Distinct epigenomic patterns are associated with haploinsufficiency and predict risk genes of developmental disorders

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

EASL Clinical Practice Guidelines on genetic cholestatic liver diseases

Genetic cholestatic liver diseases are caused by (often rare) mutations in a multitude of different genes. While these diseases differ in pathobiology, clinical presentation and prognosis, they do have several commonalities due to their cholestatic nature. These Clinical Practice Guidelines (CPGs) offer a general approach to genetic testing and management of cholestatic pruritus, while exploring diagnostic and treatment approaches for a subset of genetic cholestatic liver diseases in depth. An expert panel appointed by the European Association for the Study of the Liver has created recommendations regarding diagnosis and treatment, based on the best evidence currently available in the fields of paediatric and adult hepatology, as well as genetics. The management of these diseases generally takes place in a tertiary referral centre, in order to provide up-to-date approaches and expertise. These CPGs are intended to support hepatologists (for paediatric and adult patients), residents and other healthcare professionals involved in the management of these patients with concrete recommendations based on currently available evidence or, if not available, on expert opinion.

Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes

Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each gene's differential expression fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing datasets from rare disease and in-vitro stimulus response experiments. Compared to the standard approach, our method adjusts the bias in discovery towards highly variable genes, and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. With that, our method provides a novel view on DE and contributes towards bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease causing genes and enhance the discovery of therapeutic targets.

Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity

Copy number losses (deletions) are a major contributor to the etiology of severe genetic disorders. Although haploinsufficient genes play a critical role in deletion pathogenicity, current methods for deletion pathogenicity prediction fail to integrate multiple lines of evidence for haploinsufficiency at the gene level, limiting their power to pinpoint deleterious deletions associated with genetic disorders. Here we introduce DosaCNV, a deep multiple-instance learning framework that, for the first time, models deletion pathogenicity jointly with gene haploinsufficiency. By integrating over 30 gene-level features potentially predictive of haploinsufficiency, DosaCNV shows unmatched performance in prioritizing pathogenic deletions associated with a broad spectrum of genetic disorders. Furthermore, DosaCNV outperforms existing methods in predicting gene haploinsufficiency even though it is not trained on known haploinsufficient genes. Finally, DosaCNV leverages a state-of-the-art technique to quantify the contributions of individual gene-level features to haploinsufficiency, allowing for human-understandable explanations of model predictions. Altogether, DosaCNV is a powerful computational tool for both fundamental and translational research.

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data

BACKGROUND

The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.

RESULTS

Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous population genetic methods, DeepLOF utilizes a novel deep learning framework to integrate both population and functional genomic data, allowing us to pinpoint short essential genes that can hardly be predicted from population genomic data alone. Compared with previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Notably, at a false positive rate of 5%, DeepLOF detects 50% more ClinGen haploinsufficient genes than previous methods. Furthermore, DeepLOF discovers 109 novel essential genes that are too short to be identified by previous methods.

CONCLUSION

The predictive power of DeepLOF shows that it is a compelling computational method to aid in the discovery of essential genes.

NeuroCNVscore: a tissue-specific framework to prioritise the pathogenicity of CNVs in neurodevelopmental disorders

BACKGROUND

Neurodevelopmental disorders (NDDs) are associated with altered development of the brain especially in childhood. Copy number variants (CNVs) play a crucial role in the genetic aetiology of NDDs by disturbing gene expression directly at linear sequence or remotely at three-dimensional genome level in a tissue-specific manner. Despite the substantial increase in NDD studies employing whole-genome sequencing, there is no specific tool for prioritising the pathogenicity of CNVs in the context of NDDs.

METHODS

Using an XGBoost classifier, we integrated 189 features that represent genomic sequences, gene information and functional/genomic segments for evaluating genome-wide CNVs in a neuro/brain-specific manner, to develop a new tool, neuroCNVscore. We used Human Phenotype Ontology to construct an independent NDD-related set.

RESULTS

Our neuroCNVscore framework (https://github.com/lxsbch/neuroCNVscore) achieved high predictive performance (precision recall=0.82; area under curve=0.85) and outperformed an existing reference method SVScore. Notably, the predicted pathogenic CNVs showed enrichment in known genes associated with autism.

CONCLUSIONS

NeuroCNVscore prioritises functional, deleterious and pathogenic CNVs in NDDs at whole genome-wide level, which is important for genetic studies and clinical genomic screening of NDDs as well as for providing novel biological insights into NDDs.

dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations

BACKGROUND

Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features.

RESULTS

From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs.

CONCLUSION

The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.

Translational enhancement by base editing of the Kozak sequence rescues haploinsufficiency

A variety of single-gene human diseases are caused by haploinsufficiency, a genetic condition by which mutational inactivation of one allele leads to reduced protein levels and functional impairment. Translational enhancement of the spare allele could exert a therapeutic effect. Here we developed BOOST, a novel gene-editing approach to rescue haploinsufficiency loci by the change of specific single nucleotides in the Kozak sequence, which controls translation by regulating start codon recognition. We evaluated for translational strength 230 Kozak sequences of annotated human haploinsufficient genes and 4621 derived variants, which can be installed by base editing, by a high-throughput reporter assay. Of these variants, 149 increased the translation of 47 Kozak sequences, demonstrating that a substantial proportion of haploinsufficient genes are controlled by suboptimal Kozak sequences. Validation of 18 variants for 8 genes produced an average enhancement in an expression window compatible with the rescue of the genetic imbalance. Base editing of the NCF1 gene, whose monoallelic loss causes chronic granulomatous disease, resulted in the desired increase of NCF1 (p47phox) protein levels in a relevant cell model. We propose BOOST as a fine-tuned approach to modulate translation, applicable to the correction of dozens of haploinsufficient monogenic disorders independently of the causing mutation.

Pathological variants in genes associated with disorders of sex development and central causes of hypogonadism in a whole-genome reference panel of 8380 Japanese individuals

Disorders of sex development (DSD) comprises a congenital condition in which chromosomal, gonadal, or anatomical sex development is atypical. In this study, we screened for pathogenic variants in 32 genes associated with DSDs and central causes of hypogonadism (CHG) in a whole-genome reference panel including 8380 Japanese individuals constructed by Tohoku Medical Megabank Organization. Candidate pathogenic (P) or likely pathogenic (LP) variants were extracted from the ClinVar, InterVar, and Human Gene Mutation databases. Ninety-one candidate pathological variants were found in 25 genes; 28 novel candidate variants were identified. Nearly 1 in 40 (either ClinVar or InterVar P or LP) to 157 (both ClinVar and InterVar P or LP) individuals were found to be carriers of recessive DSD and CHG alleles. In these data, genes implicated in gonadal dysfunction did not show loss-of-function variants, with a relatively high tendency of intolerance for haploinsufficiency based on pLI and Episcore, both of which can be used for estimating haploinsufficiency. We report the types and frequencies of causative variants for DSD and CHG in the general Japanese population. This study furthers our understanding of the genetic causes and helps to refine genetic counseling of DSD and CHG.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Nested epistasis enhancer networks for robust genome regulation

Mammalian genomes possess multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, yet it is unclear how these enhancers coordinate to achieve this task. Here, we combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network possesses a nested multi-layer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify non-coding variant pairs associated with pathogenic genes in diseases beyond Genome-Wide Association Studies (GWAS) analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases.

A cross-disorder dosage sensitivity map of the human genome

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

Gene4HL: An Integrated Genetic Database for Hearing Loss

Hearing loss (HL) is one of the most common disabilities in the world. In industrialized countries, HL occurs in 1–2/1,000 newborns, and approximately 60% of HL is caused by genetic factors. Next generation sequencing (NGS) has been widely used to identify many candidate genes and variants in patients with HL, but the data are scattered in multitudinous studies. It is a challenge for scientists, clinicians, and biologists to easily obtain and analyze HL genes and variant data from these studies. Thus, we developed a one-stop database of HL-related genes and variants, Gene4HL (http://www.genemed.tech/gene4hl/), making it easy to catalog, search, browse and analyze the genetic data. Gene4HL integrates the detailed genetic and clinical data of 326 HL-related genes from 1,608 published studies, along with 62 popular genetic data sources to provide comprehensive knowledge of candidate genes and variants associated with HL. Additionally, Gene4HL supports the users to analyze their own genetic engineering network data, performs comprehensive annotation, and prioritizes candidate genes and variations using custom parameters. Thus, Gene4HL can help users explain the function of HL genes and the clinical significance of variants by correlating the genotypes and phenotypes in humans.

