PhD Scientific Days 2025

Poster Session II. - G: Pharmaceutical Sciences and Health Technologies

Predicting MicroRNA Effects with Consideration for RNA-Binding Proteins

Előadó neve

Mr. Váczy-Földi Máté, MSc

Neptun code

XA6S0Q

Előadó munkahelye

Institute of Pharmacology and Pharmacotherapy

Előadó telefonszáma

+36205047462

Előadó e-mail címe

vaczy.foldi.mate@semmelweis.hu

Az előadás címe

Predicting MicroRNA Effects with Consideration for RNA-Binding Proteins

Szerző(k) neve és munkahelye

Máté Váczy-Földi¹, Eszter Balogh¹, Zsolt Tamás Papp¹, Bettina Benczik¹, Péter Ferdinandy¹, Péter Bencsik², Bence Ágg¹

1: Cardiometabolic and HUN-REN-SU System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Center for Pharmacology and Drug Research & Development, Semmelweis University, Budapest, Hungary
2: Pharmahungary Group, Szeged, Hungary

Bemutatás módja

Poszter

Szekció

Poster Session II. - G: Pharmaceutical Sciences and Health Technologies

Language of the presentation

English

Preferred session

Pharmaceutical Sciences and Health Technologies

Összefoglaló szövege

Introduction
MicroRNAs (miRNAs) which are ~22 nucleotides long double-stranded non-coding RNAs regulate gene expression by binding to messenger RNAs (mRNAs) via their seed region, causing mRNA destabilization, degradation, or translational repression. However, miRNA gene expression regulation is complemented by RNA-binding proteins (RBPs) which modulate miRNA function. Therefore, including the impact of RBPs in the modelling of miRNA effect could greatly improve the precision of prediction.
Aims
We aimed to develop a miRNA effect prediction model based on a network dynamics algorithm which accounts for the modulation of miRNA function by RBPs.
Method
Our miRNA effect prediction model first constructs a network of miRNAs, mRNAs, RBPs, and their binding sites. MiRNA-mRNA interactions are identified using the miRanda software, and RBP-mRNA interactions with a Position Weight Matrix approach. MiRNA nodes and RBP nodes are then initialized with differential expression data, and theoretical values respectively. Finally, these values are propagated across the network using the propagation version of the Dynamics-Agnostic Network Models algorithm to predict mRNA values, from which relative miRNA effects could be inferred.
Results
We have completed the model implementation incorporating two RBPs, ELAVL1 and PUM2, and identified 855 495 ELAVL1 and 157 530 PUM2 binding site in the human 3’-UTR regions using a literature-based threshold. The model was applied to 3 test datasets containing 20 individual comparisons.
Conclusion
We present a novel, network-dynamics approach to miRNA effect prediction which integrates the influence of RBPs. Future work will focus on evaluation of the model against established, state of the art, prediction tools.
Funding
The 2020-1.1.5-GYORSÍTÓSÁV-2021-00011 project was funded by the Ministry for Innovation and Technology with support from the National Research Development and Innovation Fund under the 2020-1.1.5-GYORSÍTÓSÁV call programme. Project no. RRF-2.3.1-21-2022-00003 has been implemented with the support provided by the European Union. Supported by the 2024-2.1.2-EKÖP-KDP New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. This study has been supported by a Semmelweis 250+ Excellence Fellowship.

University

Semmelweis University

Supervisor

Bence Ágg MD, PhD

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

phd.section.field

in doctoral studies before complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

9071

Start

18:06

End

18:12