Integrated gut metabolome and microbiome fingerprinting reveals that dysbiosis precedes allergic inflammation in IgE-mediated pediatric cow's milk allergy.

BACKGROUND: IgE-mediated cow's milk allergy (IgE-CMA) is one of the first allergies to arise in early childhood and may result from exposure to various milk allergens, of which ß-lactoglobulin (BLG) and casein are the most important. Understanding the underlying mechanisms behind IgE-CMA is imperative for the discovery of novel biomarkers and the design of innovative treatment and prevention strategies. METHODS: We report a longitudinal in vivo murine model, in which two mice strains (BALB/c and C57Bl/6) were sensitized to BLG using either cholera toxin or an oil emulsion (n = 6 per group). After sensitization, mice were challenged orally, their clinical signs monitored, antibody (IgE and IgG1) and cytokine levels (IL-4 and IFN-γ) measured, and fecal samples subjected to metabolomics. The results of the murine models were further extrapolated to fecal microbiome-metabolome data from our population of IgE-CMA (n = 22) and healthy (n = 23) children (Trial: NCT04249973), on which polar metabolomics, lipidomics and 16S rRNA metasequencing were performed. In vitro gastrointestinal digestions and multi-omics corroborated the microbial origin of proposed metabolic changes. RESULTS: During mice sensitization, we observed multiple microbially derived metabolic alterations, most importantly bile acid, energy and tryptophan metabolites, that preceded allergic inflammation. We confirmed microbial dysbiosis, and its associated effect on metabolic alterations in our patient cohort, through in vitro digestions and multi-omics, which was accompanied by metabolic signatures of low-grade inflammation. CONCLUSION: Our results indicate that gut dysbiosis precedes allergic inflammation and nurtures a chronic low-grade inflammation in children on elimination diets, opening important new opportunities for future prevention and treatment strategies.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study.

BACKGROUND: Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. METHODS: RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. RESULTS: Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. CONCLUSION: Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Urinary excretion of polyols and sugars in children with chronic kidney disease.

BACKGROUND: The urinary concentrations of monosaccharides and polyols are used for diagnosing inborn errors of metabolism and renal tubular disorders. Reference values are age-related and depend on the method of detection. However, the influence of the renal function is often still neglected. In this study we examined the urinary excretion of monosaccharides and polyols in children with various degrees of chronic kidney disease (CKD), but with no known metabolic or renal tubular disorders. CASE DIAGNOSIS/TREATMENT: In 25 patients with CKD stage 1-5, urinary concentrations of 18 monosaccharides and polyols were measured by gas chromatography-mass spectrometry (GC-MS) in random urinary samples and were compared with age-related reference values. Serum creatinine was measured at the time of the urine sample, and the height-independent estimated glomerular filtration rate (eGFR-Pottel) was calculated. Urinary excretions of monosaccharides and polyols were above the reference values in 8-88% of all patients. A significant difference between CKD stage 1-2 compared with CKD stage 3-5 was found for allose, arabitol and sorbitol (p < 0.05) and for arabinose, fucose, myoinositol, ribitol, xylitol, and xylose (p < 0.01). CONCLUSIONS: We show that the excretion of polyols and sugars depends on eGFR, which warrants a cautious interpretation of the results in patients with CKD.

Thrombolysis for Severe Intestinal Ischemia due to Midgut Volvulus in a Neonate.

Intestinal malrotation complicated by the development of midgut volvulus presents a difficult management dilemma because of the risk of short bowel syndrome. Here, we present our experience with a case of severe intestinal ischemia after derotation in a newborn successfully managed using systemic tissue plasminogen activator. The present report supports the usefulness of thrombolysis as a therapeutic option for reperfusion of ischemic small bowel due to midgut volvulus in neonates.