<figure><img src="image_1.png" /></figure>

HAL
open science

# Effects of Ionizing Radiation in Cancer Radiotherapy

François Chevalier

► To cite this version:

François Chevalier. Effects of Ionizing Radiation in Cancer Radiotherapy. François Chevalier. MDPI, 2025, 978-3-7258-3386-3. hal-05096820

HAL Id: hal-05096820

https://normandie-univ.hal.science/hal-05096820v1

Submitted on 4 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

International Journal of
*Molecular Sciences*

Special Issue Reprint

# Effects of Ionizing Radiation in Cancer Radiotherapy

Edited by
François Chevalier

mdpi.com/journal/ijms
# Effects of Ionizing Radiation in Cancer Radiotherapy
<figure><img src="image_4.png" /></figure>
Effects of Ionizing Radiation
in Cancer Radiotherapy

Guest Editor

François Chevalier
Guest Editor

François Chevalier
CIMAP laboratory
Caen Normandy
Caen
France

Editorial Office

MDPI AG
Grosspeteranlage 5
4052 Basel, Switzerland

This is a reprint of the Special Issue, published open access by the journal *International Journal of Molecular Sciences* (ISSN 1422-0067), freely accessible at: www.mdpi.com/journal/ijms/special_issues/nte.

For citation purposes, cite each article independently as indicated on the article page online and using the guide below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name Year, Volume Number, Page Range*.

ISBN 978-3-7258-3386-3 (Hbk)

ISBN 978-3-7258-3385-6 (PDF)

https://doi.org/10.3390/books978-3-7258-3385-6

Cover image courtesy of François Chevalier

© 2025 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
# Contents

About the Editor vii

Preface ix

Mihaela Tudor, Antoine Gilbert, Charlotte Lepleux, Mihaela Temelie, Sonia Hem and Jean Armengaud et al.

A Proteomic Study Suggests Stress Granules as New Potential Actors in Radiation-Induced Bystander Effects

Reprinted from: *Int. J. Mol. Sci.* **2021**, *22*, 7957, https://doi.org/10.3390/ijms22157957 . . . . . . 1

Maria Grazia Andreassi, Nadia Haddy, Mats Harms-Ringdahl, Jonica Campolo, Andrea Borghini and François Chevalier et al.

A Longitudinal Study of Individual Radiation Responses in Pediatric Patients Treated with Proton and Photon Radiotherapy, and Interventional Cardiology: Rationale and Research Protocol of the HARMONIC Project

Reprinted from: *Int. J. Mol. Sci.* **2023**, *24*, 8416, https://doi.org/10.3390/ijms24098416 . . . . . . 21

Ilona Barbara Csordás, Eric Andreas Rutten, Tünde Szatmári, Prabal Subedi, Lourdes Cruz-Garcia and Dávid Kis et al.

The miRNA Content of Bone Marrow-Derived Extracellular Vesicles Contributes to Protein Pathway Alterations Involved in Ionising Radiation-Induced Bystander Responses

Reprinted from: *Int. J. Mol. Sci.* **2023**, *24*, 8607, https://doi.org/10.3390/ijms24108607 . . . . . . 34

Rim Jenni, Asma Chikhaoui, Imen Nabouli, Anissa Zaouak, Fatma Khanchel and Houda Hammami-Ghorbel et al.

Differential Expression of ATM, NF-KB, PINK1 and Foxo3a in Radiation-Induced Basal Cell Carcinoma

Reprinted from: *Int. J. Mol. Sci.* **2023**, *24*, 7181, https://doi.org/10.3390/ijms24087181 . . . . . . 59

Radhia M'Kacher, Bruno Colicchio, Steffen Junker, Elie El Maalouf, Leonhard Heidingsfelder and Andreas Plesch et al.

High Resolution and Automatable Cytogenetic Biodosimetry Using In Situ Telomere and Centromere Hybridization for the Accurate Detection of DNA Damage: An Overview

Reprinted from: *Int. J. Mol. Sci.* **2023**, *24*, 5699, https://doi.org/10.3390/ijms24065699 . . . . . . 77

Margarita Pustovalova, Philipp Malakhov, Anastasia Guryanova, Maxim Sorokin, Maria Suntsova and Anton Buzdin et al.

Transcriptome-Based Traits of Radioresistant Sublines of Non-Small Cell Lung Cancer Cells

Reprinted from: *Int. J. Mol. Sci.* **2023**, *24*, 3042, https://doi.org/10.3390/ijms24033042 . . . . . . 97

Lina Alhaddad, Zain Nofal, Margarita Pustovalova, Andreyan N. Osipov and Sergey Leonov

Long-Term Cultured Human Glioblastoma Multiforme Cells Demonstrate Increased Radiosensitivity and Senescence-Secretory Phenotype in Response to Irradiation

Reprinted from: *Int. J. Mol. Sci.* **2023**, *24*, 2002, https://doi.org/10.3390/ijms24032002 . . . . . . 114

Maria P. Souli, Zacharenia Nikitaki, Monika Puchalska, Kateřina Pachnerová Brabcová, Ellas Spyratou and Panagiotis Kote et al.

Clustered DNA Damage Patterns after Proton Therapy Beam Irradiation Using Plasmid DNA

Reprinted from: *Int. J. Mol. Sci.* **2022**, *23*, 15606, https://doi.org/10.3390/ijms232415606 . . . . . . 127
Lina Alhaddad, Andreyan N. Osipov and Sergey Leonov

The Molecular and Cellular Strategies of Glioblastoma and Non-Small-Cell Lung Cancer Cells
Conferring Radioresistance

Reprinted from: *Int. J. Mol. Sci.* **2022**, *23*, 13577, https://doi.org/10.3390/ijms232113577 . . . . . **148**

Mateusz Smolarz, Łukasz Skoczylas, Marta Gawin, Monika Krzyżowska, Monika Pietrowska and Piotr Widłak

Radiation-Induced Bystander Effect Mediated by Exosomes Involves the Replication Stress in Recipient Cells

Reprinted from: *Int. J. Mol. Sci.* **2022**, *23*, 4169, https://doi.org/10.3390/ijms23084169 . . . . . **191**
About the Editor

François Chevalier

Dr. François Chevalier, PhD, HDR, with a background in protein chemistry and molecular biology, received his doctoral degree in biochemistry in 2001 (INRA, Nantes, France) for the biochemical characterization of glycated proteins. He completed a 5-year post-doctoral research position in Montpellier (INRA, France) with a special focus on the global analysis of proteins using two-dimensional electrophoresis and mass spectrometry. Following a research position in Cork (UCC, Ireland) with a study of protein quality using proteomics tools, he is currently employed in CEA (DRF-IRAMIS-Cimap in Caen) as research director and team leader in radiation biology in the ARIA group (Applications in Radiobiology with Accelerated Ions).

Dr. François Chevalier is specialized in radiation-biology of radioresistant cancer cells, the hypoxic micro-environment, and bystander effects in the context of hadrontherapy.

He has published more than 70 scientific and technical papers in peer-reviewed journals.
<figure><img src="image_5.png" /></figure>
# Preface

Dear Colleagues,

For a long time, it was widely accepted that the biological effects of ionizing radiation such as cell death, DNA damage, and mutagenesis result from the direct ionization of cell structures, particularly DNA, or from indirect damage through reactive oxygen species produced by the radiolysis of water. This "targeted effect" (TE) model has been questioned by numerous observations, in which cells, that were not directly irradiated, exhibited responses similar to those of the directly irradiated cells. Therefore, it is nowadays accepted that the detrimental effects of ionizing radiation are not restricted only to the irradiated cells, but also to non-irradiated adjacent or distant cells.

The non-targeted effects (NTEs) of ionizing radiation, which include genomic instability, radiation-induced bystander effects, and abscopal effects, are defined as the occurrence of biological effects in non-irradiated cells because of the irradiation of other cells in the population. In opposition with TE, that display a linear dose-response, NTEs exhibit a non-linear dose-response, with a marked effect at low doses of radiation. The related cellular and molecular mechanisms of NTEs are still not completely understood, as they are mainly dependent on the cell type and the radiation quality. It is now widely admitted that in specific conditions, irradiated cells produce stress factors, which affect non-irradiated cells in the close environment (bystander effect) or at distance (abscopal effect). The cellular response, observed in non-irradiated cells, can be very similar to the response of irradiated cells, with a modulated intensity. NTEs involve the secretion or the release by irradiated cells of a broad range of stress factors, from cytokines and specifically secreted molecules to reactive oxygen species or oxidized cellular wastes. In the case of communication between neighborhood cells, the stress factors can disseminate through gap junctions, or in the case of distance communication, through small vesicles containing various embedded molecules. NTEs are commonly studied as low-dose radiation effects in radioprotection, in association with genomic instability, mutation induction, and secondary cancer risk.

In a radiotherapy context, TE and NTE can be involved at the same time and, in the case of NTE, it could present several risks of complications when the irradiated area is very close to a sensitive organ. On the other hand, NTEs could increase the biological effect of the radiotherapy on distant non-irradiated cancer cells (such as metastases) with immune-associated effects (abscopal effect) or on non-irradiated cancer cells adjacent to cancer cells specifically targeted with radioactive antibodies (positive bystander effect).

I am pleased to share with you this Special Issue on the "Targeted and non-targeted effects of ionizing radiation in the context of cancer radiotherapy".

François Chevalier
Guest Editor
<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /></figure>

Article

# A Proteomic Study Suggests Stress Granules as New Potential Actors in Radiation-Induced Bystander Effects

Mihaela Tudor <fnref n="1" />,<fnref n="2" />, Antoine Gilbert <fnref n="3" />, Charlotte Lepleux <fnref n="3" />, Mihaela Temelie <fnref n="1" />, Sonia Hem <fnref n="4" />, Jean Armengaud <fnref n="5" /> ID,
Emilie Brotin <fnref n="6" />, Siamak Haghdoost <fnref n="3" />, Diana Savu <fnref n="1" /> ID and François Chevalier <fnref n="3" />,* ID

<figure><img src="image_8.png" /></figure>

check for
updates

**Citation:** Tudor, M.; Gilbert, A.; Lepleux, C.; Temelie, M.; Hem, S.; Armengaud, J.; Brotin, E.; Haghdoost, S.; Savu, D.; Chevalier, F. A Proteomic Study Suggests Stress Granules as New Potential Actors in Radiation-Induced Bystander Effects.
*Int. J. Mol. Sci.* **2021**, *22*, 7957.
https://doi.org/10.3390/ijms22157957

Academic Editor: Sabrina Angelini

Received: 24 June 2021

Accepted: 20 July 2021

Published: 26 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_9.png" /></figure>

**Copyright:** © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Abstract:** Besides the direct effects of radiations, indirect effects are observed within the surrounding non-irradiated area; irradiated cells relay stress signals in this close proximity, inducing the so-called radiation-induced bystander effect. These signals received by neighboring unirradiated cells induce specific responses similar with those of direct irradiated cells. To understand the cellular response of bystander cells, we performed a 2D gel-based proteomic study of the chondrocytes receiving the conditioned medium of low-dose irradiated chondrosarcoma cells. The conditioned medium was directly analyzed by mass spectrometry in order to identify candidate bystander factors involved in the signal transmission. The proteomic analysis of the bystander chondrocytes highlighted 20 proteins spots that were significantly modified at low dose, implicating several cellular mechanisms, such as oxidative stress responses, cellular motility, and exosomes pathways. In addition, the secretomic analysis revealed that the abundance of 40 proteins in the conditioned medium of 0.1 Gy irradiated chondrosarcoma cells was significantly modified, as compared with the conditioned medium of non-irradiated cells. A large cluster of proteins involved in stress granules and several proteins involved in the cellular response to DNA damage stimuli were increased in the 0.1 Gy condition. Several of these candidates and cellular mechanisms were confirmed by functional analysis, such as 8-oxodG quantification, western blot, and wound-healing migration tests. Taken together, these results shed new lights on the complexity of the radiation-induced bystander effects and the large variety of the cellular and molecular mechanisms involved, including the identification of a new potential actor, namely the stress granules.

**Keywords:** chondrosarcoma; bystander signaling; proteomic analysis; secretome; stress granules

## 1. Introduction

Healthy normal tissues protection and patient recovery without sequelæ are key factors in modern cancer radiation therapy (RT). Certainly, radiation-induced side effects raise some concern due to the subsequent growth in morbidity among paediatric and adult patients. Models used in RT were initially developed from data collected after photon radiation. Emerging protocols of RT with protons or heavier particle, such as carbon

<footnote n="1">¹ Department of Life and Environmental Physics, HoriaHulubei National Institute of Physics and Nuclear Engineering, 077125 Magurele, Romania; mihaela.tudor@nipne.ro (M.T.); mihaela.temelie@nipne.ro (M.T.); dsavu@nipne.ro (D.S.)</footnote>

<footnote n="2">² Faculty of Biology, University of Bucharest, 050095 Bucharest, Romania</footnote>

<footnote n="3">³ UMR6252 CIMAP, Team Applications in Radiobiology with Accelerated Ions, CEA-CNRS-ENSICAEN-Université de Caen Normandie, 14000 Caen, France; antoine.gilbert@ganil.fr (A.G.); charlotte.lepleux@gmail.com (C.L.); siamak.haghdoost@ganil.fr (S.H.)</footnote>

<footnote n="4">⁴ BPMP, Montpellier University, CNRS, INRAE, Institut Agro, 34000 Montpellier, France; sonia.hem@supagro.inra.fr</footnote>

<footnote n="5">5 Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France; jean.armengaud@cea.fr</footnote>

<footnote n="6">⁶ ImpedanCELL Platform, Federative Structure 4206 ICORE, NormandieUniv, UNICAEN, Inserm U1086 ANTICIPE, Biology and Innovative Therapeutics for Ovarian Cancers Group (BioTICLA), Comprehensive Cancer Center F. Baclesse, 14000 Caen, France; e.brotin@baclesse.unicancer.fr</footnote>

<footnote>* Correspondence: chevalier@ganil.fr; Tel.: +33-(0)231-454-564</footnote>
ions in advanced medical facilities, have widely changed the way of thinking about local tumor control and the impact on healthy tissues [1,2]. Particle therapy (hadrontherapy) with protons has the advantage of a minimal exit dose after energy deposition in the target volume, and hence better sparing of critical structures in the vicinity of the tumor. Moreover, RT with carbon ions represents an exciting radiation modality, which combines the physical advantages of protons, excepting for an exit fragmentation tail, with higher radiobiological effectiveness [3]. Carbon ion therapy is expected to diminish the radiation morbidity rate. However, the multitude of combinations of radiation quality (linear energy transfer, energy, dose rate, dose, etc.) and tissue biological status (cell culture conditions, genetic background, etc.) does not ease the building of a relevant model for healthy tissue or tumor exposure during RT.

Irradiated cells may release signals which can induce biological alterations of neighboring non-irradiated cells termed the "radiation-induced bystander effects" (RIBE) [4,5]. There is no concord on a precise designation of RIBE, which involves distinct signal-mediated effects within or outside the irradiated volume [6]. Several cellular mechanisms have been suggested to be involved in the transmission of bystander signals by irradiated cells, including the secretion of soluble factors in the extracellular matrix, or the direct communication via gap junctions [7]. This phenomenon was observed in vivo in a context of major local inflammation, linked with a global imbalance of oxidative metabolism that makes its analysis challenging using in vitro model systems [8].

Several studies have aimed to identify the mechanisms of the radiation-induced bystander effect using proteomic tools. The protein composition of exosomes secreted by irradiated UM-SCC6 (human head-and-neck cancer cells) was investigated by direct mass spectrometry analysis [9], and showed a large number of proteins modulated (425 up-regulated and 47 down-regulated), belonging to different cellular processes, including the response to radiation, the metabolism of radical oxygen species and the DNA repair. Several soluble factors secreted from irradiated WEHI 164 (mouse fibrosarcoma cell line) were identified with a proteomic approach [10], including heat shock cognate, annexin A1, angiopoietin-2, and stress-induced phosphoprotein 1. The same proteomic approach was used to study a bystander communication between irradiated and non-irradiated fish [11]. In bystander fish, several modulated proteins were similar to those induced in irradiated fish, including hemoglobin subunit beta and hyperosmotic glycine-rich protein.

