Genetic Disease Genes Research Articles

Current trends and research topics regarding organoids: A bibliometric analysis of global research from 2000 to 2023

The use of animal models for biological experiments is no longer sufficient for research related to human life and disease. The development of organ tissues has replaced animal models by mimicking the structure, function, development and homeostasis of natural organs. This provides more opportunities to study human diseases such as cancer, infectious diseases and genetic disorders. In this study, bibliometric methods were used to analyze organoid-related articles published over the last 20+ years to identify emerging trends and frontiers in organoid research. A total of 13,143 articles from 4125 institutions in 86 countries or regions were included in the analysis. The number of papers increased steadily over the 20-year period. The United States was the leading country in terms of number of papers and citations. Harvard Medical School had the highest number of papers published. Keyword analysis revealed research trends and focus areas such as organ tissues, stem cells, 3D culture and tissue engineering. In conclusion, this study used bibliometric and visualization methods to explore the field of organoid research and found that organ tissues are receiving increasing attention in areas such as cancer, drug discovery, personalized medicine, genetic disease modelling and gene repair, making them a current research hotspot and a future research trend.

Efficient repair of human genetic defect by CRISPR/Cas9-mediated interlocus gene conversion

Abstract DNA double-strand breaks (DSBs) induced by gene-editing tools are primarily repaired through non-homologous end joining (NHEJ) or homology-directed repair (HDR) using synthetic DNA templates. However, error-prone NHEJ may result in unexpected indels at the targeted site. For most genetic disorders, precise HDR correction using exogenous homologous sequence is ideal. But, the therapeutic application of HDR might be especially challenging given the requirement for the codelivery of exogenous DNA templates with toxicity into cells, and the low efficiency of HDR could also limit its clinical application. In this study, we efficiently repair pathogenic mutations in HBB coding regions of hematopoietic stem cells (HSCs) using CRISPR/Cas9-mediated gene conversion (CRISPR/GC) using the paralog gene HBD as the internal template. After transplantation, these edited HSCs successfully repopulate the hematopoietic system and generate erythroid cells with significantly reduced thalassemia propensity. Moreover, a range of pathogenic gene mutations causing β-thalassemia in HBB coding regions were effectively converted to normal wild-type sequences without exogenous DNA templates using CRISPR/GC. This highlights the promising potential of CRISPR/GC, independent of synthetic DNA templates, for genetic disease gene therapy.

DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.

The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as "gene prioritization," is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the "guilt by association" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in ZDHHC8, DGCR5, and CATG00000022283 among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at http://git.dml.ir/z_rahaie/DGP to address obstacles in existing gene prioritization studies identifying candidate genes.

Recent Advances in Proteinuric Kidney Disease/Nephrotic Syndrome: Lessons from Knockout/Transgenic Mouse Models.

Proteinuria is known to be associated with all-cause and cardiovascular mortality, and nephrotic syndrome is defined by the level of proteinuria and hypoalbuminemia. With advances in medicine, new causative genes for genetic kidney diseases are being discovered increasingly frequently. We reviewed articles on proteinuria/nephrotic syndrome, focal segmental glomerulosclerosis, membranous nephropathy, diabetic kidney disease/nephropathy, hypertension/nephrosclerosis, Alport syndrome, and rare diseases, which have been studied in mouse models. Significant progress has been made in understanding the genetics and pathophysiology of kidney diseases thanks to advances in science, but research in this area is ongoing. In the future, genetic analyses of patients with proteinuric kidney disease/nephrotic syndrome may ultimately lead to personalized treatment options.

End-to-end interpretable disease-gene association prediction.

Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between diseases and genes. Recently, deep learning methods have achieved tremendous success in identifying candidate genes for genetic diseases. The gene prediction problem can be modeled as a link prediction problem based on the features of nodes and edges of the gene-disease graph. However, most existing researches either build homogeneous networks based on one single data source or heterogeneous networks based on multi-source data, and artificially define meta-paths, so as to learn the network representation of diseases and genes. The former cannot make use of abundant multi-source heterogeneous information, while the latter needs domain knowledge and experience when defining meta-paths, and the accuracy of the model largely depends on the definition of meta-paths. To address the aforementioned challenges above bottlenecks, we propose an end-to-end disease-gene association prediction model with parallel graph transformer network (DGP-PGTN), which deeply integrates the heterogeneous information of diseases, genes, ontologies and phenotypes. DGP-PGTN can automatically and comprehensively capture the multiple latent interactions between diseases and genes, discover the causal relationship between them and is fully interpretable at the same time. We conduct comprehensive experiments and show that DGP-PGTN outperforms the state-of-the-art methods significantly on the task of disease-gene association prediction. Furthermore, DGP-PGTN can automatically learn the implicit relationship between diseases and genes without manually defining meta paths.

Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks.

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs' ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.

Network approaches for identification of human genetic disease genes

The identification of genes causing a genetic disease is still an important issue in the biomedical field because the list of disease genes is still incomplete while it determines the early diagnosis and treatment of fatal genetic diseases such as autism, cancer, drug resistance, and secondary hypertension. Genes associated with a particular disease or similar diseases tend to reside in the same region in a biological network and their location on the network can be predicted. Many network analysis methods have been proposed to solve this problem so far. This review first helps readers access and master the basic concepts of biological networks, disease genes, and their properties. Then, the main content is devoted to the analysis and evaluation of analytical methods recently used to find disease genes on two networks: protein-protein interaction (PPI) and cellular signaling network (CSN). We reported typical problems of identification of primary genes that cause genetic diseases and modern techniques that were widely used for solving those problems. For each technique, we also represented key algorithms so that the audience can exactly implement them for their experiments. In particular, we evaluated the performance of these algorithms in prediction of disease genes and suggested the context for their usage. Finally, the implications of the methods are discussed and some future research directions are proposed. Taken together, disease genes can often be identified from network data by two approaches: network-based methods and machine learning-based methods, and the network-based approach

Constructing human genetic disease database in Bangladesh

In the advent of technological revolution, genetic interaction has become a crucial aspect in the understanding of any diseases. Information on individual genetic variation is now used in translational genomics to implement precise diagnosis and personalized medicine. Finding causal genes for genetic diseases or exploring interaction of genes in diseases with genetic predisposition is the first step to getting insights into such diseases. The human genome project made a paradigm shift in thinking, especially in the developed countries affected by non-communicable diseases. Some cutting-edge technologies, including gene therapy and genome editing, hold the promise in better diagnosis and treatment of common to rare genetic diseases. Scientific communities are trying hard to accumulate all the genetic disease information from publicly available platforms. A genetic disease database of a country serves as a depository. Many developed and a few developing countries have already developed genetic disease databases, which could benefit early diagnosis and proper patient management. Unfortunately, Bangladesh is lagging behind in this aspect. It is imperative to develop genetic disease database in Bangladesh because of its large population of patients with genetic disease. In this review, we discuss the reasons for constructing a genetic disease database and how this database can help to fight against challenges arising from the genetic diseases in Bangladesh.

CRISPR Detection and Research on Screening Mutant Gene TTN of Moyamoya Disease Family Based on Whole Exome Sequencing.

Moyamoya disease (MMD) has a high incidence in Asian populations and demonstrates some degree of familial clustering. Whole-exome sequencing (WES) is useful in establishing key related genes in familial genetic diseases but is time-consuming and costly. Therefore, exploring a new method will be more effective for the diagnosis of MMD. We identified familial cohorts showing MMD susceptibility and performed WES on 5 affected individuals to identify susceptibility loci, which identified point mutation sites in the titin (TTN) gene (rs771533925, rs559712998 and rs72677250). Moreover, TTN mutations were not found in a cohort of 50 sporadic MMD cases. We also analyzed mutation frequencies and used bioinformatic predictions to reveal mutation harmfulness, functions and probabilities of disease correlation, the results showed that rs771533925 and rs72677250 were likely harmful mutations with GO analyses indicating the involvement of TTN in a variety of biological processes related to MMD etiology. CRISPR-Cas12a assays designed to detect TTN mutations provided results consistent with WES analysis, which was further confirmed by Sanger sequencing. This study recognized TTN as a new familial gene marker for moyamoya disease and moreover, demonstrated that CRISPR-Cas12a has the advantages of rapid detection, low cost and simple operation, and has broad prospects in the practical application of rapid detection of MMD mutation sites.

