PhD Scientific Days 2026

Poster Session 2.B - Molecular Medicine

Analysis of Time-Course Omics Data from Mitosis

Előadó neve

Dr. Kerestély, Márk

Neptune code

FP0TED

Előadó munkahelye

Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary

Előadó telefonszáma

+36305362937

Előadó e-mail címe

kerestely.mark@stud.semmelweis.hu

Az előadás címe

Analysis of Time-Course Omics Data from Mitosis

Szerző(k) neve és munkahelye

Márk Kerestély¹, Ede Migh², Vivien Miczán², Péter Csermely¹, Péter Horváth², Dániel Veres³

1: Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary
2: Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC); Szeged, Hungary
3: Turbine Ltd., Budapest, Hungary

Bemutatás módja

Poszter

Szekció

Poster Session 2.B - Molecular Medicine

Language of the presentation

Hungarian

Preferred session

Molecular Medicine

Összefoglaló szövege

Introduction: Mitosis is fundamental to the life of eukaryotic multicellular organisms. Its medical significance is demonstrated by the fact that its dysregulation is a hallmark of cancer. Due to its importance, mitosis is a widely studied process: a wealth of multi-omics and molecular interaction data is available, yet the system-level integration of these data poses a major challenge.
Aims: We are building a systems-level dynamic network model of mitosis based on time-course multi-omics data, high-resolution 3D confocal imaging, and data from external databases (e.g., MitoCheck). Initially, our aims were to identify differentially abundant proteins (DAPs) and differentially expressed genes (DEGs) in the time-course proteomics and transcriptomics data from HeLa cells across the phases of mitosis.
Method: The time-course proteomics data from 40 subsections of mitosis, generated by the combination of the “Regression Plane” concept with Computer-Aided Microscopy Isolation (CAMI) and the “Deep Visual Proteomics” method, and a pilot transcriptomics dataset from the metaphase and telophase were analysed with Limma (Linear Models for Microarray Data) to identify DAPs and DEGs. The STRING database was used to annotate biological functions.
Results: In the proteomics dataset, we identified 720 DAPs among 4335 analysed protein groups (FDR<0.1), whereas in the transcriptomics dataset, we identified 101 DEGs among 5998 analysed transcripts (FDR<0.1). As expected, these proteins and transcripts were enriched (FDR<0.05) in mitosis-related processes. Concurrently, we observed enrichment in protein translation–related processes.
Conclusion: Crucial quality control steps showed that the available time-course omics data is suitable for use in dynamic modelling. According to the gene set enrichment analysis, we need to consider the regulation of protein translation alongside mitotic signalling in our model.
Funding: Supported by the Thematic Excellence Program (Tématerületi Kiválósági Program TKP2021-EGA-24) of the Ministry for Innovation and Technology in Hungary, within the framework of the Molecular Biology thematic program of the Semmelweis University. Supported by the 2025-2.1.2-EKÖP-KDP-2025-00007 University Research Scholarship Programme of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

University

Semmelweis University

Supervisor

Prof. Péter Csermely, Dr. Veres Dániel

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 after complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

8257

Start

18:54

End

18:57