In one of our previous studies, we used a medium transfer approach to study RIBE [12]. Chondrosarcoma cells were irradiated with X-rays or C-ions (0.05 to 8 Gy) and then the supernatant, containing the signals emitted by irradiated cells, was transferred into flasks with non-irradiated chondrocytes. We use different technical strategies, such as clonogenic assay, multiplex ELISA analysis of conditioned medium, and flow cytometry for cell cycle analysis of direct irradiated and bystander cells. Our results showed a significant reduction in chondrocyte survival after transfer of the conditioned medium from chondrosarcoma cells irradiated with low doses (0.05 and 0.01 Gy) of X-rays and C-ions. By diluting this medium, the phenomenon decreased proportionally, confirming the presence of bystander factors. Some of these factors were partially observed using multiplex analysis of cell cytokines. Taken together, these results showed the capacity of chondrosarcoma cells to secrete bystander signals, particularly at a low irradiation dose, and the capacity of chondrocyte cells to receive these signals [12].

The goal of this study was to better understand the intercellular communication between the irradiated chondrosarcoma cells and the bystander chondrocytes using proteomics [5]. These approaches allowed us to propose new bystander candidates and cellular responses potentially involved in these non-targeted effects. Some of these results were presented at the 45th Annual Meeting of the European Radiation Research Society in Lund, Sweden [13].
## 2. Results

### 2.1. Secretome Analysis of the Conditioned Medium of Low Doses Irradiated Chondrosarcoma Cells

The dataset acquired on the 12 samples comprises 1,338,540 high-resolution MS/MS spectra. First, as we expected, the presence of contaminants due to fetal calf serum, an interpretation of the MS/MS spectra dataset against the "Bostaurus" theoretical annotated genome was performed, prior to interrogating the "Homo sapiens" theoretical coding sequences.

A total of 357,084 MS/MS spectra were interpreted in this first search round. We identified a large number of bovine serum proteins: 889 were validated with at least two peptides of different sequences. The most abundant proteins were: serum albumin with a 21,209 spectral count (i.e., 6% of the total), but this ratio is by far lower than for a serum analysis which shows that the washes were effective but far to be sufficiently exhaustive to remove all traces of bovine proteins. It is interesting to note the BSA rate for each sample: around 2% for the six samples (3 samples 0 Gy and 3 samples 0.1 Gy) with x10 PBS washes to 10–12% for the other samples with x5 PBS washes, showing the necessity to extensively wash the cells prior to incubation and extraction of the secreted proteins.

In a second search round, we analyzed the yet unassigned MS/MS spectra against the SwissProt human database. In this case, a total of 547,589 MS/MS spectra were unambiguously assigned to peptide sequences. With this dataset, we validated 1522 additional proteins identified with at least two distinct peptides. So, we observed a greater number of proteins compared to the "Bostaurus" request. This double round interpretation confirmed that we had more than just serum proteins in these samples, and certified the presence of the human proteins.

A comparison was then carried out in order to identify the overabundant proteins in the irradiated comparison (0.1 Gy) versus control condition (three biological replicates x two analytical replicates) following the "PatternLab for proteomics" procedure. Considering the condition with 10X PBS washes, a total of 87 groups of proteins were found significantly modulated (p-value ≤ 0.05 and fold change ≥ 1.5×), with a total of 55 more abundant and 32 less abundant proteins. From these protein groups, several accessions associated with bovine origin were removed and, finally, 40 were associated with a known human accession. The abundance of 24 proteins were increased while 16 were decreased in the conditioned medium of irradiated cells (Table 1).

We focused on proteins specifically which significantly increased in the conditioned medium of low-dose irradiated cells. Polyadenylate-binding protein 1 (P11940) was over-secreted 23.8 times in the conditioned medium of SW1353 cells irradiated with 0.1 Gy X-rays when compared with the conditioned medium of non-irradiated SW1353 cells. It is interesting to note that several ribosomal proteins increased in the conditioned medium of low-dose irradiated cells (60S ribosomal protein L34; 60S ribosomal protein L7a; 60S ribosomal protein L8; 40S ribosomal protein S2; 40S ribosomal protein S6; Ubiquitin-40S ribosomal protein S27a).

Several other proteins were identified, in relation with the oxidative response and red/ox status (Acetyl-CoA acetyltransferase; Transmembrane protein 189), cadherin binding (Septin-7), cell migration (Profilin-2) or the response to DNA damage stimulus (E3 ubiquitin-protein ligase RBBP6). Several of these proteins were reported to be involved in extracellular exosomes (glyoxalase domain-containing protein 4; protein HSPD1; S-methyl-5'-thioadenosine phosphorylase; Endoplasmic reticulum aminopeptidase 1).
<table><caption>**Table 1.** List of 40 modulated proteins (24 up-regulated and 16 down-regulated) in the conditioned medium of low-dose irradiated chondrosarcoma cells, as compared with non-irradiated chondrosarcoma cells.</caption><thead><tr><th>Accession</th><th>Name</th><th>pI</th><th>Mass (Da)</th><th>Tfold *</th><th>p-Value</th></tr></thead><tbody><tr><td>P11940</td><td>Polyadenylate-binding protein 1</td><td>9.85</td><td>106.299</td><td>23.83</td><td>0.0264</td></tr><tr><td>P24752</td><td>Acetyl-CoAacetyltransferase, mitochondrial</td><td>8.39</td><td>58.871</td><td>4.00</td><td>0.0051</td></tr><tr><td>A5PLL7</td><td>Transmembraneprotein 189</td><td>9.07</td><td>107.703</td><td>3.33</td><td>0.0278</td></tr><tr><td>P08243</td><td>Asparagine synthetase [glutamine-hydrolyzing]</td><td>6.86</td><td>67.256</td><td>3.17</td><td>0.0155</td></tr><tr><td>P35080</td><td>Profilin-2</td><td>8.87</td><td>77.777</td><td>2.86</td><td>0.0423</td></tr><tr><td>P49207</td><td>60S ribosomal protein L34</td><td>10.64</td><td>21.824</td><td>2.50</td><td>0.0011</td></tr><tr><td>Q9HC38</td><td>Glyoxalase domain-containing protein 4</td><td>7.94</td><td>68.961</td><td>2.11</td><td>0.0191</td></tr><tr><td>P62424</td><td>60S ribosomal protein L7a</td><td>10.54</td><td>33.547</td><td>2.00</td><td>0.0150</td></tr><tr><td>P62753</td><td>40S ribosomal protein S6</td><td>10.74</td><td>31.799</td><td>2.00</td><td>0.0064</td></tr><tr><td>Q16181</td><td>Septin-7</td><td>8.97</td><td>244.012</td><td>2.00</td><td>0.0076</td></tr><tr><td>P27361</td><td>Mitogen-activated protein kinase 3</td><td>9.14</td><td>69.163</td><td>2.00</td><td>0.0250</td></tr><tr><td>O60763</td><td>General vesicular transport factor p115</td><td>5.88</td><td>159.184</td><td>1.91</td><td>0.0191</td></tr><tr><td>P62979</td><td>Ubiquitin-40S ribosomal protein S27a</td><td>9.64</td><td>19.523</td><td>1.84</td><td>0.0356</td></tr><tr><td>Q53SE2</td><td>Uncharacterized protein HSPD1</td><td>8.32</td><td>70.924</td><td>1.83</td><td>0.0186</td></tr><tr><td>P62917</td><td>60S ribosomal protein L8</td><td>11.15</td><td>32.789</td><td>1.79</td><td>0.0017</td></tr><tr><td>Q9NQR4</td><td>Omega-amidase NIT2</td><td>6.73</td><td>47.093</td><td>1.75</td><td>0.0335</td></tr><tr><td>P15880</td><td>40S ribosomal protein S2</td><td>10.37</td><td>34.399</td><td>1.67</td><td>0.0156</td></tr><tr><td>O75367</td><td>Core histone macro-H2A.1</td><td>9.75</td><td>68.531</td><td>1.67</td><td>0.0379</td></tr><tr><td>Q13126</td><td>S-methyl-5'-thioadenosine phosphorylase</td><td>9.17</td><td>186.699</td><td>1.64</td><td>0.0031</td></tr><tr><td>Q96D15</td><td>Reticulocalbin-3</td><td>5.05</td><td>46.220</td><td>1.64</td><td>0.0047</td></tr><tr><td>Q7Z6E9</td><td>E3 ubiquitin-protein ligase RBBP6</td><td>9.65</td><td>98.855</td><td>1.58</td><td>0.0385</td></tr><tr><td>O00370</td><td>LINE-1 retrotransposable element ORF2 protein</td><td>9.51</td><td>5.633.488</td><td>1.57</td><td>0.0179</td></tr><tr><td>Q9NZ08</td><td>Endoplasmic reticulum aminopeptidase 1</td><td>9.00</td><td>202.092</td><td>1.50</td><td>0.0219</td></tr><tr><td>P22087</td><td>rRNA 2'-O-methyltransferase fibrillarin</td><td>10.19</td><td>41.124</td><td>1.50</td><td>0.0219</td></tr><tr><td>O15145</td><td>Actin-related protein 2/3 complex subunit 3</td><td>9.60</td><td>28.754</td><td>-1.67</td><td>0.0379</td></tr><tr><td>Q13257</td><td>Mitotic spindle assembly checkpoint protein MAD2A</td><td>6.30</td><td>55.269</td><td>-1.67</td><td>0.0250</td></tr><tr><td>O15498</td><td>Synaptobrevin homolog YKT6</td><td>8.79</td><td>96.462</td><td>-1.90</td><td>0.0171</td></tr><tr><td>Q9UJS0</td><td>Calcium-binding mitochondrial carrier protein Aralar2</td><td>9.33</td><td>119.145</td><td>-2.00</td><td>0.0011</td></tr><tr><td>Q00325</td><td>Phosphate carrier protein, mitochondrial</td><td>9.34</td><td>63.151</td><td>-2.00</td><td>0.0409</td></tr><tr><td>Q8WXF1</td><td>Paraspeckle component 1</td><td>8.96</td><td>74.454</td><td>-2.08</td><td>0.0003</td></tr><tr><td>P10155</td><td>60 kDa SS-A/Ro ribonucleoprotein</td><td>9.63</td><td>350.653</td><td>-2.10</td><td>0.0160</td></tr><tr><td>Q5JXB2</td><td>Putative ubiquitin-conjugating enzyme E2 N-like</td><td>9.15</td><td>95.474</td><td>-2.11</td><td>0.0030</td></tr><tr><td>P53004</td><td>Biliverdin reductase A</td><td>6.47</td><td>41.158</td><td>-2.18</td><td>0.0405</td></tr><tr><td>Q9Y230</td><td>RuvB-like 2</td><td>5.40</td><td>54.097</td><td>-2.24</td><td>0.0169</td></tr><tr><td>O75153</td><td>Clustered mitochondria protein homolog</td><td>6.34</td><td>190.902</td><td>-2.29</td><td>0.0427</td></tr><tr><td>Q9HD20</td><td>Manganese-transporting ATPase 13A1</td><td>9.48</td><td>70.012</td><td>-2.50</td><td>0.0029</td></tr><tr><td>P18754</td><td>Regulator of chromosome condensation</td><td>5.79</td><td>42.947</td><td>-2.67</td><td>0.0108</td></tr><tr><td>Q09328</td><td>Alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyl transferase A</td><td>9.09</td><td>255.616</td><td>-2.75</td><td>0.0003</td></tr><tr><td>Q14195</td><td>Dihydropyrimidinase-related protein 3</td><td>8.69</td><td>171.819</td><td>-2.85</td><td>0.0245</td></tr><tr><td>P13645</td><td>Keratin, type I cytoskeletal 10</td><td>6.00</td><td>75.115</td><td>-2.87</td><td>0.0138</td></tr></tbody></table>

Forty proteins were statistically modulated in the conditioned medium of chondrosarcoma cells irradiated at a low dose (0.1 Gy), when compared with non-irradiated cells; twenty-four proteins were statistically highly expressed. Some of them were involved in key metabolic pathways and were suspected to participate in radiation-induced bystander signaling. These accessions were analyzed according to potential interaction networks with a STRING functional enrichment analysis (Figure 1). A clear and dense cluster can be observed in the middle of the string network, and the accessions all rely on the ribonucleosome compartment (GO:1990904) and the cytoplasmic stress granules (GO:0010494).

<footnote>* Tfold is positive for accessions up regulated in the conditioned medium of irradiated cells, and negative for accessions down regulated in the conditioned medium of irradiated cells.</footnote>
<figure><img src="image_10.png" /><figcaption>**Figure 1.** Bystander secretome network. Protein-protein interaction network constructed with protein accession from the list of 40 modulated proteins (24 up-regulated and 16 down-regulated) in the medium-conditioned/secretomic analysis. The network was constructed on STRING database.</figcaption></figure>

## 2.2. Quantification of 8-oxoG in the Conditioned Medium of Low Doses Irradiated Chondrosarcoma Cells

To further study the potential impact of oxidative stress on irradiated cells and their corresponding conditioned medium, a quantification of 8-OXO dG was performed in the conditioned media of SW1353 cells irradiated at different doses (Figure 2). A significant increase in 8-OXO dG concentration was observed in the conditioned media of samples irradiated with 0.1 Gy when compared with non-irradiated samples. The tendency of these 8-OXO dG concentration showed a maximum with 0.1 Gy (about 1.4 ng/mL) and then a decrease with doses of 0.2 and 0.5 Gy to reach the basal level observed with non-irradiated samples (about 0.8 ng/mL).

<figure><img src="image_11.png" /><figcaption>**Figure 2.** Quantification of 8-oxo-dG in the conditioned medium of chondrosarcoma cells irradiated with different doses of X-rays. (* = *p* < 0.05).</figcaption></figure>
## 2.3. Whole-Cell Proteome Variations of Chondrocytes in Responses to the Conditioned Medium of Low Doses Irradiated Chondrosarcoma Cells

Quantitative changes in proteins were analyzed by comparing the proteomic map of bystander chondrocytes receiving the conditioned medium of low-dose irradiated chondrosarcoma cells or non-irradiated chondrosarcoma cells (control). A total of 1085 proteins were detected on silver-stained 2D-PAGE gels performed with 250 µg proteins per gel. To analyze the bystander-responsive proteins, significant differences in spot volume (from 25% variation) between control and "treated" samples were assessed and protein spots displaying significant up- or down-expression were regarded as candidates and submitted to MS analysis for identification after trypsin proteolysis.

On the whole, 18 spots, representing 1.6% of all spots on the experiment (Figure 3), showed significant variations (*p* < 0.05); green spots and red spots were over-expressed and under-expressed in the bystander condition, respectively (i.e., cells receiving medium from 0.1 Gy irradiated chondrosarcoma cells). Following a mass spectrometry analysis, 9 proteins were identified as increased (green) and 11 proteins were identified as decreased (red) in the treated sample compared to the control condition (Table 2). Proteins involved in cell-junction and adhesion (Actin, Desmoplakin) as well as cell migration (Microtubule-associated protein RP, Tropomyosin alpha-1 chain, CAP-G protein) were identified and differentially modulated. In addition, several proteins participating in protein secretion and an interleukin signaling pathway (cyclophilin A, PSME1, 60S acidic ribosomal protein P0, Hspa9, 26S proteasome regulatory subunit 7) were observed too. It was also interesting to notice the implication of thioredoxin (involved in cell redox homeostasis; 27% increased) and several proteins related to exosome formation (Keratin type II and eukaryotic translation initiation factor 3 subunit I).