Chemical Genetic Validation of GWAS-derived Disease Loci

Background: Genome-wide association studies (GWAS) have opened the door to unprecedented large-scale identification of susceptibility loci for human diseases and traits. However, it is still a great challenge to validate these loci and elucidate how these sequence variants give rise to the genetic and phenotypic changes. Because many drug targets are genetic disease genes and the general drug mode of action (MoA, agonist or antagonist) is in line with the consequence of target gene mutations (loss-of-function (LOF) or gain-of-function (GOF)), here we propose a chemical genetic method to address the above issues of GWAS. Objective: This study intends to use chemical genetics information to validate GWAS-derived disease loci and interpret their underlying pathogenesis. Method: We conducted a comprehensive comparative analysis on GWAS data and drug/target information (chemical genetics information). Results: We have identified hundreds of GWAS-derived disease loci which are linked to drug target genes and have matched disease traits and drug indications. It is interesting to note that more than 40% genes have been recognized as disorder factors, indicating the potential power of chemical genetic validation. The pathogenesis of these loci was inferred by the corresponding drug MoA. Some inferences were supported by prior experimental observations; some were interpreted in terms of microRNA regulation, codon usage bias, and transcriptional regulation, in particular the transcription factor-binding affinity variation induced by disease-causing mutations. Conclusion: In summary, chemical genetics information is useful to validate GWAS-derived disease loci and to interpret their underlying pathogenesis as well, which has important implications not only in medical genetics but also in methodology evaluation of GWAS.

Polypeptide N-acetylgalactosaminyltransferase-Associated Phenotypes in Mammals.

Mucin-type O-glycosylation involves the attachment of glycans to an initial O-linked N-acetylgalactosamine (GalNAc) on serine and threonine residues on proteins. This process in mammals is initiated and regulated by a large family of 20 UDP-GalNAc: polypeptide N-acetylgalactosaminyltransferases (GalNAc-Ts) (EC 2.4.1.41). The enzymes are encoded by a large gene family (GALNTs). Two of these genes, GALNT2 and GALNT3, are known as monogenic autosomal recessive inherited disease genes with well characterized phenotypes, whereas a broad spectrum of phenotypes is associated with the remaining 18 genes. Until recently, the overlapping functionality of the 20 members of the enzyme family has hindered characterizing the specific biological roles of individual enzymes. However, recent evidence suggests that these enzymes do not have full functional redundancy and may serve specific purposes that are found in the different phenotypes described. Here, we summarize the current knowledge of GALNT and associated phenotypes.

Disease gene prediction with privileged information and heteroscedastic dropout.

MotivationRecently, machine learning models have achieved tremendous success in prioritizing candidate genes for genetic diseases. These models are able to accurately quantify the similarity among disease and genes based on the intuition that similar genes are more likely to be associated with similar diseases. However, the genetic features these methods rely on are often hard to collect due to high experimental cost and various other technical limitations. Existing solutions of this problem significantly increase the risk of overfitting and decrease the generalizability of the models.ResultsIn this work, we propose a graph neural network (GNN) version of the Learning under Privileged Information paradigm to predict new disease gene associations. Unlike previous gene prioritization approaches, our model does not require the genetic features to be the same at training and test stages. If a genetic feature is hard to measure and therefore missing at the test stage, our model could still efficiently incorporate its information during the training process. To implement this, we develop a Heteroscedastic Gaussian Dropout algorithm, where the dropout probability of the GNN model is determined by another GNN model with a mirrored GNN architecture. To evaluate our method, we compared our method with four state-of-the-art methods on the Online Mendelian Inheritance in Man dataset to prioritize candidate disease genes. Extensive evaluations show that our model could improve the prediction accuracy when all the features are available compared to other methods. More importantly, our model could make very accurate predictions when >90% of the features are missing at the test stage.Availability and implementationOur method is realized with Python 3.7 and Pytorch 1.5.0 and method and data are freely available at: https://github.com/juanshu30/Disease-Gene-Prioritization-with-Privileged-Information-and-Heteroscedastic-Dropout.

