Potential serum biomarkers from a metabolomics study of autism

Journal of Psychiatry and Neuroscience

Volumn 41, Issue 1, 2016, Pages 27-37

  Wang, Han ^a   Liang, Shuang ^a   Wang, Maoqing ^a   Gao, Jingquan ^a   Sun, Caihong ^a   Wang, Jia ^a   Xia, Wei ^a   Wu, Shiying ^a   Sumner, Susan J ^c   Zhang, Fengyu ^d   Sun, Changhao ^a   Wu, Lijie ^b  

^a HARBIN MEDICAL UNIVERSITY   (China)

^b SAS INSTITUTE INC   (United States)

^c RTI INTERNATIONAL   (United States)

^d JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACETYLCARNITINE; BIOLOGICAL MARKER; DECANOYLCARNITINE; DOCOSAHEXAENOIC ACID; PHYTOSPHINGOSINE; PREGNANETRIOL; SPHINGOSINE 1 PHOSPHATE; UNCLASSIFIED DRUG; LYSOPHOSPHOLIPID; SPHINGOSINE; SPHINGOSINE 1-PHOSPHATE;

ARTICLE; AUTISM; CHILD; CONTROLLED STUDY; ELECTROSPRAY; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; METABOLOMICS; PRESCHOOL CHILD; QUESTIONNAIRE; SENSITIVITY AND SPECIFICITY; TIME OF FLIGHT MASS SPECTROMETRY; ULTRA PERFORMANCE LIQUID CHROMATOGRAPHY; VALIDATION STUDY; ANALOGS AND DERIVATIVES; ASIAN CONTINENTAL ANCESTRY GROUP; BLOOD; BLOOD ANALYSIS; CHINA; COHORT ANALYSIS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; LEAST SQUARE ANALYSIS; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL; TANDEM MASS SPECTROMETRY;

ASIAN CONTINENTAL ANCESTRY GROUP; AUTISTIC DISORDER; BIOMARKERS; BLOOD CHEMICAL ANALYSIS; CHILD; CHILD, PRESCHOOL; CHINA; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; COHORT STUDIES; DOCOSAHEXAENOIC ACIDS; FEMALE; HUMANS; LEAST-SQUARES ANALYSIS; LOGISTIC MODELS; LYSOPHOSPHOLIPIDS; MALE; METABOLOMICS; ROC CURVE; SPHINGOSINE; TANDEM MASS SPECTROMETRY;

EID: 84949949547     PISSN: 11804882     EISSN: 14882434     Source Type: Journal    
DOI: 10.1503/jpn.140009     Document Type: Article

References (83)

