Human monogenic disorders — a source of novel drug targets

Shared etiology of Mendelian and complex disease supports drug discovery

BACKGROUND

Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.

METHODS

In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.

RESULTS

Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.

CONCLUSIONS

Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Shared etiology of Mendelian and complex disease supports drug discovery

Background

Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.

Methods

In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.

Results

Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.

Conclusions

Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

From target discovery to clinical drug development with human genetics

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records

BACKGROUND

The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery.

METHODS

We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR).

FINDINGS

After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25).

INTERPRETATION

Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions.

FUNDING

National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

A Drug Discovery Approach for an Effective Pain Therapy through Selective Inhibition of Nav1.7

Chronic pain is a widespread disorder affecting millions of people and is insufficiently addressed by current classes of analgesics due to significant long-term or high dosage side effects. A promising approach that was recently proposed involves the systemic inhibition of the voltage-gated sodium channel Nav1.7, capable of cancelling pain perception completely. Notwithstanding numerous attempts, currently no drugs have been approved for the inhibition of Nav1.7. The task is complicated by the difficulty of creating a selective drug for Nav1.7, and avoiding binding to the many human paralogs performing fundamental physiological functions. In our work, we obtained a promising set of ligands with up to 5-40-fold selectivity and reaching 5.2 nanomolar binding affinity by employing a proper treatment of the problem and an innovative differential in silico screening procedure to discriminate for affinity and selectivity against the Nav paralogs. The absorption, distribution, metabolism, and excretion (ADME) properties of our top-scoring ligands were also evaluated, with good to excellent results. Additionally, our study revealed that the top-scoring ligand is a stereoisomer of an already-approved drug. These facts could reduce the time required to bring a new effective and selective Nav1.7 inhibitor to the market.

Anti-Selection & Genetic Testing in Insurance: An Interdisciplinary Perspective

Anti-selection occurs when information asymmetry exists between insurers and applicants. When an applicant knows they are at high risk of loss, but the insurer does not, the applicant may try to use this knowledge differential to secure insurance at a lower premium that does not match risk.

Assembling Disease Networks From Causal Interaction Resources

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

Immunity and Genetics at the Revolving Doors of Diagnostics in Primary Immunodeficiencies

Primary immunodeficiencies (PIDs) are a large and growing group of disorders commonly associated with recurrent infections. However, nowadays, we know that PIDs often carry with them consequences related to organ or hematologic autoimmunity, autoinflammation, and lymphoproliferation in addition to simple susceptibility to pathogens. Alongside this conceptual development, there has been technical advancement, given by the new but already established diagnostic possibilities offered by new genetic testing (e.g., next-generation sequencing). Nevertheless, there is also the need to understand the large number of gene variants detected with these powerful methods. That means advancing beyond genetic results and resorting to the clinical phenotype and to immunological or alternative molecular tests that allow us to prove the causative role of a genetic variant of uncertain significance and/or better define the underlying pathophysiological mechanism. Furthermore, because of the rapid availability of results, laboratory immunoassays are still critical to diagnosing many PIDs, even in screening settings. Fundamental is the integration between different specialties and the development of multidisciplinary and flexible diagnostic workflows. This paper aims to tell these evolving aspects of immunodeficiencies, which are summarized in five key messages, through introducing and exemplifying five clinical cases, focusing on diseases that could benefit targeted therapy.

Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia

High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions.

Monogenic Immune Diseases Provide Insights Into the Mechanisms and Treatment of Chronic Graft-Versus-Host Disease

Chronic graft-versus-host disease (GvHD) has become a leading cause of morbidity and mortality following allogeneic hematopoietic stem cell transplantation (HSCT) and can burden patients with devastating and lifelong health effects. Our understanding of the pathogenic mechanisms underlying chronic GvHD remains incomplete and this lack of understanding is reflected by lack of clear therapeutic approaches to steroid refractory disease. Observations predominantly from mouse models and human correlative studies currently support a three phase model for the initiation and development of chronic GvHD: 1) early inflammation and tissue damage triggers the innate immune system. This leads to inflammatory cytokine/chemokine patterns that recruit effector immune cell populations; 2) chronic inflammation causes the loss of central and peripheral tolerance mechanisms leading to emergence of pathogenic B and T cell populations that promote autoimmune and alloimmune reactions; 3) the dysregulated immunity causes altered macrophage polarization, aberrant tissue repair leading to scarring and end organ fibrosis. This model has led to the evaluation of many new therapies aimed at limiting inflammation, targeting dysregulated signaling pathways and restoring tolerance mechanisms. However, chronic GvHD is a multisystem disease with complex clinical phenotypes and it remains unclear as to which cluster of patients will respond best to specific therapeutic strategies. However, it is possible to gain novel insights from immune-related monogenic diseases. These diseases either share common clinical manifestations, replicate steps from the three phase chronic GvHD model or serve as surrogates for perfectly targeted drugs being investigated in chronic GvHD therapy. In this review, we will summarize the evidence from these monogenic immune related diseases that provide insight into pathogenic pathways in chronic GvHD, rationales for current therapies and novel directions for future drug discovery.

