BACKGROUND: Sialic acid storage diseases (SSDs) are severe autosomal recessive neurodegenerative disorders caused by a transport defect across the lysosomal membrane, which leads to accumulation of sialic acid in tissues, fibroblasts, and urine. Defective free sialic acid transport can be established by quantification of free sialic acid in urine. METHODS: Urine sample size was adjusted to the equivalent of 100 nmol of creatinine. After addition of 2-keto-3-deoxy-d-glycero-d-galactonononic acid as internal standard, samples were diluted with water to an end volume of 250 microL. We used 10 microL for HPLC-tandem mass spectrometric analysis in the negative electrospray ionization mode, monitoring transitions m/z 308.3-->m/z 86.9 (sialic acid) and m/z 267.2-->m/z 86.9 (internal standard). The overall method was validated and studied for ion suppression, interfering compounds, and pH effects. Samples from controls (n = 72) and SSD patients (n = 3) were analyzed. RESULTS: The limit of detection was 3 micromol/L. Intraassay imprecision (CV; n = 10) was 6%, 3%, and 2% at 30, 130, and 1000 mmol/mol creatinine, respectively; corresponding interassay CV (n = 10) were 5%, 5%, and 2%. Recovery was 109% (100-1000 mmol/mol creatinine). The mean (SD) [range] excretion rates (mmol/mol creatinine) were 31.3 (16.6) [0.7-56.9] at 0-1 year (n = 20), 21.2 (9.8) [6.3-38.3] at 1-3 years (n = 15), 14.4 (8.2) [1.7-32.9] at 3-10 years (n = 25), and 4.6 (2.6) [0-9.8] above age 10 years (n = 12). SSD patients 1.2, 3.9, and 12 years of age had concentrations of 111.5, 54.2, and 36.1 mmol/mol creatinine, respectively. CONCLUSIONS: The HPLC-tandem MS method for free sialic acid in urine is more rapid, accurate, sensitive, selective, and robust than earlier methods and may serve as a candidate reference method for free sialic acid in diagnosis of SSD.
BACKGROUND:Sialic acid storage diseases (SSDs) are severe autosomal recessive neurodegenerative disorders caused by a transport defect across the lysosomal membrane, which leads to accumulation of sialic acid in tissues, fibroblasts, and urine. Defective free sialic acid transport can be established by quantification of free sialic acid in urine. METHODS: Urine sample size was adjusted to the equivalent of 100 nmol of creatinine. After addition of 2-keto-3-deoxy-d-glycero-d-galactonononic acid as internal standard, samples were diluted with water to an end volume of 250 microL. We used 10 microL for HPLC-tandem mass spectrometric analysis in the negative electrospray ionization mode, monitoring transitions m/z 308.3-->m/z 86.9 (sialic acid) and m/z 267.2-->m/z 86.9 (internal standard). The overall method was validated and studied for ion suppression, interfering compounds, and pH effects. Samples from controls (n = 72) and SSDpatients (n = 3) were analyzed. RESULTS: The limit of detection was 3 micromol/L. Intraassay imprecision (CV; n = 10) was 6%, 3%, and 2% at 30, 130, and 1000 mmol/mol creatinine, respectively; corresponding interassay CV (n = 10) were 5%, 5%, and 2%. Recovery was 109% (100-1000 mmol/mol creatinine). The mean (SD) [range] excretion rates (mmol/mol creatinine) were 31.3 (16.6) [0.7-56.9] at 0-1 year (n = 20), 21.2 (9.8) [6.3-38.3] at 1-3 years (n = 15), 14.4 (8.2) [1.7-32.9] at 3-10 years (n = 25), and 4.6 (2.6) [0-9.8] above age 10 years (n = 12). SSDpatients 1.2, 3.9, and 12 years of age had concentrations of 111.5, 54.2, and 36.1 mmol/mol creatinine, respectively. CONCLUSIONS: The HPLC-tandem MS method for free sialic acid in urine is more rapid, accurate, sensitive, selective, and robust than earlier methods and may serve as a candidate reference method for free sialic acid in diagnosis of SSD.
Authors: Maria I Solano; Adrian R Woolfitt; Tracie L Williams; Carrie L Pierce; Larisa V Gubareva; Vasiliy Mishin; John R Barr Journal: Anal Chem Date: 2017-02-21 Impact factor: 6.986
Authors: Xiao-Yan Wen; Maja Tarailo-Graovac; Koroboshka Brand-Arzamendi; Anke Willems; Bojana Rakic; Karin Huijben; Afitz Da Silva; Xuefang Pan; Suzan El-Rass; Robin Ng; Katheryn Selby; Anju Mary Philip; Junghwa Yun; X Cynthia Ye; Colin J Ross; Anna M Lehman; Fokje Zijlstra; N Abu Bakar; Britt Drögemöller; Jacqueline Moreland; Wyeth W Wasserman; Hilary Vallance; Monique van Scherpenzeel; Farhad Karbassi; Martin Hoskings; Udo Engelke; Arjan de Brouwer; Ron A Wevers; Alexey V Pshezhetsky; Clara Dm van Karnebeek; Dirk J Lefeber Journal: JCI Insight Date: 2018-12-20
Authors: Clara D M van Karnebeek; Luisa Bonafé; Xiao-Yan Wen; Maja Tarailo-Graovac; Sara Balzano; Beryl Royer-Bertrand; Angel Ashikov; Livia Garavelli; Isabella Mammi; Licia Turolla; Catherine Breen; Dian Donnai; Valérie Cormier-Daire; Delphine Heron; Gen Nishimura; Shinichi Uchikawa; Belinda Campos-Xavier; Antonio Rossi; Thierry Hennet; Koroboshka Brand-Arzamendi; Jacob Rozmus; Keith Harshman; Brian J Stevenson; Enrico Girardi; Giulio Superti-Furga; Tammie Dewan; Alissa Collingridge; Jessie Halparin; Colin J Ross; Margot I Van Allen; Andrea Rossi; Udo F Engelke; Leo A J Kluijtmans; Ed van der Heeft; Herma Renkema; Arjan de Brouwer; Karin Huijben; Fokje Zijlstra; Torben Heise; Thomas Boltje; Wyeth W Wasserman; Carlo Rivolta; Sheila Unger; Dirk J Lefeber; Ron A Wevers; Andrea Superti-Furga Journal: Nat Genet Date: 2016-05-23 Impact factor: 38.330
Authors: Yonnie Wu; Richard C Laughlin; David C Henry; Darryl E Krueger; JoAn S Hudson; Cheng-Yi Kuan; Jian He; Jason Reppert; Jeffrey P Tomkins Journal: BMC Cell Biol Date: 2007-08-16 Impact factor: 4.241