1
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Moran AB, Elgood-Hunt G, van der Burgt YEM, Wuhrer M, Mesker WE, Tollenaar RAEM, Spencer DIR, Lageveen-Kammeijer GSM. Serum N-Glycosylation RPLC-FD-MS Assay to Assess Colorectal Cancer Surgical Interventions. Biomolecules 2023; 13:896. [PMID: 37371476 DOI: 10.3390/biom13060896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/16/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
A newly developed analytical strategy was applied to profile the total serum N-glycome of 64 colorectal cancer (CRC) patients before and after surgical intervention. In this cohort, it was previously found that serum N-glycome alterations in CRC were associated with patient survival. Here, fluorescent labeling of serum N-glycans was applied using procainamide and followed by sialic acid derivatization specific for α2,6- and α2,3-linkage types via ethyl esterification and amidation, respectively. This strategy allowed efficient separation of specific positional isomers on reversed-phase liquid chromatography-fluorescence detection-mass spectrometry (RPLC-FD-MS) and complemented the previous glycomics data based on matrix-assisted laser desorption/ionization (MALDI)-MS that did not include such separations. The results from comparing pre-operative CRC to post-operative samples were in agreement with studies that identified a decrease in di-antennary structures with core fucosylation and an increase in sialylated tri- and tetra-antennary N-glycans in CRC patient sera. Pre-operative abundances of N-glycans showed good performance for the classification of adenocarcinoma and led to the revisit of the previous MALDI-MS dataset with regard to histological and clinical data. This strategy has the potential to monitor patient profiles before, during, and after clinical events such as treatment, therapy, or surgery and should also be further explored.
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Affiliation(s)
- Alan B Moran
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
- Ludger Ltd., Culham Science Centre, Abingdon OX14 3EB, UK
| | | | - Yuri E M van der Burgt
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Wilma E Mesker
- Department of Surgery, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | | | - Guinevere S M Lageveen-Kammeijer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
- Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, 9713 AV Groningen, The Netherlands
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2
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Treacy EP, Vencken S, Bosch AM, Gautschi M, Rubio‐Gozalbo E, Dawson C, Nerney D, Colhoun HO, Shakerdi L, Pastores GM, O'Flaherty R, Saldova R. Abnormal N-glycan fucosylation, galactosylation, and sialylation of IgG in adults with classical galactosemia, influence of dietary galactose intake. JIMD Rep 2021; 61:76-88. [PMID: 34485021 PMCID: PMC8411110 DOI: 10.1002/jmd2.12237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Classical galactosemia (CG) (OMIM #230400) is a rare disorder of carbohydrate metabolism, due to deficiency of galactose-1-phosphate uridyltransferase (EC 2.7.7.12). The pathophysiology of the long-term complications, mainly cognitive, neurological, and female infertility remains poorly understood. OBJECTIVES This study investigated (a) the association between specific IgG N-glycosylation biomarkers (glycan peaks and grouped traits) and CG patients (n = 95) identified from the GalNet Network, using hydrophilic interaction ultraperformance liquid chromatography and (b) a further analysis of a GALT c.563A-G/p.Gln188Arg homozygous cohort (n = 49) with correlation with glycan features with patient Full Scale Intelligence Quotient (FSIQ), and (c) with galactose intake. RESULTS A very significant decrease in galactosylation and sialylation and an increase in core fucosylation was noted in CG patients vs controls (P < .005). Bisected glycans were decreased in the severe GALT c.563A-G/p.Gln188Arg homozygous cohort (n = 49) (P < .05). Logistic regression models incorporating IgG glycan traits distinguished CG patients from controls. Incremental dietary galactose intake correlated positively with FSIQ for the p.Gln188Arg homozygous CG cohort (P < .005) for a dietary galactose intake of 500 to 1000 mg/d. Significant improvements in profiles with increased galactose intake were noted for monosialylated, monogalactosylated, and monoantennary glycans. CONCLUSION These results suggest that N-glycosylation abnormalities persist in CG patients on dietary galactose restriction which may be modifiable to a degree by dietary galactose intake.
