Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis

The autism susceptibility kinase, TAOK2, phosphorylates eEF2 and modulates translation

Genes implicated in translation control have been associated with autism spectrum disorders (ASDs). However, some important genetic causes of autism, including the 16p11.2 microdeletion, bear no obvious connection to translation. Here, we use proteomics, genetics, and translation assays in cultured cells and mouse brain to reveal altered translation mediated by loss of the kinase TAOK2 in 16p11.2 deletion models. We show that TAOK2 associates with the translational machinery and functions as a translational brake by phosphorylating eukaryotic elongation factor 2 (eEF2). Previously, all signal-mediated regulation of translation elongation via eEF2 phosphorylation was believed to be mediated by a single kinase, eEF2K. However, we show that TAOK2 can directly phosphorylate eEF2 on the same regulatory site, but functions independently of eEF2K signaling. Collectively, our results reveal an eEF2K-independent signaling pathway for control of translation elongation and suggest altered translation as a molecular component in the etiology of some forms of ASD.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Autistic spectrum disorder (ASD) - Gene, molecular and pathway signatures linking systemic inflammation, mitochondrial dysfunction, transsynaptic signalling, and neurodevelopment

Background

Despite advances in autism spectrum disorder (ASD) research and the vast genomic, transcriptomic, and proteomic data available, there are still controversies regarding the pathways and molecular signatures underlying the neurodevelopmental disorders leading to ASD.

Purpose

To delineate these underpinning signatures, we examined the two largest gene expression meta-analysis datasets obtained from the brain and peripheral blood mononuclear cells (PBMCs) of 1355 ASD patients and 1110 controls.

Methods

We performed network, enrichment, and annotation analyses using the differentially expressed genes, transcripts, and proteins identified in ASD patients.

Results

Transcription factor network analyses in up- and down-regulated genes in brain tissue and PBMCs in ASD showed eight main transcription factors, namely: BCL3, CEBPB, IRF1, IRF8, KAT2A, NELFE, RELA, and TRIM28. The upregulated gene networks in PBMCs of ASD patients are strongly associated with activated immune-inflammatory pathways, including interferon-α signaling, and cellular responses to DNA repair. Enrichment analyses of the upregulated CNS gene networks indicate involvement of immune-inflammatory pathways, cytokine production, Toll-Like Receptor signalling, with a major involvement of the PI3K-Akt pathway. Analyses of the downregulated CNS genes suggest electron transport chain dysfunctions at multiple levels. Network topological analyses revealed that the consequent aberrations in axonogenesis, neurogenesis, synaptic transmission, and regulation of transsynaptic signalling affect neurodevelopment with subsequent impairments in social behaviours and neurocognition. The results suggest a defense response against viral infection.

Conclusions

Peripheral activation of immune-inflammatory pathways, most likely induced by viral infections, may result in CNS neuroinflammation and mitochondrial dysfunction, leading to abnormalities in transsynaptic transmission, and brain neurodevelopment.

Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues.

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

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.

Large 22q13.3 deletions perturb peripheral transcriptomic and metabolomic profiles in Phelan-McDermid syndrome

Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused at least in part by haploinsufficiency of the SHANK3 gene, due to sequence variants in SHANK3 or subtelomeric 22q13.3 deletions. Phenotypic differences have been reported between PMS participants carrying small "class I" mutations and large "class II" mutations; however, the molecular perturbations underlying these divergent phenotypes remain obscure. Using peripheral blood transcriptome and serum metabolome profiling, we examined the molecular perturbations in the peripheral circulation associated with a full spectrum of PMS genotypes spanning class I (n = 37) and class II mutations (n = 39). Transcriptomic data revealed 52 genes with blood expression profiles that tightly scale with 22q.13.3 deletion size. Furthermore, we uncover 208 underexpressed genes in PMS participants with class II mutations, which were unchanged in class I mutations. These genes were not linked to 22q13.3 and were strongly enriched for glycosphingolipid metabolism, NCAM1 interactions, and cytotoxic natural killer (NK) immune cell signatures. In silico predictions estimated a reduction in CD56+ CD16- NK cell proportions in class II mutations, which was validated by mass cytometry time of flight. Global metabolomics profiling identified 24 metabolites that were significantly altered in PMS participants with class II mutations and confirmed a general reduction in sphingolipid metabolism. Collectively, these results provide new evidence linking PMS participants carrying class II mutations with decreased expression of cytotoxic cell signatures, reduced relative proportions of NK cells, and lower sphingolipid metabolism. These findings highlight alternative avenues for therapeutic development and offer new mechanistic insights supporting genotype-to-phenotype associations in PMS.

