children. This should stimulate further development of exhaled metabolites-based point-of-care tests in asthma.

Prebiotic diet normalizes aberrant immune and behavioral phenotypes in a mouse model of autism spectrum disorder.

Autism spectrum disorder (ASD) is a cluster of neurodevelopmental disorders characterized by deficits in communication and behavior. Increasing evidence suggests that the microbiota-gut-brain axis and the likely related immune imbalance may play a role in the development of this disorder. Gastrointestinal deficits and gut microbiota dysfunction have been linked to the development or severity of autistic behavior. Therefore, treatments that focus on specific diets may improve gastrointestinal function and aberrant behavior in individuals with ASD. In this study, we investigated whether a diet containing specific prebiotic fibers, namely, 3% galacto-oligosaccharide/fructo-oligosaccharide (GOS/FOS; 9:1), can mitigate the adverse effects of in utero exposure to valproic acid (VPA) in mice. Pregnant BALB/cByJ dams were injected with VPA (600 mg/kg, sc.) or phosphate-buffered saline (PBS) on gestational day 11 (G11). Male offspring were divided into four groups: (1) in utero PBS-exposed with a control diet, (2) in utero PBS-exposed with GOS/FOS diet, (3) in utero VPA-exposed with a control diet, and (4) in utero VPA-exposed with GOS/FOS diet. Dietary intervention started from birth and continued throughout the duration of the experiment. We showed that the prebiotic diet normalized VPA-induced alterations in male offspring, including restoration of key microbial taxa, intestinal permeability, peripheral immune homeostasis, reduction of neuroinflammation in the cerebellum, and impairments in social behavior and cognition in mice. Overall, our research provides valuable insights into the gut-brain axis involvement in ASD development. In addition, dietary interventions might correct the disbalance in gut microbiota and immune responses and, ultimately, might improve detrimental behavioral outcomes in ASD.

Food, nutrition, and autism: from soil to fork.

Food and nutrition-related factors have the potential to impact development of autism spectrum disorder (ASD) and quality of life for people with ASD, but gaps in evidence exist. On 10 November 2022, Tufts University's Friedman School of Nutrition Science and Policy and Food and Nutrition Innovation Institute hosted a 1-d meeting to explore the evidence and evidence gaps regarding the relationships of food and nutrition with ASD. This meeting report summarizes the presentations and deliberations from the meeting. Topics addressed included prenatal and child dietary intake, the microbiome, obesity, food-related environmental exposures, mechanisms and biological processes linking these factors and ASD, food-related social factors, and data sources for future research. Presentations highlighted evidence for protective associations with prenatal folic acid supplementation and ASD development, increases in risk of ASD with maternal gestational obesity, and the potential for exposure to environmental contaminants in foods and food packaging to influence ASD development. The importance of the maternal and child microbiome in ASD development or ASD-related behaviors in the child was reviewed, as was the role of discrimination in leading to disparities in environmental exposures and psychosocial factors that may influence ASD. The role of child diet and high prevalence of food selectivity in children with ASD and its association with adverse outcomes were also discussed. Priority evidence gaps identified by participants include further clarifying ASD development, including biomarkers and key mechanisms; interactions among psychosocial, social, and biological determinants; interventions addressing diet, supplementation, and the microbiome to prevent and improve quality of life for people with ASD; and mechanisms of action of diet-related factors associated with ASD. Participants developed research proposals to address the priority evidence gaps. The workshop findings serve as a foundation for future prioritization of scientific research to address evidence gaps related to food, nutrition, and ASD.

A robust microbiome signature for autism spectrum disorder across different studies using machine learning.