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Affiliation(s)
- Eileen P. Treacy
- National Centre for Inherited Metabolic Disorders, The Mater Misericordiae University HospitalDublinIreland
- Department of PaediatricsTrinity College DublinDublinIreland
- UCD School of MedicineUniversity College DublinDublinIreland
| | | | - Annet M. Bosch
- Department of Pediatrics, Division of Metabolic DisordersEmma Children's Hospital, Amsterdam Gastroenterology, Endocrinology & Metabolism, Amsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Matthias Gautschi
- Department of Paediatrics and Institute of Clinical ChemistryInselspital, University Hospital BernBernSwitzerland
| | - Estela Rubio‐Gozalbo
- Department of Pediatrics/Laboratory of Clinical GeneticsMaastricht University Medical CentreMaastrichtThe Netherlands
| | - Charlotte Dawson
- Department of EndocrinologyUniversity Hospitals Birmingham NHS Foundation TrustBirminghamUK
| | - Darragh Nerney
- National Centre for Inherited Metabolic Disorders, The Mater Misericordiae University HospitalDublinIreland
| | - Hugh Owen Colhoun
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and TrainingDublinIreland
| | - Loai Shakerdi
- National Centre for Inherited Metabolic Disorders, The Mater Misericordiae University HospitalDublinIreland
| | - Gregory M. Pastores
- National Centre for Inherited Metabolic Disorders, The Mater Misericordiae University HospitalDublinIreland
| | - Roisin O'Flaherty
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and TrainingDublinIreland
- Department of ChemistryMaynooth UniversityKildareIreland
| | - Radka Saldova
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and TrainingDublinIreland
- UCD School of Medicine, College of Health and Agricultural Sciences (CHAS), University College Dublin (UCD)DublinIreland
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3
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Lipiński P, Stępień KM, Ciara E, Tylki-Szymańska A, Jezela-Stanek A. Skeletal and Bone Mineral Density Features, Genetic Profile in Congenital Disorders of Glycosylation: Review. Diagnostics (Basel) 2021; 11:diagnostics11081438. [PMID: 34441372 PMCID: PMC8391432 DOI: 10.3390/diagnostics11081438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/04/2021] [Accepted: 08/04/2021] [Indexed: 11/25/2022] Open
Abstract
Congenital disorders of glycosylation (CDGs) are a heterogeneous group of disorders with impaired glycosylation of proteins and lipids. These conditions have multisystemic clinical manifestations, resulting in gradually progressive complications including skeletal involvement and reduced bone mineral density. Contrary to PMM2-CDG, all remaining CDG, including ALG12-CDG, ALG3-CDG, ALG9-CDG, ALG6-CDG, PGM3-CDG, CSGALNACT1-CDG, SLC35D1-CDG and TMEM-165, are characterized by well-defined skeletal dysplasia. In some of them, prenatal-onset severe skeletal dysplasia is observed associated with early death. Osteoporosis or osteopenia are frequently observed in all CDG types and are more pronounced in adults. Hormonal dysfunction, limited mobility and inadequate diet are common risk factors for reduced bone mineral density. Skeletal involvement in CDGs is underestimated and, thus, should always be carefully investigated and managed to prevent fractures and chronic pain. With the advent of new therapeutic developments for CDGs, the severity of skeletal complications may be reduced. This review focuses on possible mechanisms of skeletal manifestations, risk factors for osteoporosis, and bone markers in reported paediatric and adult CDG patients.
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Affiliation(s)
- Patryk Lipiński
- Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, 04-730 Warsaw, Poland;
- Correspondence:
| | - Karolina M. Stępień
- Adult Inherited Metabolic Diseases, Salford Royal NHS Foundation Trust, Salford M6 8HD, UK;
| | - Elżbieta Ciara
- Department of Medical Genetics, The Children’s Memorial Health Institute, 04-730 Warsaw, Poland;
| | - Anna Tylki-Szymańska
- Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, 04-730 Warsaw, Poland;
| | - Aleksandra Jezela-Stanek
- Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland;
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4
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Hendel JL, Gardner RA, Spencer DIR. Automation of Immunoglobulin Glycosylation Analysis. EXPERIENTIA SUPPLEMENTUM (2012) 2021; 112:173-204. [PMID: 34687010 DOI: 10.1007/978-3-030-76912-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The development of reliable, affordable, high-resolution glycomics technologies that can be used for many samples in a high-throughput manner are essential for both the optimization of glycosylation in the biopharmaceutical industry as well as for the advancement of clinical diagnostics based on glycosylation biomarkers. We will use this chapter to review the sample preparation processes that have been used on liquid-handling robots to obtain high-quality glycomics data for both biopharmaceutical and clinical antibody samples. This will focus on glycoprotein purification, followed by glycan or glycopeptide generation, derivatization and enrichment. The use of liquid-handling robots for glycomics studies on other sample types beyond antibodies will not be discussed here. We will summarize our thoughts on the current status of the field and explore the benefits and challenges associated with developing and using automated platforms for sample preparation. Finally, the future outlook for the automation of glycomics will be discussed along with a projected impact on the field in general.
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Affiliation(s)
- Jenifer L Hendel
- Ludger Limited, Culham Science Centre, Abingdon, Oxfordshire, UK
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5
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de Haan N, Falck D, Wuhrer M. Monitoring of immunoglobulin N- and O-glycosylation in health and disease. Glycobiology 2020; 30:226-240. [PMID: 31281930 PMCID: PMC7225405 DOI: 10.1093/glycob/cwz048] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 12/11/2022] Open
Abstract
Protein N- and O-glycosylation are well known co- and post-translational modifications of immunoglobulins. Antibody glycosylation on the Fab and Fc portion is known to influence antigen binding and effector functions, respectively. To study associations between antibody glycosylation profiles and (patho) physiological states as well as antibody functionality, advanced technologies and methods are required. In-depth structural characterization of antibody glycosylation usually relies on the separation and tandem mass spectrometric (MS) analysis of released glycans. Protein- and site-specific information, on the other hand, may be obtained by the MS analysis of glycopeptides. With the development of high-resolution mass spectrometers, antibody glycosylation analysis at the intact or middle-up level has gained more interest, providing an integrated view of different post-translational modifications (including glycosylation). Alongside the in-depth methods, there is also great interest in robust, high-throughput techniques for routine glycosylation profiling in biopharma and clinical laboratories. With an emphasis on IgG Fc glycosylation, several highly robust separation-based techniques are employed for this purpose. In this review, we describe recent advances in MS methods, separation techniques and orthogonal approaches for the characterization of immunoglobulin glycosylation in different settings. We put emphasis on the current status and expected developments of antibody glycosylation analysis in biomedical, biopharmaceutical and clinical research.
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Affiliation(s)
- Noortje de Haan
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - David Falck
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands
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6
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Colombo M, Asadi Shehni A, Thoma I, McGurnaghan SJ, Blackbourn LAK, Wilkinson H, Collier A, Patrick AW, Petrie JR, McKeigue PM, Saldova R, Colhoun HM. Quantitative levels of serum N-glycans in type 1 diabetes and their association with kidney disease. Glycobiology 2020; 31:613-623. [PMID: 33245334 DOI: 10.1093/glycob/cwaa106] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/30/2020] [Accepted: 11/07/2020] [Indexed: 12/14/2022] Open
Abstract
We investigated associations of quantitative levels of N-glycans with hemoglobin A1c (HbA1c), renal function and renal function decline in type 1 diabetes. We measured 46 total N-glycan peaks (GPs) on 1565 serum samples from the Scottish Diabetes Research Network Type 1 Bioresource Study (SDRNT1BIO) and a pool of healthy donors. Quantitation of absolute abundance of each GP used 2AB-labeled mannose-3 as a standard. We studied cross-sectional associations of GPs and derived measures with HbA1c, albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), and prospective associations with incident albuminuria and final eGFR. All GPs were 1.4 to 3.2 times more abundant in SDRTN1BIO than in the healthy samples. Absolute levels of all GPs were slightly higher with higher HbA1c, with strongest associations for triantennary trigalactosylated disialylated, triantennary trigalactosylated trisialylated structures with core or outer arm fucose, and tetraantennary tetragalactosylated trisialylated glycans. Most GPs showed increased abundance with worsening ACR. Lower eGFR was associated with higher absolute GP levels, most significantly with biantennary digalactosylated disialylated glycans with and without bisect, triantennary trigalactosylated trisialylated glycans with and without outer arm fucose, and core fucosylated biantennary monogalactosylated monosialylated glycans. Although several GPs were inversely associated prospectively with final eGFR, cross-validated multivariable models did not improve prediction beyond clinical covariates. Elevated HbA1c is associated with an altered N-glycan profile in type 1 diabetes. Although we could not establish GPs to be prognostic of future renal function decline independently of HbA1c, further studies to evaluate their impact in the pathogenesis of diabetic kidney disease are warranted.