Modeling gene × environment interactions in PTSD using human neurons reveals diagnosis-specific glucocorticoid-induced gene expression

Post-traumatic stress disorder (PTSD) can develop following severe trauma, but the extent to which genetic and environmental risk factors contribute to individual clinical outcomes is unknown. Here, we compared transcriptional responses to hydrocortisone exposure in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons and peripheral blood mononuclear cells (PBMCs) from combat veterans with PTSD (n = 19 hiPSC and n = 20 PBMC donors) and controls (n = 20 hiPSC and n = 20 PBMC donors). In neurons only, we observed diagnosis-specific glucocorticoid-induced changes in gene expression corresponding with PTSD-specific transcriptomic patterns found in human postmortem brains. We observed glucocorticoid hypersensitivity in PTSD neurons, and identified genes that contribute to this PTSD-dependent glucocorticoid response. We find evidence of a coregulated network of transcription factors that mediates glucocorticoid hyper-responsivity in PTSD. These findings suggest that induced neurons represent a platform for examining the molecular mechanisms underlying PTSD, identifying biomarkers of stress response, and conducting drug screening to identify new therapeutics.

Rare variants and the oligogenic architecture of autism

Most large-scale genetic studies of autism have focused on the discovery of genes by proving an enrichment of de novo mutations (DNMs) in autism probands or characterizing polygenic risk based on the association of common variants. We present evidence in support of an oligogenic model where two or more ultrarare mutations of more modest effect are preferentially transmitted to children with autism. Such private gene-disruptive mutations are enriched in families where there are multiple affected individuals, emerged two or three generations ago, and map to genes not previously associated with autism. Although no single gene has reached statistical significance, this class of variation should be considered along with genetic and nongenetic factors to better explain the etiology of this complex trait.

The Changes of Amygdala Transcriptome in Autism Rat Model After Arginine Vasopressin Treatment

Background

Some studies have shown that arginine vasopressin (AVP) can significantly improve the social interaction disorder of autism, but the mechanism remains unclear.

Methods

Female Wistar rats were intraperitoneally injected with VPA or normal saline at embryonic day 12.5 to establish an autism model or normal control in their offspring. Male offspring prenatally exposed to VPA were randomly assigned to two groups: the VPA-induced autism model group and the AVP group. The rats in the AVP group were treated with intranasal AVP at postnatal day (PND) 21 and for 3 weeks. The VPA-induced autism model group was given the same dose of normal saline in the same way. Behavioral responses were evaluated in the open field and three-chambered social test apparatus; the expression levels of AVP in serum were detected by enzyme-linked immunosorbent assay kit, and the gene expression levels on the amygdala were measured by RNA-seq at PND42.

Results

Intranasal administration of AVP can significantly improve the social interaction disorder and elevate the levels of AVP in serum. Transcriptome sequencing results showed that 518 differently expressed genes (DEGs) were identified in the VPA-induced autism model group compared with the control in this study. Gene Ontology biological process enrichment analysis of DEGs showed that the VPA-induced autism model group had significant nervous system developmental impairments compared with the normal group, particularly in gliogenesis, glial cell differentiation, and oligodendrocyte differentiation. Gene Set Enrichment Analysis (GSEA) enrichment analysis also showed that biological process of oligodendrocyte differentiation, axoneme assembly, and axon ensheathment were inhibited in the VPA-induced autism model group. Pathway enrichment analysis of DEGs between the control and VPA-induced autism model group showed that the PI3K/AKT and Wnt pathways were significantly dysregulated in the VPA-induced autism model group. Few DEGs were found when compared with the transcriptome between the VPA-induced autism model group and the AVP treatment group. GSEA enrichment analysis showed deficits in oligodendrocyte development and function were significantly improved after AVP treatment; the pathways were mainly enriched in the NOTCH, mitogen-activated protein kinase, and focal adhesion signaling pathways, but not in the PI3K/AKT and Wnt pathways. The expression patterns analysis also showed the same results.

Conclusion

AVP can significantly improve the social interaction disorder of VPA-induced autism model, and AVP may target behavioral symptoms in autism by modulating the vasopressin pathways, rather than primary disease mechanisms.

X Chromosome Inactivation Timing is Not eXACT: Implications for Autism Spectrum Disorders

The etiology of autism spectrum disorders (ASD) is complex, involving different combinations of genetic and environmental factors. My lab's approach has been to investigate DNA methylation as a tractable genome-wide modification at the interface of these complex interactions, reflecting past and future events in the molecular pathogenesis of ASD. Since X-linked genes were enriched in DNA methylation differences discovered from cord blood from newborns later diagnosed with ASD, this has prompted me to review and revisit the recent advancements in the field of X chromosome inactivation (XCI), particularly in humans and other primates. In this Perspective, I compare XCI mechanisms in different mammalian species, including the finding of the noncoding transcript XACT associated with X chromosome erosion in human pluripotent stem cells and recent findings from non-human primate post-implantation embryos. I focus on the experimentally challenging peri- and post-implantation stages of human development when the timing of XCI is prolonged and imprecise in humans. Collectively, this research has raised some important unanswered questions involving biased sex ratios in human births and the male bias in the incidence of ASD.

Abnormal mTOR Activity in Pediatric Autoimmune Neuropsychiatric and MIA-Associated Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by the early onset of communication and behavioral problems. ASD is highly heritable; however, environmental factors also play a considerable role in this disorder. A significant part of both syndromic and idiopathic autism cases could be attributed to disorders caused by mammalian target of rapamycin (mTOR)-dependent translation deregulation. This narrative review analyzes both bioinformatic and experimental evidence that connects mTOR signaling to the maternal autoantibody-related (MAR) autism spectrum and autoimmune neuropsychiatric disorders simultaneously. In addition, we reconstruct a network presenting the interactions between the mTOR signaling and eight MAR ASD genes coding for ASD-specific maternal autoantibody target proteins. The research discussed in this review demonstrates novel perspectives and validates the need for a subtyping of ASD on the grounds of pathogenic mechanisms. The utter necessity of designing ELISA-based test panels to identify all antibodies related to autism-like behavior is also considered.

Convergent Canonical Pathways in Autism Spectrum Disorder from Proteomic, Transcriptomic and DNA Methylation Data

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with extensive genetic and aetiological heterogeneity. While the underlying molecular mechanisms involved remain unclear, significant progress has been facilitated by recent advances in high-throughput transcriptomic, epigenomic and proteomic technologies. Here, we review recently published ASD proteomic data and compare proteomic functional enrichment signatures with those of transcriptomic and epigenomic data. We identify canonical pathways that are consistently implicated in ASD molecular data and find an enrichment of pathways involved in mitochondrial metabolism and neurogenesis. We identify a subset of differentially expressed proteins that are supported by ASD transcriptomic and DNA methylation data. Furthermore, these differentially expressed proteins are enriched for disease phenotype pathways associated with ASD aetiology. These proteins converge on protein–protein interaction networks that regulate cell proliferation and differentiation, metabolism, and inflammation, which demonstrates a link between canonical pathways, biological processes and the ASD phenotype. This review highlights how proteomics can uncover potential molecular mechanisms to explain a link between mitochondrial dysfunction and neurodevelopmental pathology.

Genetic contributions to autism spectrum disorder

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Molecular biomarkers to track clinical improvement following an integrative treatment model in autistic toddlers

OBJECTIVES

Identifying an objective, laboratory-based diagnostic tool (e.g. changes in gene expression), when used in conjunction with disease-specific clinical assessment, could increase the accuracy of the effectiveness of a therapeutic intervention.

METHODS

We assessed the association between treatment outcome and blood RNA expression before the therapeutic intervention to post-treatment (after 1 year) of five autism spectrum disorder (ASD) toddlers who underwent an intensive cognitive-behavioural intervention integrated with psychomotor and speech therapy.

RESULTS

We found 113 significant differentially expressed genes enriched for the nervous system, immune system, and transcription and translation-related pathways. Some of these genes, as MALAT-1, TSPO, and CFL1, appear to be promising candidates.

CONCLUSIONS

Our findings show that changes in peripheral gene expression could be used in conjunction with clinical scales to monitor a rehabilitation intervention's effectiveness in toddlers affected by ASD. These results need to be validated in a larger cohort.

Do Autism Spectrum and Autoimmune Disorders Share Predisposition Gene Signature Due to mTOR Signaling Pathway Controlling Expression?

Autism spectrum disorder (ASD) is characterized by uncommon genetic heterogeneity and a high heritability concurrently. Most autoimmune disorders (AID), similarly to ASD, are characterized by impressive genetic heterogeneity and heritability. We conducted gene-set analyses and revealed that 584 out of 992 genes (59%) included in a new release of the SFARI Gene database and 439 out of 871 AID-associated genes (50%) could be attributed to one of four groups: 1. FMRP (fragile X mental retardation protein) target genes, 2. mTOR signaling network genes, 3. mTOR-modulated genes, and 4. vitamin D3-sensitive genes. With the exception of FMRP targets, which are obviously associated with the direct involvement of local translation disturbance in the pathological mechanisms of ASD, the remaining categories are represented among AID genes in a very similar percentage as among ASD predisposition genes. Thus, mTOR signaling pathway genes make up 4% of ASD and 3% of AID genes, mTOR-modulated genes-31% of both ASD and AID genes, and vitamin D-sensitive genes-20% of ASD and 23% of AID genes. The network analysis revealed 3124 interactions between 528 out of 729 AID genes for the 0.7 cutoff, so the great majority (up to 67%) of AID genes are related to the mTOR signaling pathway directly or indirectly. Our present research and available published data allow us to hypothesize that both a certain part of ASD and AID comprise a connected set of disorders sharing a common aberrant pathway (mTOR signaling) rather than a vast set of different disorders. Furthermore, an immune subtype of the autism spectrum might be a specific type of autoimmune disorder with an early manifestation of a unique set of predominantly behavioral symptoms.

Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder

Evaluation of expression profile in autism spectrum disorder (ASD) patients is an important approach to understand possible similar functional consequences that may underlie disease pathophysiology regardless of its genetic heterogeneity. Induced pluripotent stem cell (iPSC)-derived neuronal models have been useful to explore this question, but larger cohorts and different ASD endophenotypes still need to be investigated. Moreover, whether changes seen in this in vitro model reflect previous findings in ASD postmortem brains and how consistent they are across the studies remain underexplored questions. We examined the transcriptome of iPSC-derived neuronal cells from a normocephalic ASD cohort composed mostly of high-functioning individuals and from non-ASD individuals. ASD patients presented expression dysregulation of a module of co-expressed genes involved in protein synthesis in neuronal progenitor cells (NPC), and a module of genes related to synapse/neurotransmission and a module related to translation in neurons. Proteomic analysis in NPC revealed potential molecular links between the modules dysregulated in NPC and in neurons. Remarkably, the comparison of our results to a series of transcriptome studies revealed that the module related to synapse has been consistently found as upregulated in iPSC-derived neurons-which has an expression profile more closely related to fetal brain-while downregulated in postmortem brain tissue, indicating a reliable association of this network to the disease and suggesting that its dysregulation might occur in different directions across development in ASD individuals. Therefore, the expression pattern of this network might be used as biomarker for ASD and should be experimentally explored as a therapeutic target.

Identification of aberrant innate and adaptive immunity based on changes in global gene expression in the blood of adults with autism spectrum disorder

Background

Autism spectrum disorder (ASD) is characterized as a neurodevelopmental disorder, and one of the main hypotheses regarding its cause is genetic factors. A previous meta-analysis of seven microarray studies and one RNA sequencing (RNA-seq) study using the blood of children with ASD identified dysregulation of gene expressions relevant to the immune system. In this study, we explored changes in global gene expression as the phenotype of ASD in the blood of adults with ASD.

Methods

We recruited an RNA-seq cohort (ASD vs. control; n = 6 each) and a replication cohort (ASD vs. control; n = 19 each) and conducted RNA-seq to explore changes in global gene expression. We then subjected the significantly up- and downregulated genes to gene ontology (GO) and core analyses. Weighted gene correlation network analysis (WGCNA) was performed with all 11,617 genes detected in RNA-seq to identify the ASD-specific gene network.

Results

In total, 117 significantly up- and 83 significantly downregulated genes were detected in the ASD compared with the control group, respectively (p < 0.05 and q < 0.05). GO analysis revealed that the aberrant innate and adaptive immunity were more obvious in the 117 upregulated than in the 83 downregulated genes. WGCNA with core analysis revealed that one module including many immune-related genes was associated with the natural killer cell signaling pathway. In the results for the replication cohort, significant changes with same trend found in RNA-seq data were confirmed for MAFB (p = 0.046), RPSAP58 (p = 0.030), and G2MK (p = 0.004).

Limitations

The sample size was relatively small in both the RNA-seq and replication cohorts. This study examined the mRNA expression level, so the interaction between mRNA and protein remains unclear. The expression changes between children and adults with ASD were not compared because only adults with ASD were targeted.

Conclusions

The dysregulated gene expressions confirmed in the blood of adults with ASD were relevant to the dysfunction of innate and adaptive immunity. These findings may aid in understanding the pathogenesis of ASD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-021-02154-7.

Expression Changes in Epigenetic Gene Pathways Associated With One-Carbon Nutritional Metabolites in Maternal Blood From Pregnancies Resulting in Autism and Non-Typical Neurodevelopment

The prenatal period is a critical window for the development of autism spectrum disorder (ASD). The relationship between prenatal nutrients and gestational gene expression in mothers of children later diagnosed with ASD or non-typical development (Non-TD) is poorly understood. Maternal blood collected prospectively during pregnancy provides insights into the effects of nutrition, particularly one-carbon metabolites, on gene pathways and neurodevelopment. Genome-wide transcriptomes were measured with microarrays in 300 maternal blood samples in Markers of Autism Risk in Babies-Learning Early Signs. Sixteen different one-carbon metabolites, including folic acid, betaine, 5'-methyltretrahydrofolate (5-MeTHF), and dimethylglycine (DMG) were measured. Differential expression analysis and weighted gene correlation network analysis (WGCNA) were used to compare gene expression between children later diagnosed as typical development (TD), Non-TD and ASD, and to one-carbon metabolites. Using differential gene expression analysis, six transcripts (TGR-AS1, SQSTM1, HLA-C, and RFESD) were associated with child outcomes (ASD, Non-TD, and TD) with genome-wide significance. Genes nominally differentially expressed between ASD and TD significantly overlapped with seven high confidence ASD genes. WGCNA identified co-expressed gene modules significantly correlated with 5-MeTHF, folic acid, DMG, and betaine. A module enriched in DNA methylation functions showed a suggestive protective association with folic acid/5-MeTHF concentrations and ASD risk. Maternal plasma betaine and DMG concentrations were associated with a block of co-expressed genes enriched for adaptive immune, histone modification, and RNA processing functions. These results suggest that the prenatal maternal blood transcriptome is a sensitive indicator of gestational one-carbon metabolite status and changes relevant to children's later neurodevelopmental outcomes. LAY SUMMARY: Pregnancy is a time when maternal nutrition could interact with genetic risk for autism spectrum disorder. Blood samples collected during pregnancy from mothers who had a prior child with autism were examined for gene expression and nutrient metabolites, then compared to the diagnosis of the child at age three. Expression differences in gene pathways related to the immune system and gene regulation were observed for pregnancies of children with autism and non-typical neurodevelopment and were associated with maternal nutrients.

