An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders

A novel framework for functional annotation of variants of uncertain significance in ID/ASD risk gene CC2D1A

Intellectual disability (ID) and autism spectrum disorder (ASD) are genetically heterogeneous with hundreds of identified risk genes, most affecting only a few patients. Novel missense variants in these genes are being discovered as clinical exome sequencing is now routinely integrated into diagnosis, yet most of them are annotated as variants of uncertain significance (VUS). VUSs are a major roadblock in using patient genetics to inform clinical action. We developed a framework to characterize VUSs in Coiled-coil and C2 domain containing 1A (CC2D1A), a gene causing autosomal recessive ID with comorbid ASD in 40% of cases. We analyzed seven VUSs (p.Pro319Leu, p.Ser327Leu, p.Gly441Val, p.Val449Met, p.Thr580Ile, p.Arg886His and p.Glu910Lys) from four cases of individuals with ID and ASD. Variants were cloned and overexpressed in HEK293 individually and in their respective heterozygous combination. CC2D1A is a signaling scaffold that positively regulates PKA-CREB signaling by repressing phosphodiesterase 4D (PDE4D) to prevent cAMP degradation. After testing multiple parameters including direct interaction between PDE4D and CC2D1A, cAMP levels and CREB activation, we found that the most sensitive readout was CREB transcriptional activity using a luciferase assay. Compared to WT CC2D1A, five VUSs (p.Pro319Leu, p.Gly441Val, p.Val449Met, p.Thr580Ile, and p.Arg886His) led to significantly blunted response to forskolin induced CREB activation. This luciferase assay approach can be scaled up to annotate ~150 CC2D1A VUSs that are currently listed in ClinVar. Since CREB activation is a common denominator for multiple ASD/ID genes, our paradigm can also be adapted for their VUSs.

Transthyretin attenuates TDP-43 proteinopathy by autophagy activation via ATF4 in FTLD-TDP

TAR DNA-binding protein-43 (TDP-43) proteinopathies are accompanied by the pathological hallmark of cytoplasmic inclusions in the neurodegenerative diseases, including frontal temporal lobar degeneration-TDP and amyotrophic lateral sclerosis. We found that transthyretin accumulates with TDP-43 cytoplasmic inclusions in frontal temporal lobar degeneration-TDP human patients and transgenic mice, in which transthyretin exhibits dramatic expression decline in elderly mice. The upregulation of transthyretin expression was demonstrated to facilitate the clearance of cytoplasmic TDP-43 inclusions through autophagy, in which transthyretin induces autophagy upregulation via ATF4. Of interest, transthyretin upregulated ATF4 expression and promoted ATF4 nuclear import, presenting physical interaction. Neuronal expression of transthyretin in frontal temporal lobar degeneration-TDP mice restored autophagy function and facilitated early soluble TDP-43 aggregates for autophagosome targeting, ameliorating neuropathology and behavioural deficits. Thus, transthyretin conducted two-way regulations by either inducing autophagy activation or escorting TDP-43 aggregates targeted autophagosomes, suggesting that transthyretin is a potential modulator therapy for neurological disorders caused by TDP-43 proteinopathy.

Whole-genome sequencing combined RNA-sequencing analysis of patients with mutations in SET binding protein 1

SET binding protein 1 (SETBP1) is essential for human development, and pathogenic germline variants in SETBP1 lead to a recognizable developmental syndrome and variable clinical features. In this study, we assessed a patient with facial dysmorphism, intellectual disability and delayed motor development. Whole genome sequencing identified a novel de novo variation of the SETBP1 (c.2631C > A; p. S877R) gene, which is located in the SKI domain, as a likely pathogenic variant for the proband’s phenotype. RNA sequencing was performed to investigate the potential molecular mechanism of the novel variation in SETBP1. In total, 77 and 38 genes were identified with aberrant expression and splicing, respectively. Moreover, the biological functions of these genes were involved in DNA/protein binding, expression regulation, and the cell cycle, which may advance our understanding of the pathogenesis of SETBP1 in vivo.

De Novo ZMYND8 variants result in an autosomal dominant neurodevelopmental disorder with cardiac malformations

PURPOSE

ZMYND8 encodes a multidomain protein that serves as a central interactive hub for coordinating critical roles in transcription regulation, chromatin remodeling, regulation of super-enhancers, DNA damage response and tumor suppression. We delineate a novel neurocognitive disorder caused by variants in the ZMYND8 gene.

METHODS

An international collaboration, exome sequencing, molecular modeling, yeast two-hybrid assays, analysis of available transcriptomic data and a knockdown Drosophila model were used to characterize the ZMYND8 variants.