<figure><img src="image_12.png" /><figcaption>**Figure 3.** Proteome changes following bystander effect. Whole cell extracts from T/C-28A2 bystander cells receiving the conditioned medium of (1) low-dose irradiated chondrosarcoma cells or (2) non-irradiated chondrosarcoma cells were analyzed and compared by 2DE. One representative gel of (1) is shown. A total of 250 micrograms proteins were separated using 18-cm pH 4-7 pI range strips for the first dimension, and 12% acrylamide gels for the second dimension. Differentially expressed spots were delineated either in green (induced in (1) cells) or in red (repressed in (1) cells).</figcaption></figure>
<table><caption>**Table 2.** List of 20 modulated proteins in the proteome of bystander cells receiving the conditioned medium of low-dose irradiated chondrosarcoma cells, as compared with bystander cells receiving the conditioned medium non-irradiated chondrosarcoma cells.</caption><thead><tr><th>Spot Number</th><th>Fold</th><th>pI *</th><th>MW *</th><th>Accession</th><th>Names</th><th>HighestMean</th><th>GO—Biological Process</th></tr></thead><tbody><tr><td>16</td><td>1.31</td><td>4.79</td><td>20</td><td>P60709</td><td>Actin</td><td>Bystander 0.1 Gy</td><td>Cell junction assembly positive regulation of protein secretion;</td></tr><tr><td>39</td><td>1.62</td><td>6.69</td><td>26</td><td>P62937</td><td>Cyclophilin A</td><td>Bystander 0.1 Gy</td><td>interleukin-12-mediated signaling pathway</td></tr><tr><td>85</td><td>1.38</td><td>5.11</td><td>63</td><td>Q15691</td><td>Microtubule-associated protein RP</td><td>Bystander 0.1 Gy</td><td>cell migration</td></tr><tr><td>93</td><td>1.31</td><td>5.99</td><td>57</td><td>Q06323</td><td>PSME1</td><td>Bystander 0.1 Gy</td><td>interleukin-1-mediated signaling pathway; tumor necrosis factor-mediated signaling pathway</td></tr><tr><td>116</td><td>1.27</td><td>4.87</td><td>23</td><td>P15924</td><td>Desmoplakin</td><td>Bystander 0.1 Gy</td><td>adherent junction organization;</td></tr><tr><td>116</td><td></td><td></td><td></td><td>P10599</td><td>Thioredoxin</td><td></td><td>cell-cell adhesion</td></tr><tr><td>126</td><td>1.63</td><td>4.67</td><td>56</td><td>P09493</td><td>Tropomyosin alpha-1 chain</td><td>Bystander 0.1 Gy</td><td>cell redox homeostasis;</td></tr><tr><td>138</td><td>1.98</td><td>4.18</td><td>145</td><td>P04264</td><td>Keratin, type II</td><td>Bystander 0.1 Gy</td><td>negative regulation of cell migration</td></tr><tr><td>145</td><td>1.47</td><td>4.67</td><td>98</td><td>Q9BTY7</td><td>Protein HGH1 homolog</td><td>Bystander 0.1 Gy</td><td>Extracellular exosome unknown (interact with Peptidyl-prolyl cis-trans isomerase and Heat shock protein 90)</td></tr><tr><td>65</td><td>1.81</td><td>5.86</td><td>72</td><td>P06733</td><td>Alpha-enolase</td><td>Bystander 0 Gy</td><td>negative regulation of cell growth</td></tr><tr><td>65</td><td></td><td></td><td></td><td>P05388</td><td>60S acidic ribosomal protein P0</td><td></td><td>interleukin-12-mediated signaling pathway</td></tr><tr><td>77</td><td>1.56</td><td>5.79</td><td>130</td><td>P01876</td><td>IGHA1 protein</td><td>Bystander 0 Gy</td><td>Extracellular exosome</td></tr><tr><td>91</td><td>1.29</td><td>5.63</td><td>140</td><td>P11142</td><td>HSC 70 protein</td><td>Bystander 0 Gy</td><td>cytokine-mediated signaling pathway</td></tr><tr><td>97</td><td>1.32</td><td>5.64</td><td>141</td><td>P38646</td><td>Hspa9</td><td>Bystander 0 Gy</td><td>interleukin-12-mediated signaling pathway</td></tr><tr><td>113</td><td>1.27</td><td>6.25</td><td>79</td><td>P40121</td><td>CAP-G protein</td><td>Bystander 0 Gy</td><td>Protein motility</td></tr><tr><td>121</td><td>1.47</td><td>6.15</td><td>94</td><td>P35998</td><td>26S proteasome regulatory subunit 7</td><td>Bystander 0 Gy</td><td>interleukin-1-mediated signaling pathway</td></tr><tr><td>123</td><td>1.31</td><td>6.56</td><td>136</td><td>P49368</td><td>T-complex protein 1 subunit gamma</td><td>Bystander 0 Gy</td><td>Extracellular exosome</td></tr><tr><td>127</td><td>1.31</td><td>4.91</td><td>87</td><td>P60709</td><td>Actin</td><td>Bystander 0 Gy</td><td>Cell junction assembly</td></tr><tr><td>128</td><td>1.36</td><td>5.03</td><td>87</td><td>Q16186</td><td>Proteasomal ubiquitin receptor ADRM1</td><td>Bystander 0 Gy</td><td>Proteasome complex</td></tr><tr><td>134</td><td>1.26</td><td>5.68</td><td>73</td><td>Q13347</td><td>Eukaryotic translation initiation factor 3 subunit I</td><td>Bystander 0 Gy</td><td>Extracellular exosome</td></tr></tbody></table>

Using this list of altered proteins (Table 2), we analyzed the corresponding accessions according to potential interaction networks with a STRING functional enrichment analysis (Figure 4), as previously performed in the case of the secretomic analysis. Again, a dense cluster with several accessions linked together many times (PPIA, TXN, HSPA9, ENO1, HSPA8, RPLP0, CCT3) could be observed. According to this analysis, several accessions associated with interleukin signaling pathways and extracellular exosomes were observed (Table 2).

<figure><img src="image_13.png" /><figcaption>**Figure 4.** Bystander whole-cell proteome network. Protein-protein interaction network constructed with protein accession from the list of 20 modulated proteins (9 up-regulated and 11 down-regulated) in the bystander proteomic analysis. The network was constructed on STRING database.</figcaption></figure>

<footnote>*: pI (iso-electric point) and MW (kDa) according to the 2D gel location. There were 9 up-regulated (highest mean with Bystander 0.1 Gy) and 11 down-regulated (highest mean with Bystander 0 Gy).</footnote>
## 2.4. Quantification and Validation of Proteomic Biomarkers

To validate the results of the 2D-gel analysis, the abundances of several proteins were assayed with specific antibodies using protein extracts from samples already used in 2D-GELs selected on the basis of biological functions and fold changes. An equivalent amount of proteins from each sample were loaded, and loading controls (alpha-tubulin and GAPDH) were used in addition. As shown in Figure 5, the expression levels for cyclophilin A, thioredoxin, alpha-enolase, RPLP0, HSC70, HSPA9, and CCT3 were analyzed and quantified by western blotting. The seven proteins displayed a coherent modulation when compared with the 2D-gel proteomic analysis. As an example, in the case of cyclophilin A, a fold change of +1.62 (+62%) was observed by 2D-gel analysis, and an increase of +37% was observed by western blotting when comparing the bystander chondrocytes receiving the conditioned medium of chondrosarcoma cells irradiated at 0.1 Gy with the non-irradiated chondrosarcoma cells (Supplementary Materials). Two proteins (cyclophilin A and thioredoxin) were observed as increased in the condition “0.1 Gy” by western blotting analysis; and five proteins (alpha-enolase, RPLP0, HSC70, HSPA9 and CCT3) were observed as decreased in the condition “0.1 Gy” by western blotting analysis. These proteins appear as good biomarker candidates involved in the cellular response in bystander cells.

<figure><img src="image_14.png" /><figcaption>**Figure 5.** Western blotting analysis of cyclophilin A, thioredoxin, alpha-enolase, RPLP0, HSC70, HSPA9, and CCT3 in a whole-cell extracts from T/C-28A2 bystander cells receiving the conditioned medium of low-dose irradiated chondrosarcoma cells (0.1 Gy) or non-irradiated chondrosarcoma cells (CTR).</figcaption></figure>

## 2.5. Chondrocyte Motility in Extracellular Matrix Affected by Exogenous Stresses

When taken at 24 h, no difference can be observed on chondrocytes between the conditioned medium of control and 0.1 Gy irradiated chondrosarcoma (Figure 6). On the contrary, when taken at 6 h, a significant difference was observed between the conditioned
medium of control and 0.1 Gy irradiated chondrosarcoma. Indeed, chondrocyte motility significantly increased from the time points 5 h to 13 h using the conditioned medium of chondrosarcoma irradiated with 0.1 Gy.

<figure><img src="image_15.png" /><figcaption>**Figure 6.** The conditioned medium of low-dose irradiated chondrosarcoma cells transiently increased the motility of T/C-28A2 bystander cells. Wound-healing assay showed the ability of cell migration in each group at 6 h (**top**) and 24 h (**bottom**) of contact with the conditioned media. (* = *p* < 0.05).</figcaption></figure>

**3. Discussion**

The aim of the present proteomic work was to highlight: (i) potential new effectors in the radiation-induced bystander effect, using a comparative secretomic analysis of conditioned media, and (ii) the corresponding cellular response in bystander cells, using a comparative gel-based proteomic analysis of bystander cells. This double strategy is pertinent for applications required without a priori analysis of different cellular compartments, within the same cellular system [5].

A highly enriched compartment, in relation with stress granules (SGs), was observed following our secretomic analysis. Stress granules are described to be non-membrane bound cytoplasmic entities, and are formed following a cellular stress to minimize damages and promote cell survival. Many membrane-less organelles exist within the cell cytosol such as SGs and P-bodies. These organelle forms are commonly referred to as bio molecular condensates [14]. The formation of SGs has been suggested to regulate gene expression during stress. These assemblies sequester specific proteins and RNAs during stress, thereby providing a layer of post-transcriptional gene adaptation with the potential to affect directly
mRNA levels, protein translation, and cell survival [15]. In addition to mRNA, SGs mainly contain 40S ribosomal subunits, translation initiation factors such as eIF4G, and RNA-binding proteins (RBPs) [16]. Different cellular stresses can promote SGs formation, such as endogenous stress (hypoxia, low nutrients) and environmental stressors (genotoxic drugs, heat shock, oxidants, or radiations) [17,18]. Exposure of cells to low doses of UVC induces the formation of SGs [19]; according to this study, cells were blocked in G1 phase of the cell cycle in order to repair DNA damages induced by UVC irradiation, simultaneously to the accumulation of the SGs in the cytoplasm. Such significant enrichment of stress granule proteins in the conditioned medium of chondrosarcoma cells irradiated with 0.1 Gy of X-rays is completely unexpected for several reasons: (i) SGs were never described to participate to any intercellular communication, nor bystander effects, (ii) SGs were not described to be secreted by cells, specifically (as cell cytokines) or non-specifically (in cell cargo, such as exosomes, or after cell death), (iii) SGs were not described to display any biological activity with the capacity to transmit a cellular stress to other cells, until now, these biomolecular condensates were supposed to act as a protective structure of protein translation machinery [14]. For all these reasons, it is mandatory to take this result carefully, and not to give definitive conclusions before any additional experiments performed with other cell lines.

In addition to SGs-related proteins, several other protein groups were observed with a significant enrichment in the conditioned medium of chondrosarcoma cells irradiated with 0.1 Gy. Several proteins already observed in exosomes were identified (glyoxalase domain-containing protein 4, uncharacterized protein HSPD1, omega-amidase NIT2, S-methyl-5'-thioadenosine phosphorylase, and rRNA 2'-O-methyltransferase fibrillarin). Such proteins, without any direct biological links between them, could be involved in exosome traffic, thus reinforcing the potential role of exosomes in radiation-induced bystander effects. The lack of knowledge regarding several proteins such as HSPD1 highlights the difficulty to understand and characterize such a complex multi-parameter effect and calls for new experiments aimed at deciphering their functions.

In addition to these unknown potential bystander effectors, several well-known cellular pathways were also observed. Proteins related to oxidative stress were also observed in the conditioned medium of chondrosarcoma cells irradiated with 0.1 Gy. The mitochondrial acetyl-CoA acetyltransferase was observed amongst the most increased protein abundances (in position two in our list), with a four times fold increase as compared with the conditioned medium of non-irradiated chondrosarcoma cells (Table 1). This enzyme is involved in the acetyl-CoA biosynthetic process (GO:0006085) and exerts a central function in the last step of the mitochondrial beta-oxidation pathway, an aerobic process breaking down fatty acids into acetyl-CoA [20]. Its activity is reversible and it can also catalyze the condensation of two acetyl-CoA molecules into aceto-acetyl-CoA [21].

Moreover, the transmembrane protein 189 increased by 3.33 times as compared with the conditioned medium of non-irradiated chondrosarcoma cells. This accession, also named "Plasmanylethanolamine desaturase", is involved in plasmalogen biogenesis in the endoplasmic reticulum, and is involved in antioxidative (GO:0055114) and signaling mechanisms [22]. An increase in the Profilin 2 protein level with a 2.88 factor was observed. This protein involved in the structure of the cytoskeleton could act as a negative regulator of epithelial cell migration (GO:0010633), as described previously [23]. The reticulocalbin-3 protein (increased 1.64 times) can induce similar effects on cell motility [24]; this protein chaperone exerts an anti-fibrotic activity by negatively regulating the secretion of type I and type III collagens (GO:0032964).

The E3 ubiquitin-protein ligase RBBP6, a protein related to DNA damages, was also observed in the conditioned medium of chondrosarcoma cells irradiated with 0.1 Gy (GO:0006974). This protein, known as being possibly involved in assembly of the p53/TP53-MDM2 complex, results in an increase in MDM2-mediated ubiquitination and degradation of p53/TP53 [25,26], perhaps leading to both apoptosis and cell growth (by similarity) playing a role in the transmission of the radiation-induced bystander effect.
We already observed a bystander cellular response in chondrocyte receiving the conditioned medium of chondrosarcoma cells irradiated with 0.1 Gy of X-rays [12], with a reduction in cell survival and an induction of micronuclei. In order to gain insights into the cell mechanisms and the pathways involved in this bystander response, we analyzed the proteome of the cells using a gel-based proteomic strategy.

While shotgun proteomics may give access to a large list of proteins, 2D-gel-based proteomics allows for the identification of matured proteins, such as the proteolytic cleavage of polypeptide chains or post-translational modifications. Thus, this approach is valuable to assess stress-induced modifications, as already demonstrated by several authors [10,11].

Our findings include the accession identified as cyclophilin A (PPIA = P62937), which is described to be positively regulated with protein secretion and involved in the interleukin 12 (IL-12) signaling pathway. We previously observed this accession as increased in the conditioned medium from irradiated breast cancer cells [27]. IL-12 was defined as a cytokine post-translationally regulated and potentially implicated in the radio-induced apoptotic response in mammary tumor cells. IL-12, observed as increased in bystander cells, could be involved in the propagation of the bystander effect throughout non-irradiated cells.

The accession identified as thioredoxin (TXN = P10599), which is described to be involved in the cell redox homeostasis, was found to have increased in bystander cells according to both the proteomic results and the western blot validation. This factor is believed to contribute to the regulation of transcription factors mediating cellular responses to environmental stress, including radiation [28]. In addition, we observed an increase in 8-oxo-dG in the conditioned medium of low-dose irradiated cells, a nucleotide released by the cells when it is oxidized, which is proof of the presence of oxidative stress. These two mechanisms could be linked in a global oxidative stress response, transmitted to bystander cells, and involved in the cellular response to the bystander effect.

Moreover, besides the identified oxidative stress response, a potential change of cellular motility and migration was observed using a wound healing test on non-irradiated cells receiving the conditioned media of irradiated cells. The bystander cells displayed an increased motility (Figure 5), which could be linked with a decrease in alpha-enolase expression. The nuclear form of the protein was previously identified as Myc-binding protein-1 (MBP1); this form plays a role in the negative regulation of cell growth [29]. Consequently, a decrease in MBP1 could induce an increase in cell motility and migration, at least transitively, as observed in this study after 6 h.

Finally, although SGs were significantly observed as enriched according to our secretomic analysis, no SG-related proteins were observed as modulated in the proteome of bystander cells. One explanation could be that both strategies do not analyze the same cell compartment, i.e., the conditioned medium with the secretomic analysis and the cellular proteome with the proteomic analysis. A second explanation is linked with the biochemical capacities of both strategies: with the secretomic analysis, a gel-free mass spectrometry analysis is performed, allowing for low-abundance and hydrophobic proteins/peptides; on the other hand, with the proteomic analysis, only abundant and soluble proteins can be observed. If SGs are involved in the bystander effects, they can be secreted in the conditioned medium, as observed with our secretomic analysis, but maybe a low amount is able to induce a bystander effect on the non-irradiated cells, which cannot be visualized with our proteomic study.

