scholarly journals Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders

2017 ◽  
Author(s):  
Hon-Cheong So ◽  
Yui-Hang Wong
Keyword(s):  
Drug Discovery ◽  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Lipid Lowering ◽  
De Novo Mutations ◽  
Histone Deacetylase Inhibition ◽  
Gene Set ◽  
Drug Candidates ◽  
Drug Classes ◽  
Sequencing Studies

AbstractRecent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored.In this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations. Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.

scholarly journals mTADA: a framework for analyzing de novo mutations in multiple traits

2018 ◽  
Author(s):  
Hoang T. Nguyen ◽  
Amanda Dobbyn ◽  
Joseph Buxbaum ◽  
Dalila Pinto ◽  
Shaun M Purcell ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Joint Analysis ◽  
Data Sets ◽  
Analysis Method ◽  
De Novo Mutations ◽  
Multiple Traits ◽  
Risk Genes ◽  
Trait Analysis ◽  
Chd Risk

AbstractJoint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. In addition, approaches that consider multiple traits can aid in the characterization of shared genetic etiology among those traits. In recent years, parent-offspring trio studies have reported an enrichment of de novo mutations (DNMs) in neuropsychiatric disorders. The analysis of DNM data in the context of neuropsychiatric disorders has implicated multiple putatively causal genes, and a number of reported genes are shared across disorders. However, a joint analysis method designed to integrate de novo mutation data from multiple studies has yet to be implemented. We here introduce multi pi e-trait TAD A (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. mTADA uses two single-trait analysis data sets to estimate the proportion of overlapping risk genes, and reports genes shared between and specific to the relevant disorders. We applied mTADA to >13,000 trios for six disorders: schizophrenia (SCZ), autism spectrum disorder (ASD), developmental disorders (DD), intellectual disability (ID), epilepsy (EPI), and congenital heart disease (CHD). We report the proportion of overlapping risk genes and the specific risk genes shared for each pair of disorders. A total of 153 genes were found to be shared in at least one pair of disorders. The largest percentages of shared risk genes were observed for pairs of DD, ID, ASD, and CHD (>20%) whereas SCZ, CHD, and EPI did not show strong overlaps In risk gene set between them. Furthermore, mTADA identified additional SCZ, EPI and CHD risk genes through integration with DD de novo mutation data. For CHD, using DD information, 31 risk genes with posterior probabilities > 0.8 were identified, and 20 of these 31 genes were not in the list of known CHD genes. We find evidence that most significant CHD risk genes are strongly expressed in prenatal stages of the human genes. Finally, we validated our findings for CHD and EPI in independent cohorts comprising 1241 CHD trios, 226 CHD singletons and 197 EPI trios. Multiple novel risk genes identified by mTADA also had de novo mutations in these independent data sets. The joint analysis method introduced here, mTADA, is able to identify risk genes shared by two traits as well as additional risk genes not found through single-trait analysis only. A number of risk genes reported by mTADA are identified only through joint analysis, specifically when ASD, DD, or ID are one of the two traits examined. This suggests that novel genes for the trait or a new trait might converge to a core gene list of the three traits.

scholarly journals Drug Repurposing Is a New Opportunity for Developing Drugs against Neuropsychiatric Disorders

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hyeong-Min Lee ◽  
Yuna Kim
Keyword(s):  
Drug Discovery ◽  
De Novo ◽  
Drug Repurposing ◽  
Cost Effective ◽  
Neuropsychiatric Disorders ◽  
Mechanisms Of Action ◽  
Drug Discovery And Development ◽  
Cost Effective Method ◽  
Alternative Approach ◽  
Target Effects

Better the drugs you know than the drugs you do not know. Drug repurposing is a promising, fast, and cost effective method that can overcome traditional de novo drug discovery and development challenges of targeting neuropsychiatric and other disorders. Drug discovery and development targeting neuropsychiatric disorders are complicated because of the limitations in understanding pathophysiological phenomena. In addition, traditional de novo drug discovery and development are risky, expensive, and time-consuming processes. One alternative approach, drug repurposing, has emerged taking advantage of off-target effects of the existing drugs. In order to identify new opportunities for the existing drugs, it is essential for us to understand the mechanisms of action of drugs, both biologically and pharmacologically. By doing this, drug repurposing would be a more effective method to develop drugs against neuropsychiatric and other disorders. Here, we review the difficulties in drug discovery and development in neuropsychiatric disorders and the extent and perspectives of drug repurposing.