Cord blood DNA methylome in newborns later diagnosed with autism spectrum disorder reflects early dysregulation of neurodevelopmental and X-linked genes

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with complex heritability and higher prevalence in males. The neonatal epigenome has the potential to reflect past interactions between genetic and environmental factors during early development and influence future health outcomes.

METHODS

We performed whole-genome bisulfite sequencing of 152 umbilical cord blood samples from the MARBLES and EARLI high-familial risk prospective cohorts to identify an epigenomic signature of ASD at birth. Samples were split into discovery and replication sets and stratified by sex, and their DNA methylation profiles were tested for differentially methylated regions (DMRs) between ASD and typically developing control cord blood samples. DMRs were mapped to genes and assessed for enrichment in gene function, tissue expression, chromosome location, and overlap with prior ASD studies. DMR coordinates were tested for enrichment in chromatin states and transcription factor binding motifs. Results were compared between discovery and replication sets and between males and females.

RESULTS

We identified DMRs stratified by sex that discriminated ASD from control cord blood samples in discovery and replication sets. At a region level, 7 DMRs in males and 31 DMRs in females replicated across two independent groups of subjects, while 537 DMR genes in males and 1762 DMR genes in females replicated by gene association. These DMR genes were significantly enriched for brain and embryonic expression, X chromosome location, and identification in prior epigenetic studies of ASD in post-mortem brain. In males and females, autosomal ASD DMRs were significantly enriched for promoter and bivalent chromatin states across most cell types, while sex differences were observed for X-linked ASD DMRs. Lastly, these DMRs identified in cord blood were significantly enriched for binding sites of methyl-sensitive transcription factors relevant to fetal brain development.

CONCLUSIONS

At birth, prior to the diagnosis of ASD, a distinct DNA methylation signature was detected in cord blood over regulatory regions and genes relevant to early fetal neurodevelopment. Differential cord methylation in ASD supports the developmental and sex-biased etiology of ASD and provides novel insights for early diagnosis and therapy.

Gene co-expression networks in peripheral blood capture dimensional measures of emotional and behavioral problems from the Child Behavior Checklist (CBCL)

The U.S. National Institute of Mental Health (NIMH) introduced the research domain criteria (RDoC) initiative to promote the integration of information across multiple units of analysis (i.e., brain circuits, physiology, behavior, self-reports) to better understand the basic dimensions of behavior and cognitive functioning underlying normal and abnormal mental conditions. Along those lines, this study examined the association between peripheral blood gene expression levels and emotional and behavioral problems in school-age children. Children were chosen from two age- and sex-matched groups: those with or without parental reports of any prior or current psychiatric diagnosis. RNA-sequencing was performed on whole blood from 96 probands aged 6-12 years who were medication-free at the time of assessment. Module eigengenes were derived using weighted gene co-expression network analysis (WGCNA). Associations were tested between module eigengene expression levels and eight syndrome scales from parent ratings on the Child Behavior Checklist (CBCL). Nine out of the 36 modules were significantly associated with at least one syndrome scale measured by the CBCL (i.e., aggression, social problems, attention problems, and/or thought problems) after accounting for covariates and correcting for multiple testing. Our study demonstrates that variation in peripheral blood gene expression relates to emotional and behavioral profiles in children. If replicated and validated, our results may help in identifying problem or at-risk behavior in pediatric populations, and in elucidating the biological pathways that modulate complex human behavior.

Maternal Immunity in Autism Spectrum Disorders: Questions of Causality, Validity, and Specificity

