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

Abstract

The 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.

X‑irradiation induces acute and early term inflammatory responses in atherosclerosis‑prone ApoE‑/‑ mice and in endothelial cells

“…In our study, the serum level of CXCL10 was significantly elevated in female and male ApoE -/mice 24 h after high dose thoracic irradiation, and was found to be secreted from both TICAE and TIME cells 24 h post-irradiation. In line with our in vitro data, CXCL10 gene expression was previously reported to be upregulated in human coronary artery endothelial cells after 10 Gy of fractionated X-irradiation (74), and in human umbilical vascular endothelial cells following exposure to 20 Gy of acute gamma irradiation (75).…”

Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation

“…The endothelial cell model of radiation-induced dysfunction we used has been extensively characterized in our previous work ( Ben Kacem et al., 2020 ; Heinonen et al., 2015 , 2018 ; Jaillet et al., 2017 ; Paget et al., 2019 ). In this model, at a dose of 20 Gy, the cells stopped proliferating as early as one day after irradiation and did not proliferate at all by the third day ( Ben Kacem et al., 2020 ; Paget et al., 2019 ).…”

“…In this work, we propose a systemic analysis of ionizing radiation-induced dysfunction in vascular endothelial cells using topological data analysis ( Zomorodian and Carlsson, 2004 ) and deep learning on graphs ( Hamilton et al., 2017a , b ). Both of these approaches are enhanced by systems biology methods ( Heinonen et al., 2015 , 2018 ; Herskind et al., 2016 ) that significantly improve the characterization of biological processes across the heterogeneous samples of omics layers assembled for this study. These datasets describe the proteome, targeted transcriptome, metabolome, and miRNome of primary human endothelial cells grown in vitro , and the results are aligned with experimental validation steps.…”

Radiation-induced Adaptive Response: New Potential for Cancer Treatment

“…Tissue-specific injury markers are also being identified, which could be most useful for discerning tumor and normal tissue adaptations during radiotherapy. In addition, studies of the microenvironment immune response to local tumor irradiation have demonstrated the role of normal tissue immune regulation on tumor recurrence, metastasis, and response to therapy (65)(66)(67). Investigation into molecular biomarkers of this immune response could therefore be informative in guiding more effective treatment regimens.…”