Autism genes converge on asynchronous development of shared neuron classes.

Genetic risk for autism spectrum disorder (ASD) is associated with hundreds of genes spanning a wide range of biological functions1-6. The alterations in the human brain resulting from mutations in these genes remain unclear. Furthermore, their phenotypic manifestation varies across individuals7,8. Here we used organoid models of the human cerebral cortex to identify cell-type-specific developmental abnormalities that result from haploinsufficiency in three ASD risk genes-SUV420H1 (also known as KMT5B), ARID1B and CHD8-in multiple cell lines from different donors, using single-cell RNA-sequencing (scRNA-seq) analysis of more than 745,000 cells and proteomic analysis of individual organoids, to identify phenotypic convergence. Each of the three mutations confers asynchronous development of two main cortical neuronal lineages-γ-aminobutyric-acid-releasing (GABAergic) neurons and deep-layer excitatory projection neurons-but acts through largely distinct molecular pathways. Although these phenotypes are consistent across cell lines, their expressivity is influenced by the individual genomic context, in a manner that is dependent on both the risk gene and the developmental defect. Calcium imaging in intact organoids shows that these early-stage developmental changes are followed by abnormal circuit activity. This research uncovers cell-type-specific neurodevelopmental abnormalities that are shared across ASD risk genes and are finely modulated by human genomic context, finding convergence in the neurobiological basis of how different risk genes contribute to ASD pathology.

Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries.

Balanced chromosomal abnormalities (BCAs) represent a relatively untapped reservoir of single-gene disruptions in neurodevelopmental disorders (NDDs). We sequenced BCAs in patients with autism or related NDDs, revealing disruption of 33 loci in four general categories: (1) genes previously associated with abnormal neurodevelopment (e.g., AUTS2, FOXP1, and CDKL5), (2) single-gene contributors to microdeletion syndromes (MBD5, SATB2, EHMT1, and SNURF-SNRPN), (3) novel risk loci (e.g., CHD8, KIRREL3, and ZNF507), and (4) genes associated with later-onset psychiatric disorders (e.g., TCF4, ZNF804A, PDE10A, GRIN2B, and ANK3). We also discovered among neurodevelopmental cases a profoundly increased burden of copy-number variants from these 33 loci and a significant enrichment of polygenic risk alleles from genome-wide association studies of autism and schizophrenia. Our findings suggest a polygenic risk model of autism and reveal that some neurodevelopmental genes are sensitive to perturbation by multiple mutational mechanisms, leading to variable phenotypic outcomes that manifest at different life stages.

CSMD1 regulates brain complement activity and circuit development.

Complement proteins facilitate synaptic elimination during neurodevelopmental pruning, but neural complement regulation is not well understood. CUB and Sushi Multiple Domains 1 (CSMD1) can regulate complement activity in vitro, is expressed in the brain, and is associated with increased schizophrenia risk. Beyond this, little is known about CSMD1 including whether it regulates complement activity in the brain or otherwise plays a role in neurodevelopment. We used biochemical, immunohistochemical, and proteomic techniques to examine the regional, cellular, and subcellular distribution as well as protein interactions of CSMD1 in the brain. To evaluate whether CSMD1 is involved in complement-mediated synapse elimination, we examined Csmd1-knockout mice and CSMD1-knockout human stem cell-derived neurons. We interrogated synapse and circuit development of the mouse visual thalamus, a process that involves complement pathway activity. We also quantified complement deposition on synapses in mouse visual thalamus and on cultured human neurons. Finally, we assessed uptake of synaptosomes by cultured microglia. We found that CSMD1 is present at synapses and interacts with complement proteins in the brain. Mice lacking Csmd1 displayed increased levels of complement component C3, an increased colocalization of C3 with presynaptic terminals, fewer retinogeniculate synapses, and aberrant segregation of eye-specific retinal inputs to the visual thalamus during the critical period of complement-dependent refinement of this circuit. Loss of CSMD1 in vivo enhanced synaptosome engulfment by microglia in vitro, and this effect was dependent on activity of the microglial complement receptor, CR3. Finally, human stem cell-derived neurons lacking CSMD1 were more vulnerable to complement deposition. These data suggest that CSMD1 can function as a regulator of complement-mediated synapse elimination in the brain during development.

Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations' positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants' pathogenicity in terms of the perturbed molecular mechanisms.

TCF12 haploinsufficiency causes autosomal dominant Kallmann syndrome and reveals network-level interactions between causal loci.

Dysfunction of the gonadotropin-releasing hormone (GnRH) axis causes a range of reproductive phenotypes resulting from defects in the specification, migration and/or function of GnRH neurons. To identify additional molecular components of this system, we initiated a systematic genetic interrogation of families with isolated GnRH deficiency (IGD). Here, we report 13 families (12 autosomal dominant and one autosomal recessive) with an anosmic form of IGD (Kallmann syndrome) with loss-of-function mutations in TCF12, a locus also known to cause syndromic and non-syndromic craniosynostosis. We show that loss of tcf12 in zebrafish larvae perturbs GnRH neuronal patterning with concomitant attenuation of the orthologous expression of tcf3a/b, encoding a binding partner of TCF12, and stub1, a gene that is both mutated in other syndromic forms of IGD and maps to a TCF12 affinity network. Finally, we report that restored STUB1 mRNA rescues loss of tcf12 in vivo. Our data extend the mutational landscape of IGD, highlight the genetic links between craniofacial patterning and GnRH dysfunction and begin to assemble the functional network that regulates the development of the GnRH axis.

Assessment of network module identification across complex diseases.

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

NetSig: network-based discovery from cancer genomes.

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

GeNets: a unified web platform for network-based genomic analyses.

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

Prediction of cancer driver genes through network-based moment propagation of mutation scores.

MOTIVATION: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein-protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers. RESULTS: We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node's local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/BorgwardtLab/MoProEmbeddings. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A scored human protein-protein interaction network to catalyze genomic interpretation.

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

Disproportionate Contributions of Select Genomic Compartments and Cell Types to Genetic Risk for Coronary Artery Disease.

Large genome-wide association studies (GWAS) have identified many genetic loci associated with risk for myocardial infarction (MI) and coronary artery disease (CAD). Concurrently, efforts such as the National Institutes of Health (NIH) Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) Consortium have provided unprecedented data on functional elements of the human genome. In the present study, we systematically investigate the biological link between genetic variants associated with this complex disease and their impacts on gene function. First, we examined the heritability of MI/CAD according to genomic compartments. We observed that single nucleotide polymorphisms (SNPs) residing within nearby regulatory regions show significant polygenicity and contribute between 59-71% of the heritability for MI/CAD. Second, we showed that the polygenicity and heritability explained by these SNPs are enriched in histone modification marks in specific cell types. Third, we found that a statistically higher number of 45 MI/CAD-associated SNPs that have been identified from large-scale GWAS studies reside within certain functional elements of the genome, particularly in active enhancer and promoter regions. Finally, we observed significant heterogeneity of this signal across cell types, with strong signals observed within adipose nuclei, as well as brain and spleen cell types. These results suggest that the genetic etiology of MI/CAD is largely explained by tissue-specific regulatory perturbation within the human genome.

Comprehensive assessment of cancer missense mutation clustering in protein structures.

Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg2+, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.

MATR3 disruption in human and mouse associated with bicuspid aortic valve, aortic coarctation and patent ductus arteriosus.

Cardiac left ventricular outflow tract (LVOT) defects represent a common but heterogeneous subset of congenital heart disease for which gene identification has been difficult. We describe a 46,XY,t(1;5)(p36.11;q31.2)dn translocation carrier with pervasive developmental delay who also exhibited LVOT defects, including bicuspid aortic valve (BAV), coarctation of the aorta (CoA) and patent ductus arteriosus (PDA). The 1p breakpoint disrupts the 5' UTR of AHDC1, which encodes AT-hook DNA-binding motif containing-1 protein, and AHDC1-truncating mutations have recently been described in a syndrome that includes developmental delay, but not congenital heart disease [Xia, F., Bainbridge, M.N., Tan, T.Y., Wangler, M.F., Scheuerle, A.E., Zackai, E.H., Harr, M.H., Sutton, V.R., Nalam, R.L., Zhu, W. et al. (2014) De Novo truncating mutations in AHDC1 in individuals with syndromic expressive language delay, hypotonia, and sleep apnea. Am. J. Hum. Genet., 94, 784-789]. On the other hand, the 5q translocation breakpoint disrupts the 3' UTR of MATR3, which encodes the nuclear matrix protein Matrin 3, and mouse Matr3 is strongly expressed in neural crest, developing heart and great vessels, whereas Ahdc1 is not. To further establish MATR3 3' UTR disruption as the cause of the proband's LVOT defects, we prepared a mouse Matr3(Gt-ex13) gene trap allele that disrupted the 3' portion of the gene. Matr3(Gt-ex13) homozygotes are early embryo lethal, but Matr3(Gt-ex13) heterozygotes exhibit incompletely penetrant BAV, CoA and PDA phenotypes similar to those in the human proband, as well as ventricular septal defect (VSD) and double-outlet right ventricle (DORV). Both the human MATR3 translocation breakpoint and the mouse Matr3(Gt-ex13) gene trap insertion disturb the polyadenylation of MATR3 transcripts and alter Matrin 3 protein expression, quantitatively or qualitatively. Thus, subtle perturbations in Matrin 3 expression appear to cause similar LVOT defects in human and mouse.

Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics.

Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits, but it is challenging to pinpoint causal genes in these loci and to exploit subtle association signals. We used tissue-specific quantitative interaction proteomics to map a network of five genes involved in the Mendelian disorder long QT syndrome (LQTS). We integrated the LQTS network with GWAS loci from the corresponding common complex trait, QT-interval variation, to identify candidate genes that were subsequently confirmed in Xenopus laevis oocytes and zebrafish. We used the LQTS protein network to filter weak GWAS signals by identifying single-nucleotide polymorphisms (SNPs) in proximity to genes in the network supported by strong proteomic evidence. Three SNPs passing this filter reached genome-wide significance after replication genotyping. Overall, we present a general strategy to propose candidates in GWAS loci for functional studies and to systematically filter subtle association signals using tissue-specific quantitative interaction proteomics.

Molecular pathogenesis of congenital diaphragmatic hernia revealed by exome sequencing, developmental data, and bioinformatics.

Congenital diaphragmatic hernia (CDH) is a common and severe birth defect. Despite its clinical significance, the genetic and developmental pathways underlying this disorder are incompletely understood. In this study, we report a catalog of variants detected by a whole exome sequencing study on 275 individuals with CDH. Predicted pathogenic variants in genes previously identified in either humans or mice with diaphragm defects are enriched in our CDH cohort compared with 120 size-matched random gene sets. This enrichment was absent in control populations. Variants in these critical genes can be found in up to 30.9% of individuals with CDH. In addition, we filtered variants by using genes derived from regions of recurrent copy number variations in CDH, expression profiles of the developing diaphragm, protein interaction networks expanded from the known CDH-causing genes, and prioritized genes with ultrarare and highly disruptive variants, in 11.3% of CDH patients. These strategies have identified several high priority genes and developmental pathways that likely contribute to the CDH phenotype. These data are valuable for comparison of candidate genes generated from whole exome sequencing of other CDH cohorts or multiplex kindreds and provide ideal candidates for further functional studies. Furthermore, we propose that these genes and pathways will enhance our understanding of the heterogeneous molecular etiology of CDH.

Mutations in FGF17, IL17RD, DUSP6, SPRY4, and FLRT3 are identified in individuals with congenital hypogonadotropic hypogonadism.

