Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering

dc.contributorAalto Universityen
dc.contributor.authorHeinonen, Markus
dc.contributor.authorMilliat, Fabien
dc.contributor.authorBenadjaoud, Mohamed Amine
dc.contributor.authorFrançois, Agnés
dc.contributor.authorBuard, Valérie
dc.contributor.authorTarlet, Georges
dc.contributor.authorD'Alché-Buc, Florence
dc.contributor.authorGuipaud, Olivier
dc.contributor.departmentProfessorship Lähdesmäki Harri
dc.contributor.departmentInstitut de radioprotection et de sûreté nucléaire
dc.contributor.departmentUniversité Paris-Saclay
dc.contributor.departmentDepartment of Computer Scienceen
dc.description.abstractThe vascular endothelium is considered as a key cell compartment for the response to ionizing radiation of normal tissues and tumors, and as a promising target to improve the differential effect of radiotherapy in the future. Following radiation exposure, the global endothelial cell response covers a wide range of gene, miRNA, protein and metabolite expression modifications. Changes occur at the transcriptional, translational and post-translational levels and impact cell phenotype as well as the microenvironment by the production and secretion of soluble factors such as reactive oxygen species, chemokines, cytokines and growth factors. These radiation-induced dynamic modifications of molecular networks may control the endothelial cell phenotype and govern recruitment of immune cells, stressing the importance of clearly understanding the mechanisms which underlie these temporal processes. A wide variety of time series data is commonly used in bioinformatics studies, including gene expression, protein concentrations and metabolomics data. The use of clustering of these data is still an unclear problem. Here, we introduce kernels between Gaussian processes modeling time series, and subsequently introduce a spectral clustering algorithm. We apply the methods to the study of human primary endothelial cells (HUVECs) exposed to a radiotherapy dose fraction (2 Gy). Time windows of differential expressions of 301 genes involved in key cellular processes such as angiogenesis, inflammation, apoptosis, immune response and protein kinase were determined from 12 hours to 3 weeks post-irradiation. Then, 43 temporal clusters corresponding to profiles of similar expressions, including 49 genes out of 301 initially measured, were generated according to the proposed method. Forty-seven transcription factors (TFs) responsible for the expression of clusters of genes were predicted from sequence regulatory elements using the MotifMap system. Their temporal profiles of occurrences were established and clustered. Dynamic network interactions and molecular pathways of TFs and differential genes were finally explored, revealing key node genes and putative important cellular processes involved in tissue infiltration by immune cells following exposure to a radiotherapy dose fraction.en
dc.description.versionPeer revieweden
dc.identifier.citationHeinonen , M , Milliat , F , Benadjaoud , M A , François , A , Buard , V , Tarlet , G , D'Alché-Buc , F & Guipaud , O 2018 , ' Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering ' , PloS one , vol. 13 , no. 10 , e0204960 , pp. 1-31 . https://doi.org/10.1371/journal.pone.0204960en
dc.identifier.otherPURE UUID: 63127757-84d5-4c73-a38c-212b3489934a
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/63127757-84d5-4c73-a38c-212b3489934a
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85054424944&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/28755490/journal.pone.0204960.pdf
dc.relation.ispartofseriesPLoS ONEen
dc.relation.ispartofseriesVolume 13, issue 10en
dc.titleTemporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clusteringen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi