AIMS: We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively. METHODS: Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses. RESULTS: We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%. CONCLUSIONS: A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.
AIMS: We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively. METHODS: Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses. RESULTS: We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%. CONCLUSIONS: A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.
Authors: Srini Godevithanage; Piyumi P Kanankearachchi; Mahanama P Dissanayake; Thilak A Jayalath; Nimal Chandrasiri; Rangani P Jinasena; Ranjith P V Kumarasiri; Chulananda D A Goonasekera Journal: Kidney Blood Press Res Date: 2010-07-10 Impact factor: 2.687
Authors: Kirill A Veselkov; Lisa K Vingara; Perrine Masson; Steven L Robinette; Elizabeth Want; Jia V Li; Richard H Barton; Claire Boursier-Neyret; Bernard Walther; Timothy M Ebbels; István Pelczer; Elaine Holmes; John C Lindon; Jeremy K Nicholson Journal: Anal Chem Date: 2011-07-05 Impact factor: 6.986
Authors: Hana Janeckova; Alzbeta Kalivodova; Lukas Najdekr; David Friedecky; Karel Hron; Per Bruheim; Tomas Adam Journal: Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub Date: 2014-11-07 Impact factor: 1.245
Authors: Ana Carolina P Souza; Roberto Zatz; Rodrigo B de Oliveira; Mirela A R Santinho; Marcia Ribalta; João E Romão; Rosilene M Elias Journal: BMC Nephrol Date: 2015-04-08 Impact factor: 2.388
Authors: Karien Esterhuizen; J Zander Lindeque; Shayne Mason; Francois H van der Westhuizen; Richard J Rodenburg; Paul de Laat; Jan A M Smeitink; Mirian C H Janssen; Roan Louw Journal: Metabolomics Date: 2021-01-12 Impact factor: 4.290
Authors: Adam D Kennedy; Kirk L Pappan; Taraka R Donti; Anne M Evans; Jacob E Wulff; Luke A D Miller; V Reid Sutton; Qin Sun; Marcus J Miller; Sarah H Elsea Journal: Mol Genet Metab Date: 2017-04-09 Impact factor: 4.797
Authors: Dana Marafi; Jawid M Fatih; Rauan Kaiyrzhanov; Matteo P Ferla; Charul Gijavanekar; Aljazi Al-Maraghi; Ning Liu; Emily Sites; Hessa S Alsaif; Mohammad Al-Owain; Mohamed Zakkariah; Ehab El-Anany; Ulviyya Guliyeva; Sughra Guliyeva; Colette Gaba; Ateeq Haseeb; Amal M Alhashem; Enam Danish; Vasiliki Karageorgou; Christian Beetz; Alaa A Subhi; Sureni V Mullegama; Erin Torti; Monisha Sebastin; Margo Sheck Breilyn; Susan Duberstein; Mohamed S Abdel-Hamid; Tadahiro Mitani; Haowei Du; Jill A Rosenfeld; Shalini N Jhangiani; Zeynep Coban Akdemir; Richard A Gibbs; Jenny C Taylor; Khalid A Fakhro; Jill V Hunter; Davut Pehlivan; Maha S Zaki; Joseph G Gleeson; Reza Maroofian; Henry Houlden; Jennifer E Posey; V Reid Sutton; Fowzan S Alkuraya; Sarah H Elsea; James R Lupski Journal: Brain Date: 2022-04-29 Impact factor: 15.255
Authors: Adam D Kennedy; Kirk L Pappan; Taraka Donti; Mauricio R Delgado; Marwan Shinawi; Toni S Pearson; Seema R Lalani; William E Craigen; V Reid Sutton; Anne M Evans; Qin Sun; Lisa T Emrick; Sarah H Elsea Journal: Front Neurosci Date: 2019-05-08 Impact factor: 4.677
Authors: Lindsay C Burrage; Lillian Thistlethwaite; Bridget M Stroup; Qin Sun; Marcus J Miller; Sandesh C S Nagamani; William Craigen; Fernando Scaglia; V Reid Sutton; Brett Graham; Adam D Kennedy; Aleksandar Milosavljevic; Brendan H Lee; Sarah H Elsea Journal: Genet Med Date: 2019-01-23 Impact factor: 8.822