Congenital hypogonadotropic hypogonadism (CHH) and its anosmia-associated form (Kallmann syndrome [KS]) are genetically heterogeneous. Among the >15 genes implicated in these conditions, mutations in FGF8 and FGFR1 account for ~12% of cases; notably, KAL1 and HS6ST1 are also involved in FGFR1 signaling and can be mutated in CHH. We therefore hypothesized that mutations in genes encoding a broader range of modulators of the FGFR1 pathway might contribute to the genetics of CHH as causal or modifier mutations. Thus, we aimed to (1) investigate whether CHH individuals harbor mutations in members of the so-called "FGF8 synexpression" group and (2) validate the ability of a bioinformatics algorithm on the basis of protein-protein interactome data (interactome-based affiliation scoring [IBAS]) to identify high-quality candidate genes. On the basis of sequence homology, expression, and structural and functional data, seven genes were selected and sequenced in 386 unrelated CHH individuals and 155 controls. Except for FGF18 and SPRY2, all other genes were found to be mutated in CHH individuals: FGF17 (n = 3 individuals), IL17RD (n = 8), DUSP6 (n = 5), SPRY4 (n = 14), and FLRT3 (n = 3). Independently, IBAS predicted FGF17 and IL17RD as the two top candidates in the entire proteome on the basis of a statistical test of their protein-protein interaction patterns to proteins known to be altered in CHH. Most of the FGF17 and IL17RD mutations altered protein function in vitro. IL17RD mutations were found only in KS individuals and were strongly linked to hearing loss (6/8 individuals). Mutations in genes encoding components of the FGF pathway are associated with complex modes of CHH inheritance and act primarily as contributors to an oligogenic genetic architecture underlying CHH.

Ataxia, dementia, and hypogonadotropism caused by disordered ubiquitination.

BACKGROUND: The combination of ataxia and hypogonadism was first described more than a century ago, but its genetic basis has remained elusive. METHODS: We performed whole-exome sequencing in a patient with ataxia and hypogonadotropic hypogonadism, followed by targeted sequencing of candidate genes in similarly affected patients. Neurologic and reproductive endocrine phenotypes were characterized in detail. The effects of sequence variants and the presence of an epistatic interaction were tested in a zebrafish model. RESULTS: Digenic homozygous mutations in RNF216 and OTUD4, which encode a ubiquitin E3 ligase and a deubiquitinase, respectively, were found in three affected siblings in a consanguineous family. Additional screening identified compound heterozygous truncating mutations in RNF216 in an unrelated patient and single heterozygous deleterious mutations in four other patients. Knockdown of rnf216 or otud4 in zebrafish embryos induced defects in the eye, optic tectum, and cerebellum; combinatorial suppression of both genes exacerbated these phenotypes, which were rescued by nonmutant, but not mutant, human RNF216 or OTUD4 messenger RNA. All patients had progressive ataxia and dementia. Neuronal loss was observed in cerebellar pathways and the hippocampus; surviving hippocampal neurons contained ubiquitin-immunoreactive intranuclear inclusions. Defects were detected at the hypothalamic and pituitary levels of the reproductive endocrine axis. CONCLUSIONS: The syndrome of hypogonadotropic hypogonadism, ataxia, and dementia can be caused by inactivating mutations in RNF216 or by the combination of mutations in RNF216 and OTUD4. These findings link disordered ubiquitination to neurodegeneration and reproductive dysfunction and highlight the power of whole-exome sequencing in combination with functional studies to unveil genetic interactions that cause disease. (Funded by the National Institutes of Health and others.).

Protein-protein interactions and genetic diseases: The interactome.

Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function.

MetaRanker 2.0: a web server for prioritization of genetic variation data.

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein-protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0.

Congenital diaphragmatic hernia candidate genes derived from embryonic transcriptomes.

Congenital diaphragmatic hernia (CDH) is a common (1 in 3,000 live births) major congenital malformation that results in significant morbidity and mortality. The discovery of CDH loci using standard genetic approaches has been hindered by its genetic heterogeneity. We hypothesized that gene expression profiling of developing embryonic diaphragms would help identify genes likely to be associated with diaphragm defects. We generated a time series of whole-transcriptome expression profiles from laser captured embryonic mouse diaphragms at embryonic day (E)11.5 and E12.5 when experimental perturbations lead to CDH phenotypes, and E16.5 when the diaphragm is fully formed. Gene sets defining biologically relevant pathways and temporal expression trends were identified by using a series of bioinformatic algorithms. These developmental sets were then compared with a manually curated list of genes previously shown to cause diaphragm defects in humans and in mouse models. Our integrative filtering strategy identified 27 candidates for CDH. We examined the diaphragms of knockout mice for one of the candidate genes, pre-B-cell leukemia transcription factor 1 (Pbx1), and identified a range of previously undetected diaphragmatic defects. Our study demonstrates the utility of genetic characterization of normal development as an integral part of a disease gene identification and prioritization strategy for CDH, an approach that can be extended to other diseases and developmental anomalies.