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by deficits in sociability and repetitive behaviour, however there is a great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as a plausible contributing factor for ASD development as individuals diagnosed with ASD often suffer from intestinal problems and show a differentiated intestinal microbial composition. Nevertheless, gut microbiome studies in ASD rarely agree on the specific bacterial taxa involved in this disorder. Regarding the potential role of gut microbiome in ASD pathophysiology, our aim is to investigate whether there is a set of bacterial taxa relevant for ASD classification by using a sibling-controlled dataset. Additionally, we aim to validate these results across two independent cohorts as several confounding factors, such as lifestyle, influence both ASD and gut microbiome studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied to 16S rRNA gene sequencing data from 117 subjects (60 ASD cases and 57 siblings) identifying 26 bacterial taxa that discriminate ASD cases from controls. The average area under the curve (AUC) of this specific set of bacteria in the sibling-controlled dataset was 81.6%. Moreover, we applied the selected bacterial taxa in a tenfold cross-validation scheme using two independent cohorts (a total of 223 samples-125 ASD cases and 98 controls). We obtained average AUCs of 74.8% and 74%, respectively. Analysis of the gut microbiome using REFS identified a set of bacterial taxa that can be used to predict the ASD status of children in three distinct cohorts with AUC over 80% for the best-performing classifiers. Our results indicate that the gut microbiome has a strong association with ASD and should not be disregarded as a potential target for therapeutic interventions. Furthermore, our work can contribute to use the proposed approach for identifying microbiome signatures across other 16S rRNA gene sequencing datasets.

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning.

BACKGROUND: In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research. RESULTS: Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods. CONCLUSIONS: We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma.

BACKGROUND: Not being well controlled by therapy with inhaled corticosteroids and long-acting ß2 agonist bronchodilators is a major concern for severe-asthma patients. The current treatment option for these patients is the use of biologicals such as anti-IgE treatment, omalizumab, as an add-on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response. METHODS: Two novel computational algorithms, machine-learning based Recursive Ensemble Feature Selection (REFS) and rule-based algorithm Logic Explainable Networks (LEN), were used on open accessible mRNA expression data from moderate-to-severe asthma patients to identify genes as predictors of omalizumab response. RESULTS: With REFS, the number of features was reduced from 28,402 genes to 5 genes while obtaining a cross-validated accuracy of 0.975. The 5 responsiveness predictive genes encode the following proteins: Coiled-coil domain- containing protein 113 (CCDC113), Solute Carrier Family 26 Member 8 (SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113, SLC26A8 PPP1R3D and LOC100131780. Literature research showed that the 4 identified responsiveness predicting genes are associated with mucosal immunity, cell metabolism, and airway remodeling. CONCLUSION AND CLINICAL RELEVANCE: Both computational methods show 4 identical genes as predictors of omalizumab response in moderate-to-severe asthma patients. The obtained high accuracy indicates that our approach has potential in clinical settings. Future studies in relevant cohort data should validate our computational approach.

An Innovative AI-based primer design tool for precise and accurate detection of SARS-CoV-2 variants of concern.

As the COVID-19 pandemic winds down, it leaves behind the serious concern that future, even more disruptive pandemics may eventually surface. One of the crucial steps in handling the SARS-CoV-2 pandemic was being able to detect the presence of the virus in an accurate and timely manner, to then develop policies counteracting the spread. Nevertheless, as the pandemic evolved, new variants with potentially dangerous mutations appeared. Faced by these developments, it becomes clear that there is a need for fast and reliable techniques to create highly specific molecular tests, able to uniquely identify VOCs. Using an automated pipeline built around evolutionary algorithms, we designed primer sets for SARS-CoV-2 (main lineage) and for VOC, B.1.1.7 (Alpha) and B.1.1.529 (Omicron). Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for the main lineage and each variant in a matter of hours. Preliminary in-silico validation showed that the sequences in the primer sets featured high accuracy. A pilot test in a laboratory setting confirmed the results: the developed primers were favorably compared against existing commercial versions for the main lineage, and the specific versions for the VOCs B.1.1.7 and B.1.1.529 were clinically tested successfully.

The relationship between immune fitness and saliva biomarkers of systemic inflammation.

Purpose: It is vital that Immune fitness, i.e., how well the immune system functions and reacts to challenges, can be reliably be examined. The current study aimed to compare immune fitness with assessments of saliva biomarkers of systemic inflammation. Methods: N = 108 healthy young adults (18-30-year-old students of Utrecht University, the Netherlands) participated in the study. A saliva sample was collected for biomarker assessment (Interleukin (IL)-1ß, IL-6, IL-8, IL-10, immunoglobulin A (IgA), and tumor necrosis factor-alpha (TNF-α), and c-reactive protein (CRP). Additionally, a survey was completed to assess immune fitness, mood, mental resilience, and quality of life. The correlations between the biomarker assessments, immune fitness and mood were determined. Results: No significant correlations between immune fitness and biomarkers of systemic inflammation were found. Significant sex differences in correlations with immune fitness were demonstrated for loneliness (significant only in men) and fatigue (significant only in women). For both sexes, immune fitness correlated significantly with anxiety, mental resilience, and quality of life. Conclusion: No significant correlations were found between immune fitness and saliva biomarkers of systemic inflammation. Immune fitness correlated significantly with anxiety, mental resilience, and quality of life. Sex differences were demonstrated in the relation of immune fitness with loneliness and fatigue. Future research should further investigate factors that may influence the relationship between immune fitness, mood, and biomarkers of systemic inflammation, including underlying psychological mechanisms of possible sex differences.

