Redes neuronales de grafos con conciencia del conocimiento para asociaciones fármaco-gen TRPV1 en el dolor periodontal
DOI:
https://doi.org/10.15517/1ehs9706Palabras clave:
Dolor periodontal; TRPV1; Grafos; Redes neuronales; Grafos de conocimiento.Resumen
TRPV1 (Receptor Potencial Transitorio Vanilloide 1) es una proteína crítica en la patogénesis del dolor persistente, activado por estímulos nocivos e intermediarios inflamatorios asociados con la periodontitis. Este estudio investiga las interacciones fármaco-gen que involucran a TRPV1 para dilucidar su papel en el dolor periodontal. Se emplearon Redes Neuronales de Grafos con Conciencia del Conocimiento (KGNs) para modelar y analizar las complejas relaciones entre fármacos, genes y receptores de dolor en tejidos periodontales. Al aprovechar conjuntos de datos biológicos, incluyendo el canal iónico TRPV1, interacciones de receptores de dolor y perfiles de expresión génica, el estudio tiene como objetivo identificar posibles dianas terapéuticas y estrategias para el manejo personalizado del dolor periodontal. Se integraron genes diferencialmente expresados (DEGs) con datos de fármacos y genes para modelar sistemas biológicos e informar la priorización terapéutica. Se emplearon tres modelos basados en grafos: Red Neuronal Convolucional de Grafos (GCN) como línea base, GCN Residual (ResGCN) para estabilidad y GCN basado en atención (AttGCN) para ponderación dinámica de nodos. Entre los modelos, ResGCN demostró un rendimiento superior con una precisión del 93.75% y la menor pérdida final, destacando su robustez en la predicción de asociaciones fármaco-gen. Este resultado apoya la utilidad potencial de ResGCN en el modelado preciso de los mecanismos de dolor mediados por TRPV1 y en la guía de la priorización terapéutica. La aplicación de KGNs ha proporcionado valiosos conocimientos sobre las interacciones fármaco-gen TRPV1 en el contexto del dolor periodontal. Los hallazgos enfatizan el potencial de usar ResGCN en la optimización y el tratamiento terapéutico. Sin embargo, persisten desafíos como la calidad de los datos y la complejidad biológica.
Referencias
Lillis K.V., Austah O., Grinceviciute R., Garlet G.P., Diogenes A. Nociceptors regulate osteoimmune transcriptomic response to infection. Sci Rep. 2023; 13: 17601.
Tao L., Yang G., Sun T., Tao J., Zhu C., Yu H., et al. Capsaicin receptor TRPV1 maintains quiescence of hepatic stellate cells in the liver via recruitment of SARM1. J Hepatol. 2023; 78: 805-19.
Munjuluri S., Wilkerson D.A., Sooch G., Chen X., White F.A., Obukhov A.G. Capsaicin and TRPV1 Channels in the Cardiovascular System: The Role of Inflammation. Cells. 2021; 11: 18
Xiao T., Sun M., Zhao C., Kang J. TRPV1: A promising therapeutic target for skin aging and inflammatory skin diseases. Front Pharmacol. 2023; 14: 1037925.
Wang S., Ko C.C., Chung M.K. Nociceptor mechanisms underlying pain and bone remodeling via orthodontic forces: toward no pain, big gain. Frontiers in pain research (Lausanne, Switzerland). 2024; 5: 1365194.
Wang S., Nie X., Siddiqui Y., Wang X., Arora V., Fan X., et al. Nociceptor Neurons Magnify Host Responses to Aggravate Periodontitis. J Dent Res. 2022; 101: 812-20.
Thammanichanon P., Kaewpitak A., Binlateh T., Pavasant P., Leethanakul C. Varied temporal expression patterns of trigeminal TRPA1 and TRPV1 and the neuropeptide CGRP during orthodontic force-induced pain. Arch Oral Biol. 2021; 128: 105170.
Maximiano T.K.E., Carneiro J.A., Fattori V., Verri W.A. TRPV1: Receptor structure, activation, modulation and role in neuro-immune interactions and pain. Cell Calcium. 2024; 119: 102870.
Juárez-Contreras R., Méndez-Reséndiz K.A., Rosenbaum T., González-Ramírez R., Morales-Lázaro S.L. TRPV1 Channel: A Noxious Signal Transducer That Affects Mitochondrial Function. Int J Mol Sci. 2020; 21: 8882
Iglesias LP, Aguiar DC, Moreira FA. TRPV1 blockers as potential new treatments for psychiatric disorders. Behavioural pharmacology. 2022; 33: 2-14.
Nicholson D.N., Greene C.S. Constructing knowledge graphs and their biomedical applications. Comput Struct Biotechnol J. 2020; 18: 1414-28.
Kumar S., Nanelia A., Mariappan R., Rajagopal A., Rajan V. Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study. JMIR Med Inform. 2022; 10: e28842.
