Role of duplicate genes in determining the tissue-selectivity of hereditary diseases

Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

BACKGROUND

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

RESULTS

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.

CONCLUSIONS

Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Predicting gene knockout effects from expression data

BACKGROUND

The study of gene essentiality, which measures the importance of a gene for cell division and survival, is used for the identification of cancer drug targets and understanding of tissue-specific manifestation of genetic conditions. In this work, we analyze essentiality and gene expression data from over 900 cancer lines from the DepMap project to create predictive models of gene essentiality.

METHODS

We developed machine learning algorithms to identify those genes whose essentiality levels are explained by the expression of a small set of "modifier genes". To identify these gene sets, we developed an ensemble of statistical tests capturing linear and non-linear dependencies. We trained several regression models predicting the essentiality of each target gene, and used an automated model selection procedure to identify the optimal model and hyperparameters. Overall, we examined linear models, gradient boosted trees, Gaussian process regression models, and deep learning networks.

RESULTS

We identified nearly 3000 genes for which we accurately predict essentiality using gene expression data of a small set of modifier genes. We show that both in the number of genes we successfully make predictions for, as well as in the prediction accuracy, our model outperforms current state-of-the-art works.

CONCLUSIONS

Our modeling framework avoids overfitting by identifying the small set of modifier genes, which are of clinical and genetic importance, and ignores the expression of noisy and irrelevant genes. Doing so improves the accuracy of essentiality prediction in various conditions and provides interpretable models. Overall, we present an accurate computational approach, as well as interpretable modeling of essentiality in a wide range of cellular conditions, thus contributing to a better understanding of the molecular mechanisms that govern tissue-specific effects of genetic disease and cancer.

Paralog Explorer: A resource for mining information about paralogs in common research organisms

Paralogs are genes which arose via gene duplication, and when such paralogs retain overlapping or redundant function, this poses a challenge to functional genetics research. Recent technological advancements have made it possible to systematically probe gene function for redundant genes using dual or multiplex gene perturbation, and there is a need for a simple bioinformatic tool to identify putative paralogs of a gene(s) of interest. We have developed Paralog Explorer (https://www.flyrnai.org/tools/paralogs/), an online resource that allows researchers to quickly and accurately identify candidate paralogous genes in the genomes of the model organisms D. melanogaster, C. elegans, D. rerio, M. musculus, and H. sapiens. Paralog Explorer deploys an effective between-species ortholog prediction software, DIOPT, to analyze within-species paralogs. Paralog Explorer allows users to identify candidate paralogs, and to navigate relevant databases regarding gene co-expression, protein-protein and genetic interaction, as well as gene ontology and phenotype annotations. Altogether, this tool extends the value of current ortholog prediction resources by providing sophisticated features useful for identification and study of paralogous genes.

Identification of genetic mechanisms for tissue-specific genetic effects based on CRISPR screens

A major challenge in genetic studies of complex diseases is to determine how the action of risk genes is restricted to a tissue or cell type. Here, we investigate tissue specificity of gene action using CRISPR screens from 786 cancer cell lines originating from 24 tissues. We find that the expression pattern of the gene across tissues explains only a minority of cases of tissue-specificity (9%), while gene amplification and the expression levels of paralogs account for 39.5% and 15.5%, respectively. In addition, the transfer of small molecules to mutant cells explains tissue-specific gene action in blood. The tissue-specific genes we found are not specific just for human cancer cell lines: we found that the tissue-specific genes are intolerant to functional mutations in the human population and are associated with human diseases more than genes that are essential across all cell types. Our findings offer important insights into genetic mechanisms for tissue specificity of human diseases.