Multiparametric Assays for Accelerating Early Drug Discovery

Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.

Applications of Functional Genomics for Drug Discovery

Many diseases, such as diabetes, autoimmune diseases, cancer, and neurological disorders, are caused by a dysregulation of a complex interplay of genes. Genome-wide association studies have identified thousands of disease-linked polymorphisms in the human population. However, detailing the causative gene expression or functional changes underlying those associations has been elusive in many cases. Functional genomics is an emerging field of research that aims to deconvolute the link between genotype and phenotype by making use of large -omic data sets and next-generation gene and epigenome editing tools to perturb genes of interest. Here we review how functional genomic tools can be used to better understand the biological interplay between genes, improve disease modeling, and identify novel drug targets. Incorporation of functional genomic capabilities into conventional drug development pipelines is predicted to expedite the development of first-in-class therapeutics.

Drug screening for human genetic diseases using iPSC models

Induced pluripotent stem cells (iPSCs) enable the generation of previously unattainable, scalable quantities of disease-relevant tissues from patients suffering from essentially any genetic disorder. This cellular material has proven instrumental for drug screening efforts on these disorders, and has facilitated the identification of novel therapeutics for patients. Here we will review the foundational technologies that have enabled iPSCs, the power and limitations of iPSC-based compound screens along with screening guidelines, and recent examples of screening efforts. Additionally we will provide a brief commentary on the future scientific roadmap using pluripotent- and 3D organoid-based, combinatorial approaches.

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).

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

DrPOCS: Drug Repositioning Based on Projection Onto Convex Sets

Drug repositioning, i.e., identifying new indications for known drugs, has attracted a lot of attentions recently and is becoming an effective strategy in drug development. In literature, several computational approaches have been proposed to identify potential indications of old drugs based on various types of data sources. In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS). With the integration of drug structure and disease phenotype information, DrPOCS predicts potential associations between drugs and diseases with matrix completion. Benchmarking results demonstrate that our proposed approach outperforms popular existing approaches with high accuracy. In addition, a number of novel predicted indications are validated with various types of evidences, indicating the predictive power of our proposed approach.

Autozygome and high throughput confirmation of disease genes candidacy

Purpose

Establishing links between Mendelian phenotypes and genes enables the proper interpretation of variants therein. Autozygome, a rich source of homozygous variants, has been successfully utilized for the high throughput identification of novel autosomal recessive disease genes. Here, we highlight the utility of the autozygome for the high throughput confirmation of previously published tentative links to diseases.

Methods

Autozygome and exome analysis of patients with suspected Mendelian phenotypes. All variants were classified according to the American College of Medical Genetics and Genomics guidelines.

Results

We highlight 30 published candidate genes (ACTL6B, ADAM22, AGTPBP1, APC, C12orf4, C3orf17 (NEPRO), CENPF, CNPY3, COL27A1, DMBX1, FUT8, GOLGA2, KIAA0556, LENG8, MCIDAS, MTMR9, MYH11, QRSL1, RUBCN, SLC25A42, SLC9A1, TBXT, TFG, THUMPD1, TRAF3IP2, UFC1, UFM1, WDR81, XRCC2, ZAK) in which we identified homozygous likely deleterious variants in patients with compatible phenotypes. We also identified homozygous likely deleterious variants in 18 published candidate genes (ABCA2, ARL6IP1, ATP8A2, CDK9, CNKSR1, DGAT1, DMXL2, GEMIN4, HCN2, HCRT, MYO9A, PARS2, PLOD3, PREPL, SCLT1, STX3, TXNRD2, WIPI2) although the associated phenotypes are sufficiently different from the original reports that they represent phenotypic expansion or potentially distinct allelic disorders.