1. Bailey A, Phillips W, Rutter M. Autism: towards an integration of clinical, genetic, neuropsychological, and neurobiological perspectives. J Child Psychol Psychiatry 1996;37:89-126
2. Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention (CDC). Prevalence of autism spectrum disorder among children aged 8 years — autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ. 2014;63:1-21
3. Hallmayer J, Glasson EJ, Bower C, et al. On the twin risk in autism. Am J Hum Genet 2002;71:941-6
4. Wang K, Zhang H, Ma D, et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 2009;459: 528-33
5. Durand CM, Betancur C, Boeckers TM, et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat Genet 2007;39:25-7
6. Sebat J, Lakshmi B, Malhotra D, et al. Strong association of de novo copy number mutations with autism. Science 2007;316:445-9
7. Weiss LA, Shen Y, Korn JM, et al. Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med 2008;358: 667-75
8. Glessner JT, Wang K, Cai G, et al. Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 2009;459:569-73
9. Weiss LA, Arking DE, Daly MJ, et al. A genome-wide linkage and association scan reveals novel loci for autism. Nature 2009;461:802-8
10. Sanders SJ, Ercan-Sencicek AG, Hus V, et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 2011; 70:863-85
11. Sanders SJ, Murtha MT, Gupta AR, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 2012;485:237-41
12. O’Roak BJ, Vives L, Girirajan S, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 2012;485:246-50
13. Neale BM, Kou Y, Liu L, et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 2012;485:242-5
14. State MW, Sestan N. Neuroscience. The emerging biology of autism spectrum disorders. Science 2012;337:1301-3
15. Anney R, Klei L, Pinto D, et al. A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet 2010;19:4072-82
16. Xia K, Guo H, Hu Z, et al. Common genetic variants on 1p132 associate with risk of autism. Mol Psychiatry 2014;19:1212-9.
17. Li M, Wang B, Zhang M, et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci U S A 2008;105: 2117-22
18. Serkova NJ, Jackman M, Brown JL, et al. Metabolic profiling of livers and blood from obese Zucker rats. J Hepatol 2006;44:956-62
19. Gloyn AL, Faber JH, Malmodin D, et al. Metabolic profiling in Maturity- onset diabetes of the young (MODY) and young onset type 2 diabetes fails to detect robust urinary biomarkers. PLoS ONE 2012; 7:e40962
20. Brindle JT, Antti H, Holmes E, et al. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 2002;8:1439-44
21. Kaddurah-Daouk R, Krishnan KR. Metabolomics: a global biochemical approach to the study of central nervous system diseases. Neuropsychopharmacology 2009;34:173-86
22. Yang J, Chen T, Sun L, et al. Potential metabolite markers of schizophrenia. Mol Psychiatry 2013;18:67-78
23. He Y, Yu Z, Giegling I, et al. Schizophrenia shows a unique metabolomics signature in plasma. Transl Psychiatry 2012;2:e149
24. Yap IK, Angley M, Veselkov KA, et al. Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls. J Proteome Res 2010;9:2996-3004
25. El-Ansary AK, Bacha AG, Al-Ayahdi LY. Plasma fatty acids as diagnostic markers in autistic patients from Saudi Arabia. Lipids Health Dis 2011;10:62
26. Ming X, Stein TP, Barnes V, et al. Metabolic perturbance in autism spectrum disorders: a metabolomics study. J Proteome Res 2012;11: 5856-62
27. Emond P, Mavel S, Aïdoud N, et al. GC-MS-based urine metabolic profiling of autism spectrum disorders. Anal Bioanal Chem 2013;405: 5291-300
28. Kuwabara H, Yamasue H, Koike S, et al. Altered metabolites in the plasma of autism spectrum disorder: a capillary electrophoresis time-of-flight mass spectroscopy study. PLoS ONE 2013;8:e73814
29. Wilson ID, Nicholson JK, Castro-Perez J, et al. High resolution “ultra performance” liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 2005;4:591-8
30. O’Connor D, Mortishire-Smith R. High-throughput bioanalysis with simultaneous acquisition of metabolic route data using ultra performance liquid chromatography coupled with time-of-flight mass spectrometry. Anal Bioanal Chem 2006;385:114-21
31. Wang M, Yang X, Wang F, et al. Calcium-deficiency assessment and biomarker identification by an integrated urinary metabonomics analysis. BMC Med 2013;11:86
