Mapping human disease-associated enzymes into Reactome allows characterization of disease groups and their interactions

Savojardo, Castrense; Baldazzi, Davide; Babbi, Giulia; Martelli, Pier Luigi; Casadio, Rita

doi:10.1038/s41598-022-22818-5

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…DisGeNET [34] (version 7.0) collects the genetic basis of human diseases. DAR [35] provides disease-pathway and disease-gene relations.…”

Section: Data Sources To Construct Megakgmentioning

confidence: 99%

MegaKG: Toward an explainable knowledge graph for early drug development

Dong,

Liu,

Wei

et al. 2024

Preprint

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In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and 9, 165, 855 edges/relations after stringent data processing. Such a massive KG can not only provide a holistic view of the entities of interest, but also generate insightful hypotheses on unknown relations by applying AI computations. We focused on the interplay of the key elements in drug development, such as genes, diseases and drugs, and aimed to facilitate practical applications that could benefit early drug development in industries. More importantly, we placed much emphasis on the exploitability of the predictions generated by MegaKG. This may greatly help researchers to assess the feasibility or design appropriate downstream validation experiments, making AI techniques more than just black-box models. In this regard, NBFNet was adopted, which combines the advantages of both traditional path-based methods and more recently developed GNN-based ones. Performance evaluation experiments indicated superior results by MegaKG. We also conducted real case studies to validate its practical utility in various scenarios, including target prediction, indication extension and drug repurposing. All these experiments highlighted the potential of MegaKG as a valuable tool in driving innovation and accelerating drug development in pharmaceutical industry.

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“…DisGeNET [34] (version 7.0) collects the genetic basis of human diseases. DAR [35] provides disease-pathway and disease-gene relations.…”

Section: Data Sources To Construct Megakgmentioning

confidence: 99%

MegaKG: Toward an explainable knowledge graph for early drug development

Dong,

Liu,

Wei

et al. 2024

Preprint

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“…The Disease And Reactome (DAR) database ( Savojardo et al, 2022 ) provides a wealth of information on enzymes, including their relationships with Reactome pathways, molecular interactions within the pathways, and tissue expression levels as recorded in the Human Protein Atlas ( Uhlén et al, 2015 ).…”

Section: Online Resources and Databases For Rare Diseasesmentioning

confidence: 99%

“…The dataset is enriched with experimentally proven impact of clinical mutations on interactions, TABLE 1 For each enzyme class, the table lists the number of enzymes associated with Orphanet RDs, the number of the corresponding Orphanet diseases, and the number of the corresponding Reactome roots and pathways. The data were derived from DAR database (Savojardo et al, 2022) and also with the non-clinical mutations which are found to impact protein functionality. So far, the dataset contains over 7,900 interactions involving about 2,500 interactors.…”

Section: Gene and Protein Network Databasesmentioning

confidence: 99%

Resources and tools for rare disease variant interpretation

Licata

Via

Turina

et al. 2023

Front. Mol. Biosci.

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Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

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Tracking the enzyme-response mechanism of tannic acid-embedded chitosan/γ-polyglutamic acid hydrogel

Han,

Wang,

et al. 2024

Commun Mater

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The design of enzyme-response hydrogels has attracted increasing interest in cell therapy, biomedical research, and tissue engineering. Their rational design usually depends on the enzyme-response mechanism and have focused on behavior improvement, drug delivery, and state transition of hydrogels. However, no enzyme-response mechanism has yet been systematically investigated. Here, we construct a tunable platform of tannic acid-embedded chitosan/γ-polyglutamic acid hydrogel to study the enzyme-response mechanism. We track the roles of gallic acid hydrolyzed from tannic acid in altering the structure and properties of the hydrogel to get insights into the mechanism. The gallic acid inside the hydrogel enhances hydrogel crosslinking, increasing the mechanical properties and pH sensitivity but reducing thickness, porosity, and swelling behavior. The gallic acid outside the hydrogel increases the positive potential and superficial hydrophobicity of the hydrogel. These findings will aid the rational design of other enzyme-response hydrogels in more extensive self-adaptive fields.

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Mapping human disease-associated enzymes into Reactome allows characterization of disease groups and their interactions

Cited by 4 publications

References 19 publications

MegaKG: Toward an explainable knowledge graph for early drug development

MegaKG: Toward an explainable knowledge graph for early drug development

Resources and tools for rare disease variant interpretation

Tracking the enzyme-response mechanism of tannic acid-embedded chitosan/γ-polyglutamic acid hydrogel

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