Conclusions

Our results should facilitate the timely relabeling of these candidate disease genes in relevant databases to improve the yield of clinical genomic sequencing.

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.

Evolutionary and genetic features of drug targets

In the modern drug discovery pipeline, identification of novel drug targets is a critical step. Despite rapid progress in developing biomedical techniques, it is still a great challenge to find promising new targets from the ample space of human genes. This fact is partially responsible for the situation of "more investments, fewer drugs" in the pharmaceutical industry. A series of recent researches revealed that successfully targeted genes share some common evolutionary and genetic features, which means that the knowledge accumulated in modern evolutionary biology and genetics is very helpful to identify potential drug targets and to find new drugs as well. In this article, we comprehensively summarize the links between human drug targets and genetic diseases and their evolutionary origins, with an attempt to introduce these novel concepts and their medical implications to the biomedical community.

A genomics-based systems approach towards drug repositioning for rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data.

We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA.

Results

Our algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets (“direct genetics-based” approach), our algorithm (“indirect genetics-based”) achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01).

Conclusions

In summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.

Classical Galactosaemia and CDG, the N-Glycosylation Interface. A Review

Classical galactosaemia is a rare disorder of carbohydrate metabolism caused by galactose-1-phosphate uridyltransferase (GALT) deficiency (EC 2.7.7.12). The disease is life threatening if left untreated in neonates and the only available treatment option is a long-term galactose restricted diet. While this is lifesaving in the neonate, complications persist in treated individuals, and the cause of these, despite early initiation of treatment, and shared GALT genotypes remain poorly understood. Systemic abnormal glycosylation has been proposed to contribute substantially to the ongoing pathophysiology. The gross N-glycosylation assembly defects observed in the untreated neonate correct over time with treatment. However, N-glycosylation processing defects persist in treated children and adults.Congenital disorders of glycosylation (CDG) are a large group of over 100 inherited disorders affecting largely N- and O-glycosylation.In this review, we compare the clinical features observed in galactosaemia with a number of predominant CDG conditions.We also summarize the N-glycosylation abnormalities, which we have described in galactosaemia adult and paediatric patients, using an automated high-throughput HILIC-UPLC analysis of galactose incorporation into serum IgG with analysis of the corresponding N-glycan gene expression patterns and the affected pathways.

The necessity for in vivo functional analysis in human medical genetics

Approximately 50% of all congenital anomalies cannot be linked to any specific genetic etiology, but in recent years cost effective high throughput sequencing has emerged as an efficient strategy for identifying single nucleotide polymorphisms (SNPs) associated with disease. However, in many cases there is not enough evidence to determine if these SNPs underlie disease. To bridge this gap in our understanding advances in functional analyses are warranted. Several preclinical model systems are currently being utilized to provide such evidence, including the advantageous zebrafish embryo. While every system exhibits disadvantages and caveats, a new era of multidisciplinary research has evolved, which uses a broad spectrum of functional analysis tools. This approach will make it possible to identify potential therapeutic targets for both common and rare human disorders.

A Call for Systematic Research on Solute Carriers

Solute carrier (SLC) membrane transport proteins control essential physiological functions, including nutrient uptake, ion transport, and waste removal. SLCs interact with several important drugs, and a quarter of the more than 400 SLC genes are associated with human diseases. Yet, compared to other gene families of similar stature, SLCs are relatively understudied. The time is right for a systematic attack on SLC structure, specificity, and function, taking into account kinship and expression, as well as the dependencies that arise from the common metabolic space.

Tailored Pig Models for Preclinical Efficacy and Safety Testing of Targeted Therapies

Despite enormous advances in translational biomedical research, there remains a growing demand for improved animal models of human disease. This is particularly true for diseases where rodent models do not reflect the human disease phenotype. Compared to rodents, pig anatomy and physiology are more similar to humans in cardiovascular, immune, respiratory, skeletal muscle, and metabolic systems. Importantly, efficient and precise techniques for genetic engineering of pigs are now available, facilitating the creation of tailored large animal models that mimic human disease mechanisms at the molecular level. In this article, the benefits of genetically engineered pigs for basic and translational research are exemplified by a novel pig model of Duchenne muscular dystrophy and by porcine models of cystic fibrosis. Particular emphasis is given to potential advantages of using these models for efficacy and safety testing of targeted therapies, such as exon skipping and gene editing, for example, using the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated system. In general, genetically tailored pig models have the potential to bridge the gap between proof-of-concept studies in rodents and clinical trials in patients, thus supporting translational medicine.