32. Fan L, Zhang W, Yin M, et al. Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform. Acta Oncol (Madr) 2012;51:473-9
33. Wang C, Feng R, Sun D, et al. Metabolic profiling of urine in young obese men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC/Q-TOF MS). J Chromatogr B Analyt Technol Biomed Life Sci 2011;879:2871-6
34. Volkmar FR, Cicchetti DV, Dykens E, et al. An evaluation of the Autism Behavior Checklist. J Autism Dev Disord 1988;18:81-97
35. Ozonoff S, Goodlin-Jones BL, Solomon M. Evidence-based assessment of autism spectrum disorders in children and adolescents. J Clin Child Adolesc Psychol 2005;34:523-40
36. Frankenburg WK, Dodds JB. The Denver developmental screening test. J Pediatr 1967;71:181-91
37. First LR, Palfrey JS. The infant or young child with developmental delay. N Engl J Med 1994;330:478-83
38. Bijlsma S, Bobeldijk I, Verheij ER, et al. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem 2006;78:567-74
39. Sangster T, Major H, Plumb R, et al. A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 2006;131:1075-8
40. Gika HG, Theodoridis GA, Wingate JE, et al. Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine. J Proteome Res 2007;6:3291-303
41. Guidance for Industry Bioanalytical Method Validation. US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Veterinary Medicine (CVM) 2013:5
42. Helsel DR. Summing nondetects: incorporating low-level contaminants in risk assessment. Integr Environ Assess Manag 2010;6:361-6
43. Baccarelli A, Pfeiffer R, Consonni D, et al. Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study. Chemosphere 2005;60:898-906
44. Jensen ON, Podtelejnikov AV, Mann M. Identification of the components of simple protein mixtures by high-accuracy peptide mass mapping and database searching. Anal Chem 1997;69:4741-50
45. Lim H, Eng J, Yates III Jr, et al. Identification of 2D-gel proteins: a comparison of MALDI/TOF peptide mass mapping to μ LC-ESI tandem mass spectrometry. J Am Soc Mass Spectrom 2003;14:957-70
46. Pelander A, Ojanperä I, Laks S, et al. Toxicological screening with formula-based metabolite identification by liquid chromatography/time- of-flight mass spectrometry. Anal Chem 2003;75:5710-8
47. Mahadevan S, Shah SL, Marrie TJ, et al. Analysis of metabolomic data using support vector machines. Anal Chem 2008;80:7562-70
48. Szymanska E, Saccenti E, Smilde AK, et al. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics 2012;8:3-16
49. Westerhuis JA, van Velzen EJ, Hoefsloot HC, et al. Discriminant Q2 (DQ2) for improved discrimination in PLSDA models. Metabolomics 2008;4:293-6
50. Westerhuis JA, Hoefsloot HC, Smit S, et al. Assessment of PLSDA cross validation. Metabolomics 2008;4:81-9
51. Jones OA, Spurgeon DJ, Svendsen C, et al. A metabolomics based approach to assessing the toxicity of the polyaromatic hydrocarbon pyrene to the earthworm Lumbricus rubellus. Chemosphere 2008;71:601-9
52. Perez-Enciso M, Tenenhaus M. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Hum Genet 2003;112:581-92
53. Zhang T, Wu X, Yin M, et al. Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry. Clin Chim Acta 2012;413:861-8
54. Li S, Liu H, Jin Y, et al. Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice. J Chromatogr B Analyt Technol Biomed Life Sci 2011;879:2369-75
55. Wiest MM, German JB, Harvey DJ, et al. Plasma fatty acid profiles in autism: a case-control study. Prostaglandins Leukot Essent Fatty Acids 2009;80:221-7
56. Bell JG, Miller D, MacDonald DJ, et al. The fatty acid compositions of erythrocyte and plasma polar lipids in children with autism, developmental delay or typically developing controls and the effect of fish oil intake. Br J Nutr 2010;103:1160-7
57. Stillwell W, Wassall SR. Docosahexaenoic acid: membrane properties of a unique fatty acid. Chem Phys Lipids 2003;126:1-27
58. Jump DB. The biochemistry of n-3 polyunsaturated fatty acids. J Biol Chem 2002;277:8755-8
59. Muskiet FA, Fokkema MR, Schaafsma A, et al. Is docosahexaenoic acid (DHA) essential? Lessons from DHA status regulation, our ancient diet, epidemiology and randomized controlled trials. J Nutr 2004;134:183-6
