Patent Application
20210277476
The present disclosure relates to expression-based diagnosis, prognosis and treatment of complex diseases.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with prenatal and early postnatal biological onset1-3. Genetic factors contribute to the predisposition and development of ASD with estimated heritability rates of 50-83%4,5. Large-scale genetic studies have implicated several hundred risk (rASD) genes that appear to be associated with many different pathways, cell processes, and neurodevelopmental stages6-8. This highly heterogeneous genetic landscape has raised challenges in elucidating the biological mechanisms involved in the disorder. While rigorous proof remains lacking, current evidence suggests that rASD genes fall into networks and biological processes6,7,9-13 that modulate one or more critical stages of prenatal and early postnatal brain development, including neuronal proliferation, migration, neurite growth, synapse formation and function3,8. However, these insights are mostly gained from focused studies on single rASD genes (see Courchesne et al.3 for a recent review) or based on transcriptome data of non-ASD brains9-11, leaving an incomplete picture of rASD-induced molecular changes at the individual level and relationships with early-age clinical heterogeneity.
To further complicate efforts to discern the molecular bases of ASD, the implicated rASD genes are largely identified through de novo loss-of-function mutations in their coding sequence. Such events account for less than 5% of the ASD population, and most of heritability is estimated to reside in common variants also seen in the typically developing population5,14-16. Currently, there is a paucity of data on whether ASD cases with known rASD gene mutations manifest as special subtypes of ASD with distinct molecular etiology, or whether they share mechanisms with the general ASD population.
To address these fundamental questions, it is important to understand what molecular processes are perturbed in prenatal and early postnatal life in ASD individuals, assess how they vary among subjects, and evaluate how these perturbations relate to rASD genes and early-age ASD clinical symptoms. It is expected that the genetic changes in ASD alter gene expression and signaling in the early-age developing brain3,7,11,17. Therefore, capturing dysregulated gene expression at prenatal and early postnatal ages may help unravel the underlying molecular organization of ASD. Unfortunately, doing so is particularly challenging as ASD brain tissue cannot be obtained at these early stages, and all available postmortem ASD brains are from much older ages, well beyond the ages when rASD genes are at peak expression and the disorder begins. However, in contrast to living neurons that have a limited time window for proliferation and maturation, other cell types constantly regenerate, such as blood cells. Given the strong genetic basis of ASD, some dysregulated developmental signals may continually reoccur in blood cells and thus be studied postnatally18-20.
Reinforcing this notion, it was demonstrated that genes that are broadly expressed across many tissues are major contributors to the overall heritability of complex traits21, and it was postulated that this could be relevant to ASD. Lending credence to this, previous studies have reported the enrichment of differentially expressed genes in ASD blood for the regulatory targets of CHD819 and FMR122 genes, two well-known rASD genes. Similarly, lymphoblastoid cells of ASD cases and iPS-derived models of fragile-X syndrome show over-expression of mir-181 with a potential role in the disorder23. Likewise, leukocytes from ASD toddlers show perturbations in biological processes, such as cell proliferation, differentiation, and microtubules24-28, and these coincide with dysregulated processes seen in neural progenitor cells (NPCs) and neurons, derived from iPS cells from ASD subjects29,30. Ultimately, establishing the signatures of ASD in other tissues will be important to facilitate the study of the molecular basis of the disorder in living ASD subjects in the first years of life.
In an embodiment, transcriptomic data from leukocytes, stems cell models, and the developing brain are leveraged to study the underlying architecture of transcriptional dysregulation in ASD, its connection to rASD genes, and its association with prenatal development and clinical outcomes of ASD toddlers. Specifically, a conserved dysregulated gene network was discovered by analyzing leukocyte transcriptomic data from 1-4 years old ASD and typically developing (TD) toddlers. The dysregulated network is enriched for pathways known to be perturbed in ASD neurons, impacts highly expressed processes in prenatal brain development, and is dysregulated in iPS cell-derived neurons from ASD cases. Consistent with the omnigenic model of complex traits21, this disclosure shows that rASD genes across diverse functional groups converge upon and regulate this core network. Importantly, this core network is disrupted to different levels of severity across ASD individuals, and is correlated with clinical severity in individual ASD toddlers. Thus, these results demonstrate how the heterogeneous genetic basis of ASD converges on a biologically relevant core network, capturing the underlying possible molecular etiology of ASD.
The invention is about the network activity and its diagnostic and prognostic power, which can then also be combined with effective conventional treatment therapies. The network can be constructed by different methods. However, the network activity in this invention has a specific meaning which is different than previously discussed network activity in the art. In this disclosure, the network activity is measured by the co-expression activity of the interacting genes. However, in the prior art, the overall fold change pattern of genes in the network is used as the measure of activity. Here, the approach of the disclosure is applied on ASD and shows that the network co-expression activity is predictive of ASD symptom severity. However, this approach is not limited to ASD and can be used to compare any two biological conditions, including other diseases. There are some prior art that the network activity is highest prior to a disease status and is gone when the disease is established. In that prior art view, the network activity is an indicator that a disease status is about to occur and does not have diagnostic or prognostic power. The present invention provides that the network activity has diagnostic and prognostic capacity. Moreover, the network activity in the prior art is based on the transcriptome measurements of impacted tissues. However, in the present invention, network activity is measured in a surrogate tissue which is not diseases. This is of critical importance to brain-related disorders, as direct access to the impacted tissue is not feasible.
This invention relates to systems approaches for distinguishing two or more conditions, such as individuals with neurodevelopmental disorders from typically developing control subjects. Specifically, it provides methods to identify and evaluate the co-expression activity of a network of biomolecules (such as genes mRNA or proteins) that indicates and/or correlates with the underlying pathobiology of a complex disease and/or disorder. In further embodiments, the invention demonstrates that the network activity has prognostic value and correlates with the severity of a complex disease or disorder. In further embodiments, the invention demonstrates that the network activity in a surrogate tissue, such as blood, is informative about the diagnosis status of disorders related to brain, for which direct access to impacted tissue is not feasible. In yet another embodiment, the invention shows that the network co-expression activity relates to the genetic basis of a complex disease and/or disorder.
This disclosure includes methods to measure the activity of a network of biomolecules (such as genes) based on the co-expression levels. The biomolecules involved in the network could be prioritized based on the comparison of two or more conditions. In certain aspects, this disclosure uses a selected transcriptome to build the model. The transcripts can be prioritized by differential expression analysis.
In embodiments, a network of selected biomolecules is constructed. This network demonstrates how the biomolecules interact with one another. This disclosure demonstrates that the approach is flexible on the type of the constructed network. Specifically, this disclosure shows that the method works with networks that are constructed based on prior existing knowledge or are purely data driven, or a combination of thereof. The activity of the constructed network from the biomolecules can be measured based on the co-expression strength of interactions present in the network. Specifically, first the co-expression strength will be measured for interaction present in the network. Next, the distribution of co-expression strengths will be compared to a distribution that is expected by chance. Accordingly, a significance level can be assigned to the co-expression strength of the constructed network. This disclosure provides a computer-implemented program that measures the network activity. This disclosure demonstrates that co-expression can be measured by either correlation or mutual information-based approaches. This disclosure demonstrates that the network activity can be measured at both the group level (e.g., disorder vs control) and the sample-based level. This disclosure shows that the network activity can be an indicator of disease state in samples from both impacted tissue as well as a surrogate tissue.
The network activity can be linked to the genetic basis of a complex disease or disorder. This disclosure shows that the devised network activity measure can be related to the underlying genetic basis of a complex disease/disorder and as such provides insights on the functionality of gene mutations. The network activity can be linked to the pathobiology of a complex disease/disorder. This disclosure shows that identified network activity in surrogate tissues such as blood can be linked to the underlying pathobiology of disease/disorder and as such can: 1) provide mechanistic insights about the disease/disorder; 2) highlights the molecular pathways that are directly associated to the disease/disorder (e.g., brain in the neurodevelopmental disorders) in individuals with the disease/disorder. This information can be leveraged to select known drugs for administration to treat the disease/disorder of impacted individuals. The network activity can have prognostic values and stratify the population of individuals with a complex disease/disorder. Since the network activity is related to the underlying pathobiology of the disease, this information can inform on the treatment options. The invention shows that the disease severity is correlated with the network activity.
The present invention provides in embodiments a method of diagnosing, prognosing and treating a disease in a subject comprising: (a) obtaining a biological sample of the subject; (b) measuring expression patterns of more than two molecular markers in the blood sample; (c) comparing the molecular marker patterns with healthy controls for gene regulatory mechanisms, signaling pathways and protein interactions to determine a dysregulated network in the subject based on a co-expression pattern of interacting genes in the network; and optionally (d) administering an effective disease therapy to the subject.
The present invention provides a method wherein the molecular marker is selected from DNA, RNA, protein, metabolites, glycans, and lipids. The present invention provides a method wherein the biological sample is blood. The present invention provides a method wherein the disease is autism. The present invention provides a method wherein the disease is ASD and the markers are genes selected from RAS/ERK, PI3K/AKT and WNT/β-catenin pathway genes. The present invention provides a method wherein the biological sample is a non-neurologic tissue sample, and wherein the disease is a neurologic disease.
The present invention provides a method further comprising: determining a change in co-expression strength or a correlation between any two molecular markers in the blood sample; and diagnosing a disease of disorder using the change or the correlation.