Targeted Next-Generation Sequencing for Clinical Diagnosis of 561 Mendelian Diseases

Background

Targeted next-generation sequencing (NGS) is a cost-effective approach for rapid and accurate detection of genetic mutations in patients with suspected genetic disorders, which can facilitate effective diagnosis.

Methodology/Principal Findings

We designed a capture array to mainly capture all the coding sequence (CDS) of 2,181 genes associated with 561 Mendelian diseases and conducted NGS to detect mutations. The accuracy of NGS was 99.95%, which was obtained by comparing the genotypes of selected loci between our method and SNP Array in four samples from normal human adults. We also tested the stability of the method using a sample from normal human adults. The results showed that an average of 97.79% and 96.72% of single-nucleotide variants (SNVs) in the sample could be detected stably in a batch and different batches respectively. In addition, the method could detect various types of mutations. Some disease-causing mutations were detected in 69 clinical cases, including 62 SNVs, 14 insertions and deletions (Indels), 1 copy number variant (CNV), 1 microdeletion and 2 microduplications of chromosomes, of which 35 mutations were novel. Mutations were confirmed by Sanger sequencing or real-time polymerase chain reaction (PCR).

Conclusions/Significance

Results of the evaluation showed that targeted NGS enabled to detect disease-causing mutations with high accuracy, stability, speed and throughput. Thus, the technology can be used for the clinical diagnosis of 561 Mendelian diseases.

Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes

Despite large-scale cancer genomics studies, key somatic mutations driving cancer, and their functional roles, remain elusive. Here we propose that analysis of comorbidities of Mendelian diseases with cancers provides a novel, systematic way to discover new cancer genes. If germline genetic variation in Mendelian loci predisposes bearers to common cancers, the same loci may harbor cancer-associated somatic variation. Compilations of clinical records spanning over 100 million patients provide an unprecedented opportunity to assess clinical associations between Mendelian diseases and cancers. We systematically compare these comorbidities against recurrent somatic mutations from more than five thousand patients across many cancers. Using multiple measures of genetic similarity, we show that a Mendelian disease and comorbid cancer indeed have genetic alterations of significant functional similarity. This result provides a basis to identify candidate drivers in cancers including melanoma and glioblastoma. Some Mendelian diseases demonstrate “pan-cancer” comorbidity and shared genetics across cancers.

A comparative study of disease genes and drug targets in the human protein interactome

BACKGROUND

Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear.

RESULTS

In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes.

CONCLUSIONS

The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

Medical genetics-based drug repurposing for Alzheimer's disease

Alzheimer's disease (AD) is a disease that threatens the elderly. No efficient therapeutic method is currently available to combat AD. Drug repurposing has provided a new route for AD drug discovery, and medical genetics has shown potential in target-based drug repurposing. We compared AD-associated genes with approved drug targets and found that three are targeted by 23 approved drugs. Thus, these drugs may be used to treat AD according to the medical genetic information of the targets. In vitro and in vivo experiments revealed that four drugs, all of which are angiotensin-converting enzyme (ACE) inhibitors, had potential to treat AD.

Allelic variation, aneuploidy, and nongenetic mechanisms suppress a monogenic trait in yeast

Clinically relevant features of monogenic diseases, including severity of symptoms and age of onset, can vary widely in response to environmental differences as well as to the presence of genetic modifiers affecting the trait’s penetrance and expressivity. While a better understanding of modifier loci could lead to treatments for Mendelian diseases, the rarity of individuals harboring both a disease-causing allele and a modifying genotype hinders their study in human populations. We examined the genetic architecture of monogenic trait modifiers using a well-characterized yeast model of the human Mendelian disease classic galactosemia. Yeast strains with loss-of-function mutations in the yeast ortholog (GAL7) of the human disease gene (GALT) fail to grow in the presence of even small amounts of galactose due to accumulation of the same toxic intermediates that poison human cells. To isolate and individually genotype large numbers of the very rare (∼0.1%) galactose-tolerant recombinant progeny from a cross between two gal7Δ parents, we developed a new method, called “FACS-QTL.” FACS-QTL improves upon the currently used approaches of bulk segregant analysis and extreme QTL mapping by requiring less genome engineering and strain manipulation as well as maintaining individual genotype information. Our results identified multiple distinct solutions by which the monogenic trait could be suppressed, including genetic and nongenetic mechanisms as well as frequent aneuploidy. Taken together, our results imply that the modifiers of monogenic traits are likely to be genetically complex and heterogeneous.