Targeting the Transcriptome Through Globally Acting Components

Transcription is a step in gene expression that defines the identity of cells and its dysregulation is associated with diseases. With advancing technologies revealing molecular underpinnings of the cell with ever-higher precision, our ability to view the transcriptomes may have surpassed our knowledge of the principles behind their organization. The human RNA polymerase II (Pol II) machinery comprises thousands of components that, in conjunction with epigenetic and other mechanisms, drive specialized programs of development, differentiation, and responses to the environment. Parts of these programs are repurposed in oncogenic transformation. Targeting of cancers is commonly done by inhibiting general or broadly acting components of the cellular machinery. The critical unanswered question is how globally acting or general factors exert cell type specific effects on transcription. One solution, which is discussed here, may be among the events that take place at genes during early Pol II transcription elongation. This essay turns the spotlight on the well-known phenomenon of promoter-proximal Pol II pausing as a step that separates signals that establish pausing genome-wide from those that release the paused Pol II into the gene. Concepts generated in this rapidly developing field will enhance our understanding of basic principles behind transcriptome organization and hopefully translate into better therapies at the bedside.

X-CNV: genome-wide prediction of the pathogenicity of copy number variations

Background

Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success.

Results

We have developed a novel computational framework X-CNV (www.unimd.org/XCNV), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups.

Conclusions

The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00945-4.

GPCards: An integrated database of genotype-phenotype correlations in human genetic diseases

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The patient-level genotype-phenotype correlations in GPCards accelerated the development of medical genetics.

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The integrated 62 genomic sources provide comprehensive information for interpreting pathogenicity.

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Analysis function in GPCards would help users to decipher their data of genotype-phenotype correlations.

Genotype–phenotype correlations are the basis of precision medicine of human genetic diseases. However, it remains a challenge for clinicians and researchers to conveniently access detailed individual-level clinical phenotypic features of patients with various genetic variants. To address this urgent need, we manually searched for genetic studies in PubMed and catalogued 8,309 genetic variants in 1,288 genes from 17,738 patients with detailed clinical phenotypic features from 1,855 publications. Based on genotype–phenotype correlations in this dataset, we developed an user-friendly online database called GPCards (http://genemed.tech/gpcards/), which not only provided the association between genetic diseases and disease genes, but also the prevalence of various clinical phenotypes related to disease genes and the patient-level mapping between these clinical phenotypes and genetic variants. To accelerate the interpretation of genetic variants, we integrated 62 well-known variant-level and gene-level genomic data sources, including functional predictions, allele frequencies in different populations, and disease-related information. Furthermore, GPCards enables automatic analyses of users’ own genetic data, comprehensive annotation, prioritization of candidate functional variants, and identification of genotype–phenotype correlations using custom parameters. In conclusion, GPCards is expected to accelerate the interpretation of genotype–phenotype correlations, subtype classification, and candidate gene prioritisation in human genetic diseases.

Identification of haploinsufficient genes from epigenomic data using deep forest

Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been developed. However, most of those computational methods suffer from study bias, experimental noise and instability, resulting in unsatisfactory identification of haploinsufficient genes. To address those challenges, we propose a deep forest model, called HaForest, to identify haploinsufficient genes. The multiscale scanning is proposed to extract local contextual representations from input features under Linear Discriminant Analysis. After that, the cascade forest structure is applied to obtain the concatenated features directly by integrating decision-tree-based forests. Meanwhile, to exploit the complex dependency structure among haploinsufficient genes, the LightGBM library is embedded into HaForest to reveal the highly expressive features. To validate the effectiveness of our method, we compared it to several computational methods and four deep learning algorithms on five epigenomic data sets. The results reveal that HaForest achieves superior performance over the other algorithms, demonstrating its unique and complementary performance in identifying haploinsufficient genes. The standalone tool is available at https://github.com/yangyn533/HaForest.

Novel candidate genes in esophageal atresia/tracheoesophageal fistula identified by exome sequencing

The various malformations of the aerodigestive tract collectively known as esophageal atresia/tracheoesophageal fistula (EA/TEF) constitute a rare group of birth defects of largely unknown etiology. Previous studies have identified a small number of rare genetic variants causing syndromes associated with EA/TEF. We performed a pilot exome sequencing study of 45 unrelated simplex trios (probands and parents) with EA/TEF. Thirteen had isolated and 32 had nonisolated EA/TEF; none had a family history of EA/TEF. We identified de novo variants in protein-coding regions, including 19 missense variants predicted to be deleterious (D-mis) and 3 likely gene-disrupting (LGD) variants. Consistent with previous studies of structural birth defects, there is a trend of increased burden of de novo D-mis in cases (1.57-fold increase over the background mutation rate), and the burden is greater in constrained genes (2.55-fold, p = 0.003). There is a frameshift de novo variant in EFTUD2, a known EA/TEF risk gene involved in mRNA splicing. Strikingly, 15 out of 19 de novo D-mis variants are located in genes that are putative target genes of EFTUD2 or SOX2 (another known EA/TEF gene), much greater than expected by chance (3.34-fold, p value = 7.20e-5). We estimated that 33% of patients can be attributed to de novo deleterious variants in known and novel genes. We identified APC2, AMER3, PCDH1, GTF3C1, POLR2B, RAB3GAP2, and ITSN1 as plausible candidate genes in the etiology of EA/TEF. We conclude that further genomic analysis to identify de novo variants will likely identify previously undescribed genetic causes of EA/TEF.