The Organ-Disease Annotations (ODiseA) database of hereditary diseases and inflicted tissues

Hereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable. Well-established resources, such as the Online Mendelian Inheritance in Man (OMIM) database and Human Phenotype Ontology (HPO), report on a spectrum of disease manifestations, yet do not highlight the main inflicted tissues. The Organ-Disease Annotations (ODiseA) database contains 4,357 thoroughly-curated annotations for 2,181 hereditary diseases and 45 inflicted tissues. Additionally, ODiseA reports 692 annotations of 635 diseases and the pathogenic tissues where they emerge. ODiseA can be queried by disease, disease gene, or inflicted tissue. Owing to its expansive, high-quality annotations, ODiseA serves as a valuable and unique tool for biomedical and computational researchers studying genotype-phenotype relationships of hereditary diseases. ODiseA is available at https://netbio.bgu.ac.il/odisea.

SEC23A rescues SEC23B-deficient congenital dyserythropoietic anemia type II

Congenital dyserythropoietic anemia type II (CDAII) results from loss-of-function mutations in SEC23B. In contrast to humans, SEC23B-deficient mice deletion do not exhibit CDAII but die perinatally with pancreatic degeneration. Here, we demonstrate that expression of the full SEC23A protein (the SEC23B paralog) from the endogenous regulatory elements of Sec23b completely rescues the SEC23B-deficient mouse phenotype. Consistent with these data, while mice with erythroid-specific deletion of either Sec23a or Sec23b do not exhibit CDAII, we now show that mice with erythroid-specific deletion of all four Sec23 alleles die in mid-embryogenesis with features of CDAII and that mice with deletion of three Sec23 alleles exhibit a milder erythroid defect. To test whether the functional overlap between the SEC23 paralogs is conserved in human erythroid cells, we generated SEC23B-deficient HUDEP-2 cells. Upon differentiation, these cells exhibited features of CDAII, which were rescued by increased expression of SEC23A, suggesting a novel therapeutic strategy for CDAII.

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Through computational analyses of large-scale tissue transcriptomes, we unveil that the chaperone system is composed of core elements that are uniformly expressed across tissues, and variable elements that are differentially expressed to fit with tissue-specific requirements. We demonstrate via a proteomic analysis that the muscle-specific signature is functional and conserved. Core chaperones are significantly more abundant across tissues and more important for cell survival than variable chaperones. Together with variable chaperones, they form tissue-specific functional networks. Analysis of human organ development and aging brain transcriptomes reveals that these functional networks are established in development and decline with age. In this work, we expand the known functional organization of de novo versus stress-inducible eukaryotic chaperones into a layered core-variable architecture in multi-cellular organisms.

Dosage-sensitive molecular mechanisms are associated with the tissue-specificity of traits and diseases

Hereditary diseases and complex traits often manifest in specific tissues, whereas their causal genes are expressed in many tissues that remain unaffected. Among the mechanisms that have been suggested for this enigmatic phenomenon is dosage-sensitive compensation by paralogs of causal genes. Accordingly, tissue-selectivity stems from dosage imbalance between causal genes and paralogs that occurs particularly in disease-susceptible tissues. Here, we used a large-scale dataset of thousands of tissue transcriptomes and applied a linear mixed model (LMM) framework to assess this and other dosage-sensitive mechanisms. LMM analysis of 382 hereditary diseases consistently showed evidence for dosage-sensitive compensation by paralogs across diseases subsets and susceptible tissues. LMM analysis of 135 candidate genes that are strongly associated with 16 tissue-selective complex traits revealed a similar tendency among half of the trait-associated genes. This suggests that dosage-sensitive compensation by paralogs affects the tissue-selectivity of complex traits, and can be used to illuminate candidate genes' modes of action. Next, we applied LMM to analyze dosage imbalance between causal genes and three classes of genetic modifiers, including regulatory micro-RNAs, pseudogenes, and genetic interactors. Our results propose modifiers as a fundamental axis in tissue-selectivity of diseases and traits, and demonstrates the power of LMM as a statistical framework for discovering treatment avenues.