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

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 addressthis 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.

Channelopathies Are a Frequent Cause of Genetic Ataxias Associated with Cerebellar Atrophy.

Cerebellar atrophy is a nonspecific imaging finding observed in a number of neurological disorders. Genetic ataxias associated with cerebellar atrophy are a heterogeneous group of conditions, rendering the approach to diagnosis challenging. To define the spectrum of genetic ataxias associated with cerebellar atrophy in a Canadian cohort and the diagnostic yield of exome sequencing for this group of conditions. A total of 92 participants from 66 families with cerebellar atrophy were recruited for this multicenter prospective cohort study. Exome sequencing was performed for all participants between 2011 and 2017 as part of 1 of 2 national research programs, Finding of Rare Genetic Disease Genes or Enhanced Care for Rare Genetic Diseases in Canada. A genetic diagnosis was established in 53% of families (35/66). Pathogenic variants were found in 21 known genes, providing a diagnosis for 31/35 families (89%), and in 4 novel genes, accounting for 4/35 families (11%). Of the families, 31/66 (47%) remained without a genetic diagnosis. The most common diagnoses were channelopathies, which were established in 9/35 families (26%). Additional clinical findings provided useful clues to specific diagnoses. We report on the high frequency of channelopathies as a cause of genetic ataxias associated with cerebellar atrophy and the utility of exome sequencing for this group of conditions.

Epidemiology of Hereditary Diseases in the Karachay-Cherkess Republic.

Prevalence and allelic heterogeneity of hereditary diseases (HDs) could vary significantly in different human populations. Current knowledge of HDs distribution in populations is generally limited to either European data or analyses of isolated populations which were performed several decades ago. Thus, an acknowledgement of the HDs prevalence in different modern open populations is important. The study presents the results of a genetic epidemiological study of hereditary diseases (HDs) in the population of the Karachay-Cherkess Republic (KChR). Clinical screening of a population of 410,367 people for the identification of HDs was conducted. The population surveyed is represented by five major ethnic groups—Karachays, Russians, Circassians, Abazins, Nogais. The study of the populations was carried out in accordance with the proprietary protocol of genetic epidemiological examination designed to identify >3500 HDs easily diagnosed during clinical examination by qualified specialists specializing in the HDs. The protocol consists of the population genetic and medical genetic sections and is intended for comprehensive population analysis based on the data on different genetic systems, including the genes of HDs, DNA polymorphisms, demographic data collected during hospital-based survey. 8950 families (with 10,125 patients) with presumably the HDs were initially identified as a result of the survey and data collection through various sources of registration (from 1156 medical workers from 163 medical institutions). A diagnosis of hereditary pathology was established in 1849 patients (from 1295 families). Two hundred and thirty nosological forms were revealed (in 1857 patients from 1295 families). The total prevalence of HDs was 1:221. Differences between populations and ethnic groups were identified: 1:350 in Russians, 1:195 in Karachays, 1:199 in Circassians, 1:218 in Abazins, 1:135 in Nogais. Frequent diseases were determined, the presence of marked genetic heterogeneity was identified during the confirmatory DNA diagnosis. To explain the reasons for the differentiation of populations by load of HD, a correlation analysis was carried out between the FST (random inbreeding) in populations and HDs load values. This analysis showed genetic drift is probably one of the leading factors determining the differentiation of KChR populations by HDs load. For the first time, the size of the load and spectrum of HDs in the populations of the KChR are determined. We have demonstrated genetic drift to be one of the main factors of the population dynamics in studied population. A significant genetic heterogeneity of HDs, both allelic and locus, was revealed in KChR.