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Affiliation(s)
- Marco Colombo
- Independent conultant, Via Palestro 16/B, 23900, Lecco, Italy
| | - Akram Asadi Shehni
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, A94 X099, Ireland
| | - Ioanna Thoma
- MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Stuart J McGurnaghan
- MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Luke A K Blackbourn
- MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Hayden Wilkinson
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, A94 X099, Ireland
| | - Andrew Collier
- School of Health and Life Sciences, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0B4, UK
| | - Alan W Patrick
- Royal Infirmary of Edinburgh, NHS Lothian, Old Dalkeith Road, Edinburgh EH16 4SA, UK
| | - John R Petrie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow G12 8TA, UK
| | - Paul M McKeigue
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK
| | - Radka Saldova
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, A94 X099, Ireland.,UCD School of Medicine, College of Health and Agricultural Science, University College Dublin, Belfield, Dublin 4, Dublin D04 V1W8, Ireland
| | - Helen M Colhoun
- MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK.,Public Health, NHS Fife, Hayfield Road, Kirkcaldy KY2 5AH, UK
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7
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de Haan N, Wuhrer M, Ruhaak L. Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation. CLINICAL MASS SPECTROMETRY (DEL MAR, CALIF.) 2020; 18:1-12. [PMID: 34820521 PMCID: PMC8600986 DOI: 10.1016/j.clinms.2020.08.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 08/18/2020] [Accepted: 08/21/2020] [Indexed: 02/01/2023]
Abstract
Over the past decades, the genome and proteome have been widely explored for biomarker discovery and personalized medicine. However, there is still a large need for improved diagnostics and stratification strategies for a wide range of diseases. Post-translational modification of proteins by glycosylation affects protein structure and function, and glycosylation has been implicated in many prevalent human diseases. Numerous proteins for which the plasma levels are nowadays evaluated in clinical practice are glycoproteins. While the glycosylation of these proteins often changes with disease, their glycosylation status is largely ignored in the clinical setting. Hence, the implementation of glycomic markers in the clinic is still in its infancy. This is for a large part caused by the high complexity of protein glycosylation itself and of the analytical techniques required for their robust quantification. Mass spectrometry-based workflows are particularly suitable for the quantification of glycans and glycoproteins, but still require advances for their transformation from a biomedical research setting to a clinical laboratory. In this review, we describe why and how glycomics is expected to find its role in clinical tests and the status of current mass spectrometry-based methods for clinical glycomics.
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Affiliation(s)
- N. de Haan
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - M. Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - L.R. Ruhaak
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands
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8
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Welsink-Karssies MM, Ferdinandusse S, Geurtsen GJ, Hollak CEM, Huidekoper HH, Janssen MCH, Langendonk JG, van der Lee JH, O'Flaherty R, Oostrom KJ, Roosendaal SD, Rubio-Gozalbo ME, Saldova R, Treacy EP, Vaz FM, de Vries MC, Engelen M, Bosch AM. Deep phenotyping classical galactosemia: clinical outcomes and biochemical markers. Brain Commun 2020; 2:fcaa006. [PMID: 32954279 PMCID: PMC7425409 DOI: 10.1093/braincomms/fcaa006] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/06/2019] [Accepted: 12/28/2019] [Indexed: 02/02/2023] Open
Abstract
Early diagnosis and dietary treatment do not prevent long-term complications, which mostly affect the central nervous system in classical galactosemia patients. The clinical outcome of patients is highly variable, and there is an urgent need for prognostic biomarkers. The aim of this study was first to increase knowledge on the natural history of classical galactosemia by studying a cohort of patients with varying geno- and phenotypes and second to study the association between clinical outcomes and two possible prognostic biomarkers. In addition, the association between abnormalities on brain MRI and clinical outcomes was investigated. Classical galactosemia patients visiting the galactosemia expertise outpatient clinic of the Amsterdam University Medical Centre were evaluated