bims-meglyc 2025-12-07 papers

Issue of 2025–12–07
two papers selected by
Silvia Radenkovic, UMC Utrecht

J Inherit Metab Dis. 2026 Jan;49(1): e70120

Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders.

Jonathan Martens, Udo F H Engelke, Ron A Wevers, Dirk J Lefeber, Purva Kulkarni.

Inherited metabolic disorders (IMDs) encompass a diverse and expanding group of rare diseases caused by genetic disruptions mainly in metabolic enzymes and transporters. Clinical diagnosis of IMDs presents significant challenges due to phenotypic heterogeneity, nonspecific symptoms, and the limited scope of current targeted biochemical assays typically available. Recent advances in mass spectrometry-based untargeted metabolomics offer promising solutions to several of these challenges by simultaneous detection and relative quantification of thousands of metabolites, not relying on any prior hypotheses. With the expansion of genetic diagnostics via whole-exome and whole-genome sequencing, metabolic insights are often crucial for understanding the pathogenicity of genetic variants of unknown significance, often enabling a clear diagnosis for patients. This review details current applications of untargeted metabolomics in IMDs, including biomarker discovery and elucidation of previously unknown pathophysiological mechanisms. Successful examples of biomarker identification in well-studied IMDs, such as pyridoxine-dependent epilepsy and phenylketonuria, are highlighted to provide novel disease insights. Additionally, we address technical and interpretation challenges inherent to this methodology, particularly concerning metabolite identification, high-dimensional data complexity, and limited patient numbers. Emerging analytical technologies and data analysis approaches are highlighted that are poised to mitigate these challenges in the upcoming years. Finally, we provide an outlook on future directions, emphasizing the complementary roles of targeted and untargeted metabolomics and the prospects for the identification of new therapeutic targets as well as therapy monitoring for the clinical management of IMDs.

Keywords: biomarkers; dark matter of the metabolome; de‐VUSing; diagnostics; inborn errors of metabolism; mass spectrometry; metabolic disease discovery; untargeted metabolomics

DOI: https://doi.org/10.1002/jimd.70120

Mol Genet Metab. 2025 Nov 13. pii: S1096-7192(25)00280-X. [Epub ahead of print]147(1): 109288

A proposed nosology of inherited disorders of the extracellular matrix (ECM): Insights from the IEMbase and dyadic classification.

Cecilia Giunta, Giulio Marcionelli, Carlos R Ferreira, Nenad Blau, Marianne Rohrbach.

We have identified 252 inherited disorders of the extracellular matrix (ECM) caused by 154 different gene defects and have proposed a classification system in 8 categories based on their mode of action: 1. Disorders of ECM glycoproteins, 2. Disorders of ECM proteoglycans, 3. Disorders of proteins in TGF-beta signaling pathway, 4. Disorders of fibrillar collagens, 5. Disorders of fibrillar collagen processing and maturation, 6. Disorders of non-fibrillar collagens, 7. Other disorders of connective tissue with bone fragility and 8. Other disorders of connective tissue. Additionally, using information from IEMbase, we have described the clinical involvement of 18 organs and systems, as well as essential laboratory investigations for each type of ECM disorder. Skeletal, ocular, neurological and dysmorphic manifestations were the most prevalent, occurring in 18 %, 12 %, 10 %, and 10 % of ECM disorders, respectively. This was followed by cardiovascular, dermatological, ear-related, muscular, digestive, endocrine, and hematological symptoms (3-7 %). Among the skeletal symptoms, those affecting joints, spine, upper limbs, lower limbs and mineralization were the most common with rates of 25.8 %, 18.0 %, 14.3 %, 14.1 % and 11.5 %, respectively. 27.4 % of the disorders display a single phenotype, with skeletal issues being the most common at 17.8 % and ocular abnormalities 12.2 %. Conversely, 72.6 % of disorders have multiple phenotypes, with LTBP4-related Cutis laxa (10 phenotypes) and SMAD4- related Myhre Syndrome (gain of function) at the end of the spectrum with up to 11 phenotypes. The information provided in this study, including our proposed dyadic classification system for ECM disorders, may be useful for healthcare providers caring for individuals with conditions associated with ECM problems.

Keywords: Collagen; Elastin; Glycoproteins; Glycosaminoglycans; Proteoglycans

DOI: https://doi.org/10.1016/j.ymgme.2025.109288