1H-NMR-Based Metabolomics in Autism Spectrum Disorder and Pediatric Acute-Onset Neuropsychiatric Syndrome

We recently described a unique plasma metabolite profile in subjects with pediatric acute-onset neuropsychiatric syndrome (PANS), suggesting pathogenic models involving specific patterns of neurotransmission, neuroinflammation, and oxidative stress. Here, we extend the analysis to a group of patients with autism spectrum disorder (ASD), as a consensus has recently emerged around its immune-mediated pathophysiology with a widespread involvement of brain networks. This observational case-control study enrolled patients referred for PANS and ASD from June 2019 to May 2020, as well as neurotypical age and gender-matched control subjects. Thirty-four PANS outpatients, fifteen ASD outpatients, and twenty-five neurotypical subjects underwent physical and neuropsychiatric evaluations, alongside serum metabolomic analysis with 1H-NMR. In supervised models, the metabolomic profile of ASD was significantly different from controls (p = 0.0001), with skewed concentrations of asparagine, aspartate, betaine, glycine, lactate, glucose, and pyruvate. Metabolomic separation was also observed between PANS and ASD subjects (p = 0.02), with differences in the concentrations of arginine, aspartate, betaine, choline, creatine phosphate, glycine, pyruvate, and tryptophan. We confirmed a unique serum metabolomic profile of PANS compared with both ASD and neurotypical subjects, distinguishing PANS as a pathophysiological entity per se. Tryptophan and glycine appear as neuroinflammatory fingerprints of PANS and ASD, respectively. In particular, a reduction in glycine would primarily affect NMDA-R excitatory tone, overall impairing downstream glutamatergic, dopaminergic, and GABAergic transmissions. Nonetheless, we found metabolomic similarities between PANS and ASD that suggest a putative role of N-methyl-D-aspartate receptor (NMDA-R) dysfunction in both disorders. Metabolomics-based approaches could contribute to the identification of novel ASD and PANS biomarkers

Pediatric Acute-onset Neuropsychiatric Syndrome (PANS): new insights

Introduction - Pediatric Acute-onset Neuropsychiatric Syndrome (PANS) is a clinically heterogeneous disorder presenting with: unusually abrupt onset of obsessive-compulsive disorder (OCD) or severe eating restrictions, with at least two concomitant cognitive, behavioral, or affective symptoms such as anxiety, obsessive-compulsive behavior, and irritability/depression. PANS consists of the expression of various serological alterations sustained by a supposed autoimmune/inflammatory disease. Sleep disorders represent one of the most frequent manifestations of PANS (around 80%). Objectives - The Study N.1 describes the clinical and laboratory variables of 39 PANS children. The Study N.2 describes the clinical and polysomnographic features of 23 PANS children identifying the relationships between sleep disorders and other PANS symptoms. The Study N.3 explores potential serum biomarkers in 34 PANS patients and 25 neurotypical subjects through the metabolomics approach. Study N.1 - Methods: Using the Artificial Neural Networks (ANNs) analysis, the putative associations between PANS working criteria were explored by the Auto Contractive Map (Auto-CM) system, a mapping method able to compute the multidimensional association of strength of each variable with all other variables. Results: The PANS symptoms were strictly linked to one another on the semantic connectivity map, shaping a central ‘‘diamond’’ encompassing anxiety, irritability/oppositional defiant disorder symptoms, OCD, behavioral regression, sensory motor abnormalities, school performance deterioration, sleep disturbances, and emotional lability/depression. The map also showed that the emotional lability/depression resulted as a highly connected hub linked to autoimmune disease in pregnancy, allergic and atopic disorders, and low Natural Killer percentage. Also anxiety symptoms were shown to be strongly related with recurrent infectious disease. Conclusion: It was shown a very specific constellation of symptoms having strong links to laboratory and clinical variables consistent with PANS feature. Study N.2 - Methods: It describes both clinical and polysomnographic variables and studies the relationships between them using a data mining approach with ANNs analysis. Results: Polysomnography showed abnormality in 17 out 23 subjects. In particular, 8/17 children had ineffective sleep, 10/17 fragmented sleep, 47.1% Periodic Limb Movement Disorder (PLMD) and 64.7% REM-Sleep Without Atonia (RSWA). Most patients had more than one sleep disorder. Among the 19/23 patients with Tic/Tourette Disorder, 8/19 showed PLMD and 10/19 RSWA. ANNs analysis and the Auto-CM exploited the links among the spectrum of variables revealing the simultaneous connections among them. Conclusion: Sleep disorders represent, for prevalence and impact on quality of life, a cardinal symptom in patients with PANS with a potential impact on the prognosis of this condition. Study N.3 - Methods: Serum samples were obtained from each patient/subject and were analyzed through the Nuclear Magnetic Resonance Spectroscopy. Subsequently, multivariate and univariate statistical analyses, as well as Receiving Operator Curves (ROC) were performed. Results: Significant differences in the concentrations of several metabolites were observed, suggesting the involvement of specific patterns of neurotransmission (tryptophan, glycine, histamine/histidine) as well as a more general state of neuroinflammation and oxidative stress (glutamine, 2-Hydroxybutyrate and tryptophan-kynurenine pathway) in the disorder. Conclusions: It was found a unique plasma metabolic profile in PANS patients, significantly different from healthy children. This metabolomics study offers new insights into biological mechanisms implicated in PANS

