bims-meglyc 2023-09-10 papers

J Adv Res. 2023 Sep 06. pii: S2090-1232(23)00239-4. [Epub ahead of print]

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities.

Hans J C T Wessels, Purva Kulkarni, Maurice van Dael, Anouk Suppers, Esther Willems, Fokje Zijlstra, Else Kragt, Jolein Gloerich, Pierre-Olivier Schmit, Stuart Pengelley, Kristina Marx, Alain J van Gool, Dirk J Lefeber.

INTRODUCTION: The human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease.
OBJECTIVES: Here, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics.
METHODS: Comparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data.
RESULTS: Glycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors.
CONCLUSION: Our work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

Keywords: blood plasma; clinical applications; congenital disorders of glycosylation; glycoproteomics; glycosylation

DOI: https://doi.org/10.1016/j.jare.2023.09.002

Mol Genet Genomic Med. 2023 Sep 08. e2283

Amino acid ratio combinations as biomarkers for discriminating patients with pyruvate dehydrogenase complex deficiency from other inborn errors of metabolism.

Anisha Verma, April N Lehman, Hatice Gokcan, Lorna Cropcho, Danielle Black, Steven F Dobrowolski, Jerry Vockley, Jirair K Bedoyan.

BACKGROUND: Pyruvate dehydrogenase complex deficiency (PDCD) is a mitochondrial neurometabolic disorder of energy deficit, with incidence of about 1 in 42,000 live births annually in the USA. The median and mean ages of diagnosis of PDCD are about 12 and 31 months, respectively. PDCD is a major cause of primary lactic acidosis with concomitant elevation in blood alanine (Ala) and proline (Pro) concentrations depending on phenotypic severity. Alanine/Leucine (Ala/Leu) ≥4.0 and Proline/Leucine (Pro/Leu) ≥3.0 combination cutoff from dried blood spot specimens was used as a biomarker for early identification of neonates/infants with PDCD. Further investigations were needed to evaluate the sensitivity (SN), specificity (SP), and clinical utility of such amino acid (AA) ratio combination cutoffs in discriminating PDCD from other inborn errors of metabolism (IEM) for early identification of such patients.
METHODS: We reviewed medical records of patients seen at UPMC in the past 11 years with molecularly or enzymatically confirmed diagnosis. We collected plasma AA analysis data from samples prior to initiation of therapeutic interventions such as total parenteral nutrition and/or ketogenic diet. Conditions evaluated included organic acidemias, primary mitochondrial disorders (MtDs), fatty acid oxidation disorders (FAOD), other IEMs on current newborn screening panels, congenital cardiac great vessel anomalies, renal tubular acidosis, and non-IEMs. The utility of specific AA ratio combinations as biomarkers were evaluated using receiver operating characteristic curves, correlation analysis, principal component analysis, and cutoff SN, SP, and positive predictive value determined from 201 subjects with broad age range.
RESULTS: Alanine/Lysine (Ala/Lys) and Ala/Leu as well as (Ala + Pro)/(Leu + Lys) and Ala/Leu ratio combinations effectively discriminated subjects with PDCD from those with other MtDs and IEMs on current newborn screening panels. Specific AA ratio combinations were significantly more sensitive in identifying PDCD than Ala alone or combinations of Ala and/or Pro in the evaluated cohort of subjects. Ala/Lys ≥3.0 and Ala/Leu ≥5.0 as well as (Ala + Pro)/(Leu + Lys) ≥2.5 and Ala/Leu ≥5.0 combination cutoffs identified patients with PDCD with 100% SN and ~85% SP.
CONCLUSIONS: With the best predictor of survival and positive cognitive outcome in PDCD being age of diagnosis, PDCD patients would benefit from use of such highly SN and SP AA ratio combination cutoffs as biomarkers for early identification of at-risk newborns, infants, and children, for early intervention(s) with known and/or novel therapeutics for this disorder.

Keywords: amino acid ratio combinations; biomarkers; inborn errors of metabolism; pyruvate dehydrogenase complex deficiency

DOI: https://doi.org/10.1002/mgg3.2283

Nat Rev Dis Primers. 2023 Sep 07. 9(1): 47

Glycogen storage diseases.

DOI: https://doi.org/10.1038/s41572-023-00462-1