### 4. Conclusions

Overall, the proteomic analysis underlines the modulation of the abundance of several bystander-related proteins; modulation that was confirmed by western blot and their physiological effects revealed by functional techniques in some instances. The stress granules related to oxidative stress-coping mechanisms were identified for the first time as potential attractive biomarkers of RIBE in the conditioned medium of irradiated cells. The next step of this analysis would be a deep analysis of the role of stress granules in
bystander effect transmission, using, for example, cellular models with defects in the stress granules formation processes. In addition, several proteins involved in intercellular signaling, oxidative stress response, and cell motility were also determined in bystander cells in response to such conditioned medium. The results obtained strengthen our previous results concerning the factors involved in the radiation-induced bystander effect at low doses of irradiation, including interleukin.

Taken together, the findings of this study pinpointed the complexity of the mechanisms involved in the radiation-induced bystander effect and the power of a proteomic analysis to bring into light new biomarker candidates of this phenomenon [5].

# 5. Methods

## 5.1. Cell Culture

Two cell lines were used during this study, a chondrosarcoma cell line, SW1353 (CLS Cell Lines Service GmbH, Eppelheim, Germany) and a chondrocyte cell line, T/C28-A2 (gift from Prof. Mary B. Goldring, Hospital for Special Surgery, Weill Medical College of Cornell University, New York, NY, USA), as previously described [12]. These cells were cultured in the same culture medium, minimum essential medium Eagle (MEM, M5650, Sigma-Aldrich, Saint-Louis, MI, USA), supplemented with 5% fetal calf serum, 2 mM L-glutamine, and 1% antibiotics (penicillin-streptomycin solution, Sigma-Aldrich). All experiments were performed in humidified atmosphere with 5% CO₂ and physioxia conditions with 2% O₂ at 37 °C, in a Heracell™ 150i Tri-Gas incubator.

The bystander factors, secreted by chondrosarcoma cells, were first evaluated by direct mass spectrometry analysis. The intracellular bystander response in chondrocytes was analyzed using a gel-based strategy.

## 5.2. Experimental Strategy to Characterize the Bystander Effect induced by Low Doses Irradiated Chondrosarcoma Cells

In order to study the bystander effect between irradiated chondrosarcoma cells (SW1353 cell line) and non-irradiated chondrocytes (T/C28-A2 cell line), we selected a medium-transfer protocol, and we kept the same treatment strategy with all our endpoints (Figure 7). This simplified process allowed us to compare the cell responses of non-irradiated cells, receiving the conditioned medium from irradiated cells.

X-rays irradiations were performed, as previously described [12,30], at room temperature (20 °C) with a tube tension of 225 kV, a copper filter, and an intensity of 1 mA corresponding to a dose rate of 0.2 Gy/min on the Pxi XradSmart 225cX irradiator, dedicated to preclinical research. The dose rate was measured inside flasks with thermoluminescent dosimeters in the irradiation conditions. Thermoluminescent dosimeters were preliminary calibrated on a 15-cm-thick virtual water phantom thanks to reference dose measurements performed with a calibrated ionization chamber following the "American Association of Physicists in Medicine protocol," developed by the Radiation Therapy Committee Task Group 61, for reference dosimetry of low- and medium-energy X-rays for radiotherapy and radiobiology. The dose rate was finally corrected from the used tube current.

Immediately after irradiation with 0.1 Gy of X-rays, chondrosarcoma cells were cultured with fresh medium. After the incubation period, the conditioned medium was removed from the cells and centrifuged to discard detached cells and cell's debris.

Then, this conditioned medium was directly analyzed (using three independent biological replicates), or used on non-irradiated chondrocytes to study a potential bystander effect (Figure 7).
<figure><img src="image_16.png" /><figcaption>**Figure 7.** Schematic representation of experiments followed for the medium transfer protocols. Chondrosarcoma cells (SW1353 cell line) were irradiated at confluence in T25 flasks with X-rays or sham irradiated. Immediately after irradiation, the medium was changed with fresh new medium and incubated for 24 h. Then, the conditioned medium was centrifuged and collected. This conditioned medium (three independent biological replicates) was then analyzed for proteomic composition (secretomic analysis by direct MS analysis) or 8-oxo-dG quantification; or transferred to non-irradiated chondrocytes (T/C-28A2 cell line) for 24 h in T25 flasks with the same cell density. Then, cells were collected for proteomic composition (2D gel comparison and WB validation) or woundhealing test.</figcaption></figure>

### 5.3. Preparation of Conditioned Medium and Shotgun Proteomics Analysis

In the case of the secretome analysis, just before irradiation with X-rays, the complete medium was removed from the flasks and the cell monolayer was extensively washed with PBS. This step was mandatory to reduce the presence in the conditioned medium of bovine serum albumin that could prevent the identification of other proteins. Then, chondrosarcoma cells were irradiated at a low dose (0.1 Gy X-ray) with a serum-free medium. After irradiation, the medium was changed with fresh/serum-free medium and two wash procedures were performed for each irradiation condition and with three independent biological replicates (12 samples). After 24 h, the conditioned medium was removed from the flasks and analyzed by tandem mass spectrometry with technical replicates, thus 24 nanoLC-MS/MS analytical runs.

SW1353 cells were irradiated at confluence and, immediately after irradiation, the monolayer was washed with PBS several times (5× and 10×), and then 3 mL of serum-free medium was added. After 24 h, the conditioned medium was removed from the flasks, centrifuged (2000× g), and stored at -80 °C. These experiments were performed in triplicates. Proteins from the 12 samples (2 irradiations, 0 and 0.1 Gy, 2 washing conditions, i.e., X5 and X10 times with PBS, 3 biological replicates) were first precipitated with TCA. For this, 250 µL of trichloroacetic acid at 50% (w/v) were added to 1 mL of conditioned medium. Precipitated proteins were collected by centrifugation for 15 min at 16,000 g and then dissolved into 30 µL of LDS1X (Invitrogen). The samples were heated at 99 °C for 5 min, briefly centrifuged, and then loaded onto a 4-12% gradient 10-well NuPAGE (Invitrogen) polyacrylamide gel. After a short electrophoresis (5 min), the gel was stained with Coomassie blue safe staining (Invitrogen, Waltham, MA, USA) for 5 min. The polyacrylamide bands corresponding to the whole exoproteomes were sliced and treated with dithiothreitol and iodoacetamide, as recommended [31]. Then, the proteins were subjected to trypsin proteolysis to generate peptides. Each peptide
fraction was analyzed twice by nanoLC-MS/MS (analytical duplicates) in data-dependent mode with a Q-Exactive HF (Thermo) mass spectrometer coupled with an Ultimate 3000 chromatography system (Thermo), resulting in 24 runs of high-resolution tandem mass spectrometry. For each peptide fraction, a volume of 10 µL (out of 50 µL) was injected on a nanoscale 500-mm C18 PepMap TM 100 (5 mm × 300 µm I.D., Thermo) column operated, as previously described [32], except that the gradient of acetonitrile (from 4% to 40% of a solution of 80% CH3CN, 20% H2O, 0.1% formic acid) was extended to 120 min for deepening the analysis. MS spectra of peptide ions were acquired at a resolution of 60,000. Only peptide ions with 2+ or 3+ charge were selected for fragmentation according to a Top20 method and using a dynamic exclusion of 10 sec. MS/MS spectra of fragment ions were acquired at a resolution of 15,000.

MS/MS spectra were interpreted using the MASCOT search engine, version 2.5.1 (Matrix Science, Boston, MA, USA), with fixed carbamidomethyl modification of cysteines, variable oxidation of methionines, and deamidation of asparagines and glutamines, a maximum of two missed cleavages, mass tolerance of 5 ppm and 0.02 Da on parent ions and secondary ions, respectively. Peptides with a score above the query identity threshold (*p* value below 0.05) were selected and parsed with the Irma software [33]. Only proteins with at least two different peptides were validated. The decoy search option of Mascot was systematically activated to estimate the FDR (<1%). Abundances of the proteins were evaluated based on their spectral counts.

### 5.4. Determination of Extracellular 8-oxo-dG in the Conditioned Media

The media were thawed and 1 mL of each sample was used for the determination of 8-oxo-dG using an ELISA-based method (Health Biomarkers, Stockholm, Sweden, AB). Briefly, one ml of cell culture medium was loaded on a solid-phase-extraction column, followed by a washing step and elution of 8-oxo-dG according to protocols provided by the company Health Biomarker Sweden AB, as previously described [34]. The eluates were concentrated by freeze-drying and dissolved in PBS, pH 7.4, to a volume of 1 mL and the clean-up process was repeated once more to purify 8-oxo-dG. Then, the samples were dissolved in PBS, pH 7.4, to a volume of 1 mL. Based on protocol from kit-provider, 90 µL aliquots of samples were mixed with 50 µL of the primary antibody and transferred to 96-well ELISA plates coated with 8-oxo-dG. After overnight incubation at 4 °C, the plates were washed 3 times by washing solution. Next, 140 µL of HRP-conjugated secondary antibody (goat anti-mouse IgG-HRP, Scandinavian Diagnostic Services, Uppsala, Sweden) was added to each well and incubated for 2 h at room temperature. The wells were washed 3 times with the washing solution. Then, 140 µL of tetramethylbenzidine liquid substrate (ICN BiomedicalsInc, Costa Mesa, CA, USA) was added to each well. The samples were incubated for 15 min at room temperature. The reaction was terminated by adding 70 µL of 2 M H₃PO₄ (Merck Millipore, Darmstadt, Germany). The absorbance was read at 450 nm using an automatic ELISA plate reader. All samples were analyzed in triplicate. Standard curves for 8-oxo-dG (from 0.05 up to 10 ng/mL) were established for each plate and the quantity of 8-oxo-dG calculated based on the standard curve and expressed as ng/mL medium.

### 5.5. Medium-Transfer Protocol from Irradiated Cells to Non-Irradiated Cells

Irradiated SW1353 cells and T/C-28a2 bystander cells were plated in T25 cm² flasks at confluence. As previously described [12], immediately after irradiation with X-rays, the medium of irradiated flasks was changed with fresh medium and, after 24 h in contact with irradiated SW1353 cells (to allow the bystander factors to be released), this medium was collected (Figure 7). The condition medium was then centrifuged (2000 g) and transferred in flasks of the same size (T25 cm²) containing bystander T/C-28a2 cells at confluence. Bystander cells were kept in contact with the conditioned medium for 24 h and then harvested. The cell pellet was washed with PBS and the dry pellet was kept at 80 °C, until protein extraction.
## 5.6. Gel-Based Proteomic Study of Bystander Chondrocytes

### 5.6.1. Chemicals

TRIS base, urea, thiourea, CHAPS, iodoacetamide, TEMED, low-melt agarose, Triton X-100, spermine, phosphatase inhibitor cocktail, and bromophenol blue were obtained from Sigma-Aldrich (St. Louis, MO, USA). The protease inhibitor cocktail (Complete Mini EDTA-free) was from Roche Diagnostics (Mannheim, Germany); IPG buffers, IPG strips (pH 4-7) were purchased from VWR (acrylamide was obtained from Bio-Rad (Hercules, CA, USA); and SDS, glycerol, DTT, and TGS 10X were from Eudomedex (Mundolshein, France). All other reagents were of analytical grade.

### 5.6.2. Protein Extraction and Solubilisation

Proteins were extracted from TC-28/Ac cells (dry pellet) in a sample buffer containing 7 M urea, 2 M thiourea, 4% CHAPS, 0.05% Triton X100, 65 mM DTT, 40 mM spermine, protease, and phosphatase inhibitor cocktails. This suspension was centrifuged at 28,000 g for 60 min, supernatants were collected, and the protein content was estimated using the Bradford method [35]. Proteins were then precipitated using the 2D clean-up kit (GE Healthcare, Chicago, IL, USA) and the pellet was solubilized with TUC solution (7M urea, 2M thiourea, 4% CHAPS) and quantified with the 2D quant kit (GE Healthcare).

### 5.6.3. Strip Rehydration with Protein Samples: "Sample In-Gel Rehydration"

A protein sample (250 µg) was mixed with rehydration buffer (RB): 7M urea, 2M thiourea, 4% CHAPS, 0.05% triton X100, 0.5% ampholytes (IPG buffer 4-7 GE) and adjusted to the correct volume to rehydrate 18 cm strip (here, 320 µL). Strips were then placed acrylamide face down in the focusing tray equipped with platinum electrode embedded into the running tray (Protean IEF, Bio-Rad, Hercules, CA, USA) and passively re hydrated at 20°C without electricity for 16 h, and then actively rehydrated at 50 V during 9 h, as previously described [27,35,36]. During protein focalization, small electrode wicks were placed between acrylamide and electrode. These paper wicks (Ref 1654071, Electrode wicks, Bio-Rad, Hercules, CA, USA) were, in advance, soaked with water in order to absorb salts and other contaminant species during active rehydration. The IPG strips were then focused according to the following program: 500 V for 1 h, a linear ramp to 1000 V for 1 h, a linear ramp to 10000 V for 33 KV-1 h, and finally 10000 V for 24 KV-1 h.

### 5.6.4. IPG Strips Equilibration and Second Dimension

The strips were incubated in the first equilibration solution (50 mM Tris-HCl pH 8.8, 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS) with 130 mM DTT, and then in the second equilibration solution (50 mM Tris-HCl pH 8.8, 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS) with 130 mM iodoacetamide.

Strips were then embedded using 1% (w/v) low-melt agarose on the top of the acrylamide gel and trapped using plastic blockers, as described previously [35]. SDS-PAGE was carried out on a 12% acrylamide gel, using the Dodeca Cell electrophoresis unit (Bio-Rad, Hercules, CA, USA).

### 5.6.5. Gel Staining and Picture Acquisition

Gels were stained with silver nitrate, as previously described, with some modifications. Briefly, gels were first fixed at least 1 h with 30% ethanol and 5% acetic acid; washed 3 times 10 min with water; sensibilized 1 min with 0.02% sodium thiosulfate; washed 2 min with water; stained 30 min with 0.2% silver nitrate and 0.011% formaldehyde; washed 10 s with water; developed 5 min with 85 mM sodium carbonate, 0.00125% sodium thiosulfate and 0.011% formaldehyde; stopped with 0.33 M TRIS and 1.7% acetic acid; and stored with 5% acetic acid with 2% DMSO [37].

Gels were scanned to images right after staining to limit the polychromatic color of spots. Images were acquired with a GS 800 densitometer (Bio-Rad, Hercules, CA, USA).
5.6.6. Image Analysis

Images from stained gels were analyzed using the Samespots software v4.5 (Non-linear Dynamics, UK). Gels were grouped to create a global analysis with all conditions. Spots of each samples were compared between conditions, and spots were numbered with the same detection parameters, as previously described [35]. A multivariate statistical analysis was performed using the statistic mode of the Samespots software (Non-linear Dynamics, UK). Spots with significant differences (modulation of +/−20% and ANOVA *t*-test *p* < 0.05) were chosen. Spots of interest were selected for subsequent protein identification by mass spectrometry analysis and were picked up using the corresponding preparative silver stained gels.