Clinical application of modern genetic analysis technology

In recent years, genetic analysis technology has been widely used in diagnosis of complex and difficult disease, neonatal and high-risk population disease screening, pre-implantation diagnosis, risk assessment of genetic disease recurrence and gene therapy.Clinicians need to better understand the purpose and timing of using genetic analysis technology, only understand the advantages and limitations of these technologies, the genetic analysis technology can be truly applied to clinical practice, it is helpful to improve the ability and level of diagnosis and treatment of here-ditary diseases. Key words: Genetic analysis; Gene sequencing; Genetic variation; Gene mutation

B.06 Whole exome sequencing in genetic ataxias associated with cerebellar atrophy: the Canadian experience

Background: Cerebellar atrophy is characterized by loss of cerebellar tissue, with evidence on brain imaging of enlarged interfolial spaces compared to the foliae. Genetic ataxias associated with cerebellar atrophy are a heterogeneous group of disorders. We investigated the prevalence in Canada and the diagnostic yield of whole exome sequencing (WES) for this group of conditions. Methods: Between 2011 and 2017, WES was performed in 91 participants with cerebellar atrophy as part of one of two national research programs, Finding of Rare Genetic Disease Genes (FORGE) or Enhanced Care for Rare Genetic Diseases in Canada (Care4Rare). Results: A genetic diagnosis was established in 58% of cases (53/91). Pathogenic variants were found in 24 known genes, providing a diagnosis for 46/53 participants (87%), and in four novel genes, accounting for 7/53 cases (13%). 38/91 cases (42%) remained unsolved. The most common diagnoses were channelopathies in 12/53 patients (23%) and mitochondrial disorders in 9/53 (17%). Inheritance was autosomal recessive in the majority of cases. Additional clinical findings provided useful clues to some of the diagnoses. Conclusions: This is the first report on the prevalence of genetic ataxias associated with cerebellar atrophy in Canada, and the utility of WES for this group of conditions.

Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates.

Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

Subcellular localization of mutated β-catenins with different incidences of cis-peptide bonds at the Xaa246-P247 site in HepG2 cells.

Mutations may ultimately change the local conformation of proteins; however, little attention has been paid to alterations in protein function caused by the incidence of cis-peptide bonds (ICPB) in mammalian cells. In this study, a statistical approach, coimmunoprecipitation, and immunofluorescence staining have been used to confirm that S246→Y and S246→W missense mutations, which help increase the ICPB in Xaa246-P247 (Xaa is any amino acid) in human β-catenin, can reduce the interactions between β-catenin and adenomatous polyposis coli (APC) and between β-catenin and Ca2+-dependent cell adhesion molecule family in epithelial tissue (E-cadherin), eventually leading to increased nuclear migration of β-catenin in the HepG2 cell line (an immortalized cell line consisting of human liver carcinoma cells). Conversely, S246→L and S246→M missense mutations, which reduce the ICPB in Xaa246-P247 in human β-catenin, can enhance interactions between β-catenin and APC and between β-catenin and E-cadherin, leading to decreased nuclear migration of β-catenin. These results not only indicate that a change in the ICPB may be an important cause of functional protein changes but also provide a new basis for the study of genetic disease prediction, gene diagnosis, individualized treatment, and protein modification at the gene level for clinicians and other professionals.—Yu, S., Zhang, Y., Wu, Y., Yang, H., Chen, Y., Yang, Y., Zhang, Z. Subcellular localization of mutated β-catenins with different incidences of cis-peptide bonds at the Xaa246-P247 site in HepG2 cells.

Xenopus: An Undervalued Model Organism to Study and Model Human Genetic Disease

The function of normal and defective candidate genes for human genetic diseases, which are rapidly being identified in large numbers by human geneticists and the biomedical community at large, will be best studied in relevant and predictive model organisms that allow high-speed verification, analysis of underlying developmental, cellular and molecular mechanisms, and establishment of disease models to test therapeutic options. We describe and discuss the pros and cons of the frog Xenopus, which has been extensively used to uncover developmental mechanisms in the past, but which is being underutilized as a biomedical model. We argue that Xenopus complements the more commonly used mouse and zebrafish as a time- and cost-efficient animal model to study human disease alleles and mechanisms.