Congenital disorders of N-glycosylation (CDG) are a large group of rare metabolic disorders caused by defects in the most common post-translational modification of proteins. CDGs are often difficult to diagnose as they are manifested with non-specific symptoms and signs. Analysis of serum transferrin (TRF) isoforms, as the classical procedure used to identify a CDG patient, enables to predict pathological steps in the N-linked glycosylation process. We devised a new strategy based on liquid chromatography-mass spectrometry (LC-MS) for the analysis of TRF isoforms by combining a simple and fast sample preparation with a specific chromatographic cleanup/separation step followed by mass-spectrometric measurement. Single TRF isoform masses were obtained through reconstruction of multiply charged electrospray data collected by quadrupole-MS technology. Hereby, we report the first analyzed serum samples obtained from 20 CDG patients and 100 controls. The ratio of desialylated isoforms to total TRF was calculated for patients and controls. CDG-Type I patients showed higher amounts of bi-sialo isoform (range: 6.7-29.6%) compared to controls (<5.5%, mean percentage 3.9%). CDG-Type II pattern showed an increased peak of tri-sialo isoforms. The mean percentage of tri-sialo-TRF was 9.3% (range: 2.9-12.9%) in controls, which was lower than that obtained from two patients with COG5-CDG and MAN1B1-CDG (18.5 and 24.5%). Intraday and between-day imprecisions were less than 9 and 16%, respectively, for bi-sialo- A nd less than 3 and 6% for tri-sialo-TRF. This LC-MS-based approach provides a simple, sensitive and fast analytical tool for characterizing CDG disorders in a routine clinical biochemistry while improving diagnostic accuracy and speeding clinical decision-making.

A new strategy implementing mass spectrometry in the diagnosis of congenital disorders of n-glycosylation (cdg) / Casetta B.; Malvagia S.; Funghini S.; Martinelli D.; Dionisi-Vici C.; Barone R.; Fiumara A.; Donati M.A.; Guerrini R.; La Marca G.. - In: CLINICAL CHEMISTRY AND LABORATORY MEDICINE. - ISSN 1434-6621. - STAMPA. - 0:(2020), pp. 1-10. [10.1515/cclm-2020-0650]

A new strategy implementing mass spectrometry in the diagnosis of congenital disorders of n-glycosylation (cdg)

Guerrini R.;La Marca G.
Conceptualization
2020

Abstract

Congenital disorders of N-glycosylation (CDG) are a large group of rare metabolic disorders caused by defects in the most common post-translational modification of proteins. CDGs are often difficult to diagnose as they are manifested with non-specific symptoms and signs. Analysis of serum transferrin (TRF) isoforms, as the classical procedure used to identify a CDG patient, enables to predict pathological steps in the N-linked glycosylation process. We devised a new strategy based on liquid chromatography-mass spectrometry (LC-MS) for the analysis of TRF isoforms by combining a simple and fast sample preparation with a specific chromatographic cleanup/separation step followed by mass-spectrometric measurement. Single TRF isoform masses were obtained through reconstruction of multiply charged electrospray data collected by quadrupole-MS technology. Hereby, we report the first analyzed serum samples obtained from 20 CDG patients and 100 controls. The ratio of desialylated isoforms to total TRF was calculated for patients and controls. CDG-Type I patients showed higher amounts of bi-sialo isoform (range: 6.7-29.6%) compared to controls (<5.5%, mean percentage 3.9%). CDG-Type II pattern showed an increased peak of tri-sialo isoforms. The mean percentage of tri-sialo-TRF was 9.3% (range: 2.9-12.9%) in controls, which was lower than that obtained from two patients with COG5-CDG and MAN1B1-CDG (18.5 and 24.5%). Intraday and between-day imprecisions were less than 9 and 16%, respectively, for bi-sialo- A nd less than 3 and 6% for tri-sialo-TRF. This LC-MS-based approach provides a simple, sensitive and fast analytical tool for characterizing CDG disorders in a routine clinical biochemistry while improving diagnostic accuracy and speeding clinical decision-making.
2020
0
1
10
Goal 3: Good health and well-being for people
Casetta B.; Malvagia S.; Funghini S.; Martinelli D.; Dionisi-Vici C.; Barone R.; Fiumara A.; Donati M.A.; Guerrini R.; La Marca G.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1210456
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