Epigenetic landscape of small cell lung cancer: small image of a giant recalcitrant disease

Small cell lung cancer (SCLC) is a particular subtype of lung cancer with high mortality. Recent advances in understanding SCLC genomics and breakthroughs of immunotherapy have substantially expanded existing knowledge and treatment modalities. However, challenges associated with SCLC remain enigmatic and elusive. Most of the conventional drug discovery approaches targeting altered signaling pathways in SCLC end up in the 'grave-yard of drug discovery', which mandates exploring novel approaches beyond inhibiting cell signaling pathways. Epigenetic modifications have long been documented as the key contributors to the tumorigenesis of almost all types of cancer, including SCLC. The last decade witnessed an exponential increase in our understanding of epigenetic modifications for SCLC. The present review highlights the central role of epigenetic regulations in acquiring neoplastic phenotype, metastasis, aggressiveness, resistance to chemotherapy, and immunotherapeutic approaches of SCLC. Different types of epigenetic modifications (DNA/histone methylation or acetylation) that can serve as predictive biomarkers for prognostication, treatment stratification, neuroendocrine lineage determination, and development of potential SCLC therapies are also discussed. We also review the utility of epigenetic targets/epidrugs in combination with first-line chemotherapy and immunotherapy that are currently under investigation in preclinical and clinical studies. Altogether, the information presents the inclusive landscape of SCLC epigenetics and epidrugs that will help to improve SCLC outcomes.

Promoter CpG Density Predicts Downstream Gene Loss-of-Function Intolerance

The aggregation and joint analysis of large numbers of exome sequences has recently made it possible to derive estimates of intolerance to loss-of-function (LoF) variation for human genes. Here, we demonstrate strong and widespread coupling between genic LoF intolerance and promoter CpG density across the human genome. Genes downstream of the most CpG-rich promoters (top 10% CpG density) have a 67.2% probability of being highly LoF intolerant, using the LOEUF metric from gnomAD. This is in contrast to 7.4% of genes downstream of the most CpG-poor (bottom 10% CpG density) promoters. Combining promoter CpG density with exonic and promoter conservation explains 33.4% of the variation in LOEUF, and the contribution of CpG density exceeds the individual contributions of exonic and promoter conservation. We leverage this to train a simple and easily interpretable predictive model that outperforms other existing predictors and allows us to classify 1,760 genes-which are currently unascertained in gnomAD-as highly LoF intolerant or not. These predictions have the potential to aid in the interpretation of novel variants in the clinical setting. Moreover, our results reveal that high CpG density is not merely a generic feature of human promoters but is preferentially encountered at the promoters of the most selectively constrained genes, calling into question the prevailing view that CpG islands are not subject to selection.

Candesartan treatment enhances liposome penetration and anti-tumor effect via depletion of tumor stroma and normalization of tumor vessel

The poor penetration of nanoparticles in solid tumors has been a critical factor limiting the clinical benefits of nanomedicine. Therefore, we depleted the dense extracellular matrix (ECM) and normalized tumor vessels to enhance drug delivery and therapeutic efficacy. We used candesartan as an angiotensin system inhibitor, which reduced ECM content and facilitated "vascular normalization" by targeting the angiotensin-signaling axis, resulting in improved anti-cancer therapeutic effects. We also combined candesartan with PEGylated liposome-encapsulated zoledronic acid (ZOL) (PEG-ZOL-LPs) to assess how this affected anti-tumor therapy. Our findings indicated that the migration of 4T1 mouse breast cancer cells was inhibited by candesartan. Moreover, the ECM depletion (including collagen I and hyaluronan) by candesartan was achieved through the downregulation of TGF-β1 in vitro, consistent with in vivo results. Furthermore, treatment groups that received candesartan also had significantly decreased tumor vessel permeability and proportions of circulating endothelial progenitor cells (CEPCs) in the serum, which resulted in normalization of tumor vasculature and improved delivery of PEG-ZOL-LPs. Finally, the positive effect candesartan in terms of tumor growth was found not to have an impact of the efficacy of the PEG-ZOL-LPs treatment. This unexpected lack of effect of candesartan on the performance of PEG-ZOL-LPs would be due to dynamics of the effect of both treatments. It might be possible that a different protocol of administration could lead to a synergistic effect. Graphical abstract The schematic illustration showed that candesartan favored depletion of tumor stroma and tumor vascular normalization to improve the anti-cancer efficacy of PEG-ZOL-LPs.

Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans

De novo mutations (DNMs) significantly contribute to sporadic diseases, particularly in neuropsychiatric disorders. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) provide effective methods for detecting DNMs and prioritizing candidate genes. However, it remains a challenge for scientists, clinicians, and biologists to conveniently access and analyse data regarding DNMs and candidate genes from scattered publications. To fill the unmet need, we integrated 580 799 DNMs, including 30 060 coding DNMs detected by WES/WGS from 23 951 individuals across 24 phenotypes and prioritized a list of candidate genes with different degrees of statistical evidence, including 346 genes with false discovery rates <0.05. We then developed a database called Gene4Denovo (http://www.genemed.tech/gene4denovo/), which allowed these genetic data to be conveniently catalogued, searched, browsed, and analysed. In addition, Gene4Denovo integrated data from >60 genomic sources to provide comprehensive variant-level and gene-level annotation and information regarding the DNMs and candidate genes. Furthermore, Gene4Denovo provides end-users with limited bioinformatics skills to analyse their own genetic data, perform comprehensive annotation, and prioritize candidate genes using custom parameters. In conclusion, Gene4Denovo conveniently allows for the accelerated interpretation of DNM pathogenicity and the clinical implication of DNMs in humans.

EM-mosaic detects mosaic point mutations that contribute to congenital heart disease

BACKGROUND

The contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.

METHODS

We developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available.

RESULTS

EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.

CONCLUSIONS

We estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease

Non-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the sizes and number of enhancers linked to a gene reflect its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and they are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved a robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This solves a puzzle about why genes associated with human disease are depleted of cis-eQTLs (cis-expression quantitative trait loci), suggesting that this relationship might complicate gene identification in causal genome-wide association studies (GWASs) using eQTL information, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

Autism risk genes are evolutionarily ancient and maintain a unique feature landscape that echoes their function

Previous research on autism risk (ASD), developmental regulatory (DevReg), and central nervous system (CNS) genes suggests they tend to be large in size, enriched in nested repeats, and mutation intolerant. The relevance of these genomic features is intriguing yet poorly understood. In this study, we investigated the feature landscape of these gene groups to discover structural themes useful in interpreting their function, developmental patterns, and evolutionary history. ASD, DevReg, CNS, housekeeping, and whole genome control (WGC) groups were compiled using various resources. Multiple gene features of interest were extracted from NCBI/UCSC Bioinformatics. Residual variation intolerance scores, Exome Aggregation Consortium pLI scores, and copy number variation data from Decipher were used to estimate variation intolerance. Gene age and protein-protein interactions (PPI) were estimated using Ensembl and EBI Intact databases, respectively. Compared to WGC: ASD, DevReg, and CNS genes are longer, produce larger proteins, maintain greater numbers/density of conserved noncoding elements and transposable elements, produce more transcript variants, and are comparatively variation intolerant. After controlling for gene size, mutation tolerance, and clinical association, ASD genes still retain many of these same features. In addition, we also found that ASD genes that are extremely mutation intolerant have larger PPI networks. These data support many of the recent findings within the field of autism genetics but also expand our understanding of the evolution of these broad gene groups, their potential regulatory complexity, and the extent to which they interact with the cellular network. Autism Res 2019, 12: 860-869. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism risk genes are more ancient compared to other genes in the genome. As such, they exhibit physical features related to their age, including long gene and protein size and regulatory sequences that help to control gene expression. They share many of these same features with other genes that are expressed in the brain and/or are associated with prenatal development.

An interaction-based model for neuropsychiatric features of copy-number variants

Variably expressive copy-number variants (CNVs) are characterized by extensive phenotypic heterogeneity of neuropsychiatric phenotypes. Approaches to identify single causative genes for these phenotypes within each CNV have not been successful. Here, we posit using multiple lines of evidence, including pathogenicity metrics, functional assays of model organisms, and gene expression data, that multiple genes within each CNV region are likely responsible for the observed phenotypes. We propose that candidate genes within each region likely interact with each other through shared pathways to modulate the individual gene phenotypes, emphasizing the genetic complexity of CNV-associated neuropsychiatric features.