5.6.7. Mass Spectrometry Analysis of 2D-Spots

Gel spots 2D were manually cut and prepared, as previously described [38]. The LC-MS/MS experiments were performed using a U3000 NCS nano-high-performance liquid chromatography (Thermo Fisher Scientific Inc, Waltham, MA, USA) system and a Q-Exactive Plus Orbitrap mass spectrometer. Next, 6 µL of peptides were loaded onto a pre-column (Thermo Scientific PepMap 100 C18, 5 µm particle size, 100 Å pore size, 300 µm i.d. × 5 mm length) from the Ultimate 3000 autosampler with 0.05% TFA for 3 min at a flow rate of 10 µL/min. Separation of peptides was performed by reverse-phase chromatography at a flow rate of 300 nL/min on a Thermo Scientific reverse-phase nano column (Thermo Scientific PepMap C18, 2 µm particle size, 100 Å pore size, 75 µm i.d. × 50 cm length). After the 3 min period, the column valve was switched to allow elution of peptides from the pre-column onto the analytical column. Solvent A was water + 0.1% FA and solvent B was 80% ACN, 20% water + 0.1% FA. The linear gradient employed was 4–40% of solvent B in 19 min, then 40–90% of solvent B from 19 to 20 min. The total run time was 35 min including a high organic wash step and re-equilibration step. The nanoHPLC and the spectrometer were coupled by nano-electrospray source. Peptides were transferred to the gaseous phase with positive ion electrospray ionization at 1.7 kV. Mass spectrometry data was processed using the Proteome Discoverer software (Version 1.4.0.288, Thermo Fisher Scientific, Bremen, Germany) and the search engine employed in local was Mascot (version.2.4, Matrix Science, Boston, MA, USA). The mass spectrometry data was searched against SwissProt with taxonomy Homo sapiens (20215) with the following parameters: trypsin as enzyme, 1 missed cleavage allowed, and carbamidomethylation of Cystein were used as fixed modifications, and N-terminal acetylation, deamidation of asparagine and glutamine, Nterminal-pyroglutamylation of glutamine and glutamate, oxidation of methionine were used as variable modifications. Mass tolerance was set to 10 ppm on full scans and 0.02 Da for fragment ions. Proteins were validated once they contained at least two peptides with a *p*-value < 0.05) and a false discovery rate <1%.

5.7. *Western Blotting Analysis*

Chondrosarcoma cells (SW1353) were irradiated with a 0.1 Gy dose using the XS-
TRAHL XRC 160 machine of IFIN-HH, followed by a media transfer to the chondrocyte
(T/C28a2) after 24 h, as previously described [12]. At 24 h, after the media transfer, the
bystander cells (T/C-28a2) were washed with PBS. The cell pellet was resuspended with
homemade RIPA lysis buffer supplemented with protease inhibitors (Roche) and incu-
bated for 30 min on a cold rack, followed by a 15 min centrifugation at 12,000 × g at room
temperature. Protein concentration was determined for all samples with a Bradford assay
(Thermo Scientific). Laemmli buffer was added to the sample and denatured at 95 °C
for 5 min, followed by a 1 min centrifugation at 16,000 xg. Samples were separated on
SDS-poly-acrylamide (15%) gel electrophoresis (SDS-PAGE) using a TV100 electrophoresis
unit, run at 110 V for about 1 h, followed by transfer on a PVDF membrane using a TV 100
Electroblotter, at 210 mA for 1 h and blocked with Tris-buffered saline with 0.05% Tween
20 (TBS-T) buffer with 5% milk on slow agitation for 1 h. Membranes were incubated
overnight at 4 °C on agitation with the following antibodies: anti-HSPA9 (MA1-91639,
Thermo Scientific), anti-HSC70 (PA5-24624, Thermo Scientific), anti-CCT3 (PA5-78953, Thermo Scientific), anti-EN01 (MA5-17627, Thermo Scientific), anti-RPLP0 (PA5-89335, Thermo Scientific), anti-cyclophilin A (39-1100, Thermo Scientific), anti-thioredoxin 1 (MA5-14941, Thermo Scientific), anti-α-Tubulin (T5168, Sigma-Aldrich) or GAPDH (sc-32233, Santa Cruz Biotechnology), in the concentrations recommended by the manufacturer. Membranes were then washed 3 times with TBS-T for 10 min, followed by an incubation with specific secondary antibody conjugated with horseradish peroxidase for 2 h at room temperature on agitation, covered from light in concentrations of 1:500 (Goat anti-Rabbit IgG (H+L), 32460, Thermo Scientific, Waltham, MA, USA) and 1:1000 (Goat anti-Mouse IgG (H+L) Poly-HRP, 32230, Thermo Scientific). For α-Tubulin, the primary antibody was diluted 1:30,000 and the incubation times were shortened at 30 min. Membranes were washed 3 times with TBS-T for 10 min and then treated with ECL reagent (Thermo Scientific). Development of blots was carried out with a Biospectrum Imaging System (UVP LLC, Upland, CA, USA) using the Vision Works LS software. Image analysis was carried out using the Quantity One (Bio-Rad, Hercules, CA, USA) software. Both irradiated and sham control data were expressed, normalizing the intensity of the protein of interest to the corresponding α-Tubulin or GAPDH band for each sample. Samples were analyzed in triplicates (Sup data WB blots).

## 5.8. Wound-Healing Assay (IncuCyte® Live-Cell Analysis Systems)

In order to further study the impact of the conditioned medium on chondrocyte motility, a wound-healing test was performed on chondrocytes using the conditioned medium of chondrosarcoma cell X-rays irradiated with 0 Gy (as control) and 0.1 Gy (as treated sample). This conditioned medium was taken from chondrosarcoma cell after 6- and 24-h incubations. Then, chondrocytes motility was followed during 24 h in contact with these conditioned media. A wound-healing assay was applied to evaluate cell migration ability. Next, 1.5 × 10⁴ T/C28a2 cells/well were seeded in 96-well IncuCyte® ImageLock Plates in media. Cells were seeded at a density of 70 to 80%. After 24 h, cells were scratched by IncuCyte® WoundMaker to build an artificial wound. Afterwards, the media was removed, cells were washed two times with PBS, and conditioned media were added on cells. Cells were cultured at 37 °C and 5% CO₂ and monitored using an IncuCyte® S3 (Sartorius). The migrating distance was measured for 24 h. Data were analyzed by the Cell Migration Analysis software module (Sartorius).

## 5.9. Statistical Analyses

Secretome statistical analysis. Spectral counts for each condition and each protein were normalized as recommended [39]. Abundances of the proteins, based on their normalized spectral counts, were compared according to the PatternLab Tfold comparison [40]. Only proteins with statistical significance (*p* value below 0.05) and with a Tfold increase or decrease in at least 50% compared to control were considered as differentially abundant. Two-dimensional-gels statistical analysis. Statistical analyzes were carried out following 3 independent experiments, using the *t*-test function of the Progenesis SameSpots software. Datasets were considered as significantly different when *p* < 0.05 (*). 8-oxo-dG statistical analysis. Statistical analyzes were carried out following 3 independent experiments, using the *t*-test function of the Excel Software in order to compare the 0 Gy condition to every other condition. Datasets were considered as significantly different when *p* < 0.05 (*). Western blotting statistical analysis. Statistical analyzes were carried out following 4 independent experiments, each made at least in 4 replicates, using the *t*-test function (= *t*-test) of the Excel Software in order to compare the 0 and 0.1 Gy condition, after normalization of the 0 Gy condition. Datasets were considered as significantly different when *p* < 0.05 (*). IncuCyte wound-healing migration test statistical analysis. Statistical analyzes were carried out following 2 independent experiments, each made at least in triplicats, using the *t*-test function (= *t*-test) of the Excel Software in order to compare each time point of 0 and 0.1 Gy condition. Datasets were considered as significantly different when *p* < 0.05 (*).
5.10. *Mass Spectrometry Data*

The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE [41] partner repository with the dataset identifier PXD024953 and project doi:10.6019/PXD024953 in the case of secretome and PXD025187 in the case of proteome analyses.

In the case of secretome analysis, the reviewers may access this private dataset using
reviewer_pxd024953@ebi.ac.uk as Username and iotcSWYp as Password.

In the case of proteome analysis, the reviewers may access this private dataset using
reviewer_pxd025187@ebi.ac.uk as Username and imUEHInL as Password. These data will
be automatically accessible after publication.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.3390/ijms22157957/s1.

**Author Contributions:** C.L., F.C. and J.A. performed the direct M.S. analysis of secretome; C.L. and F.C. performed the 2D gels experiments; C.L., F.C. and S.H. (Sonia Hem) performed the proteomic analysis of 2D gel spots; M.T. (Mihaela Tudor), M.T. (Mihaela Temelie) and D.S. performed the western blotting analyses; S.H. (Siamak Haghdoost) performed the 8-oxodG quantification; M.T. (Mihaela Tudor), A.G. and E.B. performed the wound healing tests; M.T. (Mihaela Tudor), A.G., D.S. and F.C. performed the statistical analysis; F.C. and D.S. conceived the study and its design; F.C. drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by grants from the Region Normandy, the RIN emergence 2015–2018 project IRHEMME (IRradiation du cartilage lors d’une Hadronthérapie: Effets bystander, Modifications structurales et fragmentation de la Matrice Extracellulaire), and the RIN CPIER 2018-2020 HABIONOR European project, co-funded by the Normandy County Council, the French State in the framework of the interregional development Contract “Vallée de la Seine” 2015–2020. (HAdronBIOlogie en NORmandie: Programme de recherché en Radiobiologie pour l’hadronthérapie au centre ARCHADE). This work was also supported by “Agence Nationale de la Recherche,” Equipex Rec-Hadron (ANR-10-EQPX-1401); by grants of EDF (Electricité de France), the funding from Life Sciences group of the four-way national agreement CEA-EDF-IRSN-Areva (2016-2018) and the funding from Life Sciences group of the four-way national agreement CEA-EDF-IRSN-FRAMATOME (2019-2021); by the CEA and Normandy region for the PhD funding of CL; by Romanian Ministry of Education and Research, grants numbers PN 19060203/2019, by the French Institute of Romania and the French Ministry of Foreign Affairs-Partenariat Hubert Curien, (PHC BRANCUSI 2019, 43535RF).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank the personnel at CIRIL platform (Cimap Caen, France) for C-ion dosimetry and helpful discussion and the GANIL (Caen, France) for providing us the C-ion beam, as well as Jean-Charles Gaillard (Li2D) for his help with the tandem mass spectrometry measurements. The carbon ion experiments were performed according to the projects P1146-H, (F. Chevalier), from the iPAC committee proposal for High Energy beam line on interdisciplinary research at GANIL.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Durante, M. New Challenges in High-Energy Particle Radiobiology. *Br. J. Radiol. 2014*, *87*, 20130626. [CrossRef]

2. Noël, G.; Feuvret, L.; Ferrand, R.; Mazeron, J.-J. Treatment with charged particles beams: Hadrontherapy part I: Physical basis and clinical experience of treatment with protons. *Cancer Radiother. 2003*, *7*, 321–339. [CrossRef]

3. Thariat, J.; Valable, S.; Laurent, C.; Haghdoost, S.; Pérès, E.A.; Bernaudin, M.; Sichel, F.; Lesueur, P.; Césaire, M.; Petit, E.; et al. Hadrontherapy Interactions in Molecular and Cellular Biology. *Int. J. Mol. Sci.* **2020**, *21*, 133. [CrossRef]

4. Mothersill, C.; Seymour, C. Radiation-Induced Bystander Effects: Past History and Future Directions. *Radiat. Res.* **2001**, *155*, 759–767. [CrossRef]
5. Chevalier, F.; Hamdi, D.H.; Saintigny, Y.; Lefaix, J.-L. Proteomic Overview and Perspectives of the Radiation-Induced Bystander Effects. *Mutat. Res./Rev. Mutat. Res.* **2014**, *763*, 280-293. [CrossRef]

6. Blyth, B.J.; Sykes, P.J. Radiation-Induced Bystander Effects: What Are They, and How Relevant Are They to Human Radiation Exposures? *Radiat. Res.* **2011**, *176*, 139-157. [CrossRef] [PubMed]

7. Azzam, E.I.; de Toledo, S.M.; Little, J.B. Stress Signaling from Irradiated to Non-Irradiated Cells. *Curr. Cancer Drug Targets* **2004**, *4*, 53-64. [CrossRef]

8. Prise, K.M.; O'Sullivan, J.M. Radiation-Induced Bystander Signalling in Cancer Therapy. *Nat. Rev. Cancer* **2009**, *9*, 351-360. [CrossRef] [PubMed]

9. Abramowicz, A.; Wojakowska, A.; Marczak, L.; Lysek-Gladysinska, M.; Smolarz, M.; Story, M.D.; Polanska, J.; Widlak, P.; Pietrowska, M. Ionizing Radiation Affects the Composition of the Proteome of Extracellular Vesicles Released by Head-and-Neck Cancer Cells in Vitro. *J. Radiat. Res.* **2019**, *60*, 289-297. [CrossRef]

10. Desai, S.; Srambikkal, N.; Yadav, H.D.; Shetake, N.; Balla, M.M.S.; Kumar, A.; Ray, P.; Ghosh, A.; Pandey, B.N. Molecular Understanding of Growth Inhibitory Effect from Irradiated to Bystander Tumor Cells in Mouse Fibrosarcoma Tumor Model. *PLoS ONE* **2016**, *11*, e0161662. [CrossRef]

11. Smith, R.W.; Moccia, R.D.; Seymour, C.B.; Mothersill, C.E. Irradiation of Rainbow Trout at Early Life Stages Results in a Proteomic Legacy in Adult Gills. Part A; Proteomic Responses in the Irradiated Fish and in Non-Irradiated Bystander Fish. *Environ. Res.* **2018**, *163*, 297-306. [CrossRef] [PubMed]

12. Lepleux, C.; Marie-Brasset, A.; Temelie, M.; Boulanger, M.; Brotin, É.; Goldring, M.B.; Hirtz, C.; Varès, G.; Nakajima, T.; Saintigny, Y.; et al. Bystander Effectors of Chondrosarcoma Cells Irradiated at Different LET Impair Proliferation of Chondrocytes. *J. Cell Commun. Signal.* **2019**, *13*, 343-356. [CrossRef]

13. Gilbert, A.; Tudor, M.; Lepleux, C.; Rofidal, V.; Hem, S.; Almunia, C.; Armengaud, J.; Brotin, E.; Haghdoost, S.; Savu, D.; et al. Proteomic Analysis of Bystander Effects in Chondrosarcoma Cells. In Proceedings of the ERRS 45th Annual Meeting of the European Radiation Research Society, Lund, Sweden, 13-17 September 2020. p. Poster N°25.

14. Banani, S.F.; Lee, H.O.; Hyman, A.A.; Rosen, M.K. Biomolecular Condensates: Organizers of Cellular Biochemistry. *Nat. Rev. Mol. Cell Biol.* **2017**, *18*, 285-298. [CrossRef]

15. Guzikowski, A.R.; Chen, Y.S.; Zid, B.M. Stress-Induced MRNP Granules: Form and Function of P-Bodies and Stress Granules. *Wiley Interdiscip. Rev. RNA* **2019**, *10*, e1524. [CrossRef]

16. Kedersha, N.; Anderson, P. Mammalian Stress Granules and Processing Bodies. *Methods Enzym.* **2007**, *431*, 61-81. [CrossRef]

17. Kedersha, N.; Ivanov, P.; Anderson, P. Stress Granules and Cell Signaling: More than Just a Passing Phase? *Trends Biochem. Sci.* **2013**, *38*, 494-506. [CrossRef]

18. Mahboubi, H.; Stochaj, U. Cytoplasmic Stress Granules: Dynamic Modulators of Cell Signaling and Disease. *Biochim. Biophys. Acta Mol. Basis Dis.* **2017**, *1863*, 884-895. [CrossRef]

19. Moutaoufik, M.T.; Fatimy, R.E.; Nassour, H.; Gareau, C.; Lang, J.; Tanguay, R.M.; Mazroui, R.; Khandjian, E.W. UVC-Induced Stress Granules in Mammalian Cells. *PLoS ONE* **2014**, *9*, e112742. [CrossRef] [PubMed]

20. Wakazono, A.; Fukao, T.; Yamaguchi, S.; Hori, T.; Orii, T.; Lambert, M.; Mitchell, G.A.; Lee, G.W.; Hashimoto, T. Molecular, Biochemical, and Clinical Characterization of Mitochondrial Acetoacetyl-Coenzyme A Thiolase Deficiency in Two Further Patients. *Hum. Mutat.* **1995**, *5*, 34-42. [CrossRef] [PubMed]

21. Haapalainen, A.M.; Meriläinen, G.; Pirilä, P.L.; Kondo, N.; Fukao, T.; Wierenga, R.K. Crystallographic and Kinetic Studies of Human Mitochondrial Acetoacetyl-CoA Thiolase: The Importance of Potassium and Chloride Ions for Its Structure and Function. *Biochemistry* **2007**, *46*, 4305-4321. [CrossRef]

22. Gallego-García, A.; Monera-Girona, A.J.; Pajares-Martínez, E.; Bastida-Martínez, E.; Pérez-Castaño, R.; Iniesta, A.A.; Fontes, M.; Padmanabhan, S.; Elías-Arnanz, M. A Bacterial Light Response Reveals an Orphan Desaturase for Human Plasmalogen Synthesis. *Science* **2019**, *366*, 128-132. [CrossRef] [PubMed]

23. Mouneimne, G.; Hansen, S.D.; Selfors, L.M.; Petrak, L.; Hickey, M.M.; Gallegos, L.L.; Simpson, K.J.; Lim, J.; Gertler, F.B.; Hartwig, J.H.; et al. Differential Remodeling of Actin Cytoskeleton Architecture by Profilin Isoforms Leads to Distinct Effects on Cell Migration and Invasion. *Cancer Cell* **2012**, *22*, 615-630. [CrossRef] [PubMed]

24. Martínez-Martínez, E.; Ibarrola, J.; Fernández-Celis, A.; Santamaria, E.; Fernández-Irigoyen, J.; Rossignol, P.; Jaisser, F.; López-Andrés, N. Differential Proteomics Identifies Reticulocalbin-3 as a Novel Negative Mediator of Collagen Production in Human Cardiac Fibroblasts. *Sci. Rep.* **2017**, *7*, 12192. [CrossRef]

25. Miotto, B.; Chibi, M.; Xie, P.; Koundrioukoff, S.; Moolman-Smook, H.; Pugh, D.; Debatisse, M.; He, F.; Zhang, L.; Defossez, P.-A. The RBBP6/ZBTB38/MCM10 Axis Regulates DNA Replication and Common Fragile Site Stability. *Cell Rep.* **2014**, *7*, 575-587. [CrossRef]

26. Chibi, M.; Meyer, M.; Skepu, A.; G Rees, D.J.; Moolman-Smook, J.C.; Pugh, D.J.R. RBBP6 Interacts with Multifunctional Protein YB-1 through Its RING Finger Domain, Leading to Ubiquitination and Proteosomal Degradation of YB-1. *J. Mol. Biol.* **2008**, *384*, 908-916. [CrossRef]

27. Chevalier, F.; Depagne, J.; Hem, S.; Chevillard, S.; Bensimon, J.; Bertrand, P.; Lebeau, J. Accumulation of Cyclophilin A Isoforms in Conditioned Medium of Irradiated Breast Cancer Cells. *Proteomics* **2012**, *12*, 1756-1766. [CrossRef] [PubMed]
28. Wei, S.J.; Botero, A.; Hirota, K.; Bradbury, C.M.; Markovina, S.; Laszlo, A.; Spitz, D.R.; Goswami, P.C.; Yodoi, J.; Gius, D. Thioredoxin Nuclear Translocation and Interaction with Redox Factor-1 Activates the Activator Protein-1 Transcription Factor in Response to Ionizing Radiation. *Cancer Res.* **2000**, *60*, 6688–6695.

29. Ghosh, A.K.; Steele, R.; Ray, R.B. Functional Domains of C-Myc Promoter Binding Protein 1 Involved in Transcriptional Repression and Cell Growth Regulation. *Mol. Cell. Biol.* **1999**, *19*, 2880–2886. [CrossRef]

30. Chevalier, F.; Hamdi, D.H.; Lepleux, C.; Temelie, M.; Nicol, A.; Austry, J.B.; Lesueur, P.; Vares, G.; Savu, D.; Nakajima, T.; et al. High LET Radiation Overcomes In Vitro Resistance to X-Rays of Chondrosarcoma Cell Lines. *Technol. Cancer Res. Treat.* **2019**, *18*, 1533033819871309. [CrossRef]

31. Hartmann, E.M.; Allain, F.; Gaillard, J.-C.; Pible, O.; Armengaud, J. Taking the Shortcut for High-Throughput Shotgun Proteomic Analysis of Bacteria. *Methods Mol. Biol.* **2014**, *1197*, 275–285. [CrossRef]

32. Klein, G.; Mathé, C.; Biola-Clier, M.; Devineau, S.; Drouineau, E.; Hatem, E.; Marichal, L.; Alonso, B.; Gaillard, J.-C.; Lagniel, G.; et al. RNA-Binding Proteins Are a Major Target of Silica Nanoparticles in Cell Extracts. *Nanotoxicology* **2016**, *10*, 1555–1564. [CrossRef]

33. Dupierris, V.; Masselon, C.; Court, M.; Kieffer-Jaquinod, S.; Bruley, C. A Toolbox for Validation of Mass Spectrometry Peptides Identification and Generation of Database: IRMA. *Bioinformatics* **2009**, *25*, 1980–1981. [CrossRef] [PubMed]

34. Shakeri Manesh, S.; Sangsuwan, T.; Pour Khavari, A.; Fotouhi, A.; Emami, S.N.; Haghdoost, S. MTH1, an 8-Oxo-2'-Deoxyguanosine Triphosphatase, and MYH, a DNA Glycosylase, Cooperate to Inhibit Mutations Induced by Chronic Exposure to Oxidative Stress of Ionising Radiation. *Mutagenesis* **2017**, *32*, 389–396. [CrossRef]

35. Dépagne, J.; Chevalier, F. Technical Updates to Basic Proteins Focalization Using IPG Strips. *Proteome Sci* **2012**, *10*, 54. [CrossRef]

36. Chevalier, F.; Hirtz, C.; Sommerer, N.; Kelly, A.L. Use of Reducing/Nonreducing Two-Dimensional Electrophoresis for the Study of Disulfide-Mediated Interactions between Proteins in Raw and Heated Bovine Milk. *J. Agric. Food Chem.* **2009**, *57*, 5948–5955. [CrossRef]

37. Chevalier, F.; Centeno, D.; Rofidal, V.; Tauzin, M.; Martin, O.; Sommerer, N.; Rossignol, M. Different Impact of Staining Procedures Using Visible Stains and Fluorescent Dyes for Large-Scale Investigation of Proteomes by MALDI-TOF Mass Spectrometry. *J. Proteome Res.* **2006**, *5*, 512–520. [CrossRef] [PubMed]

38. Saintigny, Y.; Chevalier, F.; Bravard, A.; Dardillac, E.; Laurent, D.; Hem, S.; Dépagne, J.; Radicella, J.P.; Lopez, B.S. A Threshold of Endogenous Stress Is Required to Engage Cellular Response to Protect against Mutagenesis. *Sci. Rep.* **2016**, *6*, 29412. [CrossRef]

39. Liu, H.; Sadygov, R.G.; Yates, J.R. A Model for Random Sampling and Estimation of Relative Protein Abundance in Shotgun Proteomics. *Anal. Chem.* **2004**, *76*, 4193–4201. [CrossRef]

40. Carvalho, P.C.; Fischer, J.S.G.; Chen, E.I.; Yates, J.R.; Barbosa, V.C. PatternLab for Proteomics: A Tool for Differential Shotgun Proteomics. *BMC Bioinform.* **2008**, *9*, 316. [CrossRef]

41. Perez-Riverol, Y.; Csordas, A.; Bai, J.; Bernal-Llinares, M.; Hewapathirana, S.; Kundu, D.J.; Inuganti, A.; Griss, J.; Mayer, G.; Eisenacher, M.; et al. The PRIDE Database and Related Tools and Resources in 2019: Improving Support for Quantification Data. *Nucleic Acids Res.* **2019**, *47*, D442–D450. [CrossRef] [PubMed]
<figure><img src="image_17.png" /></figure>

Protocol

# A Longitudinal Study of Individual Radiation Responses in Pediatric Patients Treated with Proton and Photon Radiotherapy, and Interventional Cardiology: Rationale and Research Protocol of the HARMONIC Project

Maria Grazia Andreassi<sup>1, ID</sup>, Nadia Haddy<sup>2</sup>, Mats Harms-Ringdahl<sup>3</sup>, Jonica Campolo<sup>4 ID</sup>, Andrea Borghini<sup>1 ID</sup>, François Chevalier<sup>5,6 ID</sup>, Jochen M. Schwenk<sup>7 ID</sup>, Brice Fresneau<sup>8,9 ID</sup>, Stephanie Bolle<sup>10</sup>, Manuel Fuentes<sup>11 ID</sup> and Siamak Haghdoost<sup>3,5,6,* ID</sup>

<figure><img src="image_18.png" /></figure>

check for
updates

**Citation:** Andreassi, M.G.; Haddy, N.; Harms-Ringdahl, M.; Campolo, J.; Borghini, A.; Chevalier, F.; Schwenk, J.M.; Fresneau, B.; Bolle, S.; Fuentes, M.; et al. A Longitudinal Study of Individual Radiation Responses in Pediatric Patients Treated with Proton and Photon Radiotherapy, and Interventional Cardiology: Rationale and Research Protocol of the HARMONIC Project.
*Int. J. Mol. Sci.* **2023**, *24*, 8416.
https://doi.org/10.3390/ijms24098416

Academic Editor: David A. Gewirtz

Received: 15 March 2023

Revised: 1 May 2023

Accepted: 3 May 2023

Published: 8 May 2023

<figure><img src="image_19.png" /></figure>

**Copyright:** © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).

**Abstract:** The Health Effects of Cardiac Fluoroscopy and Modern Radiotherapy (photon and proton) in Pediatrics (HARMONIC) is a five-year project funded by the European Commission that aimed to improve the understanding of the long-term ionizing radiation (IR) risks for pediatric patients. In this paper, we provide a detailed overview of the rationale, design, and methods for the biological aspect of the project with objectives to provide a mechanistic understanding of the molecular pathways involved in the IR response and to identify potential predictive biomarkers of individual response involved in long-term health risks. Biological samples will be collected at three time points: before the first exposure, at the end of the exposure, and one year after the exposure. The average whole-body dose, the dose to the target organ, and the dose to some important out-of-field organs will be estimated. State-of-the-art analytical methods will be used to assess the levels of a set of known biomarkers and also explore high-resolution approaches of proteomics and miRNA transcriptomes to provide an integrated assessment. By using bioinformatics and systems biology, biological pathways and novel pathways involved in the response to IR exposure will be deciphered.

**Keywords:** radiotherapy; ionizing radiation; proton radiotherapy; pediatric oncology; HARMONIC project; congenital heart disease; cardiac catheterization; radiation biomarkers

<footnote n="1">1. CNR National Research Council Institute of Clinical Physiology, 56125 Pisa, Italy; andreassi@ifc.cnr.it (M.G.A.); aborghini@ifc.cnr.it (A.B.)</footnote>

<footnote n="2">2 Radiation Epidemiology Team, Center for Research in Epidemiology and Population Health, INSERM U1018, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France; nadia.haddy@gustaveroussy.fr</footnote>

<footnote n="3">3 Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, 10691 Stockholm, Sweden; mats.harms-ringdahl@su.se</footnote>

<footnote n="4">4 CNR National Research Council Institute of Clinical Physiology, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; jonica.campolo@cnr.it</footnote>

<footnote n="5">5 UMR6252 CIMAP, CEA-CNRS-ENSICAEN-University of Caen Normandy, 14000 Caen, France; francois.chevalier@cea.fr</footnote>

<footnote n="6">6 Advanced Resource Center for HADrontherapy in Europe (ARCHADE), 14000 Caen, France</footnote>

<footnote n="7">7 Affinity Proteomics, SciLifeLab, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH—Royal Institute of Technology, 10044 Stockholm, Sweden; jochen.schwenk@scilifelab.se</footnote>

<footnote n="8">8 Department of Children and Adolescents Oncology, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France; brice.fresneau@gustaveroussy.fr</footnote>

<footnote n="9">9 Cancer and Radiation Team, Center for Research in Epidemiology and Population Health, INSERM U1018, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France</footnote>

<footnote n="10">10 Department of Radiation Therapy, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France</footnote>

<footnote n="11">11 Department of Medicine and General Service of Cytometry, Proteomics Unit, Cancer Research Centre-IBMCC, CSIC-USAL, IBSAL, Campus Miguel de Unamuno s/n, University of Salamanca-CSIC, 37007 Salamanca, Spain</footnote>

<footnote>* Correspondence: siamak.haghdoost@su.se or siamak.haghdoost@unicaen.fr</footnote>
# 1. Introduction

The use of ionizing radiation (IR) in medicine represents significant benefits for the medical care of patients. Indeed, IR remains one of the major therapeutic options for cancer treatment [1]. IR is also used for diagnostic and therapeutic imaging, particularly for pediatric patients with congenital or acquired heart disease, who may receive one or more cardiac catheterization procedures as part of their management [2–4]. While benefits for the patients largely outweigh the risk, the potential adverse health effects of exposure to IR are particularly important to be explored in populations of young patients who are more radiosensitive and, nowadays, survive their disease for decades [3,4].

IR is a well-known risk factor for cancer induction, and recent studies support the existence of an excess cancer risk, even at low doses of radiation [5]. Children are especially vulnerable to the oncogenic effects of IR. Moreover, the oncogenic effects of IR require a long latent period (from years to decades) that varies with the type of malignancy; therefore, an infant or child has a longer lifetime risk for developing radiation-induced cancers than an adult. Radiation-induced second malignancies are one of the most serious adverse effects following radiotherapy of primary cancers in childhood cancer [6]. Typically, radiation-induced malignancies develop in normal tissue within radiotherapy fields with a latency period of 5–10 years for hematologic malignancies and 10–60 years for solid tumors [6]. Many clinical studies reported an increased risk for second primary cancer, histopathologically different from the first tumor, in organs inside as well as outside the primary beam [6]. Interestingly, it was reported that leukemias and carcinomas are more often seen in organs receiving low-dose radiation (out-of-field dose), whereas sarcomas are more common in tissues or organ receiving high-dose radiation (in-field doses) [7]. However, the exact mechanism and dose-response relationship for radiation-induced malignancy, for both in-field and out-of-field doses, are not well understood; thus, it is necessary to investigate how radiotherapy, photons, and protons impact carcinogenic risk in childhood cancer management [7]. For instance, the therapeutic use of proton beams has the potential to provide a better depth-dose profile and remarkable reduction in the dose to the adjacent normal tissues compared with photon beams [7]. Additionally, there is growing evidence supporting an increased risk for late adverse non-cancer conditions [8], including cardiac and vascular effects. Thus, late adverse effects of radiotherapy have been observed on large vessels, causing cerebrovascular [9,10] and cardiovascular [11,12] diseases.

Nowadays, there is also evidence for a significant elevation of cancer risk in patients with acquired as well as congenital heart diseases (CHD) in response to repeated radiological exposures [13,14]. However, more data are needed to better define the “malignant price of cardiac care” [15].

The risk estimates of long-term health effects of low doses of IR (cancer and non-cancer) are still incomplete, particularly for pediatric patients. Large patient cohorts, extended follow-up, validated clinical data, and reliable dosimetry for the cohorts are needed to address this challenge. The integration of epidemiological and biological research through panels of biomarkers, together with a mechanistic understanding of the cellular responses to a particular dose and radiation quality, will provide powerful means to improve risk estimates, leading to a better quantification of the magnitude of risks associated with low-dose exposures, e.g., for out-of-field organs.

The Health Effects of Cardiac Fluoroscopy and Modern Radiotherapy in Pediatrics (HARMONIC) is a five-year project funded by the European Commission to improve understanding of the long-term health risks from medical ionizing radiation exposure in children and young patients (https://harmonicproject.eu/, accessed on 4 March 2023). The HARMONIC project uses an integrated approach of conventional epidemiology complemented by non-invasive imaging and molecular epidemiology to assess cancer and non-cancer outcomes in pediatric patients treated with modern radiotherapy techniques (such as proton therapy) for cancer and X-ray-guided interventional catheterization procedures for CHD.
The purpose of the manuscript is to describe the bioanalytical research goals of the HARMONIC project, the rationale, and the study design, including the enrolment, end-points, and expected results of the study.

The general objective of the bioanalytical studies of the HARMONIC project is to provide a mechanistic understanding of the molecular pathways and the cellular responses that are triggered by the medical applications of IR in pediatric patients.

A mechanistic understanding, together with the identification of biomarkers for in-dividuals at increased risk to develop adverse health effects, has the potential to increase the power of epidemiological studies regarding health effects caused by IR [16–18]. Such biomarkers may be useful in identifying susceptible individuals who are more vulnerable to radiation damage for whom individualized treatment can be considered (by radiation sparing policy or attempts to pharmacologic or dietary radioprotection).