The present invention provides a method further comprising: evaluating co-expression or correlation of molecules or markers in the blood sample, where the molecules are RNA, protein, metabolites, glycans, lipids, or DNA markers, and wherein the markers can be obtained from tissue or fluids.
The present invention provides a method further comprising: building a network from markers that change between two different conditions.
The present invention provides a method further comprising: determining co-expression magnitudes using either correlation and information theory based metrics.
The present invention provides a method further comprising: determining a correlation between the magnitude of co-expression with a disease severity or prognosis.
The present invention provides a method further comprising: determining differences in magnitude of co-expression or correlation or changes in co-expression or correlation associated with another metric; and determining a distinct subtype of a disorder using the differences.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject comprising: (a) obtaining a biological sample of the subject; (b)measuring expression patterns of more than two molecular markers in the blood sample; (c)comparing the molecular marker patterns with healthy controls for gene regulatory mechanisms, signaling pathways and protein interactions to determine a dysregulated network in the subject based on a co-expression pattern of interacting genes in the network; (d) administering an effective disease therapy to the subject; and (e) determining an effect of the therapy on a co-expression/correlation activity of the network.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject wherein the molecular marker is selected from DNA, RNA, protein, metabolites, glycans, and lipids.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject wherein the effective disease therapy is a first treatment is connected to subjects in a first subgroup of the disorder, and a second treatment connected to subjects in a second subgroup of the disorder.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject wherein the biological sample is blood.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject wherein the disease is autism.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject wherein the disease is ASD and the markers are genes selected from RAS/ERK, PI3K/AKT and WNT/β-catenin pathway genes.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject wherein the biological sample is a non-neurologic tissue sample, and wherein the disease is a neurologic disease.
The present invention provides a method of diagnosing, prognosing, and treating a disease in a subject further comprising: determining a change in co-expression strength or a correlation between any two molecular markers in the blood sample; and diagnosing a disease of disorder using the change or the correlation.
In many disease conditions, transcriptional programs in cells deviate from normal states due to dysregulations in signaling pathways, transcription factors and epigenetic marks. Therefore, this disclosure employs a systems approach to decipher network-level transcriptional perturbations in leukocyte transcriptome data. This disclosure provides that perturbations to disease-associated molecular pathways are reflected in the co-expression patterns between genes. To identify such disease-relevant dysregulations, context specific networks were built by integrating gene expression data from each condition with available knowledge on the gene interaction data. The magnitude of co-expressions between the context-specific networks was compared using a novel approach. In an exemplary embodiment, this disclosure applies this framework on Autism Spectrum Disorder (ASD), and shows that this approach can identify network-level dysregulations in ASD. This disclosure finds that this network is applicable to the underlying pathobiology of the ASD and predicts ASD symptom severity.
While ASD demonstrates a strong genetic basis, it heretofore remains elusive how implicated genes are connected to the molecular dysregulations that underlie the disorder at prenatal and early postnatal ages. Towards this, this disclosure includes an exemplary systems biology framework that integrates transcriptomic dysregulations in living ASD toddlers with current knowledge on ASD risk genes to explain ASD associated fetal-stage brain transcriptomic changes and clinical outcomes. Specifically, a dysregulated transcriptional network was found that shows elevated gene co-expression activity in ASD toddlers. This core network was robustly associated with rASD genes with likely deleterious mutations in ASD subjects. Such rASD genes have potentially large effect size on the etiology but occur in a small percentage of the ASD population48,49. This disclosure shows that many rASD genes exert their regulatory effect on this DE-ASD core network through the inter-connected RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. The connection of the DE-ASD network (that is constructed based on data drawn from the general ASD pediatric population) with high confidence rASD genes provides empirical evidence of shared mechanisms underlying ASD in both those with high penetrant rASD genes and those of other etiologies (e.g., common variants, environmental factors) in the wider ASD population.
The key aspect of our signature is that it is constructed based on transcriptomic data from young living ASD toddlers. This allows correlation of variations with the core clinical features of the same ASD toddlers. Indeed, the dysregulation degree of DE-ASD network correlated with the toddlers' ADOS social and communication deficits. Social and behavioral deficits are also suggested to be correlated with the genetic variations in ASD subjects50,51; and previous studies have established the effect of the PI3K/AKT signaling pathway (central to the DE-ASD core network and significantly altered in ASD leukocytes) on social behaviors of mouse models42,43. Together, these observations indicate that etiological roots of ASD converge on gene networks that correlate with the symptom severity in ASD individuals. Moreover, the results described in this disclosure show that stronger dysregulation of the same core network could lead to higher severity in the ASD cases. The DE-ASD core network is enriched for pathways implicated in ASD, strongly associated with high confidence rASD genes, and correlate with ASD severity. The network co-expression activity measure of this disclosure is a summary score from the strongest signal in the dataset (i.e., differentially expressed genes) at a group level (i.e., severity level).
The emerging architecture of complex traits suggests that gene mutations often propagate their effects through regulatory networks and converge on core pathways relevant to the trait21,52. This disclosure's findings support the existence of an analogous architecture for ASD, wherein rASD genes with diverse biological roles overlap in their down-stream function. Although not significantly overlapping with rASD genes, this disclosure finds that the DE-ASD network is significantly co-expressed with rASD genes in both blood and brain tissues. This disclosure also illustrates that the DE-ASD network could be controlled by rASD genes through direct transcriptional regulation or highly interconnected signaling pathways. This disclosure provides that the DE-ASD network is a primary convergence point of ASD etiologies, including its genetic basis as elaborated for rASD genes, in a large portion of the ASD population. This predicts that the spectrum of autism in such cases is correlated with the degree and mechanism of the perturbation of the DE-ASD network. A detailed analysis of iPS cell-derived ASD neurons demonstrated the dysregulation of the leukocyte-based DE-ASD network in ASD neurons, supporting the neural-level relevance of the findings to ASD etiology and its prevalence in the ASD population. Furthermore, direct clinical-level relevance is demonstrated by the high correlation found between degree of dysregulation in the DE-ASD core network and ASD symptom severity in the ASD toddlers.
The currently recognized rASD genes are not fully penetrant to the disorder, except for a handful of syndromic genes48,49,53,54. The analysis of the XP-ASD network provides some insights on how the effects of rASD genes can combine to result in ASD. Although some rASD genes could directly modulate the DE-ASD network at the transcriptional level, this disclosure's results indicate that the regulatory consequence of many rASD genes on the DE-ASD network is canalized through the PI3K/AKT, RAS/ERK, WNT and β-catenin signaling pathways. The structural and functional interrogation of the DE-ASD network localized the PI3K/AKT pathway to its epicenter and demonstrated enrichments for processes down-stream of this pathway. Moreover, this disclosure finds that high confidence rASD genes are better connected to the DE-ASD core network, suggesting that the closeness and influence of genes on these signaling pathways is correlated with their effect size on the disorder. These results articulate that perturbation of the PI3K/AKT, RAS/ERK, WNT and β-catenin signaling pathways through gene regulatory networks is an important etiological route for ASD that is associated with the disorder severity level in a relatively large fraction of the ASD population. Congruent with this hypothesis, cell and animal models of ASD have demonstrated the enrichment of high confidence rASD genes for the regulators of the RAS/ERK, PI3K/AKT, WNT and β-catenin signaling pathways3,8,11,17,42,43,46. These signaling pathways are highly conserved and pleiotropic, impacting multiple prenatal and early postnatal neural development stages from proliferation/differentiation to synaptic and neural circuit development3. Such multi-functionalities may be the underlying reason for the detection of the signal in ASD leukocytes.
This disclosure presents the largest transcriptome analysis on early-age ASD cases thus far from such settings. The analysis was focused on the strongest signal that best differentiates ASD cases from TD individuals (i.e., differentially expressed genes). Here this disclosure illustrates that the captured signal is informative about the transcriptional organization of ASD and shows how to bridge the gap between genetic and clinical outcomes. The presented framework provides methods to systematically diagnose, classify and prognostically stratify ASD cases at early postnatal ages based on the underlying molecular mechanisms. The concept of precision molecular medicine for ASD can be actualized via approaches that illuminate the early-age living biology of ASD3,17,20. ASD toddler-derived iPS cell studies show ASD is a progressive prenatal and early postnatal disorder that involves a cascade of diverse and varying molecular and cellular changes such as those resulting from dysregulation of the pathways and networks highlighted herein3,29,30. As such, dynamic, individual-based molecular assays in infants and toddlers will be essential to develop. The presented framework provides for the development of quantitative, molecular-based measures for the ASD diagnosis and prognosis by identifying specific molecular dysregulations that are observable in leukocytes of a large fraction of living ASD toddlers at young ages.
Hundreds of genes are implicated as risk factors for autism spectrum disorder (ASD). However, the mechanisms through which these genes exert their effects at early ages in ASD remain unclear. To identify such mechanisms, transcriptomics from ASD toddlers were analyzed to discover a core gene network with dysregulated gene co-expression. The identified network includes highly expressed processes in fetal-stage brain development and is dysregulated in neuron models of ASD. This disclosure identifies ASD risk genes across diverse functions are upstream and regulate this core network. In particular, many risk genes impact the core network through the RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. Finally, the dysregulation degree of this core network positively correlates with early-age ASD clinical severity. Thus, these results provide insights into how the heterogeneous genetic basis of ASD could converge on a core network with consequence on the postnatal outcome of toddlers with ASD.
This disclosure includes a systems biology framework to identify ASD-related perturbed molecular processes in the leukocyte surrogate tissue. Specifically, this framework was exploited to delineate the architecture of transcriptional dysregulation in ASD, its connection to rASD genes, and its association with prenatal brain development and postnatal socialization symptom severity in ASD. A dysregulated gene network was discovered by analyzing leukocyte transcriptomic data from 1-4 year-old toddlers with ASD and typical development (TD). This perturbed network is highly expressed, conserved and active in fetal brains. The dysregulated network is enriched for pathways known to be perturbed in ASD neurons, and is dysregulated in hiPSC-derived neurons of SHANK2 high confidence rASD gene as well as hiPSCs from subjects with ASD and brain enlargement. Consistent with the postulated structure of complex traits21,30, this disclosure shows that rASD genes in diverse functional groups converge upon and regulate this core network. Importantly, the dysregulation extent of this core network predicts the severity of socialization deficits in toddlers with ASD. Thus, the framework presented here facilitates the development of quantitative, molecular-based measures for diagnosis and prognosis of brain disorders and diseases including ASD, by identifying specific molecular dysregulations that we show are observable in leukocytes of a large fraction of toddlers with ASD.
When introducing elements of the present invention or the preferred embodiment(s) thereof, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.
It is understood that aspects and embodiments of the invention described herein include “consisting” and/or “consisting essentially of” aspects and embodiments.
Throughout this disclosure, various aspects of this invention are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.
The invention provides for the practice of the described methods herein in certain embodiments with the selection of at least 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 400, or 500 of the biomarkers (e.g., expressed genes) listed in Tables 1-4.
Pharmaceutically active: The term “pharmaceutically active” as used herein refers to the beneficial biological activity of a substance on living matter and, in particular, on cells and tissues of the human body. A “pharmaceutically active agent” or “drug” is a substance that is pharmaceutically active and a “pharmaceutically active ingredient” (API) is the pharmaceutically active substance in a drug. As used herein, pharmaceutically active agents include synthetic or naturally occurring small molecule drugs and more complex biological molecules.
Pharmaceutically acceptable: The term “pharmaceutically acceptable” as used herein means approved by a regulatory agency of the Federal or a state government or listed in the U.S. Pharmacopoeia, other generally recognized pharmacopoeia in addition to other formulations that are safe for use in animals, and more particularly in humans and/or non-human mammals.
As used herein, “preventative” treatment is meant to indicate a postponement of development of a disease, a symptom of a disease, or medical condition, suppressing symptoms that may appear, or reducing the risk of developing or recurrence of a disease or symptom. “Curative” treatment includes reducing the severity of or suppressing the worsening of an existing disease, symptom, or condition.
As used herein, the term “therapeutically effective amount” refers to those amounts that, when administered to a particular subject in view of the nature and severity of that subject's disease or condition, will have a desired therapeutic effect, e.g., an amount which will cure, prevent, inhibit, or at least partially arrest or partially prevent a target disease or condition. More specific embodiments are included in the sections below. In some embodiments, the term “therapeutically effective amount” or “effective amount” refers to an amount of a therapeutic agent that when administered alone or in combination with an additional therapeutic agent to a cell, tissue, or subject is effective to prevent or ameliorate the disease or condition such as an infection or the progression of the disease or condition. A therapeutically effective dose further refers to that amount of the therapeutic agent sufficient to result in amelioration of symptoms, e.g., treatment, healing, prevention or amelioration of the relevant medical condition, or an increase in rate of treatment, healing, prevention or amelioration of such conditions. When applied to an individual active ingredient administered alone, a therapeutically effective dose refers to that ingredient alone. When applied to a combination, a therapeutically effective dose refers to combined amounts of the active ingredients that result in the therapeutic effect, whether administered in combination, serially or simultaneously.
“Treating” or “treatment” or “alleviation” refers to therapeutic treatment wherein the object is to slow down (lessen) if not cure the targeted pathologic condition or disorder or prevent recurrence of the condition. A subject is successfully “treated” if, after receiving a therapeutic amount of a therapeutic agent, the subject shows observable and/or measurable reduction in or absence of one or more signs and symptoms of the particular disease. Reduction of the signs or symptoms of a disease may also be felt by the patient. A patient is also considered treated if the patient experiences stable disease. In some embodiments, treatment with a therapeutic agent is effective to result in the patients being disease-free 3 months after treatment, preferably 6 months, more preferably one year, even more preferably 2 or more years post treatment. In many embodiments, an effective treatment of the disease or condition, such as autism, may be other physical, visual or auditory therapies, rather than drug administration, such as are known or later recommended. These parameters for assessing successful treatment and improvement in the disease are readily measurable by routine procedures familiar to a physician of appropriate skill in the art.
As used herein, a subject in need refers to an animal, a non-human mammal or a human including a human fetus, neonate, toddler, or adult. As used herein, “animals” include a pet, a farm animal, an economic animal, a sport animal and an experimental animal, such as a cat, a dog, a horse, a cow, an ox, a pig, a donkey, a sheep, a lamb, a goat, a mouse, a rabbit, a chicken, a duck, a goose, a primate, including a monkey and a chimpanzee.
Although specific advantages have been enumerated above, various embodiments may include some, none, or all of the enumerated advantages. Other technical advantages may become readily apparent to one of ordinary skill in the art after review of the figures and description. It should be understood at the outset that, although exemplary embodiments are illustrated in the figures and specification, the principles of the present disclosure may be implemented using any number of techniques, whether currently known or not. The present claims should in no way be limited to the exemplary implementations and techniques illustrated in the figures and specification. The complete disclosures of the citations herein are hereby incorporated by reference in their entireties.
Leukocyte gene expression profiles obtained from 226 male toddlers (119 ASD and 107 TD) were analyzed. Robust linear regression modeling of the data identified 1236 differentially expressed (DE) genes (437 downregulated and 799 upregulated; FDR<0.05; Table 1). Jack-knife resampling demonstrated that the expression pattern of DE genes was not driven by a small number of cases, but rather shared between the vast majority of subjects with ASD (
In many disease conditions, transcriptional programs in cells deviate from normal states due to dysregulations in signaling pathways, transcription factors and epigenetic marks. Therefore, we developed a systems approach to decipher network-level transcriptional perturbations in leukocytes of toddlers with ASD (
To test if transcriptional programs were being modulated in ASD, merged the genes and interactions in the DE-ASD and DE-TD networks were merged, and compared the ‘co-expression magnitude’ of interactions in the merged network between ASD and TD samples31-33. This proxy for the transcriptional activity of gene networks9 demonstrated that co-expression magnitude was higher in the ASD than the TD samples (
In summary, the leukocyte transcriptional networks of the DE genes show higher than normal co-expression activity in ASD. Moreover, the dysregulation pattern is present in a large percentage of toddlers with ASD, as evidenced by the resampling analyses and the other two ASD datasets.
The potential involvement of the leukocyte-based network to gene expression patterns during brain development was assessed. By overlaying the neurodevelopmental RNA-Seq data from BrainSpan34,35 on the DE-ASD network, this disclosure identified that the DE-ASD network was enriched for highly expressed genes in the neocortex at prenatal and early postnatal periods (p-value<4.3×10−30;
To investigate the spatiotemporal activity of the DE-ASD network during brain development, the magnitude of gene co-expression within the DE-ASD network was measured at different neurodevelopmental time windows across brain regions. The highest levels of co-expression of the DE-ASD network temporally coincided with peak neural proliferation in brain development (10-19 post conception weeks3,8), after which co-expression activity gradually decreased (
rASD Genes are Associated with the DE-ASD Network
The DE-ASD network was analyzed in the context of other studies to test the relevance of the DE-ASD network to ASD. Parikshak et al. previously reported gene co-expression modules associated with cortical laminae development during prenatal and early postnatal ages11. A subset of these modules show enrichment in rASD genes11. The overlap of the leukocyte-derived network with all modules from Parikshak et al11 was examined. The DE-ASD network preferentially overlapped with rASD gene-enriched modules from that study (
With the observed overlap patterns, a test for enrichment of rASD genes in the DE-ASD network was performed. For this analysis, different rASD gene lists of different size and varying confidence levels (Methods) were considered. Surprisingly, this analysis demonstrated that rASD genes are not enriched in the DE-ASD network (p-value>0.19).
Many high confidence rASD genes have regulatory functions3,7,10. Although the perturbed DE-ASD network is not enriched for rASD genes, it overlaps with brain co-expression modules and networks containing known rASD genes. At the mechanistic level, the observed co-expression of rASD and DE genes in the prenatal brain could be due to the regulatory influence of rASD genes on the DE-ASD network, and thereby genetic alterations in rASD genes could cause the transcriptional perturbation and the increase in gene co-expression within the DE-ASD network.
To elucidate if rASD genes could regulate the DE-ASD network, this disclosure examined if the regulatory targets of rASD genes are enriched in the DE-ASD network. Indeed, it was observed that the DE-ASD network is enriched for genes regulated by two high-confidence rASD genes, CHD836-38 and FMR139 (
The DE-ASD Network is Preferentially Linked to High Confidence rASD Genes
rASD genes were often not differentially expressed in ASD leukocytes, and the DE-ASD network was therefore not enriched in rASD genes. To explore if rASD genes may nevertheless regulate the DE-ASD network, the DE-ASD network was expanded by including rASD genes. Thus, an expanded-ASD (XP-ASD) network was obtained. To construct the XP-ASD network, a similar approach to that used for the DE-ASD network was used. Briefly, a high-confidence static network of DE and 965 candidate rASD genes was built. The context-specific XP-ASD network was next inferred by retaining only the significantly co-expressed interacting pairs in ASD samples. This pruning step removed genes from the static network that were not significantly co-expressed with their known physically interacting partners or regulatory targets in ASD leukocytes. Accordingly, the XP-ASD network included a total of 316 out of 965 (36%) likely rASD genes (Table 4).
The 965 rASD genes included both high-confidence rASD genes (e.g., recurrently mutated in individuals with ASD) and low-confidence rASD genes (some even found in siblings of individuals with ASD, who developed normally). If the XP-ASD network is truly relevant to the prenatal etiology of ASD, high-confidence rASD genes would be preferentially incorporated into the XP-ASD network. By following different analytical methods, other researchers have independently categorized rASD genes into high- and low-confidence7,14,42. Importantly, a reproducible enrichment of high-confidence rASD genes in the XP-ASD network (
rASD Genes Tend to be Repressors of Genes in the DE-ASD Network
To explore how rASD genes may regulate DE genes, their interaction types were analyzed (i.e., positive or negative correlations, alluding to activator or repressor activity). Comparative analysis of interactions between DE and rASD genes in the XP-ASD network indicated a significant enrichment of negative correlations between rASD and DE genes (OR: 1.79; p-value<3.1×10−4; Fisher's exact test), suggesting a predominantly inhibitory role of rASD genes on the DE genes (
In line with a role of rASD genes as repressors, the DE-ASD network was enriched for genes that were up-regulated by the knock-down of CHD8 in neural progenitor and stem cells, but not for genes that were down-regulated36-38 (
To further test if rASD genes were predominantly repressors of genes in the DE-ASD network, an independent transcriptome dataset from the differentiation of primary human neural progenitor cells obtained from fetal brains of three donors43 was analyzed. Expression of genes in the DE-ASD network exhibit a gradual down-regulation during neural progenitor differentiation (p-value 4.4×10−6;
This disclosure identifies key pathways involved in the XP-ASD and DE-ASD networks. Biological process enrichment analysis of the XP-ASD network demonstrated it is highly enriched for signaling pathways (
These results suggest elevated co-expression activity of PI3K/AKT and its down-stream pathways in ASD leukocytes (
The DE-ASD and XP-ASD networks were further investigated using an integrated hub analysis approach (Methods). In the DE-ASD network, hub genes included the key members of the PI3K/AKT pathway including PIK3CD, AKT1 and GSK3B (
rASD Genes Regulate DE-ASD Genes Through Specific Signaling Pathways
This disclosure explored if perturbation to the rASD genes lead to the perturbation of the DE-ASD network through changes in the RAS/ERK, PI3K/AKT, and WNT/β-catenin pathways. The activity of these three pathways is chiefly mediated through changes in phosphorylation of ERK, AKT, and β-catenin proteins. Therefore, to assess the regulatory influence of rASD genes on these signaling pathways, available genome-wide mutational screening data were leveraged, wherein gene mutations were scored based on their effects on the phosphorylation state of ERK, AKT, and β-catenin proteins47. Consistent with the functional enrichment and hub analysis results, rASD genes in the XP-ASD network were significantly enriched for regulators of the RAS/ERK, PI3K/AKT, and WNT/β-catenin pathways (
In summary, the XP-ASD network decomposition results described herein suggest a modular regulatory structure for the XP-ASD network in which diverse rASD genes converge upon and dysregulate activity of the DE genes (
The DE-ASD Network is Over-Active in Neuron Models of Subjects with ASD and Brain Enlargement
These results demonstrate increased gene co-expression in the DE-ASD network in leukocytes of toddlers with ASD selected from the general population. Furthermore, the results implicate the DE-ASD network in the prenatal etiology of ASD by demonstrating its higher co-expression during fetal brain development, and its connection with high-confidence rASD genes. Also, the results suggest that the increased co-expression in the network is present in a large percentage of our ASD toddlers and is associated with the processes related to the neural proliferation and maturation.
To further validate these results, it was examined if the DE-ASD network shows increased co-expression in hiPSC-derived neural progenitors and neurons from toddlers with ASD. Thus, the previously published hiPSCs transcriptome data from 13 individuals with ASD and TD28,48 was reanalyzed, which were differentiated into neural progenitor and neuron stages. The included subjects with ASD capture macrocephaly which is an important phenotype common in many subjects with ASD. Importantly, the analysis demonstrated that the DE-ASD network is more active in these neuron models of subjects with ASD (
To further explore the potential dysregulation of DE-ASD network in hiPSC-derived neuron models of ASD and its connection with rASD genes, the transcriptional activity of DE-ASD network was analyzed in hiPSC-derived neuron models of SHANK2 high confidence rASD gene48. Importantly, this analysis indicated the high transcriptional activity of DE-ASD network in these neuron models compared with CRISPR SHANK2-corrected cell lines from the same individuals.
Collectively, these results suggest the functional relevance of identified leukocyte molecular signatures to the abnormal brain development in ASD, and the regulatory influence of high confidence rASD genes on this core network.
Network Dysregulation is Associated with ASD Severity
The potential role of the DE-ASD network activity on the development of the core clinical symptom of socialization deficits in toddlers with ASD was evaluated. To this end, it was tested if the same pattern of gene co-expression dysregulation exists across individuals at different levels of ASD severity as measured by Autism Diagnostic Observation Schedule (ADOS) social affect severity score. It was observed that the fold change patterns of DE genes are almost identical across different ASD severity levels (
These analyses have demonstrated the association of DE-ASD network with ASD-relevant pathobiological dysregulation in fetal brain. It also suggested that the dysregulation extent of the DE-ASD network could predict ASD symptom severity. However, the network level perturbations have been based on measurements of co-expression in a group of samples (e.g., a group of TD subjects, a group of ASD subjects with ADOS SA score range of 13 to 16). To more directly measure the potential diagnosis power of DE-ASD network, it was next developed a new metric to measure the dysregulations of DE-ASD network in each individual sample (Methods). This analysis demonstrated existence of two distinct subtypes of ASD (
To further explore the differences between the two ASD subtypes, we conducted differential expression analysis of each subtype versus TD diagnosis group. This analysis indicated that existence of 2834 DE genes at FDR<0.05 in the leukocyte transcriptome of first ASD subtype. However, no DE gene was identified in the second ASD subtype. This observation suggests that the dysregulation mechanisms are shared at some extent between leukocyte and brain development in the first subtype, while the ASD-relevant pathobiology for the second subtype is not reflected in the leukocyte transcriptome. These analyses have shown that the activity of DE-ASD network could be mediated by rASD genes including CHD8, FMR1, and SHANK2. It is also known that both genetics and environmental factors contribute to the development of ASD. Therefore, the results from the sample-based analysis could suggest existence of genetic etiology in the first subtype. Such genetic factors would allow the dysregulation to reoccur again and again during blood cell proliferations. However, in toddlers that environmental factors are the main driver in the development of ASD, the insult has occurred at fetal and very early postnatal ages. Such environmental insult had impacted the process of typical brain development, but the insult is not present at later time points anymore and hence there is no opportunity to identify its impact postnatally in the leukocyte transcriptome of toddlers.
While ASD has a strong genetic basis, it remains elusive how rASD genes are connected to the molecular changes underlying the disorder at prenatal and early postnatal ages. This disclosure includes a systems-biology framework to identify perturbed transcriptional programs in leukocytes, and connect them with the rASD genes and early-age symptom severity. Specifically, a dysregulated gene network was found that shows elevated gene co-expression activity in leukocytes from toddlers with ASD. This core network was robustly associated with high-confidence rASD genes. Although recurrent, high confidence rASD gene mutations occur in a small percentage of the ASD population5,14. The connection of the DE-ASD network (constructed with data from the general ASD pediatric population) with high-confidence rASD genes provides evidence of shared mechanisms underlying ASD in both individuals with highly penetrant rASD gene mutations and those with other etiologies (e.g., common variants). This disclosure further shows that many rASD genes may regulate the DE-ASD core network through the RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. This study confirms and substantially expands results from previous reports on blood transcriptome of subjects with ASD.
A key aspect of the signature is that it allows one to investigate the relationship of molecular perturbations with early-age ASD symptom severity. Indeed, it was found that the magnitude of dysregulation of the DE-ASD network is correlated with deficits in ADOS social affect scores in male toddlers of 1-4 years old. Social and behavioral deficits are also suggested to be linked with the genetic variations in subjects with ASD49,50; and previous studies have established the effect of the PI3K/AKT signaling pathway (central to the DE-ASD core network) on social behaviors in mouse models45,46. Together, these observations suggest that the etiology of ASD converges on gene networks that correlate with ASD symptom severity. Moreover, the results reinforce the hypothesis that stronger dysregulation of this core network could lead to a higher ASD severity. The DE-ASD core network is enriched for pathways implicated in ASD, strongly associated with high-confidence rASD genes, and correlate with the ASD symptom severity. However, a direct causal relationship between the co-expression activity of the network and ASD remains to be established. Moreover, the co-expression activity measure is a summary score from the strongest signal in our dataset (i.e., differentially expressed genes) at a group level (i.e., severity level). Therefore, by design, it may not comprehensively capture the heterogeneity that could exist within ASD. Future work is needed to explore the causal relationship of the pathways in the DE-ASD network to ASD development, symptoms, and the potential existence of other dysregulation mechanisms in individuals with ASD.
Emerging models of complex traits suggest that gene mutations and epigenetic changes often propagate their effects through regulatory networks and converge on core pathways relevant to the trait21,30. Our findings support the existence of an analogous architecture for ASD, wherein rASD genes with diverse biological roles converge and regulate core down-stream pathways. Although the DE-ASD network did not significantly overlap with rASD genes, we found that it was significantly co-expressed with rASD genes in both leukocyte and brain. This disclosure also shows that the DE-ASD network genes are regulated by many rASD genes through direct transcriptional regulation or by modulating highly interconnected signaling pathways. This disclosure postulates that the DE-ASD network is a primary convergence point of ASD etiologies. This predicts that the spectrum of autism in such cases reflects degree and mechanism of the perturbation of the DE-ASD network. A detailed analysis of hiPSC-derived neurons from subjects with ASD and brain enlargement demonstrated the dysregulation of the DE-ASD network in these neuron models of ASD. Furthermore, clinical relevance is demonstrated by the high correlation we found between magnitude of dysregulation in the DE-ASD core network and ASD symptom severity in the toddlers.
The vast majority of rASD genes are not fully penetrant to the disorder3,8,14,51. The analysis of the XP-ASD network sheds light on how rASD genes could potentially combine to result in ASD. Although some rASD genes could directly modulate the DE-ASD network at the transcriptional level, the results suggest that the regulatory consequence of many rASD genes on the DE-ASD network are channeled through the PI3K/AKT, RAS/ERK, WNT/β-catenin signaling pathways. The structural and functional interrogation of the XP-ASD network localized these pathways to its epicenter and demonstrated enrichment for processes down-stream of these pathways among DE genes. Moreover, it was found that high-confidence rASD genes are better connected to the DE-ASD core network, suggesting that the closeness and influence of genes on these signaling pathways is correlated with their effect size on the disorder. These results articulate that perturbation of the PI3K/AKT, RAS/ERK, WNT/β-catenin signaling pathways through gene regulatory networks may be an important etiological route for ASD that could be associated with the disorder severity level in a large fraction of the ASD population. Congruent with this hypothesis, cellular and animal models of ASD have demonstrated that high-confidence rASD genes are enriched in regulators of the RAS/ERK, PI3K/AKT, WNT/β-catenin signaling pathways3,10. These signaling pathways are highly conserved and pleiotropic, impacting multiple prenatal and early postnatal neural development stages from proliferation/differentiation to synaptic and neural circuit development3. Such multi-functionalities could be the reason that for detection of the signal in leukocytes of individuals with ASD.
Data availability: Leukocyte transcriptome data can be accessed from the NCBI Gene Expression Omnibus (GEO) database under the accession codes of GSE42133 and GSE111175. Microarray transcriptome data on the differentiation of primary human neural progenitor cells to neural cells were downloaded from the NCBI GEO accession GSE57595. Transcriptome data on hiPSC-derived neuron models of ASD and TD were downloaded NCBI GEO accession E-MTAB-6018. Human brain developmental transcriptome data were downloaded from BrainSpan.org.
Accession codes: Gene Expression Omnibus database (GSE42133; GSE111175; GSE57595; E-MTAB-6018).
Code availability: The R code for reproducing the analyses reported in this article is available as a supplementary software file as well as at: gitlab.com/LewisLabUCSD/ASD_Transcriptional_Organization.
The primary aim of this study was to associate the transcriptome dysregulations present in ASD leukocytes with the ASD risk genes. However, the currently available genetic information is mostly based on males, and less is known about the genetic basis of ASD females. Therefore, we focused on male toddlers for the transcriptome analysis; specifically, 264 male toddlers with the age range of 1 to 4 years. This included previously published transcriptome data (153 individuals)19 and new samples using a similar methodology for participant recruitment and sample collection (111 new cases). Research procedures were approved by the Institutional Review Board of the University of California, San Diego. Parents of subjects underwent Informed Consent Procedures with a psychologist or study coordinator at the time of their child's enrollment.
About 70% of toddlers were recruited from the general population as young as 12 months using an early detection strategy called the 1-Year Well-Baby Check-Up Approach52. Using this approach, toddlers who failed a broadband screen, the CSBS IT Checklist53, at well-baby visits in the general pediatric community settings were referred to our Center for a comprehensive evaluation. The remaining subjects were obtained by general community referrals. All toddlers received a battery of standardized psychometric tests by highly experienced Ph.D. level psychologists including the Autism Diagnostic Observation Schedule (ADDS; Module T, 1 or 2), the Mullen Scales of Early Learning and the Vineland Adaptive Behavior Scales. Testing sessions routinely lasted 4 hours and occurred across 2 separate days. Toddlers younger than 36 months in age at the time of initial clinical evaluation were followed longitudinally approximately every 9 months until a final diagnosis was determined at age 2-4 years. For analysis purposes, toddlers (median age, 27 months) were categorized into two groups based on their final diagnosis assessment: 1) ASD: subjects with the diagnosis of ASD or ASD features; 2) TD: toddlers with typical developments (TD).
ADOS scores at each toddler's final visit were used for correlation analyses with DE-ASD network co-expression activity scores. All but 4 toddlers were tracked and diagnosed using the appropriate module of the ADOS (i.e., ADOS Module-Toddler, Module-1, or Module-2) between the ages of 24-49 months, an age where the diagnosis of ASD is relatively stable16; the remaining 4 toddlers had their final diagnostic evaluation between the ages of 18 to 24 months.
Blood samples were usually taken at the end of the clinical evaluation sessions. To monitor health status, the temperature of each toddler was monitored using an ear digital thermometer immediately preceding the blood draw. The blood draw was scheduled for a different day when the temperature was higher than 99 Fahrenheit. Moreover, blood draw was not taken if a toddler had some illness (e.g., cold or flu), as observed by us or stated by parents. We collected four to six milliliters of blood into ethylenediaminetetraacetic-coated tubes from all toddlers. Blood leukocytes were captured and stabilized by LeukoLOCK filters (Ambion) and were immediately placed in a −20° C. freezer. Total RNA was extracted following standard procedures and manufacturer's instructions (Ambion).
RNA labeling, hybridization, and scanning was conducted at Scripps Genomic Medicine center, (CA, USA) using Illumina BeadChip technology. All arrays were scanned with the Illumina BeadArray Reader and read into Illumina GenomeStudio software (version 1.1.1). Raw Illumina probe intensities were converted to expression values using the lumi package61.
We subdivided our samples into three datasets to assess the reproducibility of the results. The primary discovery dataset composed of 275 samples from 240 male toddlers with the diagnosis of ASD and TD from the general population. Gene expressions were assayed using Illumina HT-12 platform. All arrays were scanned with the Illumina BeadArray Reader and read into Illumina GenomeStudio software (version 1.1.1). Raw Illumina probe intensities were converted to expression values using the lumi package54. We employed a three-step procedure to filter for probes with reliable expression levels. First, we only retained probes that met the detection p-value<0.05 cut-off threshold in at least 3 samples. Second, we required the probes to have expression levels above 95th percentile of negative probes in at least 50% of samples. The probes with detection p-value>0.1 across all samples were selected as negative probes and their expression levels were pooled together to estimate the 95th percentile expression level. Third, for genes represented by multiple probes, we considered the probe with highest mean expression level across our dataset, after quantile normalization of the data. These criteria led to the selection of 14,854 protein coding genes as expressed in our leukocyte transcriptome data, which is similar to the previously reported estimate of 14,555 protein coding genes (chosen based on unique Entrez IDs) for whole blood by GTEx consortium55. To ensure results are not affected by the variations in the procedure of selecting expressed genes, we replicated all of our analyses (redoing DE analysis and re-constructing HC DE and XP networks) by choosing 13,032 protein coding genes as expressed (
Quality control analysis was performed on normalized gene expression data to identify and remove 22 outlier samples from the dataset. Samples were marked as outlier if they showed low signal intensity in the microarray (average signal of two standard deviations lower than the overall mean), deviant pairwise correlations, deviant cumulative distributions, deviant multi-dimensional scaling plots, or poor hierarchical clustering, as described elsewhere18. After removing low quality samples, the primary dataset had 253 samples from 226 male toddlers including 27 technical replicates. High reproducibility was observed across technical replicates (mean Spearman correlation of 0.917 and median of 0.925). We randomly removed one of each of two technical replicates from the dataset.
The limma package56 was then applied on quantile normalized data for differential expression analysis in which moderated t-statistics was calculate by robust empirical Bayes methods57. Sample batch was used as a categorical covariate (total of two batches; both Illumina HT-12 platforms). Exploration graphs indicated that linear modeling of batch covariate was effective at removing its influence on expression values (
Additional transcriptome analyses confirmed that results are replicable at technical and biological levels. We performed transcriptome analysis on a second dataset composed of 56 randomly selected male toddlers from the primary dataset (35 ASD and 21 TD). We also analyzed a third microarray dataset composed of 48 male toddlers with 24 independent, non-overlapping toddlers with ASD, while 21 out of 24 TD cases overlapped with the primary dataset. These two datasets were assayed concurrently, but at a different time than the primary dataset. Moreover, in contrast to the primary dataset, the second and third datasets were assayed by Illumina WG-6 Chips. The pre-processing and downstream analysis of the second and third microarray datasets were conducted separately using the same approaches as the primary dataset.