Atypical carcinoid and large cell neuroendocrine carcinoma of the lung: a proteomic dataset from formalin-fixed archival samples

Here we present a dataset generated using formalin-fixed paraffin-embedded archival samples from two rare lung neuroendocrine tumor subtypes (namely, two atypical carcinoids, ACs, and two large-cell neuroendocrine carcinomas, LCNECs). Samples were subjected to a shotgun proteomics pipeline, comprising full-length protein extraction, SDS removal through spin columns, in solution trypsin digestion, long gradient liquid chromatography peptide separation and LTQ-Orbitrap mass spectrometry analysis. A total of 1260 and 2436 proteins were identified in the AC and LCNEC samples, respectively, with FDR <1%. MS data are available in the PeptideAtlas repository at http://www.peptideatlas.org/PASS/PASS00375. Keywords: Neuroendocrine tumors, Proteomics, FFPE, Archival tissues, Mass spectrometr

Effects of pre‐operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

We aimed to determine the impact of pre-operative isolation on postoperative pulmonary complications after elective surgery during the global SARS-CoV-2 pandemic. We performed an international prospective cohort study including patients undergoing elective surgery in October 2020. Isolation was defined as the period before surgery during which patients did not leave their house or receive visitors from outside their household. The primary outcome was postoperative pulmonary complications, adjusted in multivariable models for measured confounders. Pre-defined sub-group analyses were performed for the primary outcome. A total of 96,454 patients from 114 countries were included and overall, 26,948 (27.9%) patients isolated before surgery. Postoperative pulmonary complications were recorded in 1947 (2.0%) patients of which 227 (11.7%) were associated with SARS-CoV-2 infection. Patients who isolated pre-operatively were older, had more respiratory comorbidities and were more commonly from areas of high SARS-CoV-2 incidence and high-income countries. Although the overall rates of postoperative pulmonary complications were similar in those that isolated and those that did not (2.1% vs 2.0%, respectively), isolation was associated with higher rates of postoperative pulmonary complications after adjustment (adjusted OR 1.20, 95%CI 1.05-1.36, p = 0.005). Sensitivity analyses revealed no further differences when patients were categorised by: pre-operative testing; use of COVID-19-free pathways; or community SARS-CoV-2 prevalence. The rate of postoperative pulmonary complications increased with periods of isolation longer than 3 days, with an OR (95%CI) at 4-7 days or &gt;= 8 days of 1.25 (1.04-1.48), p = 0.015 and 1.31 (1.11-1.55), p = 0.001, respectively. Isolation before elective surgery might be associated with a small but clinically important increased risk of postoperative pulmonary complications. Longer periods of isolation showed no reduction in the risk of postoperative pulmonary complications. These findings have significant implications for global provision of elective surgical care