according to the International Classical Galactosemia guideline with the addition of an examination by a neurologist, serum immunoglobulin G N-glycan profiling and a brain MRI. The biomarkers of interest were galactose-1-phosphate levels and N-glycan profiles, and the clinical outcomes studied were intellectual outcome and the presence or absence of movement disorders and/or primary ovarian insufficiency. Data of 56 classical galactosemia patients are reported. The intellectual outcome ranged from 45 to 103 (mean 77 ± 14) and was <85 in 62%. Movement disorders were found in 17 (47%) of the 36 tested patients. In females aged 12 years and older, primary ovarian insufficiency was diagnosed in 12 (71%) of the 17 patients. Significant differences in N-glycan peaks were found between controls and patients. However, no significant differences in either N-glycans or galactose-1-phosphate levels were found between patients with a poor (intellectual outcome < 85) and normal intellectual outcome (intellectual outcome ≥ 85), and with or without movement disorders or primary ovarian insufficiency. The variant patients detected by newborn screening, with previously unknown geno- and phenotypes and currently no long-term complications, demonstrated significantly lower galactose-1-phospate levels than classical patients (P < 0.0005). Qualitative analysis of the MRI's demonstrated brain abnormalities in 18 of the 21 patients, more severely in patients with a lower intellectual outcome and/or with movement disorders. This study demonstrates a large variability in clinical outcome, which varies from a below average intelligence, movement disorders and in females primary ovarian insufficiency to a normal clinical outcome. In our cohort of classical galactosemia patients, galactose-1-phosphate levels and N-glycan variations were not associated with clinical outcomes, but galactose-1-phosphate levels did differentiate between classical and variant patients detected by newborn screening. The correlation between brain abnormalities and clinical outcome should be further investigated by quantitative analysis of the MR images. The variability in clinical outcome necessitates individual and standardized evaluation of all classical galactosemia patients.
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Affiliation(s)
- Mendy M Welsink-Karssies
- Division of Metabolic Disorders, Department of Pediatrics, Emma Children's Hospital, Amsterdam, UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Sacha Ferdinandusse
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Gert J Geurtsen
- Department of Medical Psychology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Carla E M Hollak
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Hidde H Huidekoper
- Department of Pediatrics, Center for Lysosomal and Metabolic Diseases, Erasmus, MC, University Medical Center, Rotterdam, the Netherlands
| | - Mirian C H Janssen
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Janneke G Langendonk
- Department of Internal Medicine, Center for Lysosomal and Metabolic Diseases, Erasmus MC University Medical Centre, Rotterdam, the Netherlands
| | - Johanna H van der Lee
- Pediatric Clinical Research Office, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.,Knowledge Institute of the Dutch Association of Medical Specialists, Utrecht, the Netherlands
| | - Roisin O'Flaherty
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and Training, Mount Merrion, Blackrock, County Dublin, Ireland
| | - Kim J Oostrom
- Psychosocial Department, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Stefan D Roosendaal
- Department of Radiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - M Estela Rubio-Gozalbo
- Department of Pediatrics, Maastricht University Medical Center, Maastricht, the Netherlands.,Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Radka Saldova
- Knowledge Institute of the Dutch Association of Medical Specialists, Utrecht, the Netherlands.,UCD School of Medicine, College of Health and Agricultural Science, University College Dublin, Dublin, Ireland
| | - Eileen P Treacy
- National Centre for Inherited Metabolic Disorders, The Mater Misericordiae University Hospital, Dublin, Ireland
| | - Fred M Vaz
- Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Maaike C de Vries
- Department of Pediatrics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marc Engelen
- Department of Pediatric Neurology, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.,Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Annet M Bosch
- Division of Metabolic Disorders, Department of Pediatrics, Emma Children's Hospital, Amsterdam, UMC, University of Amsterdam, Amsterdam, the Netherlands
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9