Oropharyngeal Microbiota Clusters in Children with Asthma or Wheeze Associate with Allergy, Blood Transcriptomic Immune Pathways, and Exacerbation Risk.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis ß-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-ß (transforming growth factor-ß) (highest in the Veillonella cluster) and Wnt/ß-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

A multiscale hybrid model for exploring the effect of Resolvin D1 on macrophage polarization during acute inflammation.

Dysregulated inflammation underlies various diseases. Specialized pro-resolving mediators (SPMs) like Resolvin D1 (RvD1) have been shown to resolve inflammation and halt disease progression. Macrophages, key immune cells that drive inflammation, respond to the presence of RvD1 by polarizing to an anti-inflammatory type (M2). However, RvD1's mechanisms, roles, and utility are not fully understood. This paper introduces a gene-regulatory network (GRN) model that contains pathways for RvD1 and other SPMs and proinflammatory molecules like lipopolysaccharides. We couple this GRN model to a partial differential equation-agent-based hybrid model using a multiscale framework to simulate an acute inflammatory response with and without the presence of RvD1. We calibrate and validate the model using experimental data from two animal models. The model reproduces the dynamics of key immune components and the effects of RvD1 during acute inflammation. Our results suggest RvD1 can drive macrophage polarization through the G protein-coupled receptor 32 (GRP32) pathway. The presence of RvD1 leads to an earlier and increased M2 polarization, reduced neutrophil recruitment, and faster apoptotic neutrophil clearance. These results support a body of literature that suggests that RvD1 is a promising candidate for promoting the resolution of acute inflammation. We conclude that once calibrated and validated on human data, the model can identify critical sources of uncertainty, which could be further elucidated in biological experiments and assessed for clinical use.

Epigenome-Wide Association Studies of the Fractional Exhaled Nitric Oxide and Bronchodilator Drug Response in Moderate-to-Severe Pediatric Asthma.

Asthma is the most prevalent pediatric chronic disease. Bronchodilator drug response (BDR) and fractional exhaled nitric oxide (FeNO) are clinical biomarkers of asthma. Although DNA methylation (DNAm) contributes to asthma pathogenesis, the influence of DNAm on BDR and FeNO is scarcely investigated. This study aims to identify DNAm markers in whole blood associated either with BDR or FeNO in pediatric asthma. We analyzed 121 samples from children with moderate-to-severe asthma. The association of genome-wide DNAm with BDR and FeNO has been assessed using regression models, adjusting for age, sex, ancestry, and tissue heterogeneity. Cross-tissue validation was assessed in 50 nasal samples. Differentially methylated regions (DMRs) and enrichment in traits and biological pathways were assessed. A false discovery rate (FDR) < 0.1 and a genome-wide significance threshold of p < 9 × 10-8 were used to control for false-positive results. The CpG cg12835256 (PLA2G12A) was genome-wide associated with FeNO in blood samples (coefficient= -0.015, p = 2.53 × 10-9) and nominally associated in nasal samples (coefficient = -0.015, p = 0.045). Additionally, three CpGs were suggestively associated with BDR (FDR < 0.1). We identified 12 and four DMRs associated with FeNO and BDR (FDR < 0.05), respectively. An enrichment in allergic and inflammatory processes, smoking, and aging was observed. We reported novel associations of DNAm markers associated with BDR and FeNO enriched in asthma-related processes.

Amino Acid-Metabolizing Enzymes in Advanced High-Grade Serous Ovarian Cancer Patients: Value of Ascites as Biomarker Source and Role for IL4I1 and IDO1.

The molecular mechanisms contributing to immune suppression in ovarian cancer are not well understood, hampering the successful application of immunotherapy. Amino acid-metabolizing enzymes are known to contribute to the immune-hostile environment of various tumors through depletion of amino acids and production of immunosuppressive metabolites. We aimed to collectively evaluate the activity of these enzymes in high-grade serous ovarian cancer patients by performing targeted metabolomics on plasma and ascites samples. Whereas no indication was found for enhanced l-arginine or l-glutamine metabolism by immunosuppressive enzymes in ovarian cancer patients, metabolism of l-tryptophan by indoleamine 2,3-dioxygenase 1 (IDO1) was significantly elevated compared to healthy controls. Moreover, high levels of l-phenylalanine- and l-tyrosine-derived metabolites associated with interleukin 4 induced 1 (IL4I1) activity were found in ovarian cancer ascites samples. While l-tryptophan is a major substrate of both IDO1 and IL4I1, only its enhanced conversion into l-kynurenine by IDO1 could be detected, despite the observed activity of IL4I1 on its other substrates. In ascites of ovarian cancer patients, metabolite levels were higher compared to those in plasma, demonstrating the value of utilizing this fluid for biomarker identification. Finally, elevated metabolism of l-phenylalanine and l-tyrosine by IL4I1 correlated with disease stage, pointing towards a potential role for IL4I1 in ovarian cancer progression.

Reduced Immune Fitness and Job Performance: Absenteeism, Presenteeism, and Associated Costs for the Dutch Economy.

Reduced immune fitness can have a significant negative impact on work performance. The aim of the current study was to evaluate the impact of reduced immune fitness on job performance and associated costs for the Dutch economy. Data from n = 425 Dutch working adults (18-65 years old) who completed an online survey were analyzed to evaluate the number of days of absenteeism (not going to work) and presenteeism (working while sick) due to reduced immune fitness, and the performance level on days worked with reduced immune fitness. Data from for the year 2019 were analyzed. Participants reported 2.9 absenteeism days and 19 presenteeism days, with an average performance reduction of 22.8% when working on days with reduced immune fitness. Significantly more days of absenteeism and presenteeism were reported by women, individuals with a poorer immune fitness, and those with underlying disease. Performance impairment on days worked while experiencing reduced immune fitness was significantly greater among individuals with a younger age at the junior career level, those with underlying disease, and among highly educated individuals. The associated costs of reduced immune fitness were estimated at €4.3 billion for absenteeism and €6.4 billion for presenteeism. Together, the costs of reduced immune fitness for the Dutch economy in 2019 were estimated at €10.7 billion. These findings demonstrate that reduced immune fitness has a significant negative impact on the Dutch economy.

Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate-to-severe asthma.

BACKGROUND: Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics; however, its association with asthma control has not been explored. We aimed to investigate whether the gastrointestinal microbiome can be used to discriminate between uncontrolled and controlled asthma in children. METHODS: 143 and 103 feces samples were obtained from 143 children with moderate-to-severe asthma aged 6 to 17 years from the SysPharmPediA study. Patients were classified as controlled or uncontrolled asthmatics, and their microbiome at species level was compared using global (alpha/beta) diversity, conventional differential abundance analysis (DAA, analysis of compositions of microbiomes with bias correction), and machine learning [Recursive Ensemble Feature Selection (REFS)]. RESULTS: Global diversity and DAA did not find significant differences between controlled and uncontrolled pediatric asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 86% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples. CONCLUSION: Controlled and uncontrolled children with asthma can be differentiated based on their gastrointestinal microbiome using machine learning, specifically REFS. Our results show an association between asthma control and the gastrointestinal microbiome. This suggests that the gastrointestinal microbiome may be a potential biomarker for treatment responsiveness and thereby help to improve asthma control in children.

The interaction between intestinal bacterial metabolites and phosphatase and tensin homolog in autism spectrum disorder.

Intestinal bacteria-associated para-cresyl sulfate (pCS) and 4-ethylphenyl sulfate (4EPS) are elevated in autism spectrum disorder (ASD). Both metabolites can induce ASD-like behaviors in mice, but the molecular mechanisms are not known. Phosphatase and tensin homolog (PTEN) is a susceptibility gene for ASD. The present study investigated the relation between pCS and 4EPS and PTEN in ASD in a valproic acid (VPA)-induced murine ASD model and an in vitro LPS-activated microglial model. The VPA-induced intestinal inflammation and compromised permeability in the distal ileum was not associated with changes of PTEN expression and phosphorylation. In contrast, VPA reduced PTEN expression in the hippocampus of mice. In vitro results show that pCS and 4EPS reduced PTEN expression and derailed innate immune response of BV2 microglial cells. The PTEN inhibitor VO-OHpic did not affect innate immune response of microglial cells. In conclusion, PTEN does not play a role in intestinal inflammation and compromised permeability in VPA-induced murine model for ASD. Although pCS and 4EPS reduced PTEN expression in microglial cells, PTEN is not involved in the pCS and 4EPS-induced derailed innate immune response of microglial cells. Further studies are needed to investigate the possible involvement of reduced PTEN expression in the ASD brain regarding synapse function and neuronal connectivity.

Medication use in uncontrolled pediatric asthma: Results from the SysPharmPediA study.

BACKGROUND: Uncontrolled pediatric asthma has a large impact on patients and their caregivers. More insight into determinants of uncontrolled asthma is needed. We aim to compare treatment regimens, inhaler techniques, medication adherence and other characteristics of children with controlled and uncontrolled asthma in the: Systems Pharmacology approach to uncontrolled Paediatric Asthma (SysPharmPediA) study. MATERIAL AND METHODS: 145 children with moderate to severe doctor-diagnosed asthma (91 uncontrolled and 54 controlled) aged 6-17 years were enrolled in this multicountry, (Germany, Slovenia, Spain, and the Netherlands) observational, case-control study. The definition of uncontrolled asthma was based on asthma symptoms and/or exacerbations in the past year. Patient-reported adherence and clinician-reported medication use were assessed, as well as lung function and inhalation technique. A logistic regression model was fitted to assess determinants of uncontrolled pediatric asthma. RESULTS: Children in higher asthma treatment steps had a higher risk of uncontrolled asthma (OR (95%CI): 3.30 (1.56-7.19)). The risk of uncontrolled asthma was associated with a larger change in FEV1% predicted post and pre-salbutamol (OR (95%CI): 1.08 (1.02-1.15)). Adherence and inhaler techniques were not associated with risk of uncontrolled asthma in this population. CONCLUSION: This study showed that children with uncontrolled moderate-to-severe asthma were treated in higher treatment steps compared to their controlled peers, but still showed a higher reversibility response to salbutamol. Self-reported adherence and inhaler technique scores did not differ between controlled and uncontrolled asthmatic children. Other determinants, such as environmental factors and differences in biological profiles, may influence the risk of uncontrolled asthma in this moderate to severe asthmatic population.

Effects of a nutritional intervention on impaired behavior and cognitive function in an emphysematous murine model of COPD with endotoxin-induced lung inflammation.

One cluster of the extrapulmonary manifestations in chronic obstructive pulmonary disease (COPD) is related to the brain, which includes anxiety, depression and cognitive impairment. Brain-related comorbidities are related to worsening of symptoms and increased mortality in COPD patients. In this study, a murine model of COPD was used to examine the effects of emphysema and repetitive pulmonary inflammatory events on systemic inflammatory outcomes and brain function. In addition, the effect of a dietary intervention on brain-related parameters was assessed. Adult male C57Bl/6J mice were exposed to elastase or vehicle intratracheally (i.t.) once a week on three consecutive weeks. Two weeks after the final administration, mice were i.t. exposed to lipopolysaccharide (LPS) or vehicle for three times with a 10 day interval. A dietary intervention enriched with omega-3 PUFAs, prebiotic fibers, tryptophan and vitamin D was administered from the first LPS exposure onward. Behavior and cognitive function, the degree of emphysema and both pulmonary and systemic inflammation as well as blood-brain barrier (BBB) integrity and neuroinflammation in the brain were assessed. A lower score in the cognitive test was observed in elastase-exposed mice. Mice exposed to elastase plus LPS showed less locomotion in the behavior test. The enriched diet seemed to reduce anxiety-like behavior over time and cognitive impairments associated with the presented COPD model, without affecting locomotion. In addition, the enriched diet restored the disbalance in splenic T-helper 1 (Th1) and Th2 cells. There was a trend toward recovering elastase plus LPS-induced decreased expression of occludin in brain microvessels, a measure of BBB integrity, as well as improving expression levels of kynurenine pathway markers in the brain by the enriched diet. The findings of this study demonstrate brain-associated comorbidities - including cognitive and behavioral impairments - in this murine model for COPD. Although no changes in lung parameters were observed, exposure to the specific enriched diet in this model appeared to improve systemic immune disbalance, BBB integrity and derailed kynurenine pathway which may lead to reduction of anxiety-like behavior and improved cognition.

Human Milk Oligosaccharide 2'-Fucosyllactose Inhibits Ligand Binding to C-Type Lectin DC-SIGN but Not to Langerin.

Human milk oligosaccharides (HMOs) and their most abundant component, 2'-Fucosyllactose (2'-FL), are known to be immunomodulatory. Previously, it was shown that HMOs and 2'-FL bind to the C-type lectin receptor DC-SIGN. Here we show, using a ligand-receptor competition assay, that a whole mixture of HMOs from pooled human milk (HMOS) and 2'-FL inhibit the binding of the carbohydrate-binding receptor DC-SIGN to its prototypical ligands, fucose and the oligosaccharide Lewis-B, (Leb) in a dose-dependent way. Interestingly, such inhibition by HMOS and 2'-FL was not detected for another C-type lectin, langerin, which is evolutionarily similar to DC-SIGN. The cell-ligand competition assay using DC-SIGN expressing cells confirmed that 2'-FL inhibits the binding of DC-SIGN to Leb. Molecular dynamic (MD) simulations show that 2'-FL exists in a preorganized bioactive conformation before binding to DC-SIGN and this conformation is retained after binding to DC-SIGN. Leb has more flexible conformations and utilizes two binding modes, which operate one at a time via its two fucoses to bind to DC-SIGN. Our hypothesis is that 2'-FL may have a reduced entropic penalty due to its preorganized state, compared to Leb, and it has a lower binding enthalpy, suggesting a better binding to DC-SIGN. Thus, due to the better binding to DC-SIGN, 2'-FL may replace Leb from its binding pocket in DC-SIGN. The MD simulations also showed that 2'-FL does not bind to langerin. Our studies confirm 2'-FL as a specific ligand for DC-SIGN and suggest that 2'-FL can replace other DC-SIGN ligands from its binding pocket during the ligand-receptor interactions in possible immunomodulatory processes.

The Autism Spectrum Disorder-Associated Bacterial Metabolite p-Cresol Derails the Neuroimmune Response of Microglial Cells Partially via Reduction of ADAM17 and ADAM10.

The bacterial metabolite 4-methylphenol (para-cresol or p-cresol) and its derivative p-cresyl sulfate (pCS) are elevated in the urine and feces of children with autism spectrum disorder (ASD). It has been shown that p-cresol administration induces social behavior deficits and repetitive behavior in mice. However, the mechanisms of p-cresol, specifically its metabolite pCS that can reach the brain, in ASD remain to be investigated. The pCS has been shown to inhibit LPS-stimulated inflammatory response. A Disintegrin And Metalloprotease 10 (ADAM10) and A Disintegrin And Metalloprotease 17 (ADAM17) are thought to regulate microglial immune response by cleaving membrane-bound proteins. In the present study, a neuroinflammation model of LPS-activated BV2 microglia has been used to unveil the potential molecular mechanism of pCS in ASD pathogenesis. In microglial cells pCS treatment decreases the expression or maturation of ADAM10 and ADAM17. In addition, pCS treatment attenuates TNF-α and IL-6 releases as well as phagocytosis activity of microglia. In in vitro ADAM10/17 inhibition experiments, either ADAM10 or ADAM17 inhibition reduces constitutive and LPS-activated release of TNF-α, TNFR-1 and IL-6R by microglial cells, while it increases constitutive and LPS-activated microglial phagocytotic activity. The in vivo results further confirm the involvement of ADAM10 and ADAM17 in ASD pathogenesis. In in utero VPA-exposed male mice, elevated concentration in serum of p-cresol-associated metabolites pCS and p-cresyl glucuronide (pCG) is associated with a VPA-induced increased ADAM10 maturation, and a decreased ADAM17 maturation that is related with attenuated levels of soluble TNF-α and TGF-ß1 in the mice brain. Overall, the present study demonstrates a partial role of ADAM10 and ADAM17 in the derailed innate immune response of microglial cells associated with pCS-induced ASD pathogenesis.