Developmental Gene Expression Differences between Humans and Mammalian Models

Identifying the molecular programs underlying human organ development and how they differ from model species is key for understanding human health and disease. Developmental gene expression profiles provide a window into the genes underlying organ development and a direct means to compare them across species. We use a transcriptomic resource covering the development of seven organs to characterize the temporal profiles of human genes associated with distinct disease classes and to determine, for each human gene, the similarity of its spatiotemporal expression with its orthologs in rhesus macaque, mouse, rat, and rabbit. We find clear associations between spatiotemporal profiles and the phenotypic manifestations of diseases. We also find that half of human genes differ from their mouse orthologs in their temporal trajectories in at least one of the organs. These include more than 200 genes associated with brain, heart, and liver disease for which mouse models should undergo extra scrutiny.

ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues

ResponseNet v.3 is an enhanced version of ResponseNet, a web server that is designed to highlight signaling and regulatory pathways connecting user-defined proteins and genes by using the ResponseNet network optimization approach (http://netbio.bgu.ac.il/respnet). Users run ResponseNet by defining source and target sets of proteins, genes and/or microRNAs, and by specifying a molecular interaction network (interactome). The output of ResponseNet is a sparse, high-probability interactome subnetwork that connects the two sets, thereby revealing additional molecules and interactions that are involved in the studied condition. In recent years, massive efforts were invested in profiling the transcriptomes of human tissues, enabling the inference of human tissue interactomes. ResponseNet v.3 expands ResponseNet2.0 by harnessing ∼11,600 RNA-sequenced human tissue profiles made available by the Genotype-Tissue Expression consortium, to support context-specific analysis of 44 human tissues. Thus, ResponseNet v.3 allows users to illuminate the signaling and regulatory pathways potentially active in the context of a specific tissue, and to compare them with active pathways in other tissues. In the era of precision medicine, such analyses open the door for tissue- and patient-specific analyses of pathways and diseases.

Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes

Motivation

Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking.

Results

Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases.

Summary

Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.

Availability and implementation

Datasets are available as part of the Supplementary data.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of pathogenic variant enriched regions across genes and gene families

Missense variant interpretation is challenging. Essential regions for protein function are conserved among gene-family members, and genetic variants within these regions are potentially more likely to confer risk to disease. Here, we generated 2871 gene-family protein sequence alignments involving 9990 genes and performed missense variant burden analyses to identify novel essential protein regions. We mapped 2,219,811 variants from the general population into these alignments and compared their distribution with 76,153 missense variants from patients. With this gene-family approach, we identified 465 regions enriched for patient variants spanning 41,463 amino acids in 1252 genes. As a comparison, by testing the same genes individually, we identified fewer patient variant enriched regions, involving only 2639 amino acids and 215 genes. Next, we selected de novo variants from 6753 patients with neurodevelopmental disorders and 1911 unaffected siblings and observed an 8.33-fold enrichment of patient variants in our identified regions (95% C.I. = 3.90-Inf, P-value = 2.72 × 10-11). By using the complete ClinVar variant set, we found that missense variants inside the identified regions are 106-fold more likely to be classified as pathogenic in comparison to benign classification (OR = 106.15, 95% C.I = 70.66-Inf, P-value < 2.2 × 10-16). All pathogenic variant enriched regions (PERs) identified are available online through "PER viewer," a user-friendly online platform for interactive data mining, visualization, and download. In summary, our gene-family burden analysis approach identified novel PERs in protein sequences. This annotation can empower variant interpretation.

