Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
4.1
Journals
Metabolites
Special Issues
Metabolomics and Biomarker Discovery and Evaluation: The New Frontier in...
share announcement

Metabolomics and Biomarker Discovery and Evaluation: The New Frontier in the Screening, Diagnosis and Therapeutic Monitoring of Inborn Errors of Metabolism

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 8116

Special Issue Editor

Dr. Mohamed A. Elmonem

E-Mail Website
Guest Editor
Department of Clinical and Chemical Pathology, Faculty of Medicine, Cairo University, Cairo 11628, Egypt
Interests: inborn errors of metabolism; lysosomal storage disorders; tandem mass spectrometry; genetic diagnosis; next generation sequencing; biomarker discovery and validation

Special Issue Information

Dear Colleagues,

Inborn errors of metabolism (IEMs) are a wide group of monogenic disorders caused by defects in genes coding for proteins involved in various metabolic pathways, which often lead to the accumulation of abnormal metabolites in tissues and different body fluids. IEMs commonly manifest during infancy and early childhood with progressive debilitating features, and are considered a substantial social and financial challenge for many health care authorities all over the world. Most IEMs are rare, ultrarare or orphan disorders, and this together with the absence of routine genetic and biochemical diagnosis for these disorders in many parts of the world means that patients can pass undiagnosed until serious and irreversible sequalae occur. Furthermore, dependable biomarkers that can be used for severity scoring and therapeutic monitoring are still lacking for many IEMs, hindering the application of new therapeutic approaches in clinical trials. Recent advances in metabolomics have allowed for the identification of many novel metabolites that can serve as disease biomarkers for these disorders.

In this Special Issue of Metabolites we welcome research proposals which include but are not limited to the following topics:

  • Metabolomics techniques and approaches for clinical purposes;
  • Targeted vs. untargeted metabolomics in inborn errors of metabolism;
  • Biomarker discovery and validation for the screening and diagnosis of inborn errors of metabolism;
  • Biomarker discovery and validation for the therapeutic monitoring of inborn errors of metabolism;
  • Phenotype correlation with novel biomarkers in inborn errors of metabolism;
  • Genotype correlation with novel biomarkers in inborn errors of metabolism;
  • Genetics vs. metabolomics for the screening of inborn errors of metabolism.

Dr. Mohamed A. Elmonem
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • inborn errors of metabolism
  • metabolomics
  • proteomics
  • mass spectrometry
  • genetics
  • next-generation sequencing
  • phenotype–genotype correlation
  • biomarker discovery
  • biomarker validation

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

13 pages, 2202 KiB  
Article
Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots
by María Jesús Leal-Witt, Eugenia Rojas-Agurto, Manuel Muñoz-González, Felipe Peñaloza, Carolina Arias, Karen Fuenzalida, Daniel Bunout, Verónica Cornejo and Alejandro Acevedo
Metabolites 2023, 13(6), 677; https://doi.org/10.3390/metabo13060677 - 23 May 2023
Cited by 1 | Viewed by 1384
Abstract
Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism where high phenylalanine (Phe) concentrations cause irreversible intellectual disability that can be prevented by newborn screening and early treatment. Evidence suggests that PKU subjects not adherent to treatment could be at risk of [...] Read more.
Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism where high phenylalanine (Phe) concentrations cause irreversible intellectual disability that can be prevented by newborn screening and early treatment. Evidence suggests that PKU subjects not adherent to treatment could be at risk of insulin resistance (IR). We studied how Phe concentrations (PheCs) relate to IR using machine learning (ML) and derived potential biomarkers. In our cross-sectional study, we analyzed subjects with neonatal diagnoses of PKU, grouped as follows: 10 subjects who adhered to treatment (G1); 14 subjects who suspended treatment (G2); and 24 control subjects (G3). We analyzed plasma biochemical variables, as well as profiles of amino acids and acylcarnitines in dried blood spots (DBSs). Higher PheCs and plasma insulin levels were observed in the G2 group compared to the other groups. Additionally, a positive correlation between the PheCs and homeostatic measurement assessments (HOMA-IRs) was found, as well as a negative correlation between the HOMA-Sensitivity (%) and quantitative insulin sensitivity check index (QUICKI) scores. An ML model was then trained to predict abnormal HOMA-IRs using the panel of metabolites measured from DBSs. Notably, ranking the features’ importance placed PheCs as the second most important feature after BMI for predicting abnormal HOMA-IRs. Our results indicate that low adherence to PKU treatment could affect insulin signaling, decrease glucose utilization, and lead to IR. Full article
(This article belongs to the Special Issue Metabolomics and Biomarker Discovery and Evaluation: The New Frontier in the Screening, Diagnosis and Therapeutic Monitoring of Inborn Errors of Metabolism)
►▼ Show Figures