Autism spectrum disorders (ASD) are complex neurodevelopmental disorders with unknown heterogeneous aetiologies. Epidemiological studies have found an association between maternal infection and development of ASD in the offspring, and clinical findings reveal a state of immune dysregulation in the pre- and postnatal period of affected subjects. Maternal immune activation (MIA) has been proposed to mediate this association by altering fetal neurodevelopment and leading to autism. Although animal models have supported a causal link between MIA and development of ASD, their validity needs to be explored. Moreover, considering that only a small proportion of affected offspring develop autism, and that MIA has been implicated in related diseases such as schizophrenia, a key unsolved question is how disease specificity and phenotypic outcome are determined. Here, we have integrated preclinical and clinical evidence, including the use of animal models for establishing causality, to explore the role of maternal infections in ASD. A proposed priming/multi-hit model may offer insights into the clinical heterogeneity of ASD, its convergence with related disorders, and therapeutic strategies.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Update on Atypicalities of Central Nervous System in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous, behaviorally defined, neurodevelopmental disorder that has been modeled as a brain-based disease. The behavioral and cognitive features of ASD are associated with pervasive atypicalities in the central nervous system (CNS). To date, the exact mechanisms underlying the pathophysiology of ASD still remain unknown and there is currently no cure or effective treatment for this disorder. Many publications implicated the association of ASD with inflammation, immune dysregulation, neurotransmission dysfunction, mitochondrial impairment and cell signaling dysregulation. This review attempts to highlight evidence of the major pathophysiology of ASD including abnormalities in the brain structure and function, neuroglial activation and neuroinflammation, glutamatergic neurotransmission, mitochondrial dysfunction and mechanistic target of rapamycin (mTOR) signaling pathway dysregulation. Molecular and cellular factors that contributed to the pathogenesis of ASD and how they may affect the development and function of CNS are compiled in this review. However, findings of published studies have been complicated by the fact that autism is a very heterogeneous disorder; hence, we addressed the limitations that led to discrepancies in the reported findings. This review emphasizes the need for future studies to control study variables such as sample size, gender, age range and intelligence quotient (IQ), all of which that could affect the study measurements. Neuroinflammation or immune dysregulation, microglial activation, genetically linked neurotransmission, mitochondrial dysfunctions and mTOR signaling pathway could be the primary targets for treating and preventing ASD. Further research is required to better understand the molecular causes and how they may contribute to the pathophysiology of ASD.

Transcriptome signatures from discordant sibling pairs reveal changes in peripheral blood immune cell composition in Autism Spectrum Disorder

Notwithstanding several research efforts in the past years, robust and replicable molecular signatures for autism spectrum disorders from peripheral blood remain elusive. The available literature on blood transcriptome in ASD suggests that through accurate experimental design it is possible to extract important information on the disease pathophysiology at the peripheral level. Here we exploit the availability of a resource for molecular biomarkers in ASD, the Italian Autism Network (ITAN) collection, for the investigation of transcriptomic signatures in ASD based on a discordant sibling pair design. Whole blood samples from 75 discordant sibling pairs selected from the ITAN network where submitted to RNASeq analysis and data analyzed by complementary approaches. Overall, differences in gene expression between affected and unaffected siblings were small. In order to assess the contribution of differences in the relative proportion of blood cells between discordant siblings, we have applied two different cell deconvolution algorithms, showing that the observed molecular signatures mainly reflect changes in peripheral blood immune cell composition, in particular NK cells. The results obtained by the cell deconvolution approach are supported by the analysis performed by WGCNA. Our report describes the largest differential gene expression profiling in peripheral blood of ASD subjects and controls conducted by RNASeq. The observed signatures are consistent with the hypothesis of immune alterations in autism and an increased risk of developing autism in subjects exposed to prenatal infections or stress. Our study also points to a potential role of NMUR1, HMGB3, and PTPRN2 in ASD.

Transcriptomic abnormalities in peripheral blood in bipolar disorder, and discrimination of the major psychoses

We performed a transcriptome-wide meta-analysis and gene co-expression network analysis to identify genes and gene networks dysregulated in the peripheral blood of bipolar disorder (BD) cases relative to unaffected comparison subjects, and determined the specificity of the transcriptomic signatures of BD and schizophrenia (SZ). Nineteen genes and 4 gene modules were significantly differentially expressed in BD cases. Thirteen gene modules were shown to be differentially expressed in a combined case-group of BD and SZ subjects called "major psychosis", including genes biologically linked to apoptosis, reactive oxygen, chromatin remodeling, and immune signaling. No modules were differentially expressed between BD and SZ cases. Machine-learning classifiers trained to separate diagnostic classes based solely on gene expression profiles could distinguish BD cases from unaffected comparison subjects with an area under the curve (AUC) of 0.724, as well as BD cases from SZ cases with AUC = 0.677 in withheld test samples. We introduced a novel and straightforward method called "polytranscript risk scoring" that could distinguish BD cases from unaffected subjects (AUC = 0.672) and SZ cases (AUC = 0.607) significantly better than expected by chance. Taken together, our results highlighted gene expression alterations common to BD and SZ that involve biological processes of inflammation, oxidative stress, apoptosis, and chromatin regulation, and highlight disorder-specific changes in gene expression that discriminate the major psychoses.

