Evidence map›Paper›PMID 39945871›Full record

ArticleMetabolic brain disease2025

A novel approach to metabolic profiling in case models of MECP2-related disorders.

Jessica A Cooley Coleman, Bridgette A Moffitt, William C Bridges, Kelly Jones, Melanie May, Cindy Skinner, Michael J Friez, Steven A Skinner, Charles E Schwartz, Luigi Boccuto

Abstract read
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Article in Metabolic brain disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jessica A Cooley ColemanGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Bridgette A MoffittSchool of Nursing, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, SC, 29634, USA.
William C BridgesSchool of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, 29634, USA.
Kelly JonesGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Melanie MayGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Cindy SkinnerGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Michael J FriezGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Steven A SkinnerGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Charles E SchwartzGreenwood Genetic Center, Greenwood, SC, 29646, USA.
Luigi BoccutoGreenwood Genetic Center, Greenwood, SC, 29646, USA. lboccut@clemson.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic abnormalities of the MECP2 gene cause several conditions grouped under the umbrella term of MECP2-related disorders and characterized by a variety of phenotypes. We applied a functional approach to identify metabolic profiles in two patients with Rett syndrome (RTT) and one patient with MECP2 duplication syndrome (MRXSL). Such an approach is based on the Phenotype Mammalian Microarray (PM-M) technology, which is designed to assess the cellular production of energy in the presence of different compounds generating distinct metabolic environments. The findings in the three case models were compared versus 50 controls. Although the small number of samples prevented most results from reaching significant p-values when adjusted with the Benjamini-Hochberg correction, some interesting trends emerged. Some compounds indicated metabolic trends shared by the two conditions, like increased energy production in the presence of energy sources such as pectin, adenosine, and pyruvic acid, or decreased metabolic response to certain hormones. Other compounds showed opposite trends for the two disorders, like interleukin-1 beta (IL-1 beta), which caused decreased energy production in the RTT group but increased energy production in the patient with MRXSL. The response to IL-1 beta also offers valuable insights into the pathogenic mechanism and potential therapeutic approaches. The metabolic profiling of MECP2-related disorders bears a remarkable translational potential since it may be helpful to investigate the molecular abnormalities underlying the phenotypical variety in this spectrum of conditions, develop biomarkers for the identification of ideal candidates for treatments like the recently approved trofenatide, and identify potential targets for the development of novel therapeutic approaches.

Indexed as

MetabolomeMetabolomicsMethyl-CpG-Binding Protein 2Rett SyndromeX-Linked Intellectual DisabilityChildFemaleHumansMaleMECP2 protein, humanMethyl-CpG-Binding Protein 2IRAK1MECP2MECP2 duplication syndromeMECP2-related disordersRett syndromeTrofenatide

Identifiers

PMID39945871
PMCPMC11825590

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