Biomarkers in autism spectrum disorder: the old and the new
- 2.7k Downloads
Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, ‘multiple-hit’, oligogenic/polygenic underpinnings involving several loci and possibly gene–environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs.
The present paper reviews (a) similarities and differences between the concepts of ‘biomarker’ and ‘endophenotype’, (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism.
Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels.
Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.
KeywordsAutism Biobank Biomarker Endophenotype Macrocephaly Melatonin Metabolomics Oxytocin Serotonin
This work was supported by the Italian Ministry for University, Scientific Research and Technology (PRIN no. 2006058195 and no. 2008BACT54_002), the Italian Ministry of Health (RFPS-2007-5-640174 and RF-2011-02350537), the Fondazione Gaetano e Mafalda Luce (Milan, Italy), Autism Aid ONLUS (Naples, Italy), Autism Speaks (Princeton, NJ), the Autism Research Institute (San Diego, CA), the European Molecular Biology Laboratory (EMBL), and the Innovative Medicines Initiative Joint Undertaking (EU-AIMS, no. 115300).
Conflict of interest
- Baron-Cohen S (1995) Mindblindness: an essay on autism and theory of mind. Bradford/MIT, CambridgeGoogle Scholar
- Buscema M, Penco S, Grossi E (2012) A novel mathematical approach to define the genes/SNPs conferring risk or protection in sporadic amyotrophic lateral sclerosis based on auto contractive map neural networks and graph theory. Neurol Res Int. doi: 10.1155/2012/478560
- Dichter GS, Felder JN, Green SR et al (2012) Reward circuitry function in autism spectrum disorders. Social Cogn Affect Neurosci 7:160–172Google Scholar
- Elsabbagh M, Bedford R, Senju A et al (2013) What you see is what you get: contextual modulation of face scanning in typical and atypical development. Soc Cogn Affect Neurosci. doi: 10.1093/scan/nst012
- Emond P, Mavel S, Aïdoud N et al (2013) GC-MS-based urine metabolic profiling of autism spectrum disorders. Anal Bioanal Chem 405:5291–5300Google Scholar
- Gabriele S, Sacco R, Persico AM (2013) Blood serotonin levels in autism spectrum disorder: a systematic review and meta-analysis. Biol Psychiatry (in press)Google Scholar
- Jacquemont S, Curie A, des Portes V et al (2011) Epigenetic modification of the FMR1 gene in fragile X syndrome is associated with differential response to the mGluR5 antagonist AFQ056. Sci Transl Med 3:64ra1Google Scholar
- King M, Bearman P (2009) Diagnostic change and the increased prevalence of autism. Intl J Epidemiol 38:1224–1234Google Scholar
- Klin A, Jones W, Schultz R (2003) The enactive mind—from actions to cognition: lessons from autism. Phil Trans R Soc Land B Biol Sci 358:345–360Google Scholar
- Kou Y, Betancur C, Xu H et al (2012) Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability. Am J Med Genet C: Semin Med Genet 160C:130–142Google Scholar
- Krestyaninova M, Tammisto Y (2012) Services design in a collaborative network for multidisciplinary research projects. IFIP Advances in Information and Communication Technology 380 AICT:273–279Google Scholar
- Malesa E, Foss-Feig J, Yoder P, et al (2012) Predicting language and social outcomes at age 5 for later-born siblings of children with autism spectrum disorders. Autism 17:558–570Google Scholar
- Nelson EK, Piehler B, Eckels J et al (2011) LabKey Server: an open source platform for scientific data integration, analysis and collaboration. BMC Bioinforma 12:71Google Scholar
- Pan S, Rush J, Peskind ER et al (2008) Application of targeted quantitative proteomics analysis in human cerebrospinal fluid using a liquid chromatography matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometer (LC MALDI TOF/TOF) platform. J Proteome Res 7:720–730PubMedGoogle Scholar
- Persico AM, Napolioni V (2013) Autism genetics. Behav Brain Res. doi: 10.1016/j.bbr.2013.06.012
- Petrinovic MM, Künnecke B (2013) Neuroimaging endophenotypes in animal models of autism spectrum disorders: lost or found in translation? Psychopharmacology. doi: 10.1007/s00213-013-3200-z
- Rayner TF, Rocca-Serra P, Spellman PT et al (2006) A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinforma 7:489Google Scholar
- Reichelt WH, Knivsberg AM, Nodland M et al (1997) Urinary peptide levels and patterns in autistic children from seven countries, and the effect of dietary intervention after 4 years. Dev Brain Dysfunct 10:44–55Google Scholar
- Skafidas E, Testa R, Zantomio D et al (2012) Predicting the diagnosis of autism spectrum disorder using gene pathway analysis. Mol Psychiatry. doi: 10.1038/mp.2012.126
- Spellman PT, Miller M, Stewart J et al (2002) Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol 3: RESEARCH0046Google Scholar
- Steffenburg S, Gillberg C, Hellgren L et al. (1989) A twin study of autism in Denmark, Finland, Iceland, Norway and Sweden. J Child Psychol Psychiatry 30:405–416Google Scholar
- Tolopko AN, Sullivan JP, Erickson SD et al (2010) Screensaver: an open source lab information management system (LIMS) for high throughput screening facilities. BMC Bioinforma 11:260Google Scholar
- UK Biobank (2007) Protocol for a large-scale prospective epidemiological resource. March 21, 2007. Available via http://www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf?phpMyAdmin=trmKQlYdjjnQIgJ%2CfAzikMhEnx6. Accessed 12 Apr 2013
- Vorstman J, Spooren W, Gee T et al (2013) Autism genetics and how genetic studies can be used in the development of pharmaceutical compounds. Psychopharmacology (in press)Google Scholar
- Wang L, Li J, Ruan Y et al (2013) Sequencing ASMT identifies rare mutations in Chinese Han patients with autism. PLoS One 8(1):e53727Google Scholar
- Zafeiriou DI, Ververi A, Dafoulis V et al (2013) Autism spectrum disorders: the quest for genetic syndromes. Am J Med Genet B Neuropsychiatr Genet 162:327–366Google Scholar