scholarly journals Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i436-i444 ◽  
Author(s):  
Mengshi Zhou ◽  
Chunlei Zheng ◽  
Rong Xu
Keyword(s):  
Drug Discovery ◽  
Electronic Health Records ◽  
Drug Target ◽  
Cross Validation ◽  
De Novo ◽  
State Of The Art ◽  
P Value ◽  
Prediction System ◽  
Health Records ◽  
Drug Candidates

Abstract Motivation Predicting drug–target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration. Results We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision–recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value < 0.0001]. The EHR-based case–control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value < 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients’ EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases. Availability and implementation nlp.case.edu/public/data/TargetPredict.

scholarly journals Cross-Disorder Analysis of De Novo Mutations in Neuropsychiatric Disorders

2021 ◽  
Author(s):  
Kuokuo Li ◽  
Zhenghuan Fang ◽  
Guihu Zhao ◽  
Bin Li ◽  
Chao Chen ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Epileptic Encephalopathy ◽  
De Novo Mutations ◽  
Specific Expression ◽  
Functional Pathway ◽  
Specific Expression Pattern ◽  
Phenotype Similarity ◽  
Shared Genetic

AbstractThe clinical similarity among different neuropsychiatric disorders (NPDs) suggested a shared genetic basis. We catalogued 23,109 coding de novo mutations (DNMs) from 6511 patients with autism spectrum disorder (ASD), 4,293 undiagnosed developmental disorder (UDD), 933 epileptic encephalopathy (EE), 1022 intellectual disability (ID), 1094 schizophrenia (SCZ), and 3391 controls. We evaluated that putative functional DNMs contribute to 38.11%, 34.40%, 33.31%, 10.98% and 6.91% of patients with ID, EE, UDD, ASD and SCZ, respectively. Consistent with phenotype similarity and heterogeneity in different NPDs, they show different degree of genetic association. Cross-disorder analysis of DNMs prioritized 321 candidate genes (FDR < 0.05) and showed that genes shared in more disorders were more likely to exhibited specific expression pattern, functional pathway, genetic convergence, and genetic intolerance.

scholarly journals Mutation rates and the evolution of germline structure

2016 ◽  
Vol 371 (1699) ◽  
pp. 20150137 ◽  
Author(s):  
Aylwyn Scally
Keyword(s):  
Mutation Rate ◽  
De Novo ◽  
Active Role ◽  
Paternal Age ◽  
State Transitions ◽  
De Novo Mutations ◽  
Evolutionary Changes ◽  
Sequencing Studies ◽  
Paternal Age Effect ◽  
Stem Cell State

Genome sequencing studies of de novo mutations in humans have revealed surprising incongruities in our understanding of human germline mutation. In particular, the mutation rate observed in modern humans is substantially lower than that estimated from calibration against the fossil record, and the paternal age effect in mutations transmitted to offspring is much weaker than expected from our long-standing model of spermatogenesis. I consider possible explanations for these discrepancies, including evolutionary changes in life-history parameters such as generation time and the age of puberty, a possible contribution from undetected post-zygotic mutations early in embryo development, and changes in cellular mutation processes at different stages of the germline. I suggest a revised model of stem-cell state transitions during spermatogenesis, in which ‘dark’ gonial stem cells play a more active role than hitherto envisaged, with a long cycle time undetected in experimental observations. More generally, I argue that the mutation rate and its evolution depend intimately on the structure of the germline in humans and other primates. This article is part of the themed issue ‘Dating species divergences using rocks and clocks'.

scholarly journals Protein phosphatase 2A – structure, function and role in neurodevelopmental disorders