Bi-allelic Variants in IQSEC1 Cause Intellectual Disability, Developmental Delay, and Short Stature

We report two consanguineous families with probands that exhibit intellectual disability, developmental delay, short stature, aphasia, and hypotonia in which homozygous non-synonymous variants were identified in IQSEC1 (GenBank: NM_001134382.3). In a Pakistani family, the IQSEC1 segregating variant is c.1028C>T (p.Thr343Met), while in a Saudi Arabian family the variant is c.962G>A (p.Arg321Gln). IQSEC1-3 encode guanine nucleotide exchange factors for the small GTPase ARF6 and their loss affects a variety of actin-dependent cellular processes, including AMPA receptor trafficking at synapses. The ortholog of IQSECs in the fly is schizo and its loss affects growth cone guidance at the midline in the CNS, also an actin-dependent process. Overexpression of the reference IQSEC1 cDNA in wild-type flies is lethal, but overexpression of the two variant IQSEC1 cDNAs did not affect viability. Loss of schizo caused embryonic lethality that could be rescued to 2^nd instar larvae by moderate expression of the human reference cDNA. However, the p.Arg321Gln and p.Thr343Met variants failed to rescue embryonic lethality. These data indicate that the variants behave as loss-of-function mutations. We also show that schizo in photoreceptors is required for phototransduction. Finally, mice with a conditional Iqsec1 deletion in cortical neurons exhibited an increased density of dendritic spines with an immature morphology. The phenotypic similarity of the affecteds and the functional experiments in flies and mice indicate that IQSEC1 variants are the cause of a recessive disease with intellectual disability, developmental delay, and short stature, and that axonal guidance and dendritic projection defects as well as dendritic spine dysgenesis may underlie disease pathogenesis.

Paralog dependency indirectly affects the robustness of human cells

The protective redundancy of paralogous genes partly relies on the fact that they carry their functions independently. However, a significant fraction of paralogous proteins may form functionally dependent pairs, for instance, through heteromerization. As a consequence, one could expect these heteromeric paralogs to be less protective against deleterious mutations. To test this hypothesis, we examined the robustness landscape of gene loss-of-function by CRISPR-Cas9 in more than 450 human cell lines. This landscape shows regions of greater deleteriousness to gene inactivation as a function of key paralog properties. Heteromeric paralogs are more likely to occupy such regions owing to their high expression and large number of protein-protein interaction partners. Further investigation revealed that heteromers may also be under stricter dosage balance, which may also contribute to the higher deleteriousness upon gene inactivation. Finally, we suggest that physical dependency may contribute to the deleteriousness upon loss-of-function as revealed by the correlation between the strength of interactions between paralogs and their higher deleteriousness upon loss of function.

Looking to the future of zebrafish as a model to understand the genetic basis of eye disease

In this brief commentary, we provide some of our thoughts and opinions on the current and future use of zebrafish to model human eye disease, dissect pathological progression and advance in our understanding of the genetic bases of microphthalmia, andophthalmia and coloboma (MAC) in humans. We provide some background on eye formation in fish and conservation and divergence across vertebrates in this process, discuss different approaches for manipulating gene function and speculate on future research areas where we think research using fish may prove to be particularly effective.

Paralog Studies Augment Gene Discovery: DDX and DHX Genes

Members of a paralogous gene family in which variation in one gene is known to cause disease are eight times more likely to also be associated with human disease. Recent studies have elucidated DHX30 and DDX3X as genes for which pathogenic variant alleles are involved in neurodevelopmental disorders. We hypothesized that variants in paralogous genes encoding members of the DExD/H-box RNA helicase superfamily might also underlie developmental delay and/or intellectual disability (DD and/or ID) disease phenotypes. Here we describe 15 unrelated individuals who have DD and/or ID, central nervous system (CNS) dysfunction, vertebral anomalies, and dysmorphic features and were found to have probably damaging variants in DExD/H-box RNA helicase genes. In addition, these individuals exhibit a variety of other tissue and organ system involvement including ocular, outer ear, hearing, cardiac, and kidney tissues. Five individuals with homozygous (one), compound-heterozygous (two), or de novo (two) missense variants in DHX37 were identified by exome sequencing. We identified ten total individuals with missense variants in three other DDX/DHX paralogs: DHX16 (four individuals), DDX54 (three individuals), and DHX34 (three individuals). Most identified variants are rare, predicted to be damaging, and occur at conserved amino acid residues. Taken together, these 15 individuals implicate the DExD/H-box helicases in both dominantly and recessively inherited neurodevelopmental phenotypes and highlight the potential for more than one disease mechanism underlying these disorders.

