Moving from capstones toward cornerstones: successes and challenges in applying systems biology to identify mechanisms of autism spectrum disorders

Connecting the dots: The boons and banes of network modeling

Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.

Association of CDH11 with Autism Spectrum Disorder Revealed by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

A large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features - gene size, mRNA abundance, and guanine-cytosine content - affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

Functions of Gtf2i and Gtf2ird1 in the developing brain: transcription, DNA binding and long-term behavioral consequences

Gtf2ird1 and Gtf2i are two transcription factors (TFs) among the 28 genes deleted in Williams syndrome, and prior mouse models of each TF show behavioral phenotypes. Here we identify their genomic binding sites in the developing brain and test for additive effects of their mutation on transcription and behavior. GTF2IRD1 binding targets were enriched for transcriptional and chromatin regulators and mediators of ubiquitination. GTF2I targets were enriched for signal transduction proteins, including regulators of phosphorylation and WNT. Both TFs are highly enriched at promoters, strongly overlap CTCF binding and topological associating domain boundaries and moderately overlap each other, suggesting epistatic effects. Shared TF targets are enriched for reactive oxygen species-responsive genes, synaptic proteins and transcription regulators such as chromatin modifiers, including a significant number of highly constrained genes and known ASD genes. We next used single and double mutants to test whether mutating both TFs will modify transcriptional and behavioral phenotypes of single Gtf2ird1 mutants, though with the caveat that our Gtf2ird1 mutants, like others previously reported, do produce low levels of a truncated protein product. Despite little difference in DNA binding and transcriptome-wide expression, homozygous Gtf2ird1 mutation caused balance, marble burying and conditioned fear phenotypes. However, mutating Gtf2i in addition to Gtf2ird1 did not further modify transcriptomic or most behavioral phenotypes, suggesting Gtf2ird1 mutation alone was sufficient for the observed phenotypes.

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood-brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug-gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Massively Parallel Reporter Assays: Defining Functional Psychiatric Genetic Variants Across Biological Contexts

Neuropsychiatric phenotypes have long been known to be influenced by heritable risk factors, directly confirmed by the past decade of genetic studies that have revealed specific genetic variants enriched in disease cohorts. However, the initial hope that a small set of genes would be responsible for a given disorder proved false. The more complex reality is that a given disorder may be influenced by myriad small-effect noncoding variants and/or by rare but severe coding variants, many de novo. Noncoding genomic sequences-for which molecular functions cannot usually be inferred-harbor a large portion of these variants, creating a substantial barrier to understanding higher-order molecular and biological systems of disease. Fortunately, novel genetic technologies-scalable oligonucleotide synthesis, RNA sequencing, and CRISPR (clustered regularly interspaced short palindromic repeats)-have opened novel avenues to experimentally identify biologically significant variants en masse. Massively parallel reporter assays (MPRAs) are an especially versatile technique resulting from such innovations. MPRAs are powerful molecular genetics tools that can be used to screen thousands of untranscribed or untranslated sequences and their variants for functional effects in a single experiment. This approach, though underutilized in psychiatric genetics, has several useful features for the field. We review methods for assaying putatively functional genetic variants and regions, emphasizing MPRAs and the opportunities they hold for dissection of psychiatric polygenicity. We discuss literature applying functional assays in neurogenetics, highlighting strengths, caveats, and design considerations-especially regarding disease-relevant variables (cell type, neurodevelopment, and sex), and we ultimately propose applications of MPRA to both computational and experimental neurogenetics of polygenic disease risk.

Systems biology in the central nervous system: a brief perspective on essential recent advancements

As recent advances in human genetics have begun to more rapidly identify the individual genes contributing to risk of psychiatric disease, the spotlight now turns to understanding how disruption of these genes alters the brain, and thus behavior. Compared to other tissues, cellular complexity in the brain provides both a substantial challenge and a significant opportunity for systems biology approaches. Current methods are maturing that will allow for finally defining the 'parts list' for the functioning mouse and human brains, enabling new approaches to defining how the system goes awry in disorders of the CNS. However, the availability of tissue is certainly a challenge for systems biology of neuroscience, compared to systems biology of other tissues, where biopsy is feasible. This challenge is particularly notable for disorders caused by extremely rare genetic variants. Thus computational and systems biology approaches, as well as precise experimental models by way of genome editing, will play key roles in defining mechanisms for disorders, and their individual symptoms, across varied genetic etiologies. Here, we highlight recent progress in neurogenetics, postmortem genomics, cell-type specific profiling, and precision modeling toward defining mechanisms in disease.

Metabolic Dysfunction Underlying Autism Spectrum Disorder and Potential Treatment Approaches

Autism spectrum disorder (ASD) is characterized by deficits in sociability and communication, and increased repetitive and/or restrictive behaviors. While the etio-pathogenesis of ASD is unknown, clinical manifestations are diverse and many possible genetic and environmental factors have been implicated. As such, it has been a great challenge to identify key neurobiological mechanisms and to develop effective treatments. Current therapies focus on co-morbid conditions (such as epileptic seizures and sleep disturbances) and there is no cure for the core symptoms. Recent studies have increasingly implicated mitochondrial dysfunction in ASD. The fact that mitochondria are an integral part of diverse cellular functions and are susceptible to many insults could explain how a wide range of factors can contribute to a consistent behavioral phenotype in ASD. Meanwhile, the high-fat, low-carbohydrate ketogenic diet (KD), used for nearly a century to treat medically intractable epilepsy, has been shown to enhance mitochondrial function through a multiplicity of mechanisms and affect additional molecular targets that may address symptoms and comorbidities of ASD. Here, we review the evidence for the use of metabolism-based therapies such as the KD in the treatment of ASD as well as emerging co-morbid models of epilepsy and autism. Future research directions aimed at validating such therapeutic approaches and identifying additional and novel mechanistic targets are also discussed.

Quantitative Nucleotide Level Analysis of Regulation of Translation in Response to Depolarization of Cultured Neural Cells

Studies on regulation of gene expression have contributed substantially to understanding mechanisms for the long-term activity-dependent alterations in neural connectivity that are thought to mediate learning and memory. Most of these studies, however, have focused on the regulation of mRNA transcription. Here, we utilized high-throughput sequencing coupled with ribosome footprinting to globally characterize the regulation of translation in primary mixed neuronal-glial cultures in response to sustained depolarization. We identified substantial and complex regulation of translation, with many transcripts demonstrating changes in ribosomal occupancy independent of transcriptional changes. We also examined sequence-based mechanisms that might regulate changes in translation in response to depolarization. We found that these are partially mediated by features in the mRNA sequence—notably upstream open reading frames and secondary structure in the 5′ untranslated region—both of which predict downregulation in response to depolarization. Translationally regulated transcripts are also more likely to be targets of FMRP and include genes implicated in autism in humans. Our findings support the idea that control of mRNA translation plays an important role in response to neural activity across the genome.

If genetic variation could talk: What genomic data may teach us about the importance of gene expression regulation in the genetics of autism

Autism spectrum disorder (ASD) has been long known to have substantial genetic etiology. Much research has attempted to identify specific genes contributing to ASD risk with the goal of tying gene function to a molecular pathological explanation for ASD. A unifying molecular pathology would potentially increase understanding of what is going wrong during development, and could lead to diagnostic biomarkers or targeted preventative or therapeutic directions. We review past and current genetic mapping approaches and discuss major results, leading to the hypothesis that global dysregulation of gene or protein expression may be implicated in ASD rather than disturbance of brain-specific functions. If substantiated, this hypothesis might indicate the need for novel experimental and analytical approaches in order to understand this neurodevelopmental disorder, develop biomarkers, or consider treatment approaches.

Cross-generational THC exposure alters the developmental sensitivity of ventral and dorsal striatal gene expression in male and female offspring

Cannabis (Cannabis sativa, Cannabis indica) is the illicit drug most frequently abused by young men and women. The growing use of the drug has raised attention not only on the impact of direct exposure on the developing brain and behavior later in life, but also on potential cross-generational consequences. Our previous work demonstrated that adolescent exposure to Δ⁹-tetrahydrocannabinol (THC), the main psychoactive component of cannabis, affects reward-related behavior and striatal gene expression in male offspring that were unexposed to the drug during their own lifespan. The significant sex differences documented for most addiction and psychiatric disorders suggest that understanding the perturbation of the brain in the two sexes due to cannabis could provide insights about neuronal systems underpinning vulnerability to psychiatric illnesses. In the current study, we expanded our previous observations in males by analyzing the female brain for specific aberrations associated with cross-generational THC exposure. Based on the impact of adolescent development on subsequent adult behavioral pathology, we examined molecular patterns during both adolescence and adulthood. The results revealed a switch from the ventral striatum during adolescence to the dorsal striatum in adulthood in alterations of gene expression related to synaptic plasticity in both sexes. Females, however, exhibited stronger correlation patterns between genes and also showed locomotor disturbances not evident in males. Overall, the findings demonstrate cross-generational consequences of parental THC exposure in both male and female offspring.