RESULTS

ZMYND8 variants were identified in 11 unrelated individuals; 10 occurred de novo and one suspected de novo; 2 were truncating, 9 were missense, of which one was recurrent. The disorder is characterized by intellectual disability with variable cardiovascular, ophthalmologic and minor skeletal anomalies. Missense variants in the PWWP domain of ZMYND8 abolish the interaction with Drebrin and missense variants in the MYND domain disrupt the interaction with GATAD2A. ZMYND8 is broadly expressed across cell types in all brain regions and shows highest expression in the early stages of brain development. Neuronal knockdown of the DrosophilaZMYND8 ortholog results in decreased habituation learning, consistent with a role in cognitive function.

CONCLUSION

We present genomic and functional evidence for disruption of ZMYND8 as a novel etiology of syndromic intellectual disability.

Retinoic Acid Supplementation Rescues the Social Deficits in Fmr1 Knockout Mice

Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder with the underlying etiology yet incompletely understood and no cure treatment. Patients of fragile X syndrome (FXS) also manifest symptoms, e.g. deficits in social behaviors, that are core traits with ASD. Several studies demonstrated that a mutual defect in retinoic acid (RA) signaling was observed in FXS and ASD. However, it is still unknown whether RA replenishment could pose a positive effect on autistic-like behaviors in FXS. Herein, we found that RA signaling was indeed down-regulated when the expression of FMR1 was impaired in SH-SY5Y cells. Furthermore, RA supplementation rescued the atypical social novelty behavior, but failed to alleviate the defects in sociability behavior or hyperactivity, in Fmr1 knock-out (KO) mouse model. The repetitive behavior and motor coordination appeared to be normal. The RNA sequencing results of the prefrontal cortex in Fmr1 KO mice indicated that deregulated expression of Foxp2, Tnfsf10, Lepr and other neuronal genes was restored to normal after RA treatment. Gene ontology terms of metabolic processes, extracellular matrix organization and behavioral pathways were enriched. Our findings provided a potential therapeutic intervention for social novelty defects in FXS.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Drosophila functional screening of de novo variants in autism uncovers damaging variants and facilitates discovery of rare neurodevelopmental diseases

Individuals with autism spectrum disorder (ASD) exhibit an increased burden of de novo mutations (DNMs) in a broadening range of genes. While these studies have implicated hundreds of genes in ASD pathogenesis, which DNMs cause functional consequences in vivo remains unclear. We functionally test the effects of ASD missense DNMs using Drosophila through "humanization" rescue and overexpression-based strategies. We examine 79 ASD variants in 74 genes identified in the Simons Simplex Collection and find 38% of them to cause functional alterations. Moreover, we identify GLRA2 as the cause of a spectrum of neurodevelopmental phenotypes beyond ASD in 13 previously undiagnosed subjects. Functional characterization of variants in ASD candidate genes points to conserved neurobiological mechanisms and facilitates gene discovery for rare neurodevelopmental diseases.

Mapping oncogenic protein interactions for precision medicine

Normal protein‐protein interactions (normPPIs) occur with high fidelity to regulate almost every physiological process. In cancer, this highly organised and precisely regulated network is disrupted, hijacked or reprogrammed resulting in oncogenic protein‐protein interactions (oncoPPIs). OncoPPIs, which can result from genomic alterations, are a hallmark of many types of cancers. Recent technological advances in the field of mass spectrometry (MS)‐based interactomics, structural biology and drug discovery have prompted scientists to identify and characterise oncoPPIs. Disruption of oncoPPI interfaces has become a major focus of drug discovery programs and has resulted in the use of PPI‐specific drugs clinically. However, due to several technical hurdles, studies to build a reference oncoPPI map for various cancer types have not been undertaken. Therefore, there is an urgent need for experimental workflows to overcome the existing challenges in studying oncoPPIs in various cancers and to build comprehensive reference maps. Here, we discuss the important hurdles for characterising oncoPPIs and propose a three‐phase multidisciplinary workflow to identify and characterise oncoPPIs. Systematic identification of cancer‐type‐specific oncogenic interactions will spur new opportunities for PPI‐focused drug discovery projects and precision medicine.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

De novo mutations identified by whole-genome sequencing implicate chromatin modifications in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a chronic anxiety disorder with a substantial genetic basis and a broadly undiscovered etiology. Recent studies of de novo mutation (DNM) exome-sequencing studies for OCD have reinforced the hypothesis that rare variation contributes to the risk. We performed, to our knowledge, the first whole-genome sequencing on 53 parent-offspring families with offspring affected with OCD to investigate all rare de novo variants and insertions/deletions. We observed higher mutation rates in promoter-anchored chromatin loops (empirical P = 0.0015) and regions with high frequencies of histone marks (empirical P = 0.0001). Mutations affecting coding regions were significantly enriched within coexpression modules of genes involved in chromatin modification during human brain development. Four genes—SETD5, KDM3B, ASXL3, and FBL—had strong aggregated evidence and functionally converged on transcription’s epigenetic regulation, suggesting an important OCD risk mechanism. Our data characterized different genome-wide DNMs and highlighted the contribution of chromatin modification in the etiology of OCD.