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Affiliation(s)
- Hayden Wilkinson
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and Training, Blackrock, Dublin, Ireland
- CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland
- UCD School of Medicine, College of Health and Agricultural Science, University College Dublin, Dublin, Ireland
| | - Radka Saldova
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and Training, Blackrock, Dublin, Ireland
- CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland
- UCD School of Medicine, College of Health and Agricultural Science, University College Dublin, Dublin, Ireland
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10
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Rasmussen SA, Daenzer JMI, MacWilliams JA, Head ST, Williams MB, Geurts AM, Schroeder JP, Weinshenker D, Fridovich‐Keil JL. A galactose-1-phosphate uridylyltransferase-null rat model of classic galactosemia mimics relevant patient outcomes and reveals tissue-specific and longitudinal differences in galactose metabolism. J Inherit Metab Dis 2020; 43:518-528. [PMID: 31845342 PMCID: PMC7318568 DOI: 10.1002/jimd.12205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 12/04/2019] [Accepted: 12/11/2019] [Indexed: 12/14/2022]
Abstract
Classic galactosemia (CG) is a potentially lethal inborn error of metabolism, if untreated, that results from profound deficiency of galactose-1-phosphate uridylyltransferase (GALT), the middle enzyme of the Leloir pathway of galactose metabolism. While newborn screening and rapid dietary restriction of galactose prevent or resolve the potentially lethal acute symptoms of CG, by mid-childhood, most treated patients experience significant complications. The mechanisms underlying these long-term deficits remain unclear. Here we introduce a new GALT-null rat model of CG and demonstrate that these rats display cataracts, cognitive, motor, and growth phenotypes reminiscent of patients outcomes. We further apply the GALT-null rats to test how well blood biomarkers, typically followed in patients, reflect metabolic perturbations in other, more relevant tissues. Our results document that the relative levels of galactose metabolites seen in GALT deficiency differ widely by tissue and age, and that red blood cell Gal-1P, the marker most commonly followed in patients, shows no significant association with Gal-1P in other tissues. The work reported here establishes our outbred GALT-null rats as an effective model for at least four complications characteristic of CG, and sets the stage for future studies addressing mechanism and testing the efficacy of novel candidate interventions.
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Affiliation(s)
- Shauna A. Rasmussen
- Department of Human GeneticsEmory University School of Medicine, Emory UniversityAtlantaGeorgia
| | - Jennifer M. I. Daenzer
- Department of Human GeneticsEmory University School of Medicine, Emory UniversityAtlantaGeorgia
| | - Jessica A. MacWilliams
- Department of Human GeneticsEmory University School of Medicine, Emory UniversityAtlantaGeorgia
| | - S. Taylor Head
- Rollins School of Public Health, Graduate Program in BiostatisticsEmory UniversityAtlantaGeorgia
| | - Martine B. Williams
- Department of Human GeneticsEmory University School of Medicine, Emory UniversityAtlantaGeorgia
| | - Aron M. Geurts
- Gene Editing Rat Resource CenterMedical College of WisconsinMilwaukeeWisconsin
| | - Jason P. Schroeder
- Department of Human GeneticsEmory University School of Medicine, Emory UniversityAtlantaGeorgia
| | - David Weinshenker
- Department of Human GeneticsEmory University School of Medicine, Emory UniversityAtlantaGeorgia
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11
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Desaire H, Hua D. Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS. Anal Chem 2019; 92:1050-1057. [PMID: 31769656 DOI: 10.1021/acs.analchem.9b04049] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains. One promising way to address this current limitation is by using advanced machine learning techniques. In this work, we adapt a newly developed machine learning tool, the Aristotle Classifier, to bacterial classification of MALDI-TOF MS data. This tool was originally developed for classifying glycomics and glycoproteomics data, so we modified it to be well-suited for assigning mass spectral data from bacterial proteins. The classifier exceeds existing benchmarks in classifying bacteria, and it shows particularly strong performance when the samples to be identified are highly similar. The combination of mass spectrometry data and tools like the Aristotle Classifier could ameliorate the ambiguities associated with challenging bacterial classification problems.
