MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

Rappaport, Noa; Twik, Michal; Plaschkes, Inbar; Nudel, Ron; Stein, Tsippi Iny; Levitt, J.; Gershoni, Moran; Morrey, C. Paul; Safran, Marilyn; Lancet, Doron

doi:10.1093/nar/gkw1012

Cited by 494 publications

(365 citation statements)

References 41 publications

(59 reference statements)

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“…It appeared that all genes, presented in Table 1, except CST3, were associated with asthma and/or hypertension, according to the OMIM [85] and MalaCards [86] databases. However, the associations of this gene with asthma and hypertension are discussed in the literature [87][88][89].…”

Section: Prioritization Of Candidate Genesmentioning

confidence: 99%

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

et al. 2018

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Background: Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or~22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Results: Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension -713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system.

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Section: Prioritization Of Candidate Genesmentioning

confidence: 99%

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

et al. 2018

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“…Table 1A shows that multiple GWAS disease risk SNPs located within the glutamate receptor subnetwork can be annotated as enhancers associated with tobacco smoking status, chronic schizophrenia (ICD diagnostic code F20), and bipolar 1 disorder (ICD diagnostic codes F31.0 -F31.64). (37), column 6, enhancer status determined using annotation from (38,(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60), and column 7 using Hi-C data from (46,61,62). eQTL data support the results shown in column 7 (63,64).…”

Section: Gwas Disease Risk Snps and Hi-c Loops Discriminate 2 Ketaminmentioning

confidence: 61%

Ketamine’s pharmacogenomic network in human brain contains sub-networks associated with glutamate neurotransmission and with neuroplasticity

Higgins

Handelman

Allyn-Feurer

et al. 2020

Preprint

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The pharmacogenomic network responsible for the rapid antidepressant action of ketamine and concomitant adverse events in patients has been poorly defined. Integrative, multiscale biological data analytics helps explain ketamine's action. Using a validated computational pipeline, candidate ketamine-response genes and regulatory RNAs from published literature, binding affinity studies, and single nucleotide polymorphisms (SNPs) from genomewide association studies (GWAS), we identified 108 SNPs associated with 110 genes and regulatory RNAs. All of these SNPs are classified as enhancers, and additional chromatin interaction mapping in human neural cell lines and tissue shows enhancer-promoter interactions involving other network members. Pathway analysis and gene set optimization identified three composite sub-networks within the broader ketamine pharmacogenomic network. Expression patterns of ketamine network genes within the postmortem human brain are concordant with ketamine neurocircuitry based on the results of 24 published functional neuroimaging studies. The ketamine pharmacogenomic network is enriched in forebrain regions known to be rapidly activated by ketamine, including cingulate cortex and frontal cortex, and is significantly regulated by ketamine (p=6.26E-33; Fisher's exact test). The ketamine pharmacogenomic network can be partitioned into distinct enhancer sub-networks associated with: (1) glutamate neurotransmission, chromatin remodeling, smoking behavior, schizophrenia, pain, nausea, vomiting, and post-operative delirium; (2) neuroplasticity, depression, and alcohol consumption; and (3) pharmacokinetics. The component sub-networks explain the diverse action mechanisms of ketamine and its analogs.These results may be useful for optimizing pharmacotherapy in patients diagnosed with depression, pain or related stress disorders. GRIA4 (12), and many other known (8,13) and unknown pharmacodynamic targets within human brain. Ketamine and its metabolites strongly induce the expression of the CYP2B6 gene in human brain, which encodes a drug metabolizing enzyme that contributes to first-and second-pass metabolism of the drug and its metabolites (14). Recent genomewide association studies (GWAS) in humans demonstrate association of ketamine response and adverse events with enhancers of genes and long non-coding RNAs (lncRNAs) related to the roundabout guidance receptor 2 (ROBO2) gene, whose product binds members of the slit guidance ligand family (SLIT1, SLIT2) that are involved in dendrite guidance and synaptic plasticity (15,16). Like phencyclidine, a structurally related compound, ketamine induces acute dissociation, with both drugs exhibiting species-specific differences in response. In sum, the central nervous system (CNS) pathway(s) responsible for the rapid antidepressant effects of ketamine and its enantiomers in patients diagnosed with treatment-resistant depression (TRD) remain poorly defined, including emergence of on-and off-target effects and individual differences in response and adverse eve...

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“…A clearly defined set of best practices can facilitate this process, similarly to protocols for construction of biomodels [27]. External resources like Gene2Disease or MalaCards, and tools like Integrated Network and Dynamical Reasoning Assembler (INDRA) [28][29][30] can help in organizing and referencing the disease-related knowledge integrated into a map.…”

Section: Biocuration and Knowledge Representation Standardsmentioning

confidence: 99%

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

Preprint

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The Disease Maps Project builds upon a network of scientific and clinical groups that exchange best practices, share information, and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation, or serve as a scaffold for the generation of predictive mathematical models.This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and web-tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

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MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

Cited by 494 publications

References 41 publications

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

Ketamine’s pharmacogenomic network in human brain contains sub-networks associated with glutamate neurotransmission and with neuroplasticity

Community-driven roadmap for integrated disease maps

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