Annotating Cancer-Related Variants at Protein-Protein Interface with Structure-PPi

A comprehensive analysis of germline and somatic variants requires complex computational approaches that combine next-generation sequencing (NGS)-based omics data with curated annotations from public repositories. Here, we describe Structure-PPi, which facilitates the analysis of cancer-related variants onto protein 3D structures, interaction interfaces, and other important functional sites (i.e., catalytic, ligand-binding, posttranslational modification). Our approach relies on features extracted from Interactome3D, UniProtKB, InterPro, APPRIS, dbNSFP, and COSMIC databases and provides complementary information to pathogenicity prediction methods. Thus, Structure-PPi helps in the discrimination of false-positive predictions and adds both mechanistic and biological insights into the role of variants in a given cancer. An online version of the tools is available at https://rbbt.bsc.es/StructurePPI/ .

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Rare variants in the outcome of social skills group training for autism

Exome sequencing has been proposed as the first-tier genetic testing in autism spectrum disorder (ASD). Here, we performed exome sequencing in autistic individuals with average to high intellectual abilities (N = 207) to identify molecular diagnoses and genetic modifiers of intervention outcomes of social skills group training (SSGT) or standard care. We prioritized variants of clinical significance (VCS), variants of uncertain significance (VUS) and generated a pilot scheme to calculate genetic scores of rare and common variants in ASD-related gene pathways. Mixed linear models were used to test the association between the carrier status of VCS/VUS or the genetic scores with intervention outcomes measured by the social responsiveness scale. Additionally, we combined behavioral and genetic features using a machine learning (ML) model to predict the individual response. We showed a rate of 4.4% and 11.3% of VCS and VUS in the cohort, respectively. Individuals with VCS or VUS had improved significantly less after standard care than non-carriers at post-intervention (β = 9.35; p = 0.036), while no such association was observed for SSGT (β = -2.50; p = 0.65). Higher rare variant genetic scores for synaptic transmission and regulation of transcription from RNA polymerase II were separately associated with less beneficial (β = 8.30, p = 0.0044) or more beneficial (β = -6.79, p = 0.014) effects after SSGT compared with standard care at follow-up, respectively. Our ML model showed the importance of rare variants for outcome prediction. Further studies are needed to understand genetic predisposition to intervention outcomes in ASD.

A computational exploration of resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models

Whole-exome and whole-genome sequencing studies in autism spectrum disorder (ASD) have identified hundreds of thousands of exonic variants. Only a handful of them, primarily loss-of-function variants, have been shown to increase the risk for ASD, while the contributory roles of other variants, including most missense variants, remain unknown. New approaches that combine tissue-specific molecular profiles with patients' genetic data can thus play an important role in elucidating the functional impact of exonic variation and improve understanding of ASD pathogenesis. Here, we integrate spatio-temporal gene co-expression networks from the developing human brain and protein-protein interaction networks to first reach accurate prioritization of ASD risk genes based on their connectivity patterns with previously known high-confidence ASD risk genes. We subsequently integrate these gene scores with variant pathogenicity predictions to further prioritize individual exonic variants based on the positive-unlabeled learning framework with gene- and variant-score calibration. We demonstrate that this approach discriminates among variants between cases and controls at the high end of the prediction range. Finally, we experimentally validate our top-scoring de novo mutation NP_001243143.1:p.Phe309Ser in the sodium/potassium-transporting ATPase ATP1A3 to disrupt protein binding with different partners.

A missense variant rs2585405 in clock gene PER1 is associated with the increased risk of noise-induced hearing loss in a Chinese occupational population

OBJECTIVE

To investigate the potential association of cochlear clock genes (CRY1, CRY2, PER1, and PER2), the DNF gene (brain-derived neurotrophic factor), and the NTF3 gene (neurotrophin3) with susceptivity to noise-induced hearing loss (NIHL) among Chinese noise-exposed workers.

METHODS

A nested case-control study was performed with 2056 noise-exposed workers from a chemical fiber factory and an energy company who underwent occupational health examinations in 2019 as study subjects. Propensity score matching was conducted to screen cases and controls by matching sex, age, and the consumption of tobacco and alcohol. A total of 1269 participants were enrolled. Then, general information and noise exposure of the study subjects were obtained through a questionnaire survey and on-site noise detection. According to the results of audiological evaluations, the participants were divided into the case group (n = 432, high-frequency threshold shift > 25 dB) and the matched control group (n = 837, high-frequency threshold shift ≤ 25 dB) by propensity score matching. Genotyping for PER1 rs2253820 and rs2585405; PER2 rs56386336 and rs934945; CRY1 rs1056560 and rs3809236; CRY2 rs2292910 and rs6798; BDNF rs11030099, rs7124442 and rs6265; and NTF3 rs1805149 was conducted using the TaqMan-PCR technique.

RESULTS

In the dominant model and the co-dominant model, the distribution of PER1 rs2585405 genotypes between the case group and the control group was significantly different (P = 0.03, P = 0.01). The NIHL risk of the subjects with the GC genotype was 1.41 times the risk of those carrying the GG genotype (95% confidence interval (CI) of odds ratio (OR): 1.01-1.96), and the NIHL risk of the subjects with the CC genotype was 0.93 times the risk of those carrying the GG genotype (95%CI of OR: 0.71-1.21). After the noise exposure period and noise exposure intensities were stratified, in the co-dominant model, the adjusted OR values for noise intensities of ≤ 85 was 1.23 (95%CI: 0.99-1.53). In the dominant model, the adjusted OR values for noise exposure periods of ≤ 16 years and noise intensities of ≤ 85 were 1.88 (95%CI: 1.03-3.42) and 1.64 (95%CI: 1.12-2.38), respectively.

CONCLUSION

The CC/CG genotype of rs2585405 in the PER1 gene was identified as a potential risk factor for NIHL in Chinese noise-exposed workers, and interaction between rs2585405 and high temperature was found to be associated with NIHL risk.

Predicting functional consequences of mutations using molecular interaction network features

Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.

A method to delineate de novo missense variants across pathways prioritizes genes linked to autism

Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.

Genetic architecture of autism spectrum disorder: Lessons from large-scale genomic studies

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a strong genetic component. Recently developed genomic technologies, including microarray and next-generation sequencing (NGS), have enabled researchers to genetic analyses aimed at identifying genetic variations associated with ASD and to elucidate the genetic architecture of the disorder. Large-scale microarray, exome sequencing analyses, and robust statistical methods have resulted in successful gene discovery and identification of high-confidence ASD genes from among de novo and inherited variants. Efforts have been made to understand the genetic architecture of ASD using whole-genome sequencing and genome-wide association studies aimed at identifying noncoding mutations and common variants associated with ASD. In addition, the development of systems biology approaches has resulted in the integration of genetic findings with functional genomic datasets, thereby providing a unique insight into the functional convergence of ASD risk genes and their neurobiology. In this review, we summarize the latest findings of ASD genetic studies involving large cohorts and discuss their implications in ASD neurobiology and in clinical practice.

Structure and function of STAC proteins: Calcium channel modulators and critical components of muscle excitation-contraction coupling

In skeletal muscle tissue, an intriguing mechanical coupling exists between two ion channels from different membranes: the L-type voltage-gated calcium channel (Ca_V1.1), located in the plasma membrane, and ryanodine receptor 1 (RyR1) located in the sarcoplasmic reticulum membrane. Excitable cells rely on Ca_vs to initiate Ca²⁺ entry in response to action potentials. RyRs can amplify this signal by releasing Ca²⁺ from internal stores. Although this process can be mediated through Ca²⁺ as a messenger, an overwhelming amount of evidence suggests that RyR1 has recruited Ca_V1.1 directly as its voltage sensor. The exact mechanisms that underlie this coupling have been enigmatic, but a recent wave of reports have illuminated the coupling protein STAC3 as a critical player. Without STAC3, the mechanical coupling between Ca_v1.1 and RyR1 is lost, and muscles fail to contract. Various sequence variants of this protein have been linked to congenital myopathy. Other STAC isoforms are expressed in the brain and may serve as regulators of L-type Ca_Vs. Despite the short length of STACs, several points of contacts have been proposed between them and Ca_Vs. However, it is currently unclear whether STAC3 also forms direct interactions with RyR1, and whether this modulates RyR1 function. In this review, we discuss the 3D architecture of STAC proteins, the biochemical evidence for their interactions, the relevance of these connections for functional modulation, and their involvement in myopathy.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

To Ubiquitinate or Not to Ubiquitinate: TRIM17 in Cell Life and Death

TRIM17 is a member of the TRIM family, a large class of RING-containing E3 ubiquitin-ligases. It is expressed at low levels in adult tissues, except in testis and in some brain regions. However, it can be highly induced in stress conditions which makes it a putative stress sensor required for the triggering of key cellular responses. As most TRIM members, TRIM17 can act as an E3 ubiquitin-ligase and promote the degradation by the proteasome of substrates such as the antiapoptotic protein MCL1. Intriguingly, TRIM17 can also prevent the ubiquitination of other proteins and stabilize them, by binding to other TRIM proteins and inhibiting their E3 ubiquitin-ligase activity. This duality of action confers several pivotal roles to TRIM17 in crucial cellular processes such as apoptosis, autophagy or cell division, but also in pathological conditions as diverse as Parkinson's disease or cancer. Here, in addition to recent data that endorse this duality, we review what is currently known from public databases and the literature about TRIM17 gene regulation and expression, TRIM17 protein structure and interactions, as well as its involvement in cell physiology and human disorders.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

