1. Charman T, Loth E, Tillmann J, Crawley D, Wooldridge C, Goyard D, Ahmad J, Auyeung B, Ambrosino S, Banaschewski T, Baron-Cohen S, Baumeister S, Beckmann C, Bolte S, Bourgeron T, Bours C, Brammer M, Brandeis D, Brogna C, de Bruijn Y, Chakrabarti B, Cornelissen I, Acqua FD, Dumas G, Durston S, Ecker C, Faulkner J, Frouin V, Garces P, Ham L, Hayward H, Hipp J, Holt RJ, Isaksson J, Johnson MH, Jones EJH, Kundu P, Lai MC, D’Ardhuy X L, Lombardo MV, Lythgoe DJ, Mandl R, Mason L, Meyer-Lindenberg A, Moessnang C, Mueller N, O’Dwyer L, Oldehinkel M, Oranje B, Pandina G, Persico AM, Ruggeri B, Ruigrok ANV, Sabet J, Sacco R, Caceres ASJ, Simonoff E, Toro R, Tost H, Waldman J, Williams SCR, Zwiers MP, Spooren W, Murphy DGM, Buitelaar JK. {{The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation}}. {Mol Autism}. 2017; 8: 27.
BACKGROUND: The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study on biomarkers for autism spectrum disorder (ASD). The current paper describes the clinical characteristics of the LEAP cohort and examines age, sex and IQ differences in ASD core symptoms and common co-occurring psychiatric symptoms. A companion paper describes the overall design and experimental protocol and outlines the strategy to identify stratification biomarkers. METHODS: From six research centres in four European countries, we recruited 437 children and adults with ASD and 300 controls between the ages of 6 and 30 years with IQs varying between 50 and 148. We conducted in-depth clinical characterisation including a wide range of observational, interview and questionnaire measures of the ASD phenotype, as well as co-occurring psychiatric symptoms. RESULTS: The cohort showed heterogeneity in ASD symptom presentation, with only minimal to moderate site differences on core clinical and cognitive measures. On both parent-report interview and questionnaire measures, ASD symptom severity was lower in adults compared to children and adolescents. The precise pattern of differences varied across measures, but there was some evidence of both lower social symptoms and lower repetitive behaviour severity in adults. Males had higher ASD symptom scores than females on clinician-rated and parent interview diagnostic measures but not on parent-reported dimensional measures of ASD symptoms. In contrast, self-reported ASD symptom severity was higher in adults compared to adolescents, and in adult females compared to males. Higher scores on ASD symptom measures were moderately associated with lower IQ. Both inattentive and hyperactive/impulsive ADHD symptoms were lower in adults than in children and adolescents, and males with ASD had higher levels of inattentive and hyperactive/impulsive ADHD symptoms than females. CONCLUSIONS: The established phenotypic heterogeneity in ASD is well captured in the LEAP cohort. Variation both in core ASD symptom severity and in commonly co-occurring psychiatric symptoms were systematically associated with sex, age and IQ. The pattern of ASD symptom differences with age and sex also varied by whether these were clinician ratings or parent- or self-reported which has important implications for establishing stratification biomarkers and for their potential use as outcome measures in clinical trials.
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2. Krishnamoorthy KM, Gopalakrishnan A, Kumar DS, Sivasankaran SS. {{Eustachian valve-Masquerading ASD rim}}. {Indian Heart J}. 2017; 69(3): 422-3.
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3. Lombardo MV, Courchesne E, Lewis NE, Pramparo T. {{Hierarchical cortical transcriptome disorganization in autism}}. {Mol Autism}. 2017; 8: 29.
BACKGROUND: Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology. METHODS: Two ASD cortical transcriptome datasets were re-analyzed using consensus weighted gene co-expression network analysis (WGCNA) to identify common co-expression modules across datasets. Linear mixed-effect models and Bayesian replication statistics were used to identify replicable differentially expressed modules. Eigengene network analysis was then utilized to identify between-group differences in how co-expression modules interact and cluster into hierarchical meta-modular organization. Protein-protein interaction analyses were also used to determine whether dysregulated co-expression modules show enhanced interactions. RESULTS: We find replicable evidence for 10 gene co-expression modules that are differentially expressed in ASD cortex. Rather than being independent non-interacting sources of pathology, these dysregulated co-expression modules work in synergy and physically interact at the protein level. These systems-level transcriptional signals are characterized by downregulation of synaptic processes coordinated with upregulation of immune/inflammation, response to other organism, catabolism, viral processes, translation, protein targeting and localization, cell proliferation, and vasculature development. Hierarchical organization of meta-modules (clusters of highly correlated modules) is also highly affected in ASD. CONCLUSIONS: These findings highlight that dysregulation of the ASD cortical transcriptome is characterized by the dysregulation of multiple coordinated transcriptional programs producing synergistic systems-level effects that cannot be fully appreciated by studying the individual component biological processes in isolation.
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4. Loth E, Charman T, Mason L, Tillmann J, Jones EJH, Wooldridge C, Ahmad J, Auyeung B, Brogna C, Ambrosino S, Banaschewski T, Baron-Cohen S, Baumeister S, Beckmann C, Brammer M, Brandeis D, Bolte S, Bourgeron T, Bours C, de Bruijn Y, Chakrabarti B, Crawley D, Cornelissen I, Acqua FD, Dumas G, Durston S, Ecker C, Faulkner J, Frouin V, Garces P, Goyard D, Hayward H, Ham LM, Hipp J, Holt RJ, Johnson MH, Isaksson J, Kundu P, Lai MC, D’Ardhuy X L, Lombardo MV, Lythgoe DJ, Mandl R, Meyer-Lindenberg A, Moessnang C, Mueller N, O’Dwyer L, Oldehinkel M, Oranje B, Pandina G, Persico AM, Ruigrok ANV, Ruggeri B, Sabet J, Sacco R, Caceres ASJ, Simonoff E, Toro R, Tost H, Waldman J, Williams SCR, Zwiers MP, Spooren W, Murphy DGM, Buitelaar JK. {{The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders}}. {Mol Autism}. 2017; 8: 24.
BACKGROUND: The tremendous clinical and aetiological diversity among individuals with autism spectrum disorder (ASD) has been a major obstacle to the development of new treatments, as many may only be effective in particular subgroups. Precision medicine approaches aim to overcome this challenge by combining pathophysiologically based treatments with stratification biomarkers that predict which treatment may be most beneficial for particular individuals. However, so far, we have no single validated stratification biomarker for ASD. This may be due to the fact that most research studies primarily have focused on the identification of mean case-control differences, rather than within-group variability, and included small samples that were underpowered for stratification approaches. The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study worldwide that aims to identify and validate stratification biomarkers for ASD. METHODS: LEAP includes 437 children and adults with ASD and 300 individuals with typical development or mild intellectual disability. Using an accelerated longitudinal design, each participant is comprehensively characterised in terms of clinical symptoms, comorbidities, functional outcomes, neurocognitive profile, brain structure and function, biochemical markers and genomics. In addition, 51 twin-pairs (of which 36 had one sibling with ASD) are included to identify genetic and environmental factors in phenotypic variability. RESULTS: Here, we describe the demographic characteristics of the cohort, planned analytic stratification approaches, criteria and steps to validate candidate stratification markers, pre-registration procedures to increase transparency, standardisation and data robustness across all analyses, and share some ‘lessons learnt’. A clinical characterisation of the cohort is given in the companion paper (Charman et al., accepted). CONCLUSION: We expect that LEAP will enable us to confirm, reject and refine current hypotheses of neurocognitive/neurobiological abnormalities, identify biologically and clinically meaningful ASD subgroups, and help us map phenotypic heterogeneity to different aetiologies.
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5. Nakai Y, Takiguchi T, Matsui G, Yamaoka N, Takada S. {{Detecting Abnormal Voice Prosody Through Single-Word Utterances in Children With Autism Spectrum Disorders}}. {Percept Mot Skills}. 2017: 31512517716855.
Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.
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6. Rogers TD, Anacker AMJ, Kerr TM, Forsberg CG, Wang J, Zhang B, Veenstra-VanderWeele J. {{Effects of a social stimulus on gene expression in a mouse model of fragile X syndrome}}. {Mol Autism}. 2017; 8: 30.
BACKGROUND: People with fragile X syndrome (FXS) often have deficits in social behavior, and a substantial portion meet criteria for autism spectrum disorder. Though the genetic cause of FXS is known to be due to the silencing of FMR1, and the Fmr1 null mouse model representing this lesion has been extensively studied, the contributions of this gene and its protein product, FMRP, to social behavior are not well understood. METHODS: Fmr1 null mice and wildtype littermates were exposed to a social or non-social stimulus. In one experiment, subjects were assessed for expression of the inducible transcription factor c-Fos in response to the stimulus, to detect brain regions with social-specific activity. In a separate experiment, tissue was taken from those brain regions showing differential activity, and RNA sequencing was performed. RESULTS: Immunohistochemistry revealed a significantly greater number of c-Fos-positive cells in the lateral amygdala and medial amygdala in the brains of mice exposed to a social stimulus, compared to a non-social stimulus. In the prelimbic cortex, there was no significant effect of social stimulus; although the number of c-Fos-positive cells was lower in the social condition compared to the non-social condition, and negatively correlated with c-Fos in the amygdala. RNA sequencing revealed differentially expressed genes enriched for molecules known to interact with FMRP and also for autism-related genes identified in the Simons Foundation Autism Research Initiative gene database. Ingenuity Pathway Analysis detected enrichment of differentially expressed genes in networks and pathways related to neuronal development, intracellular signaling, and inflammatory response. CONCLUSIONS: Using the Fmr1 null mouse model of fragile X syndrome, we have identified brain regions, gene networks, and molecular pathways responsive to a social stimulus. These findings, and future experiments following up on the role of specific gene networks, may shed light on the neural mechanisms underlying dysregulated social behaviors in fragile X syndrome and more broadly.
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7. Srivastava S, Sahin M. {{Autism spectrum disorder and epileptic encephalopathy: common causes, many questions}}. {J Neurodev Disord}. 2017; 9: 23.
Epileptic encephalopathies represent a particularly severe form of epilepsy, associated with cognitive and behavioral deficits, including impaired social-communication and restricted, repetitive behaviors that are the hallmarks of autism spectrum disorder (ASD). With the advent of next-generation sequencing, the genetic landscape of epileptic encephalopathies is growing and demonstrates overlap with genes separately implicated in ASD. However, many questions remain about this connection, including whether epileptiform activity itself contributes to the development of ASD symptomatology. In this review, we compiled a database of genes associated with both epileptic encephalopathy and ASD, limiting our purview to Mendelian disorders not including inborn errors of metabolism, and we focused on the connection between ASD and epileptic encephalopathy rather than epilepsy broadly. Our review has four goals: to (1) discuss the overlapping presentations of ASD and monogenic epileptic encephalopathies; (2) examine the impact of the epilepsy itself on neurocognitive features, including ASD, in monogenic epileptic encephalopathies; (3) outline many of the genetic causes responsible for both ASD and epileptic encephalopathy; (4) provide an illustrative example of a final common pathway that may be implicated in both ASD and epileptic encephalopathy. We demonstrate that autistic features are a common association with monogenic epileptic encephalopathies. Certain epileptic encephalopathy syndromes, like infantile spasms, are especially linked to the development of ASD. The connection between seizures themselves and neurobehavioral deficits in these monogenic encephalopathies remains open to debate. Finally, advances in genetics have revealed many genes that overlap in ties to both ASD and epileptic encephalopathy and that play a role in diverse central nervous system processes. Increased attention to the autistic features of monogenic epileptic encephalopathies is warranted for both researchers and clinicians alike.
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8. Woodbury-Smith M, Nicolson R, Zarrei M, Yuen RKC, Walker S, Howe J, Uddin M, Hoang N, Buchanan JA, Chrysler C, Thompson A, Szatmari P, Scherer SW. {{Variable phenotype expression in a family segregating microdeletions of the NRXN1 and MBD5 autism spectrum disorder susceptibility genes}}. {NPJ Genom Med}. 2017; 2.
Autism Spectrum Disorder (ASD) is a developmental condition of early childhood onset, which impacts socio-communicative functioning and is principally genetic in etiology. Currently, more than 50 genomic loci are deemed to be associated with susceptibility to ASD, showing de novo and inherited unbalanced copy number variants (CNVs) and smaller insertions and deletions (indels), more complex structural variants (SVs), as well as single nucleotide variants (SNVs) deemed of pathological significance. However, the phenotypes associated with many of these genes are variable, and penetrance is largely unelaborated in clinical descriptions. This case report describes a family harboring two CNV microdeletions, which affect regions of NRXN1 and MBD5 – each well-established in association with risk of ASD and other neurodevelopmental disorders. Although each CNV would likely be categorized as pathologically significant, both genomic alterations are transmitted in this family from an unaffected father to the proband, and shared by an unaffected sibling. This family case illustrates the importance of recognizing that phenotype can vary among exon overlapping variants of the same gene, and the need to evaluate penetrance of such variants in order to properly inform on risks.
