PhD Scientific Days 2022
Budapest, 6-7 July 2022
Molecular Sciences III. (Poster discussion will take place on the terrace of the room during the Coffee Break)
Sponge-aware Network Perturbation Propagation Model for Improved Prediction of mRNA Targets of MicroRNAs
Előadó neve
Dr. Ágg, Bence, PhD
Előadó munkahelye
Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest
Előadó telefonszáma
+3630/634-23-49
Előadó e-mail címe
agg.bence@med.semmelweis-univ.hu
Az előadás címe
Sponge-aware Network Perturbation Propagation Model for Improved Prediction of mRNA Targets of MicroRNAs
Szerző(k) neve és munkahelye
Bence Ágg1,2, Bettina Benczik1,2, Kristóf Körmendi1, Márton Makai1, Mátyás Pétervári1,
Péter Ferdinandy1,2
1 Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
2 Pharmahungary Group, Szeged, Hungary
Bemutatás módja
Szóbeli
Szekció
Molecular Sciences III. (Poster discussion will take place on the terrace of the room during the Coffee Break)
Language of the presentation
English
Preferred session
Molecular Sciences
Összefoglaló szövege
Introduction: MicroRNAs initiate silencing of their messenger RNA (mRNA) targets based on sequence complementarity. Previously, targets selected by our network topological microRNA-target prediction software (miRNAtarget™), were successfully validated in several studies. However, for more accurate predictions the microRNA sponging effect by mRNAs need to be considered.
Aims: We aimed to implement a network dynamics framework that enables the integration of sponging effects into our microRNA-target prediction software, and to investigate its predictive performance.
Methods: To model propagation of perturbations induced by microRNA expression changes, the PRINCE (PRIoritizatioN and Complex Elucidation) algorithm was implemented and applied on microRNA-target networks constructed by miRNAtarget and extended by sponging edges. Validation was performed based on 6 comparisons in 3 Gene Expression Omnibus combined small RNA and mRNA sequencing datasets (GSE35350, GSE121702, GSE136930). Capability to predict differentially expressed targets was quantified by the area under the receiver operating characteristic curves (AUROC) for both node strength values calculated by miRNAtarget and our novel sponge-aware perturbation scores.
Results: In 4 out of the investigated 6 comparisons sponge-aware perturbation scores outperformed the node strength values. In 3 of these 4 cases the AUROC difference (0.813 vs. 0.708; 0.722 vs. 0.388; 0.579 vs. 0.557) was significant according to the DeLong test.
Conclusion: This is the first demonstration that integration of the microRNA sponging effect of mRNAs into a network perturbation propagation model of microRNA-target interactions can increase accuracy of microRNA target predictions that leads to more efficient identification of novel drug targets.
Funding: This study was supported by the National Research, Development and Innovation Office of Hungary (NKFIA 2020-1.1.6-JÖVŐ-2021-00013, “Befektetés a jövőbe”). BÁ was supported by the ÚNKP-20-4-I-SE-7 and the ÚNKP-21-4-II-SE-18 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.
University and Doctoral School
Semmelweis University, Doctoral School of Pharmaceutical Sciences
Supervisor
Prof. Dr. Péter Ferdinandy
Publication of my abstract
I do not give consent to the publication of my abstract on the website of the congress.
Kind
Szabad
Status
elfogadva
Accepted presentation method
szóbeli
Előadás fájl jóváhagyás
nem rendelkezett róla
Előadó
2693
Start
11:45
End
12:00
Authors (legacy)
Bence Ágg1,2, Bettina Benczik1,2, Kristóf Körmendi1, Márton Makai1, Mátyás Pétervári1,
Péter Ferdinandy1,2
1 Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
2 Pharmahungary Group, Szeged, Hungary