Dissecting Traditional Chinese Medicines by Omics and Bioinformatics

Traditional Chinese medicines (TCM) are a rich source of potential leads for drug development. However, there are fundamental differences between traditional Chinese medical concepts and modern pharmacology, which greatly hinder the modern development of TCM. To address this challenge, new techniques associated with genomics, transcriptomics, proteomics, metabolomics and bioinformatics have been used to dissect the pharmacological mechanisms of TCM. This review article provides an overview of the current research in this area, and illustrates the potential of omic and bioinformatic methods in TCM-based drug discovery.

Where do health benefits of flavonoids come from? Insights from flavonoid targets and their evolutionary history

Flavonoid intake is negatively correlated with the incidence of some chronic diseases including cardiovascular diseases, type II diabetes, neurodegenerative diseases, and cancers. Thus, the molecular mechanisms underlying this correlation are of great interest. Although ample attention has been given to the free radical-scavenging potential of flavonoids, the poor bioavailability of exogenous flavonoids suggests that the direct antioxidant activity is unlikely responsible for their favorable effects. This study comprehensively analyzed flavonoid targets. The results show that the main functions of these targets are associated with cancers and cardiovascular and metabolic diseases. Moreover, evolutionary analysis of these targets showed that ~1000 of the targets have homologues in human gut bacterial metagenomes. Clusters of orthologous groups of proteins (COG) analysis indicated that most of these bacterial targets are associated with bacterial metabolism. Given that the metabolism of gut microbiota is coupled with the metabolism of the host, this finding implies that flavonoids exert their benefits by regulating gut microbes. Therefore, the health benefits of flavonoids are well explained by their targets rather than their direct antioxidant potential.

Loss of Siglec-14 reduces the risk of chronic obstructive pulmonary disease exacerbation

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. COPD exacerbation, or episodic worsening of symptoms, often results in hospitalization and increased mortality rates. Airway infections by new bacterial strains, such as nontypeable Haemophilus influenzae (NTHi), are a major cause of COPD exacerbation. NTHi express lipooligosaccharides that contain sialic acids, and may interact with Siglec-14, a sialic acid recognition protein on myeloid cells that serves as an activating signal transduction receptor. A null allele polymorphism in SIGLEC14 may attenuate the inflammatory responses to NTHi by eliminating Siglec-14 expression. We asked if the loss of Siglec-14 attenuates the inflammatory response by myeloid cells against NTHi, and if the SIGLEC14-null polymorphism has any effect on COPD exacerbation. We found that NTHi interacts with Siglec-14 to enhance proinflammatory cytokine production in a tissue culture model. Inhibitors of the Syk tyrosine kinase suppress this response. Loss of Siglec-14, due to SIGLEC14-null allele homozygosity, is associated with a reduced risk of COPD exacerbation in a Japanese patient population. Taken together, Siglec-14 and its downstream signaling pathway facilitate the "infection-inflammation-exacerbation" axis of COPD disease progression, and may represent promising targets for therapeutic intervention.

Functional genomics of the rat neuromedin U receptor 1 reveals a naturally occurring deleterious allele