60. Florent S, Malaplate-Armand C, Youssef I, et al. Docosahexaenoic acid prevents neuronal apoptosis induced by soluble amyloid-beta oligomers. J Neurochem 2006;96:385-95
61. Nabekura J, Noguchi K, Witt MR, et al. Functional modulation of human recombinant gamma-aminobutyric acid type A receptor by docosahexaenoic acid. J Biol Chem 1998;273:11056-61
62. Kim HY, Akbar M, Lau A, et al. Inhibition of neuronal apoptosis by docosahexaenoic acid (22:6n-3). Role of phosphatidylserine in antiapoptotic effect. J Biol Chem 2000;275:35215-23
63. Bazan NG. Lipid signaling in neural plasticity, brain repair, and neuroprotection. Mol Neurobiol 2005;32:89-103
64. Tamiji J, Crawford DA. The neurobiology of lipid metabolism in autism spectrum disorders. Neurosignals 2010;18:98-112
65. Burdge GC, Jones AE, Wootton SA. Eicosapentaenoic and docosapentaenoic acids are the principal products of alpha-linolenic acid metabolism in young men*. Br J Nutr 2002;88:355-63
66. Haggarty P, Ashton J, Joynson M, et al. Effect of maternal polyunsaturated fatty acid concentration on transport by the human placenta. Biol Neonate 1999;75:350-9
67. Anderson GJ, Connor WE, Corliss JD. Docosahexaenoic acid is the preferred dietary n-3 fatty acid for the development of the brain and retina. Pediatr Res 1990;27:89-97
68. Kurlak LO, Stephenson TJ. Plausible explanations for effects of long chain polyunsaturated fatty acids (LCPUFA) on neonates. Arch Dis Child Fetal Neonatal Ed 1999;80:F148-54
69. Amminger GP, Berger GE, Schafer MR, et al. Omega-3 fatty acids supplementation in children with autism: a double-blind randomized, placebo-controlled pilot study. Biol Psychiatry 2007;61:551-3
70. Yui K, Koshiba M, Nakamura S, et al. Effects of large doses of arachidonic acid added to docosahexaenoic acid on social impairment in individuals with autism spectrum disorders: a double-blind, placebo-controlled, randomized trial. J Clin Psychopharmacol 2012;32:200-6
71. Richardson AJ, Ross MA. Fatty acid metabolism in neurodevelopmental disorder: a new perspective on associations between attention-deficit/hyperactivity disorder, dyslexia, dyspraxia and the autistic spectrum. Prostaglandins Leukot Essent Fatty Acids 2000;63:1-9
72. Heipertz R, Pilz H, Scholz W. The fatty acid composition of sphingomyelin from adult human cerebral white matter and changes in childhood, senium and unspecific brain damage. J Neurol 1977;216: 57-65
73. Sugita M, Dulaney JT, Moser HW. Structure and composition of sulfatides isolated from livers of patients with metachromatic leukodystrophy: galactosyl sulfatide and lactosyl sulfatide. J Lipid Res 1974;15:227-33
74. Barnea-Goraly N, Kwon H, Menon V, et al. White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry 2004;55:323-6
75. Mak-Fan KM, Morris D, Vidal J, et al. White matter and development in children with an autism spectrum disorder. Autism 2013;17:541-57
76. Spiegel S, Milstien S. The outs and the ins of sphingosine-1-phosphate in immunity. Nat Rev Immunol 2011;11:403-15
77. Schaefer GB, Lutz RE. Diagnostic yield in the clinical genetic evaluation of autism spectrum disorders. Genet Med 2006;8:549-56
78. Filipek PA, Juranek J, Nguyen MT, et al. Relative carnitine deficiency in autism. J Autism Dev Disord 2004;34:615-23
79. Celestino-Soper PB, Violante S, Crawford EL, et al. A common X-linked inborn error of carnitine biosynthesis may be a risk factor for nondysmorphic autism. Proc Natl Acad Sci U S A 2012;109:7974-81
80. Chauhan A, Gu F, Essa M, et al. Brain region-specific deficit in mitochondrial electron transport chain complexes in children with autism. J Neurochem 2011;117:209-20
81. Torrioli MG, Vernacotola S, Peruzzi L, et al. A double‐blind, parallel, multicenter comparison of L‐acetylcarnitine with placebo on the attention deficit hyperactivity disorder in fragile X syndrome boys. Am J Med Genet A 2008;146A:803-12
82. St-Pierre MV, Kullak-Ublick GA, Hagenbuch B, et al. Transport of bile acids in hepatic and non-hepatic tissues. J Exp Biol 2001;204: 1673-86
83. Nemoto Y, Toda K, Ono M, et al. Altered expression of fatty acid-metabolizing enzymes in aromatase-deficient mice. J Clin Invest 2000;105:1819-25.