In-depth and 3-dimensional exploration of the budding yeast phosphoproteome

Phosphorylation is one of the most dynamic and widespread post-translational modifications regulating virtually every aspect of eukaryotic cell biology. Here, we assemble a dataset from 75 independent phosphoproteomic experiments performed in our laboratory using Saccharomyces cerevisiae. We report 30,902 phosphosites identified from cells cultured in a range of DNA damage conditions and/or arrested in distinct cell cycle stages. To generate a comprehensive resource for the budding yeast community, we aggregate our dataset with the Saccharomyces Genome Database and another recently published study, resulting in over 46,000 budding yeast phosphosites. With the goal of enhancing the identification of functional phosphorylation events, we perform computational positioning of phosphorylation sites on available 3D protein structures and systematically identify events predicted to regulate protein complex architecture. Results reveal hundreds of phosphorylation sites mapping to or near protein interaction interfaces, many of which result in steric or electrostatic "clashes" predicted to disrupt the interaction. With the advancement of Cryo-EM and the increasing number of available structures, our approach should help drive the functional and spatial exploration of the phosphoproteome.

Cellular Models and High-Throughput Screening for Genetic Causality of Intellectual Disability

Intellectual disabilities (ID) are a type of neurodevelopmental disorder (NDD). They can have a genetic cause, including an emerging class of ID centring around Rho GTPases, such as Ras-related C3 botulinum toxin substrate 1 (RAC1). Guidelines for establishing genetic causality include the use of cellular models, which often have morphological aberrations, a long-standing hallmark of ID. Disease cellular models can facilitate high-throughput screening (HTS) of chemical or genetic perturbations, which can provide translatable biological insight. Here, we discuss a class of IDs centring around RAC1. We review novel and established cellular models of ID, including mouse and human primary cells and reprogrammed or induced neurons. Finally, we review progress and remaining challenges in the adoption of HTS methodologies by the community studying neurological disorders.

Protein folding stability and binding interactions through the lens of evolution: a dynamical perspective

While the function of a protein depends heavily on its ability to fold into a correct 3D structure, billions of years of evolution have tailored proteins from highly stable objects to flexible molecules as they adapted to environmental changes. Nature maintains the fine balance of protein folding and stability while still evolving towards new function through generations of fine-tuning necessary interactions with other proteins and small molecules. Here we focus on recent computational and experimental studies that shed light onto how evolution molds protein folding and the functional landscape from a conformational dynamics' perspective. Particularly, we explore the importance of dynamic allostery throughout protein evolution and discuss how the protein anisotropic network can give rise to allosteric and epistatic interactions.

Representative cancer-associated U2AF2 mutations alter RNA interactions and splicing

High-throughput sequencing of hematologic malignancies and other cancers has revealed recurrent mis-sense mutations of genes encoding pre-mRNA splicing factors. The essential splicing factor U2AF2 recognizes a polypyrimidine-tract splice-site signal and initiates spliceosome assembly. Here, we investigate representative, acquired U2AF2 mutations, namely N196K or G301D amino acid substitutions associated with leukemia or solid tumors, respectively. We determined crystal structures of the wild-type (WT) compared with N196K- or G301D-substituted U2AF2 proteins, each bound to a prototypical AdML polypyrimidine tract, at 1.5, 1.4, or 1.7 Å resolutions. The N196K residue appears to stabilize the open conformation of U2AF2 with an inter-RNA recognition motif hydrogen bond, in agreement with an increased apparent RNA-binding affinity of the N196K-substituted protein. The G301D residue remains in a similar position as the WT residue, where unfavorable proximity to the RNA phosphodiester could explain the decreased RNA-binding affinity of the G301D-substituted protein. We found that expression of the G301D-substituted U2AF2 protein reduces splicing of a minigene transcript carrying prototypical splice sites. We further show that expression of either N196K- or G301D-substituted U2AF2 can subtly alter splicing of representative endogenous transcripts, despite the presence of endogenous, WT U2AF2 such as would be present in cancer cells. Altogether, our results demonstrate that acquired U2AF2 mutations such as N196K and G301D are capable of dysregulating gene expression for neoplastic transformation.