Neuromedin U (NMU) plays an important role in a number of physiological processes, but the relative contribution of its two known receptors, NMUR1 and NMUR2, is still poorly understood. Here we report the existence of a SNP T1022→A (Val341→Glu) in the third exon of the rat Nmur1 gene that leads to an inactive receptor. This SNP is present within the coding region of the highly conserved NPXXY motif found within all class A type G protein-coupled receptors and translates to an NMUR1 receptor that is not expressed on the cell surface. Genetic analysis of the Nmur1 gene in a population of Sprague-Dawley rats revealed that this strain is highly heterogeneous for the inactivating polymorphism. The loss of functional NMUR1 receptors in Sprague-Dawley rats homozygous for the inactive allele was confirmed by radioligand binding studies on native tissue expressing NMUR1. The physiological relevance of this functional genomics finding was examined in two nociceptive response models. The pronociceptive effects of NMU were abolished in rats lacking functional NMUR1 receptors. The existence of naturally occurring NMUR1-deficient rats provides a novel and powerful tool to investigate the physiological role of NMU and its receptors. Furthermore, it highlights the importance of verifying the NMUR1 single nucleotide polymorphism status for rats used in physiological, pharmacological or toxicological studies conducted with NMUR1 modulators.

A truncating mutation of Alms1 reduces the number of hypothalamic neuronal cilia in obese mice

Primary cilia are ubiquitous cellular antennae whose dysfunction collectively causes various disorders, including vision and hearing impairment, as well as renal, skeletal, and central nervous system anomalies. One ciliopathy, Alström syndrome, is closely related to Bardet-Biedl syndrome (BBS), sharing amongst other phenotypic features morbid obesity. As the cellular and molecular links between weight regulation and cilia are poorly understood, we used the obese mouse strain foz/foz, bearing a truncating mutation in the Alström syndrome protein (Alms1), to help elucidate why it develops hyperphagia, leading to early onset obesity and metabolic anomalies. Our in vivo studies reveal that Alms1 localizes at the base of cilia in hypothalamic neurons, which are implicated in the control of satiety. Alms1 is lost from this location in foz/foz mice, coinciding with a strong postnatal reduction (∼70%) in neurons displaying cilia marked with adenylyl cyclase 3 (AC3), a signaling protein implicated in obesity. Notably, the reduction in AC3-bearing cilia parallels the decrease in cilia containing two appetite-regulating proteins, Mchr1 and Sstr3, as well as another established Arl13b ciliary marker, consistent with progressive loss of cilia during development. Together, our results suggest that Alms1 maintains the function of neuronal cilia implicated in weight regulation by influencing the maintenance and/or stability of the organelle. Given that Mchr1 and Sstr3 localization to remaining cilia is maintained in foz/foz animals but known to be lost from BBS knockout mice, our findings suggest different molecular etiologies for the satiety defects associated with the Alström syndrome and BBS ciliopathies.

Human genetics and genomics a decade after the release of the draft sequence of the human genome

Substantial progress has been made in human genetics and genomics research over the past ten years since the publication of the draft sequence of the human genome in 2001. Findings emanating directly from the Human Genome Project, together with those from follow-on studies, have had an enormous impact on our understanding of the architecture and function of the human genome. Major developments have been made in cataloguing genetic variation, the International HapMap Project, and with respect to advances in genotyping technologies. These developments are vital for the emergence of genome-wide association studies in the investigation of complex diseases and traits. In parallel, the advent of high-throughput sequencing technologies has ushered in the 'personal genome sequencing' era for both normal and cancer genomes, and made possible large-scale genome sequencing studies such as the 1000 Genomes Project and the International Cancer Genome Consortium. The high-throughput sequencing and sequence-capture technologies are also providing new opportunities to study Mendelian disorders through exome sequencing and whole-genome sequencing. This paper reviews these major developments in human genetics and genomics over the past decade.

Saturation of the human phenome

The phenome is the complete set of phenotypes resulting from genetic variation in populations of an organism. Saturation of a phenome implies the identification and phenotypic description of mutations in all genes in an organism, potentially constrained to those encoding proteins. The human genome is believed to contain 20-25,000 protein coding genes, but only a small fraction of these have documented mutant phenotypes, thus the human phenome is far from complete. In model organisms, genetic saturation entails the identification of multiple mutant alleles of a gene or locus, allowing a consistent description of mutational phenotypes for that gene. Saturation of several model organisms has been attempted, usually by targeting annotated coding genes with insertional transposons (Drosophila melanogaster, Mus musculus) or by sequence directed deletion (Saccharomyces cerevisiae) or using libraries of antisense oligonucleotide probes injected directly into animals (Caenorhabditis elegans, Danio rerio). This paper reviews the general state of the human phenome, and discusses theoretical and practical considerations toward a saturation analysis in humans. Throughout, emphasis is placed on high penetrance genetic variation, of the kind typically asociated with monogenic versus complex traits.