To further assess the reproducibility of the results across experimental platforms, we performed RNA-Seq experiments on 56 samples from an independent cohort of 12 (19 samples) TD and 23 (37 samples) male toddlers with ASD. None of these subjects overlapped with those in the microarray datasets. This allowed us to ensure our results are not subject nor platform (i.e., microarray vs. RNA-Seq) specific. RNA-Seq libraries were sequenced at the UCSD IGM genomics core on a HiSeq 4000. We processed the raw RNA-Seq data with our pipeline that starts with quality control with FastQC58. Low quality bases and adapters were removed using trimmomatic59. Reads were aligned to the genome using STAR60. STAR results were processed using Samtools61, and transcript quantification is done with HTseq-count62. Subsequently, low expressed genes were removed and data were log count per million (cpm) normalized (with prior read count of 1) using limma56. We performed SVA analysis63 on the normalized expression data and included the first surrogate variable as covariate to account for potential hidden confounding variables. Differential expression analysis was performed using the limma package with subjects modeled as random effects.
Additional analysis was performed on the four transcriptome datasets (one discovery and three replicates) to ensure results are: (1) robust to alterations in the analysis pipeline, (2) are not affected by the batches or potential hidden covariates, (3) are present in the vast majority of samples, and (4) are not driven by changes in the blood cell type composition between ASD and TD diagnosis groups.
ASD Risk Genes
ASD risk genes were extracted from the SFARI database42 on Dec. 7, 2016. We also included the reported risk genes from a recent meta-analysis of two large-scale genetic studies, containing genes mutated in individuals with ASD but not present in Exome Aggregation Consortium database (ExAC)14. Together, these two resources provided 965 likely rASD genes that were used for the construction of the XP-ASD networks (Table 4). Previously published genes with likely gene damaging and synonymous mutations in siblings of subjects with ASD, who developed normally were retrieved from Iossifov et al.13.
ASD high confidence risk genes were extracted from the SFARI database (genes with confidence levels of 1 and 2), Kosmicki et al.14 (recurrent gene mutations in individuals with ASD, but not present in the ExAC database), Sanders et al.15, and Chang et al.7. Strong evidence genes with de novo protein truncating variants in subjects with ASD were extracted from Kosmicki et al.14 and included rASD genes that were not in the ExAC database and have a probability of loss-of-function intolerance (pLI) score of above 0.9. Gene names in these datasets were converted to Entrez IDs using DAVID tools64.
To assess the overlap of DE-ASD networks with rASD genes, we considered our list of all rASD genes (965 genes), different lists of high confidence rASD genes (varying in size and composition) and their combinations, including all SFARI rASD genes, SFARI gene levels 1-to-3, SFARI gene levels 1 and 2, strong evidence rASD genes from Kosmicki et al.14, and strong evidence rASD genes from Sanders et al.15.
We first regressed out the interfering co-variate (i.e., batch group) from the quantile normalized expression values of the primary dataset (see the Data processing section). The Context Likelihood of Relatedness (CLR) algorithm65 was next applied on the batch corrected transcriptome data from ASD and TD diagnosis groups separately to construct two co-expression networks (technical replicates were randomly removed from the dataset prior to construction of the networks). The CLR algorithm employs a two-step procedure to infer significantly co-expressed gene pairs. First, it estimates the distribution of similarity scores for each gene based on the similarity that the gene shows with all other genes in the dataset using a mutual information metric. Second, it estimates the significance of the observed similarity score for each gene pair by testing how likely it is to have such a similarity score given the co-expression similarity score distributions of the two genes from the first step. The separate application of the CLR algorithm on ASD and TD samples provided global (i.e., all expressed genes) gene-gene co-expression similarity matrices for each diagnosis group. DE and expanded DE-and-rASD (XP) networks were next constructed from CLR-derived ASD and TD similarity matrices as detailed below.
To ensure the robustness of the results, we constructed three variants of the DE networks for each diagnosis group (i.e., ASD and TD; total of six networks). These networks varied in the number of nodes and edges, providing a tradeoff between sensitivity (number of false negative interactions) and specificity (number of false positive interactions) in our downstream analysis. Unless otherwise noted, we reported results that were reproducible in all three networks. The three networks include the high confidence network (HC; including strong evidence physical and regulatory interactions), the functional network (including interactions between previously known functionally related genes), and the full co-expression network. The full co-expression network is solely based on co-expression patterns of DE genes (i.e., all significantly co-expressed DE gene pairs with FDR<0.05 as judged by the CLR algorithm). To construct the HC and functional networks, we first retrieved the static HC and functional networks of the detected protein-coding DE genes from databases. The static HC network was obtained from the Pathway Commons database66 and was updated to include interactions from the most recent Reactome67 and BioGrid68 databases. The static functional network was extracted from the GeneMania webserver69 and included interactions supported by co-expression, protein-protein interactions, genetic interactions, co-localization, shared protein domains, and other predictions69. The backbone, static network of all DE-ASD and DE-TD networks composed of at least 96% DE genes. Static HC and functional networks were made context specific by retaining those database-derived interactions that were significantly co-expressed in the diagnosis group (The static backbone networks were shared between the DE-ASD and DE-TD networks). All figures in the main text are based on HC DE-ASD and DE-TD networks, and the results of functional and full co-expression networks are represented in the supplementary files.
By design, the HC network is smaller, more accurate, but potentially more biased as it includes genes that are more actively studied than those in the functional network. Both networks are smaller than the full co-expression network. Therefore, on average, the functional DE-ASD and DE-TD networks had 15× more interactions and 2.3× more genes than their HC counterparts. Similarly, the full DE-ASD and DE-TD networks had 6.4× more interactions and 1.05× more genes than their functional counterparts.
The XP-ASD networks were constructed using a similar approach, but from the union of protein-coding DE genes and 965 rASD genes. Our list of 965 rASD genes included genes that are ranked either as high confidence (supported with multiple studies or direct experimentation) or low confidence (some even have been found in healthy siblings of individuals with ASD). To assess the relevance of XP-ASD networks to the pathobiology of ASD, we also examined the association of XP-ASD networks with genes mutated in siblings of subjects with ASD, who developed normally. For this, we constructed two other variants of the XP-ASD networks by adding genes with likely gene damaging mutations (Siblings-LGD) and Synonymous (Siblings-Syn) mutations in our list of DE and rASD genes, separately. We next tested if these two variants of XP-ASD networks preferentially incorporated mutated genes in siblings of individuals with ASD, who developed normally. As the sole purpose of these two network variants were to test the relevance of the main XP-ASD network, they were not needed for follow up analyses. Similar to DE networks, the main figures represent results based on the HC XP-ASD network and the results for the functional and full XP-ASD networks are included in the supplement.
Unless otherwise noted, we used permutation tests to assess the significance of overlap between pairs of networks or modules. The background gene list for DE and XP networks were all protein coding genes that were expressed in our microarray experiments (see the gene expression preprocessing section for more details). DE genes did not show bias in terms of gene mutation rates and length.
Empirical permutation tests were conducted by 10,000 random draws from background gene lists and measuring the overlaps. The actual overlap was then compared to the overlap distribution of random draws and an empirical p-value was estimated. In cases where the estimated empirical p-value was zero based on 10,000 permutation tests, we performed 90,000 additional random draws to obtain a more accurate estimation. If the estimated empirical p-value was still zero, a theoretical, hypergeometric-based p-value (non-zero) was considered. Multiple testing was corrected by the Benjamini-Hochberg procedure and FDR<0.1 was considered as significant, unless otherwise noted. By design, our functional and full DE and XP networks are highly sensitive and therefore include more than 90% of queried genes. Since we required replicable significant overlap of gene sets across our networks, this feature renders the overlap analysis robust to potential biases due to the network topology.
The hub analysis of DE-ASD and XP-ASD networks were conducted by an integrated analysis of high-confidence (HC) and functional networks. By design, HC and functional networks each have their own advantages. Interactions in HC networks are presumably more accurate but potentially biased towards specific genes that are better studied. In contrast, hubs in functional networks are less susceptible to bias in knowledge on the interactome, but more prone to false positive interactions. Thus, we aimed to combine the information provided by the two networks to get a more accurate picture of hub genes. We first counted the number of interactions that each gene has in either of HC or functional networks. For the genes that were present in only one of the two networks, the interaction count of zero was considered for the other network. Then the p-value of hubness for each gene in a network (with the null hypothesis that the gene is not a hub) was determined by calculating the empirical probability of identifying a gene with the same number of interactions or higher in the network. Next, the hubness p-value score of each gene in HC and functional networks were combined together using Fisher's method:
X
2
2=−2×(ln(pHC)+ln(pfunctional))
where p refers to the empirical p-value of hubness for a gene in the HC and functional networks. X22 is the chi-squared score with two degrees of freedom. The top 5% and 7% genes with highest X22 scores were considered as hub in DE-ASD and XP-ASD networks, respectively.
We set two criteria to identify biological processes that are differentially expressed between ASD and TD diagnosis groups and are enriched in the DE-ASD networks. First, we required the biological process to significantly change between ASD and TD transcriptome samples based on GSEA70,71. Second, we required the biological process to be significantly enriched in the DE-ASD networks.
