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. 2021 May 28:15:645267.
doi: 10.3389/fnins.2021.645267. eCollection 2021.

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Federica Murgia  1 , Antonella Gagliano  2   3 , Marcello G Tanca  2 , Noga Or-Geva  4 , Aran Hendren  1   5 , Sara Carucci  2   3 , Manuela Pintor  3 , Francesca Cera  2 , Fausto Cossu  6 , Stefano Sotgiu  7 , Luigi Atzori  1 , Alessandro Zuddas  2   3
Affiliations

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Federica Murgia et al. Front Neurosci. .

Abstract

Introduction: PANS is a controversial clinical entity, consisting of a complex constellation of psychiatric symptoms, adventitious changes, and expression of various serological alterations, likely sustained by an autoimmune/inflammatory disease. Detection of novel biomarkers of PANS is highly desirable for both diagnostic and therapeutic management of affected patients. Analysis of metabolites has proven useful in detecting biomarkers for other neuroimmune-psychiatric diseases. Here, we utilize the metabolomics approach to determine whether it is possible to define a specific metabolic pattern in patients affected by PANS compared to healthy subjects.

Design: This observational case-control study tested consecutive patients referred for PANS between June 2019 to May 2020. A PANS diagnosis was confirmed according to the PANS working criteria (National Institute of Mental Health [NIMH], 2010). Healthy age and sex-matched subjects were recruited as controls.

Methods: Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male) and 25 neurotypical subjects matched for age and gender, were subjected to metabolite analysis. Serum samples were obtained from each participant and were analyzed using Nuclear Magnetic Resonance (NMR) spectroscopy. Subsequently, multivariate and univariate statistical analyses and Receiver Operator Curves (ROC) were performed.

Results: Separation of the samples, in line with the presence of PANS diagnosis, was observed by applying a supervised model (R2X = 0.44, R2Y = 0.54, Q2 = 0.44, p-value < 0.0001). The significantly altered variables were 2-Hydroxybutyrate, glycine, glutamine, histidine, tryptophan. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan metabolism, as well as glutamine and glutamate metabolism, exhibited the largest deviations from neurotypical controls.

Conclusion: We found a unique plasma metabolic profile in PANS patients, significantly differing from that of healthy children, that suggests 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. This metabolomics study offers new insights into biological mechanisms underpinning the disorder and supports research of other potential biomarkers implicated in PANS.

Keywords: biomarkers; metabolomics; neuroinflammation; oxidative stress; pediatric acute-onset neuropsychiatric syndrome.

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Conflict of interest statement

AG was on the advisory boards for Eli Lilly and Shire. She is/has been involved in clinical trials conducted by Eli Lilly, Shire, Lundbeck, Janssen, and Otsuka. She has been a speaker for Novartis, Eli Lilly, and Shire. SC was collaborating on projects from the European Union (7th Framework Program) and as a sub-investigator in sponsored clinical trials by Lundbeck Otsuka and Janssen Cilag. Travel support from Fidia Farmaceutici. AZ served in an advisory or consultancy role for Angelini, Lundbeck, Otsuka, and Edu-Pharma. He received conference support or speaker’s fee from Angelini, Otzuka, and Takeda. He is/has been involved in clinical trials conducted by Angelini, Roche, Lundbeck, Janssen, Servier, and Otsuka. He received royalties from Oxford University Press, and Giunti OS. SS received conference support or speaker’s fee from Bayer Pharma, Biogen Idec, Merck-Serono, Novartis, and Teva. He has been involved in clinical trials conducted by Bayer Pharma, Biogen and Teva. He received royalties from Documenta (Ed). The present work is unrelated to the above grants and relationships. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Comparison between PANS and Controls patients. (A,B) OPLS-DA models of the analyzed classes. PANS (white circles) vs. Controls subjects (black boxes) with the respective permutation test. (C) Bar graphs and (D) ROC curves of the metabolites exhibiting a p-value of < 0.05. U-Mann Whitney analysis was used, and subsequently Holm-Bonferroni correction was applied. White bars represent the PANS class while black bars represent the control patients. *p < 0.05.
FIGURE 2
FIGURE 2
(A) PLS correlation analysis between the metabolic profile of the enrolled subjects and the age. (B) PLS correlation analysis between the metabolic profile of the affected patients and the PANSS severity score.
FIGURE 3
FIGURE 3
Pearson correlation analysis between the concentrations of the significant metabolites and the PANSS severity score. Only glycine, tryptophan and tyrosine showed a significant correlation with the clinical parameter considered.
FIGURE 4
FIGURE 4
The metabolic pathways most altered in patients with PANS diagnosis were histidine metabolism, phenylalanine, tyrosine and tryptophan metabolism, tyrosine metabolism, glutathione metabolism, glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, glutamine and glutamate metabolism. (A) The size of the circles represent the pathway impact while the colors (varying from yellow to red) reflect the different levels of significance. (B) Also the enrichment analysis performed with the same software, confirm the same altered metabolic nets.
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