The mTOR Signaling Pathway Activity and Vitamin D Availability Control the Expression of Most Autism Predisposition Genes

Autism spectrum disorder (ASD) has a strong and complex genetic component with an estimate of more than 1000 genes implicated cataloged in SFARI (Simon′s Foundation Autism Research Initiative) gene database. A significant part of both syndromic and idiopathic autism cases can be attributed to disorders caused by the mechanistic target of rapamycin (mTOR)-dependent translation deregulation. We conducted gene-set analyses and revealed that 606 out of 1053 genes (58%) included in the SFARI Gene database and 179 out of 281 genes (64%) included in the first three categories of the database (“high confidence”, “strong candidate”, and “suggestive evidence”) could be attributed to one of the four groups: 1. FMRP (fragile X mental retardation protein) target genes, 2. mTOR signaling network genes, 3. mTOR-modulated genes, 4. vitamin D3 sensitive genes. The additional gene network analysis revealed 43 new genes and 127 new interactions, so in the whole 222 out of 281 (79%) high scored genes from SFARI Gene database were connected with mTOR signaling activity and/or dependent on vitamin D3 availability directly or indirectly. We hypothesized that genetic and/or environment mTOR hyperactivation, including provocation by vitamin D deficiency, might be a common mechanism controlling the expressivity of most autism predisposition genes and even core symptoms of autism.

Expression Analysis of BDNF, BACE1, and Their Natural Occurring Antisenses in Autistic Patients

Autism spectrum disorder (ASD) as a multifaceted neurological syndrome affects many aspects of neuropsychologic functions. Dysregulated expressions of several genes have been documented in ASD patients. The current project aimed at comparison of transcript levels of brain derived neurotrophic factor (BDNF), beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), and their natural occurring antisenses in the peripheral blood of ASD individuals (n = 50, male/female = 38/12, age (mean ± standard deviation (SD)): 6 ± 1.4, age range: 3-8) and matched healthy persons (n = 50, male/female = 37/13, age (mean ± SD): 6 ± 1.74, age range: 3-8). We demonstrated remarkable higher levels of these genes in ASD patients. BACE1 transcript levels were correlated with transcript levels of BACE1-AS in all study participants. However, BACE1 transcript levels were not correlated with participants' age. BACE1-AS and BDNF transcript levels were correlated with age in female participants. Significant correlations were detected between transcript levels of BDNF and those of other genes in all study groups. The current results render further indications for contribution of BDNF, BACE1, and their antisenses in the course of ASD and suggested expression levels of these transcripts as putative markers for this neurobehavioral disorder. Such results might be applied in clinical setting for diagnosis of complicated ASD cases.

RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation

Background

A gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC.

Methods

Genome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis.

Results

In the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation.

Limitations

Identical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis.

Conclusions

Using a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals.

A meta-analysis of two high-risk prospective cohort studies reveals autism-specific transcriptional changes to chromatin, autoimmune, and environmental response genes in umbilical cord blood

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects more than 1% of children in the USA. ASD risk is thought to arise from both genetic and environmental factors, with the perinatal period as a critical window. Understanding early transcriptional changes in ASD would assist in clarifying disease pathogenesis and identifying biomarkers. However, little is known about umbilical cord blood gene expression profiles in babies later diagnosed with ASD compared to non-typically developing and non-ASD (Non-TD) or typically developing (TD) children.

Methods

Genome-wide transcript levels were measured by Affymetrix Human Gene 2.0 array in RNA from cord blood samples from both the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) and the Early Autism Risk Longitudinal Investigation (EARLI) high-risk pregnancy cohorts that enroll younger siblings of a child previously diagnosed with ASD. Younger siblings were diagnosed based on assessments at 36 months, and 59 ASD, 92 Non-TD, and 120 TD subjects were included. Using both differential expression analysis and weighted gene correlation network analysis, gene expression between ASD and TD, and between Non-TD and TD, was compared within each study and via meta-analysis.

Results

While cord blood gene expression differences comparing either ASD or Non-TD to TD did not reach genome-wide significance, 172 genes were nominally differentially expressed between ASD and TD cord blood (log₂(fold change) > 0.1, p < 0.01). These genes were significantly enriched for functions in xenobiotic metabolism, chromatin regulation, and systemic lupus erythematosus (FDR q < 0.05). In contrast, 66 genes were nominally differentially expressed between Non-TD and TD, including 8 genes that were also differentially expressed in ASD. Gene coexpression modules were significantly correlated with demographic factors and cell type proportions.

Limitations

ASD-associated gene expression differences identified in this study are subtle, as cord blood is not the main affected tissue, it is composed of many cell types, and ASD is a heterogeneous disorder.

Conclusions

This is the first study to identify gene expression differences in cord blood specific to ASD through a meta-analysis across two prospective pregnancy cohorts. The enriched gene pathways support involvement of environmental, immune, and epigenetic mechanisms in ASD etiology.