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Affiliation(s)
- Heather Desaire
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - David Hua
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
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O'Flaherty R, Muniyappa M, Walsh I, Stöckmann H, Hilliard M, Hutson R, Saldova R, Rudd PM. A Robust and Versatile Automated Glycoanalytical Technology for Serum Antibodies and Acute Phase Proteins: Ovarian Cancer Case Study. Mol Cell Proteomics 2019; 18:2191-2206. [PMID: 31471495 PMCID: PMC6823853 DOI: 10.1074/mcp.ra119.001531] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/11/2019] [Indexed: 11/06/2022] Open
Abstract
The direct association of the genome, transcriptome, metabolome, lipidome and proteome with the serum glycome has revealed systems of interconnected cellular pathways. The exact roles of individual glycoproteomes in the context of disease have yet to be elucidated. In a move toward personalized medicine, it is now becoming critical to understand disease pathogenesis, and the traits, stages, phenotypes and molecular features that accompany it, as the disruption of a whole system. To this end, we have developed an innovative technology on an automated platform, "GlycoSeqCap," which combines N-glycosylation data from six glycoproteins using a single source of human serum. Specifically, we multiplexed and optimized a successive serial capture and glycoanalysis of six purified glycoproteins, immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), transferrin (Trf), haptoglobin (Hpt) and alpha-1-antitrypsin (A1AT), from 50 μl of human serum. We provide the most comprehensive and in-depth glycan analysis of individual glycoproteins in a single source of human serum to date. To demonstrate the technological application in the context of a disease model, we performed a pilot study in an ovarian cancer cohort (n = 34) using discrimination and classification analyses to identify aberrant glycosylation. In our sample cohort, we exhibit improved selectivity and specificity over the currently used biomarker for ovarian cancer, CA125, for early stage ovarian cancer. This technology will establish a new state-of-the-art strategy for the characterization of individual serum glycoproteomes as a diagnostic and monitoring tool which represents a major step toward understanding the changes that take place during disease.
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Affiliation(s)
- Róisín O'Flaherty
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland, A94X099
| | - Mohankumar Muniyappa
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland, A94X099
| | - Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (ASTAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore
| | - Henning Stöckmann
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland, A94X099
| | - Mark Hilliard
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland, A94X099
| | - Richard Hutson
- Cancer Research UK Clinical Centre at Leeds, St James' University Hospital, Leeds LS9 7TF, UK.
| | - Radka Saldova
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland, A94X099; UCD School of Medicine, College of Health and Agricultural Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Pauline M Rudd
- NIBRT GlycoScience Group, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland, A94X099
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Hua D, Patabandige MW, Go EP, Desaire H. The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State. Anal Chem 2019; 91:11070-11077. [PMID: 31407893 DOI: 10.1021/acs.analchem.9b01606] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
"The totality is not, as it were, a mere heap, but the whole is something besides the parts."-Aristotle. We built a classifier that uses the totality of the glycomic profile, not restricted to a few glycoforms, to differentiate samples from two different sources. This approach, which relies on using thousands of features, is a radical departure from current strategies, where most of the glycomic profile is ignored in favor of selecting a few features, or even a single feature, meant to capture the differences in sample types. The classifier can be used to differentiate the source of the material; applicable sources may be different species of animals, different protein production methods, or, most importantly, different biological states (disease vs healthy). The classifier can be used on glycomic data in any form, including derivatized monosaccharides, intact glycans, or glycopeptides. It takes advantage of the fact that changing the source material can cause a change in the glycomic profile in many subtle ways: some glycoforms can be upregulated, some downregulated, some may appear unchanged, yet their proportion-with respect to other forms present-can be altered to a detectable degree. By classifying samples using the entirety of their glycan abundances, along with the glycans' relative proportions to each other, the "Aristotle Classifier" is more effective at capturing the underlying trends than standard classification procedures used in glycomics, including PCA (principal components analysis). It also outperforms workflows where a single, representative glycomic-based biomarker is used to classify samples. We describe the Aristotle Classifier and provide several examples of its utility for biomarker studies and other classification problems using glycomic data from several sources.