The first Autumn School on Proteostasis: from molecular mechanisms to organismal consequences

The first Autumn School on Proteostasis was held at the Mediterranean Institute for Life Sciences (MedILS) in Split, Croatia, from November 12th-16th, 2018, bringing together 44 graduate students and postdoctoral fellows and 22 principal investigators from around the world. This meeting was geared towards providing students with an in-depth understanding of the field of proteostasis, with the aim of broadening their perspectives of the field. Session topics covered multiple aspects of cellular and organismal proteostasis, including fundamental principles, responses to heat shock, aging and disease, and protein folding, misfolding, and degradation. The structure of the meeting and the restricted number of participants afforded the students and postdocs the opportunity to interact with principal investigators to discuss not only their latest research, but also their career prospects and progression in a close, supportive environment.

Compensatory growth renders Tcf7l1a dispensable for eye formation despite its requirement in eye field specification

The vertebrate eye originates from the eye field, a domain of cells specified by a small number of transcription factors. In this study, we show that Tcf7l1a is one such transcription factor that acts cell-autonomously to specify the eye field in zebrafish. Despite the much-reduced eye field in tcf7l1a mutants, these fish develop normal eyes revealing a striking ability of the eye to recover from a severe early phenotype. This robustness is not mediated through genetic compensation at neural plate stage; instead, the smaller optic vesicle of tcf7l1a mutants shows delayed neurogenesis and continues to grow until it achieves approximately normal size. Although the developing eye is robust to the lack of Tcf7l1a function, it is sensitised to the effects of additional mutations. In support of this, a forward genetic screen identified mutations in hesx1, cct5 and gdf6a, which give synthetically enhanced eye specification or growth phenotypes when in combination with the tcf7l1a mutation.

SEC31A mutation affects ER homeostasis, causing a neurological syndrome

BACKGROUND

Consanguineous kindred presented with an autosomal recessive syndrome of intrauterine growth retardation, marked developmental delay, spastic quadriplegia with profound contractures, pseudobulbar palsy with recurrent aspirations, epilepsy, dysmorphism, neurosensory deafness and optic nerve atrophy with no eye fixation. Affected individuals died by the age of 4. Brain MRI demonstrated microcephaly, semilobar holoprosencephaly and agenesis of corpus callosum. We aimed at elucidating the molecular basis of this disease.

METHODS

Genome-wide linkage analysis combined with whole exome sequencing were performed to identify disease-causing variants. Functional consequences were investigated in fruit flies null mutant for the Drosophila SEC31A orthologue. SEC31A knockout SH-SY5Y and HEK293T cell-lines were generated using CRISPR/Cas9 and studied through qRT-PCR, immunoblotting and viability assays.

RESULTS

Through genetic studies, we identified a disease-associated homozygous nonsense mutation in SEC31A. We demonstrate that SEC31A is ubiquitously expressed, and that the mutation triggers nonsense-mediated decay of its transcript, comprising a practical null mutation. Similar to the human disease phenotype, knockdown SEC31A flies had defective brains and early lethality. Moreover, in line with SEC31A encoding one of the two coating layers comprising the Coat protein complex II (COP-II) complex, trafficking newly synthesised proteins from the endoplasmic reticulum (ER) to the Golgi, CRISPR/Cas9-mediated SEC31A null mutant cells demonstrated reduced viability through upregulation of ER-stress pathways.

CONCLUSION

We demonstrate through human and Drosophila genetic and in vitro molecular studies, that a severe neurological syndrome is caused by a null mutation in SEC31A, reducing cell viability through enhanced ER-stress response, in line with SEC31A's role in the COP-II complex.