Improving the classification of neuropsychiatric conditions using gene ontology terms as features

Although neuropsychiatric disorders have an established genetic background, their molecular foundations remain elusive. This has prompted many investigators to search for explanatory biomarkers that can predict clinical outcomes. One approach uses machine learning to classify patients based on blood mRNA expression. However, these endeavors typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because not all genes have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the models of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that is accurate and stable. We also show how annotation features can improve the interpretability of classifiers: as annotations are used to assign biological importance to genes, the biological importance of annotation-based features are the features themselves. In evaluating the annotation features, we find that top ranked annotations tend contain top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.

Solving for X: Evidence for sex-specific autism biomarkers across multiple transcriptomic studies

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

A Prospective Study of Environmental Exposures and Early Biomarkers in Autism Spectrum Disorder: Design, Protocols, and Preliminary Data from the MARBLES Study

BACKGROUND

Until recently, environmental factors in autism spectrum disorder (ASD) were largely ignored. Over the last decade, altered risks from lifestyle, medical, chemical, and other factors have emerged through various study designs: whole population cohorts linked to diagnostic and/or exposure-related databases, large case-control studies, and smaller cohorts of children at elevated risk for ASD.

OBJECTIVES

This study aimed to introduce the MARBLES (Markers of Autism Risk in Babies-Learning Early Signs) prospective study and its goals, motivate the enhanced-risk cohort design, describe protocols and main exposures of interest, and present initial descriptive results for the study population.

METHODS

Families having one or more previous child with ASD were contacted before or during a pregnancy, and once the woman became pregnant, were invited to enroll. Data and biological samples were collected throughout pregnancy, at birth, and until the child's third birthday. Neurodevelopment was assessed longitudinally. The study began enrolling in 2006 and is ongoing.

RESULTS

As of 30 June 2018, 463 pregnant mothers have enrolled. Most mothers ([Formula: see text]) were thirty years of age or over, including 7.9% who are fourty years of age or over. The sample includes 22% Hispanic and another 25% nonHispanic Black, Asian, or multiracial participants; 24% were born outside the United States. Retention is high: 84% of participants whose pregnancies did not end in miscarriage completed the study or are still currently active. Among children evaluated at 36 months of age, 24% met criteria for ASD, and another 25% were assessed as nonASD nontypical development.

CONCLUSION

Few environmental studies of ASD prospectively obtain early-life exposure measurements. The MARBLES study fills this gap with extensive data and specimen collection beginning in pregnancy and has achieved excellent retention in an ethnically diverse study population. The 24% familial recurrence risk is consistent with recent reported risks observed in large samples of siblings of children diagnosed with ASD. https://doi.org/10.1289/EHP535.

Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes

Prenatal exposure to maternal stress and depression has been identified as a risk factor for adverse behavioral and neurodevelopmental outcomes in early childhood. However, the molecular mechanisms through which maternal psychopathology shapes offspring development remain poorly understood. We applied transcriptome-wide screens to 149 umbilical cord blood samples from neonates born to mothers with posttraumatic stress disorder (PTSD; n = 20), depression (n = 31) and PTSD with comorbid depression (n = 13), compared to carefully matched trauma exposed controls (n = 23) and healthy mothers (n = 62). Analyses by maternal diagnoses revealed a clear pattern of gene expression signatures distinguishing neonates born to mothers with a history of psychopathology from those without. Co-expression network analysis identified distinct gene expression perturbations across maternal diagnoses, including two depression-related modules implicated in axon-guidance and mRNA stability, as well as two PTSD-related modules implicated in TNF signaling and cellular response to stress. Notably, these disease-related modules were enriched with brain-expressed genes and genetic risk loci for autism spectrum disorder and schizophrenia, which may imply a causal role for impaired developmental outcomes. These molecular alterations preceded changes in clinical measures at twenty-four months, including reductions in cognitive and socio-emotional outcomes in affected infants. Collectively, these findings indicate that prenatal exposure to maternal psychological distress induces neuronal, immunological and behavioral abnormalities in affected offspring and support the search for early biomarkers of exposures to adverse in utero environments and the classification of children at risk for impaired development.

Abnormal mTOR Activation in Autism

The mechanistic target of rapamycin (mTOR) is an important signaling hub that integrates environmental information regarding energy availability and stimulates anabolic molecular processes and cell growth. Abnormalities in this pathway have been identified in several syndromes in which autism spectrum disorder (ASD) is highly prevalent. Several studies have investigated mTOR signaling in developmental and neuronal processes that, when dysregulated, could contribute to the development of ASD. Although many potential mechanisms still remain to be fully understood, these associations are of great interest because of the clinical availability of mTOR inhibitors. Clinical trials evaluating the efficacy of mTOR inhibitors to improve neurodevelopmental outcomes have been initiated.

PTSD Blood Transcriptome Mega-Analysis: Shared Inflammatory Pathways across Biological Sex and Modes of Trauma

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module downregulated in men exposed to combat traumas, one IL-12-mediated signaling module upregulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and mitogen-activated protein kinase activity upregulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune, and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.

Hierarchical cortical transcriptome disorganization in autism

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-017-0147-7) contains supplementary material, which is available to authorized users.