2021 ◽  
Vol 134 (13) ◽  
Author(s):  
Priyanka Sandal ◽  
Chian Ju Jong ◽  
Ronald A. Merrill ◽  
Jianing Song ◽  
Stefan Strack
Keyword(s):  
Neurodevelopmental Disorders ◽  
Protein Phosphatase ◽  
Mammalian Cells ◽  
Large Scale ◽  
De Novo ◽  
Protein Phosphatase 2A ◽  
Cell Types ◽  
De Novo Mutations ◽  
Sequencing Studies ◽  
Phosphatase 2A

ABSTRACT Neurodevelopmental disorders (NDDs), including intellectual disability (ID), autism and schizophrenia, have high socioeconomic impact, yet poorly understood etiologies. A recent surge of large-scale genome or exome sequencing studies has identified a multitude of mostly de novo mutations in subunits of the protein phosphatase 2A (PP2A) holoenzyme that are strongly associated with NDDs. PP2A is responsible for at least 50% of total Ser/Thr dephosphorylation in most cell types and is predominantly found as trimeric holoenzymes composed of catalytic (C), scaffolding (A) and variable regulatory (B) subunits. PP2A can exist in nearly 100 different subunit combinations in mammalian cells, dictating distinct localizations, substrates and regulatory mechanisms. PP2A is well established as a regulator of cell division, growth, and differentiation, and the roles of PP2A in cancer and various neurodegenerative disorders, such as Alzheimer's disease, have been reviewed in detail. This Review summarizes and discusses recent reports on NDDs associated with mutations of PP2A subunits and PP2A-associated proteins. We also discuss the potential impact of these mutations on the structure and function of the PP2A holoenzymes and the etiology of NDDs.

scholarly journals Genes with de novo mutations are shared by four neuropsychiatric disorders discovered from NPdenovo database

2015 ◽  
Vol 21 (2) ◽  
pp. 290-297 ◽  
Author(s):  
Jinchen Li ◽  
Tao Cai ◽  
Yi Jiang ◽  
Huiqian Chen ◽  
Xin He ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
De Novo Mutations

scholarly journals A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies

2018 ◽  
Vol 102 (6) ◽  
pp. 1031-1047 ◽  
Author(s):  
Yuwen Liu ◽  
Yanyu Liang ◽  
A. Ercument Cicek ◽  
Zhongshan Li ◽  
Jinchen Li ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
De Novo ◽  
Whole Genome ◽  
De Novo Mutations ◽  
Risk Genes ◽  
Statistical Framework ◽  
Sequencing Studies

scholarly journals Post-transcriptionally impaired de novo mutations contribute to the genetic etiology of four neuropsychiatric disorders

2017 ◽  
Author(s):  
Fengbiao Mao ◽  
Lu Wang ◽  
Xiaolu Zhao ◽  
Zhongshan Li ◽  
Luoyuan Xiao ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Genetic Disorders ◽  
Interaction Network ◽  
Neuropsychiatric Disorders ◽  
Protein Protein Interactions ◽  
De Novo Mutations ◽  
Coding Region

AbstractWhile deleterious de novo mutations (DNMs) in coding region conferring risk in neuropsychiatric disorders have been revealed by next-generation sequencing, the role of DNMs involved in post-transcriptional regulation in pathogenesis of these disorders remains to be elucidated. Here, we identified 1,736 post-transcriptionally impaired DNMs (piDNMs), and prioritized 1,482 candidate genes in four neuropsychiatric disorders from 7,748 families. Our results revealed higher prevalence of piDNMs in the probands than in controls (P = 8.19×10−17), and piDNM-harboring genes were enriched for epigenetic modifications and neuronal or synaptic functions. Moreover, we identified 86 piDNM-containing genes forming convergent co-expression modules and intensive protein-protein interactions in at least two neuropsychiatric disorders. These cross-disorder genes carrying piDNMs could form interaction network centered on RNA binding proteins, suggesting a shared post-transcriptional etiology underlying these disorders. Our findings illustrate the significant contribution of piDNMs to four neuropsychiatric disorders, and lay emphasis on combining functional and network-based evidences to identify regulatory causes of genetic disorders.

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