Peng C., Xia F., Naseriparsa M., Osborne F. Knowledge Graphs: Opportunities and Challenges. Artif Intell Rev. 2023; 1-32.
Silva M.C., Eugénio P., Faria D., Pesquita C. Ontologies and Knowledge Graphs in Oncology Research. Cancers (Basel). 2022; 14: 1906
Sousa R.T., Silva S., Pesquita C. Explaining protein-protein interactions with knowledge graph-based semantic similarity. Comput Biol Med. 2024; 170: 108076.
Mohamed S.K., Nounu A., Nováček V. Biological applications of knowledge graph embedding models. Brief Bioinform. 2020; 22: 1679-93.
Wang H., Zu Q., Lu M., Chen R., Yang Z., Gao Y., et al. Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review. Adv Ther. 2022; 39: 4052-60.
Gan Z., Zhou D., Rush E., Panickan V.A., Ho Y.L., Ostrouchov G., et al. ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis. medRxiv : the preprint server for health sciences. J Biomed Inform. 2025; 162: 104761.
Fei H., Ren Y., Zhang Y., Ji D., Liang X. Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief Bioinform. 2020; 22: bbaa110.
Soman K., Rose P.W., Morris J.H., Akbas R.E., Smith B., Peetoom B., et al. Biomedical knowledge graph-optimized prompt generation for large language models. Bioinformatics. 2024; 40: btae560.
Renaux A., Terwagne C., Cochez M., Tiddi I., Nowé A., Lenaerts T. A knowledge graph approach to predict and interpret disease-causing gene interactions. BMC Bioinformatics. 2023; 24: 324.
Dai Y., Guo C., Guo W., Eickhoff C. Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings. Brief Bioinform. 2020; 22: bbaa256.
Liu X.Y., Mei X.Y. Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches. Front Bioeng Biotechnol. 2023; 11: 1156372.
Guo R., Zhou Y., Long H., Shan D., Wen J., Hu H., et al. Transient receptor potential Vanilloid 1-based gene therapy alleviates orthodontic pain in rats. Int J Oral Sci. 2019; 11: 11.
Yadalam P.K., Anegundi R.V., Ramadoss R., Joseph B., Veeramuthu A. Felodipine repurposed for targeting TRPV1 receptor to relieve oral cancer pain. Oral Oncol. 2022; 134: 106094.
Yadalam P.K., Natarajan P.M., Mosaddad S.A., Heboyan A. Graph neural networks-based prediction of drug gene association of P2X receptors in periodontal pain. J Oral Biol Craniofac Res. 2024; 14: 335-8.
Yadalam P.K., Natarajan P.M., Saeed M.H., Ardila C.M. Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation. Int Dent J. 2025; 75: 185-194.
Wang C., Yang Y., Song J., Nan X. Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry. J Chem Inf Model. 2024; 64: 7189-213.
Skuta C., Popr M., Muller T., Jindrich J., Kahle M., Sedlak D., et al. Probes & Drugs portal: an interactive, open data resource for chemical biology. Nat Methods. 2017; 14: 759-60.
Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013; 41(Database issue): D991-5.
Czolbe S., Pegios P., Krause O., Feragen A. Semantic similarity metrics for image registration. Med Image Anal. 2023; 87: 102830.
Kulmanov M., Smaili F.Z., Gao X., Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform. 2021; 22: bbaa199.
Yang Y., Sun Y., Li F., Guan B., Liu J.X., Shang J. MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest. IEEE Trans Neural Netw Learn Syst. 2024; 35: 15701-15709.
Jia C., Wang F., Xing B., Li S., Zhao Y., Li Y., et al. DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network. Int J Numer Method Biomed Eng. 2024; 40: e3809.
Ning Q., Zhao Y., Gao J., Chen C., Li X, Li T., et al. AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification. Brief Bioinform. 2023; 24: bbad094.
Xu X,. Li Y., Yang Z., Zhou Z. Transient receptor potential vanilloid type-1 regulates periodontal disease damage via the PI3K/AKT signaling pathway. Iran J Basic Med Sci. 2022; 25: 635-42.
Loos B.G., Dyke T.E. Van. The role of inflammation and genetics in periodontal disease. Periodontology 2000. 2020; 83: 26-39.
Spaull R.V.V., Kurian M.A. SLC6A3-Related Dopamine Transporter Deficiency Syndrome. In: Adam MP, Feldman J, Mirzaa GM, Pagon RA, Wallace SE, Amemiya A, editors. Seattle (WA); 1993.
Hu H., Zhao H., Zhong T., Dong X., Wang L., Han P., et al. Adaptive deep propagation graph neural network for predicting miRNA-disease associations. Brief Funct Genomics. 2023; 22: 453-62.
Jiao C.N., Zhou F., Liu B.M., Zheng C.H., Liu J.X., Gao Y.L. Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction. IEEE J Biomed Health Inform. 2024; 28: 1110-21.
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