GSEA identified multiple gene sets that were significantly upregulated in subjects with ASD (FDR<0.12; Table S13), using the R version of the GSEA package and the msigdb.v5.1 database (downloaded on Oct. 20, 2016)70,71. Significantly enriched processes in the DE-ASD networks were identified by examining the overlap of GSEA-identified significantly altered gene sets with the DE-ASD networks based on empirical permutation tests, and p-values were corrected for multiple testing using the Benjamini-Hochberg procedure. We excluded gene sets annotated as associated with specific reference datasets in MSigDB since their generalizability to our dataset has not been established (Table S13).
Significantly enriched Gene Ontology biological processes (GO-BP) were identified by Fisher's exact test on terms with the 10-2000 annotated genes. The terms with Benjamini-Hochberg estimated FDR<0.1 were deemed as significant. The enriched terms were next clustered based on the GO-BP tree, extracted from the Amigo database using RamiGO package in R72. The general terms with more than 1000 annotated genes that spanned two or more clusters were removed. The list of enriched GO-BP terms and their clustering are provided in Table S8.
For biological process enrichment analysis of DE-ASD networks, to ensure robustness of enrichment results, we set two criteria to consider a term as significantly enriched in the DE-ASD networks: 1) the term, in overall, is significantly up-regulated based on gene set enrichment analysis (GSEA) (FDR<0.1). There was no significantly down-regulated term (FDR<0.1) in ASD samples based on GSEA analysis. After the GSEA analysis, we excluded significant gene sets that were derived from specific datasets (e.g., gene groups that are up or down regulated in a specific dataset) as their generalizability to our dataset needs further experimental verifications; 2) the term significantly overlaps with all three DE-ASD networks (FDR<0.05), based on an empirical permutation test.
The R version of the GSEA package and msigdb.v5.1 database (downloaded on Oct. 20, 2016) was used for identification of biological processes with differential expression between ASD and TD samples 72,73.
Protein domains were downloaded from Interpro database 74 and enrichment analysis was based on the hypergeometric test. Multiple testing was corrected based on Benjamin-Hochberg procedure.
To identify genes that potentially regulate DE-ASD networks, we examined the overlap of DE-ASD networks with identified targets of human transcription factors as part of ENCODE40 and the curated Chea2016 database41. Overall, targets of 285 unique human transcription factors are assayed in the ENCODE and Chea2016 resources, and from these, 20 are currently annotated as high-confidence or suggestive evidence rASD genes by the SFARI database (SFARI levels 1 to 3). We performed overlap analysis between targets of transcription factors and each of the three DE-ASD networks separately using the hypergeometric test through the EnrichR portal73. Some of the transcription factors were assayed multiple times, providing partially different sets of target genes for these transcription factors. For such transcription factors, we had multiple p-values from the overlap analysis. Therefore, we used Fisher's method to combine the enrichment p-values across assays related to a given transcription factor during the analysis of each DE-ASD network. Next, p-values were corrected using the Benjamini-Hochberg procedure. Only transcription factors whose targets were significantly enriched in all three DE-ASD networks were considered as significantly overlapping (FDR<0.1) with the DE-ASD networks. This resulted in the identification of 97 unique transcription factors whose targets are significantly enriched in all three DE-ASD networks. From these 97, 11 transcription factors are currently annotated as high confidence or suggestive evidence rASD genes. We assessed whether rASD genes are significantly enriched among the 97 transcription factors using a Fisher's exact test.
ENCODE data38 were downloaded through the annotationHub package in R. For each experiment, a gene was considered as a regulatory target if there was a binding peak at 1000nt proximity of its transcription start sites using hg19 genome annotation. Narrow peak files were used and called peaks with confidence level FDR<0.01 were only considered. For data files with missing q-value information on called peaks, Benjamini-Hochberg FDRs were estimated based on the provided p-values. We only considered experiments with called peaks in transcription proximity of less than 3000 genes.
Fisher's exact test was used for the overlap analysis with DE-ASD networks. To assess enrichment of rASD genes among the potential regulators of DE-ASD networks, only experiments in which targets significantly overlapped (FDR<0.1) with all three context specific DE-ASD networks were considered as significant. Each experiment was annotated by asking if the cognate regulator is an rASD gene. This indicated enrichment of rASD genes among the regulators with the p-value<0.009. We confirmed the significant enrichment by considering the higher confidence rASD genes (p-value<0.018; SFARI confidence levels of 1-to-3).
Normalized RNA-Seq transcriptome data during human neurodevelopmental time periods were downloaded from the BrainSpan database on Dec. 20, 201634,35. To calculate correlations, normalized RPKM gene expression values were log2(x+1) transformed.
Microarray transcriptome data from the differentiation of primary human neural progenitor cells to neural cells43 were downloaded from the NCBI GEO database (GSE57595). The data were already quantile normalized and ComBat batch-corrected74. For genes with multiple probes, we retained the probe with the highest mean expression value.
To observe the transcriptome response of XP-ASD networks during neuron differentiation, we correlated the gene expression patterns with the developmental time points, considering the differentiation time as an ordinal variable.
ASD Induced Pluripotent Stem Cells (iPSC) Data
We obtained hiPSC data28 from subjects with ASD and TD controls from GEO (GSE67528). Gene expression counts were normalized with the TMM method75 and filtered to exclude low-expressed genes (genes with count per million greater than 1 were retained). To calculate the correlations, normalized RNA-Seq gene expression values were log2(x+1) transformed.
The subjects from this iPSC study come from our center. However, none of the iPSC subjects overlap with those included in the transcriptome datasets in this study. Moreover, the iPSC cohort includes only 8 subjects with ASD and macrocephaly, while our primary (i.e., discovery) leukocyte transcriptome is from 119 toddlers with ASD selected from general pediatric community and were not filtered based on their brain size. Moreover, the subjects participating in the two studies did not have the same age range and iPSC cohort is composed of subjects with mean and median age of 167 and 193 months, respectively (toddlers in our dataset are between 12 to 48 months old). On the sample collection, our transcriptome data are from leukocytes of subjects with ASD, while the hiPSC transcriptome is based on the reprogrammed fibroblast cells.
Data were extracted from a genome-wide mutational study that monitored the impact of gene mutations on phosphorylation status of 10 core signaling proteins47. Genes whose mutations affected the phosphorylation status of the core signaling proteins with FDR<0.1 were considered as the regulators of the cognate signaling protein. We performed additional analyses to ascertain the specificity of observed enrichment for RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways.
We measured the co-expression strength of interacting genes in DE-ASD networks based on an unsigned Pearson's correlation coefficient metric. To estimate the significance of the network activity in a set of samples, we compared the co-expression distribution of gene pairs in the network to a background distribution of co-expression values using the Wilcoxon-Mann-Whitney test in the R coin package. The network activity level was defined as z-transformed p-values of this comparison. Significant scores imply that at least some interacting gene pairs are co-expressed significantly higher than chance and hence parts of the network is potentially active. The background distribution was obtained by selecting genes with mean expression values closest to those involved in the relevant network. The unsigned correlations among these genes constituted the background distribution.
We first transformed the normalized gene expression data using a gaussian kernel estimator. Alternatively, gene expression data could be normalized to have mean zero and standard division of one. Next, the contribution of each sample to the correlation strength of interactions in a network was computed in three ways, as detailed below. It is expected that 1-2% of population to have ASD. However, in our dataset, toddlers with ASD constitute ˜50% of samples in the dataset. To ensure that this skewness is not affecting the results, we transformed the data using the statistics based on TD samples.
Permutation: iterating 100,000 times, we randomly selected 20 samples from the dataset and measured the co-expression magnitude of interactions in the network. We next rank summed each individual sample based on the measured co-expression activities in which the sample was involved in.
Analytical approach based on Pearson's correlation coefficient: The Pearson's correlation coefficient is defined as:
Therefore, the influence of one sample to the above formula can be approximated as:
|rx
Where |rx
Analytical approach based on joint distribution: The change in the activity (i.e., co-expression magnitude) of each interaction can be measured by calculating the joint probability distribution of the genes involved in the interaction. The joint probability on the transformed data could be defined as:
Or alternatively:
Or alternatively:
Where zx
To ensure that confounding elements do not influence the measured network activities, we also measured network activity on a set of random interactions, as well as interactions that are more strongly co-expressed in TD samples. The activity of network can be measured and expressed by various techniques including z-value, p-value, and effect size. These measures of network activity can be also together or independently and/or with measures of gene expression and up- and down-regulation patterns be used for the classification or prognosis of ASD symptom severity.
To measure the co-expression activity of the DE-networks during the typical brain neurodevelopmental period from BrainSpan transcriptome data, we grouped samples from every 5 consequent time periods together, starting from 8 post conception weeks and ending with 11 years old. The defined groups did not overlap in their timespan.
To measure the co-expression activity of the DE-ASD networks in iPSC-derived neurons of ASD and TD cases, we analyzed transcriptome data from Marchetto et al. study29, the largest available dataset. This dataset encompasses transcriptome data from iPSC to neuron differentiation from 8 ASD and 6 TD donors and each donor is represented with 1 to 3 different cell lines at each differentiation time point in the dataset. To measure the DE-ASD network activity at neural progenitor and neuronal stages, we randomly selected 10 samples (5 neural progenitor samples and 5 neuron samples) from each diagnosis group (i.e., ASD and TD), iterating 100 times. As the cell lines derived from the same donor tend to be highly correlated at each differentiation time point, the sample selection was done such that each donor contributed a maximum of one sample in each differentiation time point.