Revisiting Mendelian disorders through exome sequencing

Over the past several years, more focus has been placed on dissecting the genetic basis of complex diseases and traits through genome-wide association studies. In contrast, Mendelian disorders have received little attention mainly due to the lack of newer and more powerful methods to study these disorders. Linkage studies have previously been the main tool to elucidate the genetics of Mendelian disorders; however, extremely rare disorders or sporadic cases caused by de novo variants are not amendable to this study design. Exome sequencing has now become technically feasible and more cost-effective due to the recent advances in high-throughput sequence capture methods and next-generation sequencing technologies which have offered new opportunities for Mendelian disorder research. Exome sequencing has been swiftly applied to the discovery of new causal variants and candidate genes for a number of Mendelian disorders such as Kabuki syndrome, Miller syndrome and Fowler syndrome. In addition, de novo variants were also identified for sporadic cases, which would have not been possible without exome sequencing. Although exome sequencing has been proven to be a promising approach to study Mendelian disorders, several shortcomings of this method must be noted, such as the inability to capture regulatory or evolutionary conserved sequences in non-coding regions and the incomplete capturing of all exons.

Oligogenic basis of isolated gonadotropin-releasing hormone deficiency

Between the genetic extremes of rare monogenic and common polygenic diseases lie diverse oligogenic disorders involving mutations in more than one locus in each affected individual. Elucidating the principles of oligogenic inheritance and mechanisms of genetic interactions could help unravel the newly appreciated role of rare sequence variants in polygenic disorders. With few exceptions, however, the precise genetic architecture of oligogenic diseases remains unknown. Isolated gonadotropin-releasing hormone (GnRH) deficiency caused by defective secretion or action of hypothalamic GnRH is a rare genetic disease that manifests as sexual immaturity and infertility. Recent reports of patients who harbor pathogenic rare variants in more than one gene have challenged the long-held view that the disorder is strictly monogenic, yet the frequency and extent of oligogenicity in isolated GnRH deficiency have not been investigated. By systematically defining genetic variants in large cohorts of well-phenotyped patients (n = 397), family members, and unaffected subjects (n = 179) for the majority of known disease genes, this study suggests a significant role of oligogenicity in this disease. Remarkably, oligogenicity in isolated GnRH deficiency was as frequent as homozygosity/compound heterozygosity at a single locus (2.5%). Among the 22% of patients with detectable rare protein-altering variants, the likelihood of oligogenicity was 11.3%. No oligogenicity was detected among controls (P < 0.05), even though deleterious variants were present. Viewing isolated GnRH deficiency as an oligogenic condition has implications for understanding the pathogenesis of its reproductive and nonreproductive phenotypes; deciphering the etiology of common GnRH-related disorders; and modeling the genetic architecture of other oligogenic and multifactorial diseases.

Robust physical methods that enrich genomic regions identical by descent for linkage studies: confirmation of a locus for osteogenesis imperfecta

BACKGROUND

The monogenic disease osteogenesis imperfecta (OI) is due to single mutations in either of the collagen genes ColA1 or ColA2, but within the same family a given mutation is accompanied by a wide range of disease severity. Although this phenotypic variability implies the existence of modifier gene variants, genome wide scanning of DNA from OI patients has not been reported. Promising genome wide marker-independent physical methods for identifying disease-related loci have lacked robustness for widespread applicability. Therefore we sought to improve these methods and demonstrate their performance to identify known and novel loci relevant to OI.

RESULTS

We have improved methods for enriching regions of identity-by-descent (IBD) shared between related, afflicted individuals. The extent of enrichment exceeds 10- to 50-fold for some loci. The efficiency of the new process is shown by confirmation of the identification of the Col1A2 locus in osteogenesis imperfecta patients from Amish families. Moreover the analysis revealed additional candidate linkage loci that may harbour modifier genes for OI; a locus on chromosome 1q includes COX-2, a gene implicated in osteogenesis.

CONCLUSION

Technology for physical enrichment of IBD loci is now robust and applicable for finding genes for monogenic diseases and genes for complex diseases. The data support the further investigation of genetic loci other than collagen gene loci to identify genes affecting the clinical expression of osteogenesis imperfecta. The discrimination of IBD mapping will be enhanced when the IBD enrichment procedure is coupled with deep resequencing.