Graphical abstract

14 pages, 1931 KiB  
Article
A Case Study of Dysfunctional Nicotinamide Metabolism in a 20-Year-Old Male
by Karen L. DeBalsi, John H. Newman, Laura J. Sommerville, John A. Phillips III, Rizwan Hamid, Joy Cogan, Joshua P. Fessel, Anne M. Evans, Undiagnosed Diseases Network and Adam D. Kennedy
Metabolites 2023, 13(3), 399; https://doi.org/10.3390/metabo13030399 - 08 Mar 2023
Viewed by 1741
Abstract
We present a case study of a 20-year-old male with an unknown neurodegenerative disease who was referred to the Undiagnosed Diseases Network Vanderbilt Medical Center site. A previous metabolic panel showed that the patient had a critical deficiency in nicotinamide intermediates that are [...] Read more.
We present a case study of a 20-year-old male with an unknown neurodegenerative disease who was referred to the Undiagnosed Diseases Network Vanderbilt Medical Center site. A previous metabolic panel showed that the patient had a critical deficiency in nicotinamide intermediates that are generated during the biosynthesis of NAD(H). We followed up on these findings by evaluating the patient’s ability to metabolize nicotinamide. We performed a global metabolic profiling analysis of plasma samples that were collected: (1) under normal fed conditions (baseline), (2) after the patient had fasted, and (3) after he was challenged with a 500 mg nasogastric tube bolus of nicotinamide following the fast. Our findings showed that the patient’s nicotinamide N-methyltransferase (NNMT), a key enzyme in NAD(H) biosynthesis and methionine metabolism, was not functional under normal fed or fasting conditions but was restored in response to the nicotinamide challenge. Altered levels of metabolites situated downstream of NNMT and in neighboring biochemical pathways provided further evidence of a baseline defect in NNMT activity. To date, this is the only report of a critical defect in NNMT activity manifesting in adulthood and leading to neurodegenerative disease. Altogether, this study serves as an important reference in the rare disease literature and also demonstrates the utility of metabolomics as a diagnostic tool for uncharacterized metabolic diseases. Full article
(This article belongs to the Special Issue Metabolomics and Biomarker Discovery and Evaluation: The New Frontier in the Screening, Diagnosis and Therapeutic Monitoring of Inborn Errors of Metabolism)
►▼ Show Figures

Graphical abstract

18 pages, 1684 KiB  
Article
Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria
by Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Vincent Heuveline and Stefan Kölker
Metabolites 2023, 13(2), 304; https://doi.org/10.3390/metabo13020304 - 18 Feb 2023
Cited by 5 | Viewed by 1605
Abstract
Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” [...] Read more.
Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns’ and families’ burden of false positives or over-treatment. Full article
(This article belongs to the Special Issue Metabolomics and Biomarker Discovery and Evaluation: The New Frontier in the Screening, Diagnosis and Therapeutic Monitoring of Inborn Errors of Metabolism)
►▼ Show Figures

Figure 1

Review

Jump to: Research

47 pages, 4982 KiB  
Review
Inborn Errors of Purine Salvage and Catabolism
by Marcella Camici, Mercedes Garcia-Gil, Simone Allegrini, Rossana Pesi, Giulia Bernardini, Vanna Micheli and Maria Grazia Tozzi
Metabolites 2023, 13(7), 787; https://doi.org/10.3390/metabo13070787 - 24 Jun 2023
Cited by 1 | Viewed by 2371
Abstract
Cellular purine nucleotides derive mainly from de novo synthesis or nucleic acid turnover and, only marginally, from dietary intake. They are subjected to catabolism, eventually forming uric acid in humans, while bases and nucleosides may be converted back to nucleotides through the salvage [...] Read more.
Cellular purine nucleotides derive mainly from de novo synthesis or nucleic acid turnover and, only marginally, from dietary intake. They are subjected to catabolism, eventually forming uric acid in humans, while bases and nucleosides may be converted back to nucleotides through the salvage pathways. Inborn errors of the purine salvage pathway and catabolism have been described by several researchers and are usually referred to as rare diseases. Since purine compounds play a fundamental role, it is not surprising that their dysmetabolism is accompanied by devastating symptoms. Nevertheless, some of these manifestations are unexpected and, so far, have no explanation or therapy. Herein, we describe several known inborn errors of purine metabolism, highlighting their unexplained pathological aspects. Our intent is to offer new points of view on this topic and suggest diagnostic tools that may possibly indicate to clinicians that the inborn errors of purine metabolism may not be very rare diseases after all. Full article
(This article belongs to the Special Issue Metabolomics and Biomarker Discovery and Evaluation: The New Frontier in the Screening, Diagnosis and Therapeutic Monitoring of Inborn Errors of Metabolism)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop