A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity.

Hundreds of genes are implicated in autism spectrum disorder (ASD), but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here we analyzed leukocyte transcriptomics from 1- to 4-year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes highly expressed genes during fetal brain development. This network is dysregulated in human induced pluripotent stem cell-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS-ERK, PI3K-AKT and WNT-β-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms

Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing.

BackgroundBoth RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.ResultsHere we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.ConclusionThe use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility

Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing.

BackgroundBoth RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging.ResultsHere we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation.ConclusionThe use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility

Differences in regional brain structure in toddlers with autism are related to future language outcomes.

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the childs future language ability

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years.

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation

Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains

Although de novo missense mutations have been predicted to account for more cases of autism than gene-truncating mutations, most research has focused on the latter. We identified the properties of de novo missense mutations in patients with neurodevelopmental disorders (NDDs) and highlight 35 genes with excess missense mutations. Additionally, 40 amino acid sites were recurrently mutated in 36 genes, and targeted sequencing of 20 sites in 17,688 patients with NDD identified 21 new patients with identical missense mutations. One recurrent site substitution (p.A636T) occurs in a glutamate receptor subunit, GRIA1. This same amino acid substitution in the homologous but distinct mouse glutamate receptor subunit Grid2 is associated with Lurcher ataxia. Phenotypic follow-up in five individuals with GRIA1 mutations shows evidence of specific learning disabilities and autism. Overall, we find significant clustering of de novo mutations in 200 genes, highlighting specific functional domains and synaptic candidate genes important in NDD pathology.status: publishe

Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases

Gene-disruptive mutations contribute to the biology of neurodevelopmental disorders (NDDs), but most pathogenic genes are not known. We sequenced 208 candidate genes from >11,730 patients and >2,867 controls. We report 91 genes with an excess of de novo mutations or private disruptive mutations in 5.7% of patients, including 38 novel NDD genes. Drosophila functional assays of a subset bolster their involvement in NDDs. We identify 25 genes that show a bias for autism versus intellectual disability and highlight a network associated with high-functioning autism (FSIQ>100). Clinical follow-up for NAA15, KMT5B, and ASH1L reveals novel syndromic and non-syndromic forms of disease