The specific aims are to:

- identify radiation-induced biochemical responses in blood and saliva from pediatric patients exposed to medical IR;

- evaluate dose-response relationships for different radiation qualities and delivery techniques with regards to specific biochemical responses;

- search for pre-existing biomarkers of radiation sensitivity and health effects that may be useful for molecular epidemiological studies to identify patients with a potential higher risk of radiation-induced adverse health effects.

## 2. Experimental Design

The HARMONIC biological study is a prospective observational study which aims to investigate the biological changes induced by ionizing radiation exposure at various time points before and after exposure. It will focus on specific molecular biomarkers reported as 'early signs' of biological damage and long-term health effects. These include biomarkers of oxidative stress (8-hydroxy-2'-deoxyguanosine) [19–21], protein markers of inflammation (PTX3, IL-6, IL-10, TNF-α, NF-κB, MCP-1, etc.) [22,23], and genetic markers (telomere shortening and mtDNA copy numbers) [24–29].

To decipher significant intracellular pathways and novel potential biomarkers involved in response to the radiation regimes applied, four different approaches will be used: multiplexed protein profiling assays on blood plasma [30], reverse-phase protein array (RPPA) on proteins isolated from peripheral blood mononuclear cells [31], miRNA transcriptome on whole blood and saliva [32], and liquid chromatography–mass spectrometry (LC–MS) on saliva.

Finally, we will develop and implement new bioinformatic models to integrate the collected biological, clinical, and dosimetry data. that may be used in epidemiological and clinical approaches to identify patients at higher risk for radiation-induced adverse health effects, not only before starting (by analyzing the sample taken before exposure), but also after finishing the exposure (by analyzing samples taken after exposure). Biomarkers will be studied in both blood and saliva to investigate whether saliva can be used as a non-invasive sample to analyze biomarkers in large-scale molecular epidemiology studies. The overall strategy of the "biology" project is presented in Figure 1.
<figure><img src="image_20.png" /><figcaption>**Figure 1.** Overall strategy of the planned biological research in the HARMONIC project.</figcaption></figure>

### 3. Material and Equipment

#### 3.1. Study Population

This exploratory study will include 150 patients: 50 patients treated for cancer with proton therapy, 50 patients with photon therapy, and 50 patients treated with X-ray-guided interventional catheterization procedures for CHD. Specific inclusion and exclusion criteria are listed in Table 1.

<table><caption>**Table 1.** Inclusion and exclusion criteria.</caption><thead><tr><th>Radiotherapy</th><th>Interventional Cardiology</th></tr></thead><tbody><tr><td><strong>Inclusion criteria</strong><ul><li>Age at diagnosis ≤ 21 years</li><li>Informed consent of parent/guardian as well as child/patient</li><li>Patients treated for: brain tumors (except malignant gliomas); head and neck tumors (e.g., rhabdomyosarcomas and nasopharyngeal carcinoma); Hodgkin's lymphoma</li><li>Patients receiving pulmonary and chest radiation for: Ewing sarcoma; other chest sarcomas; lung metastasis of Wilms and Ewing tumors; other tumors</li><li>Patients receiving craniospinal radiation therapy for: Medulloblastoma or other tumors</li></ul></td><td><ul><li>Age of patients: 5-22 years</li><li>Patients with congenital heart disease</li><li>Informed consent of parent/guardian as well as child/patient</li></ul></td></tr><tr><td><strong>Exclusion criteria</strong><ul><li>Chromosomal abnormalities and/or genetic syndromes</li><li>Absence of informed consent</li></ul></td><td></td></tr></tbody></table>

Ethics approval has been already obtained in all participating centers. All eligible patients received an information brochure and are invited to participate in the study by the responsible physician. Informed consent is signed by the patient or his/her legal representative before entering the study. Detailed demographic, clinical and treatment data are retrieved by the attending physician and from the patient's medical electronic
record. Data protection officers (DPOs) from each organization will be involved to ensure
compliance with General Data Protection Regulations (GDPRs).

3.2. *Biological Sample Collection*

For radiotherapy patients, blood and saliva will be collected at three time points: before radiotherapy; three months after the last fraction (time point for the first follow-up); and one year after completion of the treatment. For X-ray-guided interventional catheterization procedures, biological samples will also be collected at three time points: before intervention; the same day after completion; and one year after completion. Figure 2 summarizes the protocol for the collection, preparation, and storage of biosamples.

<figure><img src="image_21.png" /><figcaption>**Figure 2.** Study design of the Harmonic project and overview of biological sample collection.</figcaption></figure>

Briefly, a maximum of 12 mL blood will be collected at each time point in three different tubes, which are as follows:

- one BD vacutainer® CPT™ tube for the isolation of lymphocytes (~4 mL);

- one vacutainer tube containing EDTA K2 (~4 mL);

- one clot activator serum separation tube (~4 mL).

The samples will be given a unique patient identification number (pseudonymization). Within two hours post collection, tubes with blood samples will be centrifuged according to standard operating procedures (SOPs) prepared by the HARMONIC consortium to obtain lymphocytes, serum, and plasma. To investigate the possible impact of pre-analytical variables, we will record and share information on the study centers, time, and calendar days when the samples are collected. We will also record and share the time that elapses between the blood draw, centrifugation, and first freezing.

Regarding saliva samples, approximately 4–5 mL of saliva will be collected at each time point in a sterile 10 mL plastic tube without any additive. Saliva samples will be divided into two aliquots, 2 mL in each. Aliquots of biological samples from each donor will be immediately stored at –80 °C (Figure 2). The type of tubes, volume, and number of each aliquots are summarized in Figure 2. At specific time points in the project, coded
samples will be shipped from a respective clinic on dry ice to a centralized Biobank, where
they will be stored at -80 °C until use.

3.3. *Biological Measures*

The samples will be analyzed by state-of-the-art methods to determine the levels of selected biomarkers. Moreover, the samples will be examined by innovative high-throughput approaches, including analyses of miRNA transcriptomes and proteomics [30–32]. The handling and analytical procedures will follow the respective SOP procedures for all biologic sampling, handling, shipping, and analysis.

**4. Detailed Procedure**

4.1. *8-Hydroxy-2'-deoxyguanosine (8-oxo-dG) and Markers of Inflammation*

The levels of 8-oxo-dG in serum and saliva will be determined using an ELISA method where the samples are essentially purified by Bond Elute columns, as previously described [19,20]. Briefly, 800 µL blood serum or saliva will be purified using a C18 solid phase Bond Elut extraction column. The purified samples will be freeze-dried and reconstituted in PBS. Then, 300 µL of the purified sample will be mixed with 150 µL of primary antibody against 8-oxo-dG and distributed in three wells of a 96-well ELISA plate pre-coated with 8-oxo-dG and then incubated at 37 °C for 120 min. Secondary antibody body will then be added followed by the staining solution in order to quantify the yield of secondary antibodies bounded to primary antibodies in each well using a 96-well automatic ELISA plate reader. Each sample will be analyzed in triplicate. A standard curve for 8-oxo-dG (0.05–10 ng/mL) will be established for each plate and the concentration of 8-oxo-dG in each sample will be calculated based on the standard curve.

4.2. *Analysis of Telomere Length (TL) and mtDNA Copy Number (mtDNA-CN)*

TL and mtDNA-CN will be measured on DNA extracted from 200 µL of biological
samples of blood and saliva samples by real-time PCR (CFX384 Touch™ Real-Time PCR
System, Bio-Rad Life Sciences) according to standardized protocols [25,26]. Briefly, TL
will be measured in genomic DNA by determining the ratio of a telomere repeat copy
number (T) to a single-copy gene (S) and copy number (T/S ratio). The relative telom-
ere length will be calculated using the following formula "T/S ratio = 2-ΔΔCt", where
ΔCt = Ct telomere - a Ct single-copy gene. The T/S ratio reflects the average length of
the telomeres across all leukocytes. For the quantification of mtDNA-CN, the NDI1 gene
in the undeleted region for the reference sequence of mtDNA will be used as an internal
control (mtNDI1) and human β-globin gene of genomic DNA (gDNA) will be amplified
by PCR in both gDNA and mtDNA. ΔCt values will be calculated from the difference
between the Ct for the β-globin gene and the Ct for the NDI1 gene and used to measure
mtDNA-CN relative to gDNA. mtDNA-CN will be calculated using the (2ΔCt) method
(ΔCt = Ct mtNDI1 - CtgDNA).

4.3. *miRNA Profiling Analysis*

Total RNA will be isolated from 500 µL of blood and saliva samples using a RiboPure™-Blood Kit (ThermoFisher, Waltham, MA, USA) and a miRNeasy Serum/Plasma Kit (QIA-GEN, Hilden, Germany), respectively, according to the manufacturer's protocol [32]. The expression profiling of miRNAs will be analyzed using the Illumina MiSeq platform. For each patient, we will carry out a small RNA sequencing experiment to characterize the different miRNA expression profiles in samples for each time point. Prepared libraries will be run on Miseq, and miRNA identification and dysregulated expression analyses will be performed using latest version of iMir software (https://www.labmedmolge.unisa.it/italiano/home/imir, accessed on 5 September 2022), a fully automated workflow for the rapid analysis of high-throughput small RNA-Seq data. Specific dysregulated miRNAs will be further validated using qRT-PCR with sequence-specific TaqMan microRNA assays and a TaqMan Universal PCR Master Mix, as opposed to AmpErase UNG (Thermo
Fisher Scientific, USA), in accordance with the manufacturer's instructions. The miRNA expression levels will be normalized to the U6 small nuclear RNA and calculated using the ΔΔCt method [32]. The target genes from differentially expressed miRNAs will be predicted using DIANA miRPath software (microrna.gr/mirpath, accessed on 5 September 2022). Then, the targets will then be further analyzed for gene ontology (GO) function enrichment terms (geneontology.org/, accessed on 5 September 2022), Kyoto Encyclopedia of Genes Genomes (KEGG) pathway classification (www.genome.jp/kegg/, accessed on 5 September 2022), and Reactome pathway databases (www.reactome.org, accessed on 5 September 2022).

### 4.4. Plasma Protein Profiling

Briefly, from the literature, a list of 90 protein markers that have previously been identified as potential markers of diseases related to the late effects of radiation exposure, particularly vascular diseases and secondary cancer, will be established. Aliquots of 100 µL plasma will be used for plasma proteome analysis using Olink's affinity proteomics platform. The approach is based on paired antibodies, coupled to unique and partially complementary oligonucleotides, and measured by quantitative real-time PCR. This dual-recognition DNA-coupled method provides high specificity and sensitivity for an analysis of at least 90 proteins in parallel [30]. We plan to analyze the 98 selected proteins in all samples from the three cohorts. Different statistical and computational models for single and multivariate analysis will then be used to identify the modified pathways.

### 4.5. Reverse-Phase Protein Arrays (RPPAs)

Reverse-phase protein arrays (RPPAs) will allow us to study protein expression levels and the activation status of cell signaling pathways. Isolated peripheral blood mononuclear cells (PBMCs) from blood collected into CPT tubes will be analyzed by customized RPPA (Proteomics Unit. IBSAL. University of Salamanca). To summarize, the cells will be lysed and the protein extract will be serially diluted with a protein lysis buffer, supplemented with proteases and phosphatase inhibitors. Five serial dilutions/sample, ranging from 2000 to 125 µg/mL, and two technical replicates per dilution will be applied on the nitrocellulose microarray membrane. In addition, a few spike-in proteins as RPPAs, such as negative and positive controls, will be included.

The membranes will be incubated with primary antibodies that target proteins of interest or without primary antibodies as negative controls. All primary antibodies for RPPA screening have been previously tested by Western blotting to assess their specificity and selectivity to the targeted protein. RPPA readout is a fluorescent signal correlated to the protein expression level. Samples will be applied on membranes in three technical replicates (spots), and the membranes will then be individually incubated with antibodies targeting one protein of interest, followed by an incubation with a fluorescent dye. Fluorescent signals will be acquired by a microarray scanner at high resolutions and minimal autofluorescent background. The NormaCurve method will be used for data quantification and normalization [31]. This method includes a normalization for (i) background fluorescence, (ii) variations in the total amount of spotted protein; and (iii) spatial bias on the membranes. The normalized values will be employed to compare the protein expression levels across samples. Briefly, for each spot, the raw fluorescent signal of the proteins will be corrected with the fluorescent signal of the negative control (signal obtained after incubating an array, without the targeted protein antibody). This corrected signal will be divided by the total amount of spotted protein, corresponding to the normalized signal. Finally, the normalized signals of all the proteins will be scaled according to the median for further comparisons and statistical analysis. For each sample, one value will be generated for each targeted protein, and further statistical analysis will be considered.
## 4.6. Saliva Protein Analysis

The saliva protein concentrations will be determined using a colorimetric protein assay (BCA Protein Assay Kit, Thermo Scientific, Waltham, MA, USA). The proteomic workflow is as follows. The iST-BCT Kit (PreOmics, Martinsried, Germany) will be used to perform a fast, reliable, and reproducible sample preparation on all the patient samples. Fifty microliters of saliva will be used as the starting material (the volume will be adjusted depending on the BCA result). Saliva proteins will be precipitated with 200 µL of ethanol at -20 °C overnight. Samples will then be centrifuged (at 17,000 × g for 5 min at 4 °C) and the supernatants will be removed. Salivary protein pellets will be re-suspended, lysed, reduced, and alkylated in 10 min at 95 °C. Proteins will be digested in one hour. Generated peptides will be cleaned before LC-MS injection. Purified tryptic digests will be separated with a predefined 60 SPD method (21 min gradient time and 200 ng peptides) on an Evosep One LC system (Evosep, Odense, Demmark). A fused silica 10 µm ID emitter (Bruker Daltonics, Waltham, MA, USA) is placed inside a nanoelectrospray source (CaptiveSpray source, Bruker Daltonics, Waltham, MA, USA). The emitter is connected to a 8 cm × 150 µm reverse-phase column, packed with 1.5 µm C18 beads. Mobile phases will comprise water and acetonitrile, buffered with 0.1% formic acid. The column will be heated to 40 °C in an oven compartment. LC is coupled online to a TIMS Q-TOF instrument (timsTOF Pro 2, Bruker Daltonics) with a diaPASEF acquisition method. Samples will be acquired using a diaPASEF method, consisting of 12 cycles, including a total of 34 mass width windows (25 Da width, from 350 to 1200 Da) with 2 mobility windows each, leaving a total of 68 windows that cover the ion mobility range (1/K0) from 0.64 to 1.37 V s/cm². Saliva proteins will be quantified using a label-free DIA approach with DIA-NN software (https://github.com/vdemichev/diann, accessed on 4 March 2023). DIA-NN version 1.8 will be used first to build an in silico predicted library from the human FASTA database (NextProt 2022-02-25), enabling the 'FASTA digest for library-free search/library generation' and 'Deep learning-based spectra' options, as well as RTs and IMs prediction. The predicted library will be used to analyze the diaPASEF dataset.

## 4.7. Radiation Doses Data

For each patient, the average whole-body dose or mean/maximum dose and non-target organ (out-of-field organ) doses will be estimated in collaboration with physicists responsible for dosimetry studies in the Harmonic project (https://harmonicproject.eu, accessed on 4 May 2023). Briefly, the strategy for dose estimation will rely on Monte Carlo simulations for CHD patients and on treatment planning systems and analytical models for cancer patients [33]. These strategies were benchmarked against measurements on physical phantoms and reference Monte Carlo simulations [34,35]. As we are analyzing plasma proteins, the total dose to the blood will also be estimated and considered.

