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Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity

Molecular Psychiatry volume 20, pages 118–125 (2015)Cite this article

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Abstract

Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1–2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1α, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.

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References

  1. Abrahams BS, Geschwind DH . Connecting genes to brain in the autism spectrum disorders. Arch Neurol 2010; 67: 395–399.

    Article  Google Scholar 

  2. Geschwind DH . Genetics of autism spectrum disorders. Trends Cogn Sci (Regul Ed) 2011; 15: 409–416.

    Article  Google Scholar 

  3. Lord C, Risi S, DiLavore PS, Shulman C, Thurm A, Pickles A . Autism from 2 to 9 years of age. Arch Gen Psychiatry 2006; 63: 694–701.

    Article  Google Scholar 

  4. Huerta M, Lord C . Diagnostic evaluation of autism spectrum disorders. Pediatr Clin North Am 2012; 59: 103–11–xi.

    Article  Google Scholar 

  5. Amaral DG . The promise and the pitfalls of autism research: an introductory note for new autism researchers. Brain Res 2011; 1380: 3–9.

    Article  CAS  Google Scholar 

  6. Canitano R . Novel treatments in autism spectrum disorders: from synaptic dysfunction to experimental therapeutics. Behav Brain Res2012; 251: 125–132.

    Article  Google Scholar 

  7. Veenstra-VanderWeele J, Blakely RD . Networking in autism: leveraging genetic, biomarker and model system findings in the search for new treatments. Neuropsychopharmacology 2012; 37: 196–212.

    Article  CAS  Google Scholar 

  8. Stessman HA, Bernier R, Eichler EE . A genotype-first approach to defining the subtypes of a complex disease. Cell 2014; 156: 872–877.

    Article  CAS  Google Scholar 

  9. Hrdlicka M, Dudova I, Beranova I, Lisy J, Belsan T, Neuwirth J et al. Subtypes of autism by cluster analysis based on structural MRI data. Eur Child Adolesc Psychiatry 2005; 14: 138–144.

    Article  Google Scholar 

  10. Banerjee-Basu S, Packer A . SFARI gene: an evolving database for the autism research community. Dis Model Mech 2010; 3: 133–135.

    Article  Google Scholar 

  11. Nakatani J, Tamada K, Hatanaka F, Ise S, Ohta H, Inoue K et al. Abnormal behavior in a chromosome-engineered mouse model for human 15q11-13 duplication seen in autism. Cell 2009; 137: 1235–1246.

    Article  Google Scholar 

  12. Horev G, Ellegood J, Lerch JP, Son Y-EE, Muthuswamy L, Vogel H et al. Dosage-dependent phenotypes in models of 16p11.2 lesions found in autism. Proc Natl Acad Sci USA 2011; 108: 17076–17081.

    Article  Google Scholar 

  13. Tabuchi K, Blundell J, Etherton MR, Hammer RE, Liu X, Powell CM et al. A neuroligin-3 mutation implicated in autism increases inhibitory synaptic transmission in mice. Science 2007; 318: 71–76.

    Article  CAS  Google Scholar 

  14. Moy SS, Nadler JJ, Young NB, Perez A, Holloway LP, Barbaro RP et al. Mouse behavioral tasks relevant to autism: phenotypes of 10 inbred strains. Behav Brain Res 2007; 176: 4–20.

    Article  Google Scholar 

  15. International Mouse Knockout Consortium, International Mouse Knockout Consortium, Collins FS, International Mouse Knockout Consortium, Rossant J, International Mouse Knockout Consortium, Wurst W . A mouse for all reasons. Cell 2007; 128: 9–13.

    Article  Google Scholar 

  16. Chadman KK, Gong S, Scattoni ML, Boltuck SE, Gandhy SU, Heintz N et al. Minimal aberrant behavioral phenotypes of neuroligin-3 R451C knockin mice. Autism Res 2008; 1: 147–158.

    Article  Google Scholar 

  17. Etherton M, Földy C, Sharma M, Tabuchi K, Liu X, Shamloo M et al. Autism-linked neuroligin-3 R451C mutation differentially alters hippocampal and cortical synaptic function. Proc Natl Acad Sci USA 2011; 108: 13764–13769.

    Article  CAS  Google Scholar 

  18. Lerch JP, Gazdzinski L, Germann J, Sled JG, Henkelman RM, Nieman BJ . Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse. Front Neuroinform 2012; 6: 6.

    Article  Google Scholar 

  19. van Eede MC, Scholz J, Chakravarty MM, Henkelman RM, Lerch JP . Mapping registration sensitivity in MR mouse brain images. Neuroimage 2013; 82: 226–236.

    Article  Google Scholar 

  20. Bock NA, Konyer NB, Henkelman RM . Multiple-mouse MRI. Magn Reson Med 2003; 49: 158–167.

    Article  Google Scholar 

  21. Lerch JP, Sled JG, Henkelman RM . MRI phenotyping of genetically altered mice. Methods Mol Biol 2011; 711: 349–361.

    Article  CAS  Google Scholar 

  22. Ellegood J, Babineau BA, Henkelman RM, Lerch JP, Crawley JN . Neuroanatomical analysis of the BTBR mouse model of autism using magnetic resonance imaging and diffusion tensor imaging. Neuroimage 2013; 70: 288–300.

    Article  Google Scholar 

  23. Nieman BJ, Lerch JP, Bock NA, Chen XJ, Sled JG, Henkelman RM . Mouse behavioral mutants have neuroimaging abnormalities. Hum Brain Mapp 2007; 28: 567–575.

    Article  Google Scholar 

  24. Lerch JP, Yiu AP, Martinez-Canabal A, Pekar T, Bohbot VD, Frankland PW et al. Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning. Neuroimage 2011; 54: 2086–2095.

    Article  Google Scholar 

  25. Brodkin ES . BALB/c mice: low sociability and other phenotypes that may be relevant to autism. Behav Brain Res 2007; 176: 53–65.

    Article  CAS  Google Scholar 

  26. McFarlane HG, Kusek GK, Yang M, Phoenix JL, Bolivar VJ, Crawley JN . Autism-like behavioral phenotypes in BTBR T+tf/J mice. Genes Brain Behav 2008; 7: 152–163.

    Article  CAS  Google Scholar 

  27. Cahill LS, Laliberté CL, Ellegood J, Spring S, Gleave JA, Eede MCV et al. Preparation of fixed mouse brains for MRI. Neuroimage 2012; 60: 933–939.

    Article  Google Scholar 

  28. Spring S, Lerch JP, Henkelman RM . Sexual dimorphism revealed in the structure of the mouse brain using three-dimensional magnetic resonance imaging. Neuroimage 2007; 35: 1424–1433.

    Article  Google Scholar 

  29. Nieman BJ, Flenniken AM, Adamson SL, Henkelman RM, Sled JG . Anatomical phenotyping in the brain and skull of a mutant mouse by magnetic resonance imaging and computed tomography. Physiol Genomics 2006; 24: 154–162.

    Article  CAS  Google Scholar 

  30. Nieman BJ, Bock NA, Bishop J, Chen XJ, Sled JG, Rossant J et al. Magnetic resonance imaging for detection and analysis of mouse phenotypes. NMR Biomed 2005; 18: 447–468.

    Article  Google Scholar 

  31. Collins DL, Neelin P, Peters TM, Evans AC . Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 1994; 18: 192–205.

    Article  CAS  Google Scholar 

  32. Avants BB, Yushkevich P, Pluta J, Minkoff D, Korczykowski M, Detre J et al. The optimal template effect in hippocampus studies of diseased populations. Neuroimage 2010; 49: 2457–2466.

    Article  Google Scholar 

  33. Dorr AE, Lerch JP, Spring S, Kabani N, Henkelman RM . High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6 J mice. Neuroimage 2008; 42: 60–69.

    Article  CAS  Google Scholar 

  34. Suzuki R, Shimodaira H . Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 2006; 22: 1540–1542.

    Article  CAS  Google Scholar 

  35. Courchesne E, Campbell K, Solso S . Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res 2011; 1380: 138–145.

    Article  CAS  Google Scholar 

  36. Raznahan A, Wallace GL, Antezana L, Greenstein D, Lenroot R, Thurm A et al. Compared to what? Early brain overgrowth in autism and the perils of population norms. Biol Psychiatry 2013; 74: 563–575.

    Article  Google Scholar 

  37. Gleave JA, Wong MD, Dazai J, Altaf M, Henkelman RM, Lerch JP et al. Neuroanatomical phenotyping of the mouse brain with three-dimensional autofluorescence imaging. Physiol Genomics 2012; 44: 778–785.

    Article  CAS  Google Scholar 

  38. Toal F, Daly EM, Page L, Deeley Q, Hallahan B, Bloemen O et al. Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study. Psychol Med 2010; 40: 1171–1181.

    Article  CAS  Google Scholar 

  39. Amaral DG, Schumann CM, Nordahl CW . Neuroanatomy of autism. Trends Neurosci 2008; 31: 137–145.

    Article  CAS  Google Scholar 

  40. Stanfield AC, McIntosh AM, Spencer MD, Philip R, Gaur S, Lawrie SM . Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies. Eur Psychiatry 2008; 23: 289–299.

    Article  Google Scholar 

  41. Kurth F, Narr KL, Woods RP, O'Neill J, Alger JR, Caplan R et al. Diminished gray matter within the hypothalamus in autism disorder: a potential link to hormonal effects? Biol Psychiatry 2011; 70: 278–282.

    Article  Google Scholar 

  42. Fatemi SH, Aldinger KA, Ashwood P, Bauman ML, Blaha CD, Blatt GJ et al. Consensus paper: pathological role of the cerebellum in autism. Cerebellum 2012; 11: 777–807.

    Article  Google Scholar 

  43. Kana RK, Libero LE, Moore MS . Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. Phys Life Rev 2011; 8: 410–437.

    Article  Google Scholar 

  44. Ey E, Leblond CS, Bourgeron T . Behavioral profiles of mouse models for autism spectrum disorders. Autism Res 2011; 4: 5–16.

    Article  Google Scholar 

  45. Bailey KR, Rustay NR, Crawley JN . Behavioral phenotyping of transgenic and knockout mice: practical concerns and potential pitfalls. ILAR J 2006; 47: 124–131.

    Article  CAS  Google Scholar 

  46. Radyushkin K, Hammerschmidt K, Boretius S, Varoqueaux F, El-Kordi A, Ronnenberg A et al. Neuroligin-3-deficient mice: model of a monogenic heritable form of autism with an olfactory deficit. Genes Brain Behav 2009; 8: 416–425.

    Article  CAS  Google Scholar 

  47. Saywell V, Viola A, Confort-Gouny S, Le Fur Y, Villard L, Cozzone PJ . Brain magnetic resonance study of Mecp2 deletion effects on anatomy and metabolism. Biochem Biophys Res Commun 2006; 340: 776–783.

    Article  CAS  Google Scholar 

  48. Ward BC, Agarwal S, Wang K, Berger-Sweeney J, Kolodny NH . Longitudinal brain MRI study in a mouse model of Rett Syndrome and the effects of choline. Neurobiol Dis 2008; 31: 110–119.

    Article  CAS  Google Scholar 

  49. Dodero L, Damiano M, Galbusera A, Bifone A, Tsaftsaris SA, Scattoni ML et al. Neuroimaging evidence of major morpho-anatomical and functional abnormalities in the BTBR T+TF/J mouse model of autism. PLoS ONE 2013; 8: e76655.

    Article  CAS  Google Scholar 

  50. Portmann T, Yang M, Mao R, Panagiotakos G, Ellegood J, Dolen G et al. Behavioral abnormalities and circuit defects in the basal ganglia of a mouse model of 16p11.2 deletion syndrome. Cell Rep 2014; 7: 1077–1092.

    Article  CAS  Google Scholar 

  51. Michalon A, Sidorov M, Ballard TM, Ozmen L, Spooren W, Wettstein JG et al. Chronic pharmacological mGlu5 inhibition corrects fragile X in adult mice. Neuron 2012; 74: 49–56.

    Article  CAS  Google Scholar 

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Acknowledgements

This work was primarily funded by the Canadian Institute for Health Research (CIHR) and the Ontario Brain Institute (OBI). JE received salary support from the Ontario Mental Health Foundation (OHMF) and RMH holds a Canada Research Chair.

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Authors and Affiliations

  1. Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada

    J Ellegood, C L Laliberté, M C van Eede, R M Henkelman & J P Lerch

  2. Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada

    E Anagnostou

  3. Laboratory of Behavioral Neurosciences, National Institute of Mental Health, Bethesda, MD, USA

    B A Babineau & J N Crawley

  4. Department of Psychiatry and Behavioral Sciences, MIND Institute, University of California Davis School of Medicine, Sacramento, CA, USA

    J N Crawley

  5. Department of Neurosciences and Cell Biology, UMDNJ—Robert Wood Johnson Medical School, Piscataway, NJ, USA

    L Lin, M Genestine & E DiCicco-Bloom

  6. The Brain-Body Institute, McMaster University, Hamilton, ON, Canada

    J K Y Lai & J A Foster

  7. Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA,

    O Peñagarikano & D H Geschwind

  8. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada

    L K Pacey & D R Hampson

  9. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA

    A A Mills

  10. Departments of Medicine and Molecular Genetics, University of Toronto, Toronto, ON, Canada

    E Tam & L R Osborne

  11. Departments of Neurology and Neurotherapeutics, Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA

    M Kouser, F Espinosa-Becerra, Z Xuan & C M Powell

  12. Child Psychiatry Branch, National Institutes of Mental Health, Bethesda, MD, USA

    A Raznahan

  13. Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA

    D M Robins

  14. Department of Mental Biology, RIKEN Brain Science Institute, Wako, Japan

    N Nakai, J Nakatani & T Takumi

  15. Department of Pharmacology, Vanderbilt Kennedy Center, Vanderbilt Brain Institute, Nashville, TN, USA

    T M Kerr, C Muller, R D Blakely & J Veenstra-VanderWeele

  16. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    R M Henkelman & J P Lerch

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  1. J Ellegood
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Correspondence to J Ellegood or J P Lerch.

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Competing interests

EA has received consultation fees from Novartis and Seaside therapeutics, and has an unrestricted grant from Sanofi Canada. JV-VW receives research funding from Seaside Therapeutics, Novartis, Roche Pharmaceuticals, Forest, Sunovion and SynapDx and sits on the advisory board for Novartis and Roche Pharmaceuticals. The remaining authors declare no conflict of interest.

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Ellegood, J., Anagnostou, E., Babineau, B. et al. Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity. Mol Psychiatry 20, 118–125 (2015). https://doi.org/10.1038/mp.2014.98

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