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

Functional Studies of Deafness-Associated Pendrin and Prestin Variants

Pendrin and prestin are evolutionary-conserved membrane proteins that are essential for normal hearing. Dysfunction of these proteins results in hearing loss in humans, and numerous deafness-associated pendrin and prestin variants have been identified in patients. However, the pathogenic impacts of many of these variants are ambiguous. Here, we report results from our ongoing efforts to experimentally characterize pendrin and prestin variants using in vitro functional assays. With previously established fluorometric anion transport assays, we determined that many of the pendrin variants identified on transmembrane (TM) 10, which contains the essential anion binding site, and on the neighboring TM9 within the core domain resulted in impaired anion transport activity. We also determined the range of functional impairment in three deafness-associated prestin variants by measuring nonlinear capacitance (NLC), a proxy for motor function. Using the results from our functional analyses, we also evaluated the performance of AlphaMissense (AM), a computational tool for predicting the pathogenicity of missense variants. AM prediction scores correlated well with our experimental results; however, some variants were misclassified, underscoring the necessity of experimentally assessing the effects of variants. Together, our experimental efforts provide invaluable information regarding the pathogenicity of deafness-associated pendrin and prestin variants.

Functional studies of deafness-associated pendrin and prestin variants

Pendrin and prestin are evolutionary conserved membrane proteins that are essential for normal hearing. Pendrin is an anion transporter required for normal development and maintenance of ion homeostasis in the inner ear, while prestin is a voltage-dependent motor responsible for cochlear amplification essential for high sensitivity and frequency selectivity of mammalian hearing. Dysfunction of these proteins result in hearing loss in humans, and numerous deafness-associated pendrin and prestin variants have been identified in patients. However, the pathogenic impacts of many of these variants are ambiguous. Here we report results from our ongoing efforts in experimentally characterizing pendrin and prestin variants using in vitro functional assays, providing invaluable information regarding their pathogenicity.

Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

A cryptic microdeletion del(12)(p11.21p11.23) within an unbalanced translocation t(7;12)(q21.13;q23.1) implicates new candidate loci for intellectual disability and Kallmann syndrome

In a patient diagnosed with both Kallmann syndrome (KS) and intellectual disability (ID), who carried an apparently balanced translocation t(7;12)(q22;q24)dn, array comparative genomic hybridization (aCGH) disclosed a cryptic heterozygous 4.7 Mb deletion del(12)(p11.21p11.23), unrelated to the translocation breakpoint. This novel discovery prompted us to consider the possibility that the combination of KS and neurological disorder in this patient could be attributed to gene(s) within this specific deletion at 12p11.21-12p11.23, rather than disrupted or dysregulated genes at the translocation breakpoints. To further support this hypothesis, we expanded our study by screening five candidate genes at both breakpoints of the chromosomal translocation in a cohort of 48 KS patients. However, no mutations were found, thus reinforcing our supposition. In order to delve deeper into the characterization of the 12p11.21-12p11.23 region, we enlisted six additional patients with small copy number variations (CNVs) and analyzed eight individuals carrying small CNVs in this region from the DECIPHER database. Our investigation utilized a combination of complementary approaches. Firstly, we conducted a comprehensive phenotypic-genotypic comparison of reported CNV cases. Additionally, we reviewed knockout animal models that exhibit phenotypic similarities to human conditions. Moreover, we analyzed reported variants in candidate genes and explored their association with corresponding phenotypes. Lastly, we examined the interacting genes associated with these phenotypes to gain further insights. As a result, we identified a dozen candidate genes: TSPAN11 as a potential KS candidate gene, TM7SF3, STK38L, ARNTL2, ERGIC2, TMTC1, DENND5B, and ETFBKMT as candidate genes for the neurodevelopmental disorder, and INTS13, REP15, PPFIBP1, and FAR2 as candidate genes for KS with ID. Notably, the high-level expression pattern of these genes in relevant human tissues further supported their candidacy. Based on our findings, we propose that dosage alterations of these candidate genes may contribute to sexual and/or cognitive impairments observed in patients with KS and/or ID. However, the confirmation of their causal roles necessitates further identification of point mutations in these candidate genes through next-generation sequencing.

CovET: A covariation-evolutionary trace method that identifies protein structure-function modules

Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.

Evolution of cisplatin resistance through coordinated metabolic reprogramming of the cellular reductive state

BACKGROUND

Cisplatin (CDDP) is a mainstay treatment for advanced head and neck squamous cell carcinomas (HNSCC) despite a high frequency of innate and acquired resistance. We hypothesised that tumours acquire CDDP resistance through an enhanced reductive state dependent on metabolic rewiring.

METHODS

To validate this model and understand how an adaptive metabolic programme might be imprinted, we performed an integrated analysis of CDDP-resistant HNSCC clones from multiple genomic backgrounds by whole-exome sequencing, RNA-seq, mass spectrometry, steady state and flux metabolomics.

