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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs
Madhu Sharma1, Indra Prakash Jha1, Smriti Chawla1
1Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi.
Abstract:
Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.
Insights
We developed a novel method for single-cell expression analysis to uncover genetic pathway-disease links. This approach aids in understanding disease mechanisms and developing precision therapies, validated using diabetes models.
Area of Science:
- Genomics
- Systems Biology
- Computational Biology
Background:
- Identifying direct links between genetic pathways and diseases is crucial for applications like precision therapy.
- Previous bulk expression studies faced limitations due to cellular heterogeneity and sample size.
Purpose of the Study:
- To introduce a novel method, single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), for dissecting pathway-disease-cell-type relationships.
- To enable a deeper understanding of disease causality and guide the development of targeted therapies.
Main Methods:
- Developed the sci-PDC method utilizing single-cell expression data.
- Applied sci-PDC to infer associations between pathways, diseases, and cell types.
- Utilized the diabetes model to track pathway-disease association variations across age and species.
Main Results:
- Highlighted reliable associations between specific diseases and genetic pathways.
- Demonstrated the utility of sci-PDC in tracking dynamic pathway-disease associations.
- Revealed insights into the suitability of mouse models for studying human diseases like diabetes.
Conclusions:
- The sci-PDC method provides a reliable tool for multidimensional analysis of pathway-disease associations.
- Results align with previous findings and offer potential for identifying drug target pathways.
- The study underscores the importance of single-cell resolution for accurate disease mechanism elucidation.
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