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Affiliation(s)
- David Hua
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | | | - Eden P Go
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Heather Desaire
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
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Colhoun HO, Rubio Gozalbo EM, Bosch AM, Knerr I, Dawson C, Brady J, Galligan M, Stepien K, O’Flaherty R, Catherine Moss C, Peter Barker P, Fitzgibbon M, Doran PP, Treacy EP. Fertility in classical galactosaemia, a study of N-glycan, hormonal and inflammatory gene interactions. Orphanet J Rare Dis 2018; 13:164. [PMID: 30231941 PMCID: PMC6146524 DOI: 10.1186/s13023-018-0906-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 09/03/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Classical Galactosaemia (CG) (OMIM #230400) is a rare inborn error of galactose metabolism caused by deficiency of the enzyme galactose-1-phosphate uridylyltransferase (GALT). Long-term complications persist in treated patients despite dietary galactose restriction with significant variations in outcomes suggesting epigenetic glycosylation influences. Primary Ovarian Insufficiency (POI) is a very significant complication affecting females with follicular depletion noted in early life. We studied specific glycan synthesis, leptin system and inflammatory gene expression in white blood cells as potential biomarkers of infertility in 54 adults with CG adults (27 females and 27 males) (age range 17-51 yr) on a galactose-restricted diet in a multi-site Irish and Dutch study. Gene expression profiles were tested for correlation with a serum Ultra-high Performance Liquid Chromatography (UPLC)-Immunoglobulin (IgG)-N-glycan galactose incorporation assay and endocrine measurements. RESULTS Twenty five CG females (93%) had clinical and biochemical evidence of POI. As expected, the CG female patients, influenced by hormone replacement therapy, and the healthy controls of both genders showed a positive correlation between log leptin and BMI but this correlation was not apparent in CG males. The strongest correlations between serum leptin levels, hormones, G-ratio (galactose incorporation assay) and gene expression data were observed between leptin, its gene and G-Ratios data (rs = - 0.68) and (rs = - 0.94) respectively with lower circulating leptin in CG patients with reduced IgG galactosylation. In CG patients (males and females analysed as one group), the key glycan synthesis modifier genes MGAT3 and FUT8, which influence glycan chain bisecting and fucosylation and subsequent cell signalling and adhesion, were found to be significantly upregulated (p < 0.01 and p < 0.05) and also the glycan synthesis gene ALG9 (p < 0.01). Both leptin signalling genes LEP and LEPR were found to be upregulated (p < 0.01) as was the inflammatory genes ANXA1 and ICAM1 and the apoptosis gene SEPT4 (p < 0.01). CONCLUSIONS These results validate our previous findings and provide novel experimental evidence for dysregulation of genes LEP, LEPR, ANXA1, ICAM1 and SEPT4 for CG patients and combined with our findings of abnormalities of IgG glycosylation, hormonal and leptin analyses elaborate on the systemic glycosylation and cell signalling abnormalities evident in CG which likely influence the pathophysiology of POI.
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Affiliation(s)
| | - Estela M. Rubio Gozalbo
- Department of Pediatrics and Department of Clinical Genetics, Maastricht University Medical Centre, P. Debyelaan 25, PO Box 5800, 6202 AZ Maastricht, The Netherlands
| | - Annet M. Bosch
- Department of Pediatrics, Division of Metabolic Disorders, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Ina Knerr
- National Centre for Inherited Metabolic Disorders, Temple Street Children’s University Hospital and Mater Misericordiae University Hospital, Dublin, Ireland
| | - Charlotte Dawson
- Department of Endocrinology, University Hospitals Birmingham NHS Foundation Trust, B15 2TH, Birmingham, UK
| | - Jennifer Brady
- Department of Clinical Biochemistry and Diagnostic Endocrinology, The Mater Misericordiae University Hospital, Dublin, Ireland
| | | | - Karolina Stepien
- Mark Holland Metabolic Unit, Salford Royal NHS Foundation Trust, M6 8HD, Manchester, UK
| | - Roisin O’Flaherty
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and Training, Mount Merrion, Blackrock, Co, Dublin, Ireland
| | - C. Catherine Moss
- Core Genomics Facility, Conway Institute, University College, Dublin, Ireland
| | - P. Peter Barker
- Core Biochemical Assay Laboratory (CBAL), Clinical Biochemistry, Box 232, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ UK
| | - Maria Fitzgibbon
- Department of Clinical Biochemistry and Diagnostic Endocrinology, The Mater Misericordiae University Hospital, Dublin, Ireland
| | | | - Eileen P. Treacy
- Department of Paediatrics, Trinity College, Dublin, Ireland
- National Centre for Inherited Metabolic Disorders, Temple Street Children’s University Hospital and Mater Misericordiae University Hospital, Dublin, Ireland
- National Centre for Inherited Metabolic Diseases, The Mater Misericordiae University Hospital, Eccles St, Dublin, 7 Ireland
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