To map the co-expression activity of the DE-ASD networks on toddlers' ADOS communication and socialization (CoSo) deficit scores, we only considered ASD samples as DE-ASD networks were constructed among DE genes between ASD and TD. ASD toddlers were grouped based on a moving window on ADOS CoSo scores with the width of 4 and a step size of 1. The number of toddlers with scores of 5 and 6 were relatively few compared to other categories. Therefore, the first window was from ADOS CoSo score 5 to 10 (window size of 6). Moreover, to avoid potential biases due to number of samples in each window, the network activities were measured based on randomly selected sets of 20 samples from each window, iterating 1000 times. The correlation of ADOS CoSo scores with the observed network activity was measured by considering the windows as ordinal values. To assess the statistical significance of observed pattern, we randomly shuffled the ADOS CoSo scores of toddlers 10,000 times and re-calculated the network activity for each permutation using the same procedure (with no internal iterations).
There are some objective differences for measuring network activity during normal brain development versus the correlation of the blood network activity with ADOS CoSo scores. While in brain transcriptome data we wanted to know if the DE-ASD networks show co-expression levels higher than background, we already knew that these networks are significantly co-expressed in ASD toddlers and were most interested to see if their change in co-expression activity is dependent on ADOS CoSo scores. Hence to map the relative activity of the DE-ASD networks in leukocytes of ASD toddlers, as a secondary analysis, we devised a more stringent test by basing the background co-expression on the same network in the TD toddlers (instead of random genes from the same samples). The distribution of co-expression scores in each ADOS CoSo score window were compared to the co-expression distribution (Wilcoxon-Mann-Whitney test) of the same network after randomly selecting the same number of samples among the TD toddlers (20 ASD samples and 20 TD samples at each iteration). Because of numerous possible combinations for selected samples, we repeated the same procedure 1000 times each with a distinct ASD and TD sample combination for all three context-specific DE-ASD networks to get the range of the network activity at each window. To assess the significance of observed distribution, we performed 10,000 times random shuffling of CoSo scores of ASD toddlers (with no internal iterations).
Our analyses illustrated the interactions in the DE-ASD network show stronger co-expression in ASD samples compared to that of TD. To identify the interactions and genes that are central to this increase in co-expression, we sorted the interactions in each of three DE-ASD networks based on their change in magnitude between ASD and TD samples. We next retained those interactions where abs(corASD)−abs(corTD)≥0.1. To identify the genes that are the main drivers of the observed increase in the co-expression magnitude, we next sorted the DE genes based on their number of connections among the retained interactions.
Almost all statistical analyses were conducted in the R programing environment (version 3.5.0; see supplementary software). For microarray data, raw Illumina probe intensities were converted to expression values using the lumi package54. We filtered out probes that were not expressed from the dataset. Through quality control assessments, we identified and removed 22 outlier samples from the microarray dataset. Data were next quantile normalized and differentially expression genes were identified using limma package56 with the experimental batch included as a covariate in the regression model. Genes with Benjamini-Hochberg corrected p-value<0.05 were deemed as differentially expressed. Surrogate variable analysis did not support presence of other co-variates in the data63. Cibersort was used to examine potential impact of cell types on the differential expression patterns76. Technical replicates were used to assess the quality of samples and then were excluded from differential expression analysis and the follow up analyses (e.g., co-expression network construction). RNA-Seq data were mapped and quantified using STAR60 and HTSeq62, respectively. Quality of RNA-Seq samples were examined using FastQC58. Surrogate variable analysis was performed to identify and remove a covariate from RNA-Seq data63. Pearson's correlation coefficient was used for the comparison of fold changes across datasets. We regressed out the covariate (i.e., the experimental batch) before calculating the co-expression. Significantly co-expressed genes were identified using the CLR package in MATLAB65, and interactions with co-expression FDR<0.05 were considered as significant. For network co-expression activity, we used unsigned Pearson's correlation coefficient to measure the co-expression magnitude of interactions. The co-expression magnitudes of interactions of two networks were compared using two-sided Wilcoxon-Mann-Whitney test. When comparing co-expression magnitudes in two different datasets, to ascertain that the number of samples do not influence the measurements, a balanced number of samples were selected randomly. In most cases we used permutation tests to empirically examine the significance of an observed overlap between two gene sets. In cases that required a large number of tests, to increase speed, we used either hypergeometric or fisher's exact tests. Fisher's exact test was used to examine the overlap of the constructed networks with Gene Ontology-biological process (GO-BP) terms. We used the RamiGO package72 to cluster significantly enriched GO-BP terms that are similar and overlapping in their gene content. If appropriate, all p-values were corrected for multiple testing. The EnrichR portal72 was used to systematically examine the enrichment of the DE-ASD networks for the regulatory targets of human transcription factors. Fisher's method was used to combine p-values from multiple assays on the same transcription factor. When applicable, we specified the sample sizes (n) within the figure legend or table description. Non-parametric tests (e.g., Wilcoxon-Mann-Whitney and permutation tests) were used to avoid strong assumptions about the distribution of data in our statistical analyses. No statistical tests were used to predetermine sample sizes, but our sample sizes were larger than those reported in previous publications18,19,25. No randomization was performed in our cohort assignment. Data collection and analysis were not performed blind to the conditions of the experiments.
The role of the PI3K/AKT pathway on cell proliferation and functioning of blood and neuron cells has been studied3,40,78-82. These effects are associated with the role of the PI3K/AKT signaling pathway in activating the mTOR and β-catenin pathways and suppressing the FOXO1 transcriptional repressor. This is of particular interest since GSEA revealed that, in addition to over-expression of PI3K/AKT pathway, the mTOR pathway and FOXO1 targets show consistent results with over-activity of PI3K/AKT signaling in ASD toddlers. Specifically, we observed the upregulation of mTOR pathway (FDR<0.044; GSEA) and its significant enrichment in DE-ASD networks (FDR<0.037 in all three DE-ASD networks; hypergeometric test). We also found an upregulation of genes in ASD toddler leukocytes that are potentially regulated by FOXO1 as well as genes that are upregulated in knock-out of FOXO1 transcriptional repressor in T regulatory cells (FDR<0.086; GSEA). The potential binding targets of FOXO1 and genes responsive to its knock-out were extracted from TRANSFAC v7.483 and Ouyang et al. study84 through msigdb.v5.1 database72, respectively.
We developed a single-sample based method to measure the network dysregulation in each sample. We show that at the molecular level, distinct subtypes of ASD exist and we can classify subclasses of ASD. Our results provide evidence on high penetrance rate of the signature in ASD.
Methods and results are based measurement of the magnitude of co-expression patterns. Our results indicate that the extent of dysregulation of coexpression in our DE-ASD network is correlated with the ASD severity level in male toddlers at the group level. To calculate the co-expression (e.g., correlation), one need to have multiple samples. Therefore, it is not possible to apply that metric to measure network-based dysregulations in each individual sample, separately. However, ideally, one wants to know if a given sample shows a dysregulation in the network of interest. Here we expand the approach to allow measuring the network activity in each individual sample. This new metric is conceptually based on the same concept that we used to measure network coexpression in a set of samples. This new metric allows diagnosis of individuals. Our individual-sample based analysis of the data demonstrated the strong dysregulation of the DE-ASD network in about 50% of ASD toddlers.
This sample based analysis reveals two clear subgroups of ASD, one subgroup that exhibits strong dysregulation of the DE-ASD network as per our submitted paper, and a second ASD subgroup that does not show such dysregulation. Thus our approach allows the identification of sub-groups in ASD.
We performed differential gene expression analysis between the two subtypes that we identified in slide 11 and TD normal controls. We found: 2834 differentially expressed genes in the ASD subgroup that exhibited the network dysregulation at FDR<0.05, covering 94% of genes that were detected as differentially expressed in the combined dataset and adding 2061 newly identified differentially expressed genes. In contrast, differential gene expression analysis of ASD toddlers who do not show the network over-activity, identified only 11 genes (FDR<0.1) as differentially expressed.
These observations may be due to a strong genetic-based origin of ASD for this subgroup. Meanwhile, nongenetic etiologies likely underlie ASD among the subgroup that did not have DE-ASD dysregulation. (further work underway to solidify our current evidence). Thus, our approach allows us to identify subgroups of ASD subjects with likely different etiologies.
Summary statistics for the boxplots (min;25% ile;median;mean;75% ile;max): background: 0.00;0.00;46.00;79.91;177.00;187.00. DEgenes: 0.00;12.00;136.00;106.43;187.00;187.00. Full DE-ASD: 0.00;14.00;141.00;108.64;187.00;187.00. Func DE-ASD: 0.00;19.25;151.00;111.66;187.00;187.00. HC DE-ASD: 0.00;42.75;171.00;124.88;187.00;187.00.
indicates data missing or illegible when filed
This application claims the priority benefit of U.S. Provisional Application No. 62/697,049, filed Jul. 12, 2018, which is incorporated herein by reference.
This invention was made with government support under Grant No. MH110558 awarded by the National Institutes of Mental Health. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2019/041618 | 7/12/2019 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62697049 | Jul 2018 | US |