Brain Organoids as Model Systems for Genetic Neurodevelopmental Disorders

Neurodevelopmental disorders (NDDs) are a group of disorders in which the development of the central nervous system (CNS) is disturbed, resulting in different neurological and neuropsychiatric features, such as impaired motor function, learning, language or non-verbal communication. Frequent comorbidities include epilepsy and movement disorders. Advances in DNA sequencing technologies revealed identifiable genetic causes in an increasingly large proportion of NDDs, highlighting the need of experimental approaches to investigate the defective genes and the molecular pathways implicated in abnormal brain development. However, targeted approaches to investigate specific molecular defects and their implications in human brain dysfunction are prevented by limited access to patient-derived brain tissues. In this context, advances of both stem cell technologies and genome editing strategies during the last decade led to the generation of three-dimensional (3D) in vitro-models of cerebral organoids, holding the potential to recapitulate precise stages of human brain development with the aim of personalized diagnostic and therapeutic approaches. Recent progresses allowed to generate 3D-structures of both neuronal and non-neuronal cell types and develop either whole-brain or region-specific cerebral organoids in order to investigate in vitro key brain developmental processes, such as neuronal cell morphogenesis, migration and connectivity. In this review, we summarized emerging methodological approaches in the field of brain organoid technologies and their application to dissect disease mechanisms underlying an array of pediatric brain developmental disorders, with a particular focus on autism spectrum disorders (ASDs) and epileptic encephalopathies.

De novo missense variants disrupting protein-protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types

BACKGROUND

Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses.

METHODS

Utilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein-protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an "ASD disrupted network." Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner.

RESULTS

Extending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types-excitatory, inhibitory, and neural progenitor-we implicate several hundred genes in risk (FDR [Formula: see text]0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system.

LIMITATIONS

Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional.

CONCLUSIONS

Disrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD.

Genetics of extreme human longevity to guide drug discovery for healthy ageing

Ageing is the greatest risk factor for most common chronic human diseases, and it therefore is a logical target for developing interventions to prevent, mitigate or reverse multiple age-related morbidities. Over the past two decades, genetic and pharmacologic interventions targeting conserved pathways of growth and metabolism have consistently led to substantial extension of the lifespan and healthspan in model organisms as diverse as nematodes, flies and mice. Recent genetic analysis of long-lived individuals is revealing common and rare variants enriched in these same conserved pathways that significantly correlate with longevity. In this Perspective, we summarize recent insights into the genetics of extreme human longevity and propose the use of this rare phenotype to identify genetic variants as molecular targets for gaining insight into the physiology of healthy ageing and the development of new therapies to extend the human healthspan.

Precision medicine - networks to the rescue

Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global "interactome" comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/.

Autism-associated missense genetic variants impact locomotion and neurodevelopment in Caenorhabditis elegans

Autism spectrum disorder (ASD) involves thousands of alleles in over 850 genes, but the current functional inference tools are not sufficient to predict phenotypic changes. As a result, the causal relationship of most of these genetic variants in the pathogenesis of ASD has not yet been demonstrated and an experimental method prioritizing missense alleles for further intensive analysis is crucial. For this purpose, we have designed a pipeline that uses Caenorhabditis elegans as a genetic model to screen for phenotype-changing missense alleles inferred from human ASD studies. We identified highly conserved human ASD-associated missense variants in their C. elegans orthologs, used a CRISPR/Cas9-mediated homology-directed knock-in strategy to generate missense mutants and analyzed their impact on behaviors and development via several broad-spectrum assays. All tested missense alleles were predicted to perturb protein function, but we found only 70% of them showed detectable phenotypic changes in morphology, locomotion or fecundity. Our findings indicate that certain missense variants in the C. elegans orthologs of human CACNA1D, CHD7, CHD8, CUL3, DLG4, GLRA2, NAA15, PTEN, SYNGAP1 and TPH2 impact neurodevelopment and movement functions, elevating these genes as candidates for future study into ASD. Our approach will help prioritize functionally important missense variants for detailed studies in vertebrate models and human cells.

Recent advances in network medicine: From disease mechanisms to new treatment strategies

Conventional reductionist approaches have guided most of our understanding in disease diagnostic and treatment. However, most diseases are not consequence of perturbations in a single protein or metabolite, but rather of the effect that these perturbations have in their cellular context. The emerging field of network medicine offers a set of tools to explore molecular networks and to retrieve insights about mechanisms of different diseases. The study of the protein interactome, the map of physical interactions among human proteins, revealed that disease proteins tend to interact with each other, linking diseases to well-defined interactome neighborhoods. These disease-associated neighborhoods have been defined as disease modules, and they can uncover the biological significance of genes identified by genetic studies, reveal molecular mechanisms that connect different phenotypes, and help identify new pharmacological strategies for disease treatment. Therefore, network medicine offers a framework in which the complexity of different aspects of multiple sclerosis can be explored in an integrative fashion, which can ultimately provide insights about disease mechanisms and treatment.

Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites

Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.

An autism-causing calcium channel variant functions with selective autophagy to alter axon targeting and behavior

Common and rare variants of the CACNA1C voltage-gated calcium channel gene have been associated with autism and other neurodevelopmental disorders including schizophrenia, bipolar disorder and ADHD. However, little is known about how CACNA1C variants affect cellular processes to alter neurodevelopment. The Timothy syndrome mutation is a rare de novo gain-of-function variant in CACNA1C that causes autism with high penetrance, providing a powerful avenue into investigating the role of CACNA1C variants in neurodevelopmental disorders. Here, we use egl-19, the C. elegans homolog of CACNA1C, to investigate the role of voltage-gated calcium channels in autism. We show that an egl-19(gof) mutation that is equivalent to the Timothy syndrome mutation can alter axon targeting and affect behavior in C. elegans. We find that wildtype egl-19 negatively regulates axon termination. The egl-19(gof) mutation represses axon termination to cause axon targeting defects that lead to the misplacement of electrical synapses and alterations in habituation to light touch. Moreover, genetic interactions indicate that the egl-19(gof) mutation functions with genes that promote selective autophagy to cause defects in axon termination and behavior. These results reveal a novel genetic mechanism whereby a de novo mutation in CACNA1C can drive alterations in circuit formation and behavior.

Autism is a disorder that affects neuronal development, leading to alterations in cognition and behavior. Imaging studies have revealed alterations in axonal connectivity as a key feature of autism. However, the underlying perturbations in cell biology that drive these alterations remain largely unknown. To address this issue, we have taken advantage of the Timothy syndrome mutation, a variant in a voltage-gated calcium channel that has the unusual property of causing autism with high penetrance. We identify a role for wild-type voltage-gated calcium channels in regulating axon targeting in C. elegans. Moreover, we find that two different versions of the Timothy syndrome mutation disrupt axon targeting. Our results suggest that the Timothy syndrome mutations disrupt axon targeting and behavior by interacting with genes that promote selective autophagy, the process through which cellular components are selected for degradation. These results reveal a mechanism through which variants in voltage-gated calcium channels can cause the disruptions in axonal connectivity that underlie autism.

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.

The pathobiology of perturbed mutant huntingtin protein-protein interactions in Huntington's disease

Mutations are at the root of many human diseases. Still, we largely do not exactly understand how they trigger pathogenesis. One, more recent, hypothesis has been that they comprehensively perturb protein-protein interaction (PPI) networks and significantly alter key biological processes. Under this premise, many rare genetic disorders with Mendelian inheritance, like Huntington's disease and several spinocerebellar ataxias, are likely to be caused by complex genotype-phenotype relationships involving abnormal PPIs. These altered PPI networks and their effects on cellular pathways are poorly understood at the molecular level. In this review, we focus on PPIs that are perturbed by the expanded pathogenic polyglutamine tract in huntingtin (HTT), the protein which, in its mutated form, leads to the autosomal dominant, neurodegenerative Huntington's disease. One aspect of perturbed mutant HTT interactions is the formation of abnormal protein species such as fibrils or large neuronal inclusions as a result of homotypic and heterotypic aberrant molecular interactions. This review focuses on abnormal PPIs that are associated with the assembly of mutant HTT aggregates in cells and their potential relevance in disease. Furthermore, the mechanisms and pathobiological processes that may contribute to phenotype development, neuronal dysfunction and toxicity in Huntington's disease brains are also discussed. This article is part of the Special Issue "Proteomics".

AMPA receptor GluA2 subunit defects are a cause of neurodevelopmental disorders

AMPA receptors (AMPARs) are tetrameric ligand-gated channels made up of combinations of GluA1-4 subunits encoded by GRIA1-4 genes. GluA2 has an especially important role because, following post-transcriptional editing at the Q607 site, it renders heteromultimeric AMPARs Ca2+-impermeable, with a linear relationship between current and trans-membrane voltage. Here, we report heterozygous de novo GRIA2 mutations in 28 unrelated patients with intellectual disability (ID) and neurodevelopmental abnormalities including autism spectrum disorder (ASD), Rett syndrome-like features, and seizures or developmental epileptic encephalopathy (DEE). In functional expression studies, mutations lead to a decrease in agonist-evoked current mediated by mutant subunits compared to wild-type channels. When GluA2 subunits are co-expressed with GluA1, most GRIA2 mutations cause a decreased current amplitude and some also affect voltage rectification. Our results show that de-novo variants in GRIA2 can cause neurodevelopmental disorders, complementing evidence that other genetic causes of ID, ASD and DEE also disrupt glutamatergic synaptic transmission.

Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology

Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in cancer. Elucidating the functional pathways altered by loss-of-function (LOF) or gain-of-function (GOF) mutations will be crucial for prioritizing cancer-causing variants and their resultant therapeutic liabilities. In this review, we highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also summarize advances in experimental and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of mutations. Together, systematic investigations of the function of GOF mutations will provide an important missing piece for cancer biology and precision therapy.

A multifaceted approach for analyzing complex phenotypic data in rodent models of autism

Autism (MIM 209850) is a multifactorial disorder with a broad clinical presentation. A number of high-confidence ASD risk genes are known; however, the contribution of non-genetic environmental factors towards ASD remains largely uncertain. Here, we present a bioinformatics resource of genetic and induced models of ASD developed using a shared annotation platform. Using this data, we depict the intricate trends in the research approaches to analyze rodent models of ASD. We identify the top 30 most frequently studied phenotypes extracted from rodent models of ASD based on 787 publications. As expected, many of these include animal model equivalents of the “core” phenotypes associated with ASD, such as impairments in social behavior and repetitive behavior, as well as several comorbid features of ASD including anxiety, seizures, and motor-control deficits. These phenotypes have also been studied in models based on a broad range of environmental inducers present in the database, of which gestational exposure to valproic acid (VPA) and maternal immune activation models comprising lipopolysaccharide (LPS) and poly I:C are the most studied. In our unique dataset of rescue models, we identify 24 pharmaceutical agents tested on established models derived from various ASD genes and CNV loci for their efficacy in mitigating symptoms relevant for ASD. As a case study, we analyze a large collection of Shank3 mouse models providing a high-resolution view of the in vivo role of this high-confidence ASD gene, which is the gateway towards understanding and dissecting the heterogeneous phenotypes seen in single-gene models of ASD. The trends described in this study could be useful for researchers to compare ASD models and to establish a complete profile for all relevant animal models in ASD research.

Analysis of Topological Parameters of Complex Disease Genes Reveals the Importance of Location in a Biomolecular Network

Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer’s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.

Evolution of resilience in protein interactomes across the tree of life

Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein-protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment.

Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule’s interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

Genetic variants might exert their functional effects via influencing molecular interaction. Here, the authors present a resource featuring almost 28,000 annotations describing the effect of small sequence changes on physical protein interactions, curated by IMEx Consortium curators.

The Relevance of Variants With Unknown Significance for Autism Spectrum Disorder Considering the Genotype-Phenotype Interrelationship

Several efforts in basic and clinical research have been contributing to unveiling the genetics behind autism spectrum disorders (ASD). However, despite these advancements, many individuals diagnosed with ASD and related neuropsychiatric conditions have been genetically investigated without elucidative results. The enormous genetic complexity of ASD-related conditions makes it a significant challenge to achieve, with a growing number of genes (close to a thousand) involved, belonging to different molecular pathways and presenting distinct genetic variations. Next-generation sequencing (NGS) is the approach most used in genetic research related to ASD, identifying de novo mutation, which is closely related to more severe clinical phenotypes, especially when they affect constrained and loss-of-function intolerant genes. On the other hand, de novo mutation findings contribute to a small percentage of the ASD population, since most of the cases and genetic variants associated with neuropsychiatric conditions are inherited and phenotypes are results of additive polygenic models, which makes statistical efforts more difficult. As a result, NGS investigation can sound vainly or unsuccessful, and new mutations on genes already related with ASD are classified as variants of unknown significance (VUS), hampering their endorsement to a clinical phenotype. This review is focused on currently available strategies to clarify the impact of VUS and to describe the efforts to identify more pieces of evidence throughout clinical interpretation and genetic curation process.

Integrating molecular networks with genetic variant interpretation for precision medicine

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine.

This article is categorized under:

Translational, Genomic, and Systems Medicine > Translational Medicine

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Analytical and Computational Methods > Computational Methods

Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations

Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fails to provide molecular insights. Moreover, complex disease phenomena often violate standard assumptions of a direct organismal phenotype to overall fitness effect relationship. Here we discuss the potential of a molecular phenotype-oriented paradigm to uniquely identify candidate disease-causing mutations from the human genetic background. By providing a direct connection between single nucleotide mutations and observable organismal and cellular phenotypes associated with disease, we suggest that molecular phenotypes can readily incorporate alongside established fitness-based methodologies to provide complementary insights to the functional impact of human mutations. Lastly, we discuss how integrated approaches between molecular phenotypes and fitness-based perspectives facilitate new insights into the molecular mechanisms underlying disease-associated mutations while also providing a platform for improved interpretation of epistasis in human disease.