RESULTS

Inactivating KEAP1 mutations or reductions in KEAP1 RNA correlated with Nrf2 activation in CDDP-resistant cells, which functionally contributed to resistance. Proteomics identified elevation of downstream Nrf2 targets and the enrichment of enzymes involved in generation of biomass and reducing equivalents, metabolism of glucose, glutathione, NAD(P), and oxoacids. This was accompanied by biochemical and metabolic evidence of an enhanced reductive state dependent on coordinated glucose and glutamine catabolism, associated with reduced energy production and proliferation, despite normal mitochondrial structure and function.

CONCLUSIONS

Our analysis identified coordinated metabolic changes associated with CDDP resistance that may provide new therapeutic avenues through targeting of these convergent pathways.

A microdeletion del(12)(p11.21p11.23) with a cryptic unbalanced translocation t(7;12)(q21.13;q23.1) implicates new candidate loci for intellectual disability and Kallmann syndrome

In an apparently balanced translocation t(7;12)(q22;q24)dn exhibiting both Kallmann syndrome (KS) and intellectual disability (ID), we detected a cryptic heterozygous 4.7 Mb del(12)(p11.21p11.23) unrelated to the translocation breakpoint. This new finding raised the possibility that KS combined with neurological disorder in this patient could be caused by gene(s) within this deletion at 12p11.21-12p11.23 instead of disrupted or dysregulated genes at the genomic breakpoints. Screening of five candidate genes at both breakpoints in 48 KS patients we recruited found no mutation, corroborating our supposition. To substantiate this hypothesis further, we recruited six additional subjects with small CNVs and analyzed eight individuals carrying small CNVs in this region from DECIPHER to dissect 12p11.21-12p11.23. We used multiple complementary approaches including a phenotypic-genotypic comparison of reported cases, a review of knockout animal models recapitulating the human phenotypes, and analyses of reported variants in the interacting genes with corresponding phenotypes. The results identified one potential KS candidate gene ( TSPAN11 ), seven candidate genes for the neurodevelopmental disorder ( TM7SF3 , STK38L , ARNTL2 , ERGIC2 , TMTC1 , DENND5B , and ETFBKMT ), and four candidate genes for KS with ID ( INTS13 , REP15 , PPFIBP1 , and FAR2 ). The high-level expression pattern in the relevant human tissues further suggested the candidacy of these genes. We propose that the dosage alterations of the candidate genes may contribute to sexual and/or cognitive impairment in patients with KS and/or ID. Further identification of point mutations through next generation sequencing will be necessary to confirm their causal roles.

Identification of risk genes for Alzheimer's disease by gene embedding

Most disease-gene association methods do not account for gene-gene interactions, even though these play a crucial role in complex, polygenic diseases like Alzheimer's disease (AD). To discover new genes whose interactions may contribute to pathology, we introduce GeneEMBED. This approach compares the functional perturbations induced in gene interaction network neighborhoods by coding variants from disease versus healthy subjects. In two independent AD cohorts of 5,169 exomes and 969 genomes, GeneEMBED identified novel candidates. These genes were differentially expressed in post mortem AD brains and modulated neurological phenotypes in mice. Four that were differentially overexpressed and modified neurodegeneration in vivo are PLEC, UTRN, TP53, and POLD1. Notably, TP53 and POLD1 are involved in DNA break repair and inhibited by approved drugs. While these data show proof of concept in AD, GeneEMBED is a general approach that should be broadly applicable to identify genes relevant to risk mechanisms and therapy of other complex diseases.

Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli

Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes.

Identification and functional evaluation of GRIA1 missense and truncation variants in individuals with ID: An emerging neurodevelopmental syndrome

GRIA1 encodes the GluA1 subunit of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors, which are ligand-gated ion channels that act as excitatory receptors for the neurotransmitter L-glutamate (Glu). AMPA receptors (AMPARs) are homo- or heteromeric protein complexes with four subunits, each encoded by different genes, GRIA1 to GRIA4. Although GluA1-containing AMPARs have a crucial role in brain function, the human phenotype associated with deleterious GRIA1 sequence variants has not been established. Subjects with de novo missense and nonsense GRIA1 variants were identified through international collaboration. Detailed phenotypic and genetic assessments of the subjects were carried out and the pathogenicity of the variants was evaluated in vitro to characterize changes in AMPAR function and expression. In addition, two Xenopus gria1 CRISPR-Cas9 F₀ models were established to characterize the in vivo consequences. Seven unrelated individuals with rare GRIA1 variants were identified. One individual carried a homozygous nonsense variant (p.Arg377Ter), and six had heterozygous missense variations (p.Arg345Gln, p.Ala636Thr, p.Ile627Thr, and p.Gly745Asp), of which the p.Ala636Thr variant was recurrent in three individuals. The cohort revealed subjects to have a recurrent neurodevelopmental disorder mostly affecting cognition and speech. Functional evaluation of major GluA1-containing AMPAR subtypes carrying the GRIA1 variant mutations showed that three of the four missense variants profoundly perturb receptor function. The homozygous stop-gain variant completely destroys the expression of GluA1-containing AMPARs. The Xenopus gria1 models show transient motor deficits, an intermittent seizure phenotype, and a significant impairment to working memory in mutants. These data support a developmental disorder caused by both heterozygous and homozygous variants in GRIA1 affecting AMPAR function.

A general calculus of fitness landscapes finds genes under selection in cancers

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

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.

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.

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.