## 4.8. Integrative Analysis of Biological Function and Networks

Integrative data analysis of multiple sets of data types will be performed to construct an interaction network of differentially expressed features (miRNAs and proteins) to elucidate the molecular mechanisms underlying the biological response to IR and to discover new potential biomarkers. In brief, each dataset from the different independent analyses (miRNA transcriptome sequencing and proteomics) will first be analyzed in relation to the available clinical parameters, e.g., age, sex, diagnosis, and background diseases, to identify significantly different features between the baseline and post-IR exposure responses. Then, the radiation-deregulated miRNAs and proteins will be analyzed by an integrative procedure using software, such as ingenuity pathway analysis (Qiagen Bioinformatics; Redwood City, CA, USA; www.qiagen.com/ingenuity, accessed on 5 March 2023), in order to identify the most significantly affected pathways, their components, and associated signaling networks.
## 4.9. Sample Size and Plan for Statistical Analysis

This study is exploratory as the number of available patients is limited. With the use of data from our previous study on leukocyte telomere length [25], priori power analysis (Spearman's correlation test) requires a sample size of 34 patients to achieve >80% power (alpha = 0.05) and to detect an effect size of 0.5 (C\*Power, version 3.1.9.2). We plan to include a target population of 100 patients from each cohort, considering the feasibility aspects of the study, the estimated level of recruitment in each participating clinical site, and a drop-out rate of 50%, aiming for a minimum of 50 participating patients in each cohort. Concerning the statistical plan, a database including clinical (diagnosis, different therapies, CT and MRI images, etc.), dosimetric, and experimental data for each patient will be created in order to perform appropriate statistical analysis. Descriptive data will be presented as frequencies with proportions for categorical variables, and either as means with corresponding SDs or medians with corresponding IQRs for continuous variables depending on the distribution. Statistical tests will include Pearson's $x^2$ test for frequencies, the Mann-Whitney U test for non-normally distributed continuous variables, and Student's t-test for normally distributed variables. Spearman's correlation test will be used to explore the association between variables and radiation doses.

Exploratory analysis, including unsupervised clustering and principal component analysis, will also be performed to stratify patients according to differential protein expression or relative protein abundance. Other soft clustering, dimensionality reduction (e.g., tSNE and UMAP), or unsupervised analyses (e.g., group-based trajectory models) will be conducted to identify groups of individuals that follow similar shifts or trends on protein relative abundance or differential protein expressions over time, considering that all time points will be performed. The differential profiles will be determined based on the data analysis for baseline and each timepoint, as well as changes from baseline values (relative and absolute change).

A mixed-effects model will be used to study the association between different biomarkers at different time points and the dosimetry data (dose, volume, and beam quality). Lastly, the differences in biomarker levels between a follow-up timepoint and baseline will be studied as a function of dosimetric indicators using general linear models, with adjustment for potential confounders (chemotherapy, disease history, medicines, BMI, etc.). Statistical significance for all analyses will be assessed using two-sided tests with an alpha level of 0.05 and adjustment for multiple comparisons.

# 5. Expected Results

The HARMONIC project addresses a crucial question regarding the health risks for pediatric patients exposed to ionizing radiation from radiotherapy or interventional cardiology (UNSCEAR report 2008) [1-4]. Over the recent years, there has been considerable technological advancements that improve the therapeutic gain of radiotherapy, i.e., maximizing the dose to the tumor while sparing the healthy tissue, [7,36], as well as implementing numerous dose reduction strategies in pediatric interventional cardiology [3,37]. The difficulties associated with cancer and non-cancer risk assessments from pediatric radiation exposure could be partly overcome with precise dose estimation and biochemical studies to better understand the mechanisms that underlie the development of disease processes and provide indicators of risk [16-18].

Radiation can induce DNA damage, especially DNA double-strand breaks (DSBs), which are the most lethal type of DNA damage and can result in mutations, chromosomal abnormalities, and the further development of cancer, as well as other severe health effects. However, the full spectra of biological mechanisms underlying the adverse health effects after irradiation are only partly understood. It has been reported that several biological pathways, such as DNA repair, inflammatory response, oxidative stress induction, as well as metabolic changes, are involved in response to IR exposure [17,38]. A multifactorial approach in conjunction with robust high-throughput technologies and integrated computational approaches, such as systems biology, may facilitate the discovery of new
pathways and biomarkers for the prediction of long-term adverse health effects of IR exposure [17,38,39].

Accordingly, biological research on the HARMONIC project will investigate the changes induced by medical radiation at the level of specific biomarkers which might be considered ‘early signs’ of tissue damage before the full development of adverse health effects. The project will focus on changes related to oxidative stress [19,20], inflammation [22,23], as well as nuclear and mitochondrial DNA damage [25,26], in order to identify the long-term health risks [21,24,27–29].

In parallel, the study will also use high-resolution approaches of proteomics and whole miRNA transcriptomes to provide an integrated assessment of bio-molecular responses to pediatric radiation exposure by identifying biological pathways that may underlie the adverse health effects of the exposures.

Of note, this study has a longitudinal design which will allow the shorter-term and longer-term biological response of IR to be compared. To account for the differences between the participants, we will anchor the data of each individual on their own baseline data. This will allow us to measure the treatment effects on an individual level immediately and up to 1 year later.

An additional aspect of this study is the comparison of biomarkers in saliva with those in blood samples. Especially in vulnerable populations, such as children [40,41], saliva offers an attractive non-invasive sampling method that is relatively inexpensive, safe, and easy to use. Saliva could be a valuable alternative as a biological source for human biomonitoring in occupational and environmental medicine [41], but further studies are needed to explore the robustness, reproducibility, and validity of salivary biomarkers in comparison to those analyzed in blood.

As the study includes a cohort of pediatric patients that will be well characterized in terms of dosimetry to “in-field” and “out-of-field” organs [33–35] (https://harmonicproject.eu, accessed on 4 March 2023), it will be possible to investigate dose-response relationships between biomarkers and doses to organs for both radiotherapy and interventional cardiology, as well as a comparison of effects of radiation quality.

The HARMONIC databases will register the individual responses for a defined set of biomarkers and facilitate studies on the mechanisms that underlie radiation-induced second/primary cancers, as well as cardiac and vascular damages. A mechanistic understanding, together with biomarkers for individuals at increased risk, has the potential to increase the power of epidemiological studies regarding the health effects of different radiotherapy modalities.

Hence, we believe that a better understanding of the underlying biological and cellular mechanisms will complement the epidemiological approach of the HARMONIC project (https://harmonicproject.eu/, accessed on 4 March 2023). This will provide a unique opportunity to gain better insight into the biological effects of medical radiation doses in pediatric patients.

In summary, the findings of this research project hold the potential to provide mechanistic insight in the molecular and cellular responses involved in the effects of IR of different radiation quality and doses. The HARMONIC project aims to improve the protection of patients and maximize the benefits from medical applications. The final expected output from the biological part of the project will be able to define the predictive biomarkers to be used for molecular epidemiology studies in order to identify patients at higher risk for adverse health effects.

**Author Contributions:** Writing—original draft and reading review: M.G.A.; reading review and editing: N.H., M.H.-R., J.C., A.B., F.C., J.M.S., B.F., S.B. and M.F.; conceptualization, supervision, and writing—review and editing, S.H. All authors have read and agreed to the published version of the manuscript.
**Funding:** The HARMONIC project (Health effects of cArdiac fluoRoscopy and MOderN radIotherapy in paediatrics) has received funding from the Euratom research and training program 2014–2018 under grant agreement No 847707.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Local Ethics Committees from the participating centers.

**Informed Consent Statement:** Written informed consent will be obtained from all patients and their parents involved in the study before the subject undergoes any study procedure.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Acknowledgments:** Collaborators: HARMONIC CONSORTIUM: Isabelle Thierry-Chef<sup>1,2,3</sup>, Christian Bäumer<sup>4</sup>, Marie-Odile Bernier<sup>5</sup>, Guillaume Boissonnat<sup>6</sup>, Lorenzo Brualla<sup>4</sup>, Vadim Chumak<sup>7</sup>, Jérémie Dabin<sup>8</sup>, Inge De Wit<sup>9</sup>, Charlotte Demoor-Goldschmidt<sup>10</sup>, Marijke De Saint-Hubert<sup>8</sup>, Steffen Dreger<sup>11</sup>, Agnès Dumas<sup>12</sup>, Gaute Døhlen<sup>13</sup>, Karin Haustermans<sup>9</sup>, Louise Tram Henriksen<sup>14</sup>, Morten Høyer<sup>14</sup>, Sofie Isebaert<sup>9</sup>, Angéla Jackson<sup>12</sup>, Andreas Jahnen<sup>15</sup>, Neige Journy<sup>12</sup>, Kristina Kjaerheim<sup>13</sup>, Yasmin Lassen<sup>14</sup>, John Maduro<sup>16</sup>, Richard McNally<sup>17</sup>, Hilde M. Olerud<sup>18</sup>, Rodney Ortiz<sup>1,2,3</sup>, Eugenio Picano<sup>19</sup>, Cécile Ronckers<sup>20</sup>, Utheya Salini Thevathas<sup>18</sup>, Adelaida Sarukhan<sup>1,2,3</sup>, Uwe Schneider<sup>21</sup>, Theresa Steinmeier<sup>22</sup>, Jacques Balosso<sup>10</sup>, Juliette Thariat<sup>10</sup>, Beate Timmermann<sup>22,4</sup>, Laura Toussaint<sup>14</sup>, Camille Vidaud<sup>12</sup>, Linda Walsh<sup>21</sup>, Martina Wette<sup>22</sup>, and Hajo Zeeb<sup>11</sup> (¹ Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain);<sup>2</sup> Universitat Pompeu Fabra (UPF), Barcelona, Spain;<sup>3</sup> CIBER Epidemiología y Salud Pública (CIBERESP), Spain;<sup>4</sup> The West German Proton Therapy Centre Essen (WPE), Essen, Germany;<sup>5</sup> Institut de Radioprotection et de Sûreté Nucléaire (IRSN), France;<sup>6</sup> Commissariat à l'Énergie Atomique & aux Energies Alternatives (CEA), France;<sup>7</sup> National Research Center for Radiation Medicine (NRCRM), Kyiv, Ukraine;<sup>8</sup> Belgian Nuclear Research Centre (SCK CEN), Belgium;<sup>9</sup> Katholieke Universiteit Leuven (KU Leuven), Leuven, Belgium;<sup>10</sup> Centre Régional François Baclesse (CRFB), Caen, France;<sup>11</sup> Leibniz-Institute for Prevention Research & Epidemiology (BIPS), Bremen, Germany;<sup>12</sup> French National Institute of Health and Medical Research (Inserm), France;<sup>13</sup> Oslo University Hospital (OUS), Oslo, Norway;<sup>14</sup> Aarhus University Hospital (AUH), Aarhus, Denmark;<sup>15</sup> Luxembourg Institute of Science and Technology (LIST), Luxembourg;<sup>16</sup> University Medical Center Groningen (UMCG), Groningen, Netherlands;<sup>17</sup> University of Newcastle upon Tyne (UNEW), Newcastle, United Kingdom;<sup>18</sup> University of South-Eastern Norway (USN), Porsgrunn, Norway;<sup>19</sup> Institute of Clinical Physiology—National Research Council (IFC-CNR), Pisa, Italy;<sup>20</sup> Princess Maxima Center for Pediatric Oncology (PMC), Utrecht, Netherlands;<sup>21</sup> University of Zurich (UZH), Zurich, Switzerland;<sup>22</sup> and University Hospital Essen (UK Essen), Essen, Germany).

**Conflicts of Interest:** The authors declare no conflict of interest.

**Abbreviations**

<table><tr><td>mtDNAcn</td><td>mtDNA copy number</td></tr><tr><td>8-Oxo-dG</td><td>8-hydroxy-2'-deoxyguanosine</td></tr><tr><td>IL-6</td><td>interleukin-6</td></tr><tr><td>IL-10</td><td>interleukin-10</td></tr><tr><td>TNF-α</td><td>tumor necrosis factor-α</td></tr><tr><td>MCP-1</td><td>monocyte chemoattractant protein-1</td></tr><tr><td>NF-kB</td><td>nuclear factor kappaB</td></tr><tr><td>PBMC</td><td>peripheral blood mononuclear cell</td></tr><tr><td>PTX3</td><td>pentraxin 3</td></tr><tr><td>PBMC</td><td>peripheral blood mononuclear cells</td></tr><tr><td>miRNA</td><td>microRNA</td></tr><tr><td>Lym</td><td>cell pellet/lymphocytes</td></tr><tr><td>PCP</td><td>plasma from CPT</td></tr><tr><td>P</td><td>plasma from EDTA</td></tr><tr><td>B</td><td>rest of blood</td></tr><tr><td>S</td><td>serum</td></tr><tr><td>SC</td><td>blood clot</td></tr><tr><td>Sal</td><td>saliva</td></tr></table>
References

1. Delaney, G.; Jacob, S.; Featherstone, C.; Barton, M. The role of radiotherapy in cancer treatment: Estimating optimal utilization from a review of evidence-based clinical guidelines. *Cancer* **2005**, *104*, 1129–1137. [CrossRef] [PubMed]

2. Brower, C.; Rehani, M.M. Radiation risk issues in recurrent imaging. *Br. J. Radiol.* **2021**, *94*, 20210389. [CrossRef] [PubMed]

3. Hill, K.D.; Frush, D.P.; Han, B.K.; Abbott, B.G.; Armstrong, A.K.; DeKemp, R.A.; Glatz, A.C.; Greenberg, S.B.; Herbert, A.S.; Justino, H.; et al. Radiation Safety in Children with Congenital and Acquired Heart Disease: A Scientific Position Statement on Multimodality Dose Optimization from the Image Gently Alliance. *JACC Cardiovasc. Imaging* **2017**, *10*, 797–818. [CrossRef] [PubMed]

4. Andreassi, M.G.; Picano, E. Reduction of radiation to children: Our responsibility to change. *Circulation* **2014**, *130*, 135–137. [CrossRef]

5. Hauptmann, M.; Daniels, R.D.; Cardis, E.; Cullings, H.M.; Kendall, G.; Laurier, D.; Linet, M.S.; Little, M.P.; Lubin, J.H.; Preston, D.L.; et al. Epidemiological Studies of Low-Dose Ionizing Radiation and Cancer: Summary Bias Assessment and Meta-Analysis. *J. Natl. Cancer Inst. Monogr.* **2020**, *2020*, 188–200. [CrossRef]

6. Pettorini, B.L.; Park, Y.S.; Caldarelli, M.; Massimi, L.; Tamburrini, G.; Di Rocco, C. Radiation-induced brain tumours after central nervous system irradiation in childhood: A review. *Childs Nerv. Syst.* **2008**, *24*, 793–805. [CrossRef]

7. Zahnreich, S.; Schmidberger, H. Childhood Cancer: Occurrence, Treatment and Risk of Second Primary Malignancies. *Cancers* **2021**, *13*, 2607. [CrossRef]

8. Kreuzer, M.; Bouffler, S. Guest editorial: Non-cancer effects of ionizing radiation—Clinical implications, epidemiological and mechanistic evidence and research gaps. *Environ. Int.* **2021**, *149*, 106286. [CrossRef]

9. El-Fayech, C.; Haddy, N.; Allodji, R.S.; Veres, C.; Diop, F.; Kahlouche, A.; Llanas, D.; Jackson, A.; Rubino, C.; Guibout, C.; et al. Cerebrovascular Diseases in Childhood Cancer Survivors: Role of the Radiation Dose to Willis Circle Arteries. *Int. J. Radiat. Oncol. Biol. Phys.* **2017**, *97*, 278–286. [CrossRef]

10. Haddy, N.; Mousannif, A.; Tukenova, M.; Guibout, C.; Grill, J.; Dhermain, F.; Pacquement, H.; Oberlin, O.; El-Fayech, C.; Rubino, C.; et al. Relationship between the brain radiation dose for the treatment of childhood cancer and the risk of long-term cerebrovascular mortality. *Brain* **2011**, *134*, 1362–1372. [CrossRef]

11. Darby, S.C.; Ewertz, M.; McGale, P.; Bennet, A.M.; Blom-Goldman, U.; Bronnum, D.; Correa, C.; Cutter, D.; Gagliardi, G.; Gigante, B.; et al. Risk of ischemic heart disease in women after radiotherapy for breast cancer. *N. Engl. J. Med.* **2013**, *368*, 987–998. [CrossRef]

12. Haddy, N.; Diallo, S.; El-Fayech, C.; Schwartz, B.; Pein, F.; Hawkins, M.; Veres, C.; Oberlin, O.; Guibout, C.; Pacquement, H.; et al. Cardiac Diseases Following Childhood Cancer Treatment: Cohort Study. *Circulation* **2016**, *133*, 31–38. [CrossRef]

13. Cohen, S.; Liu, A.; Gurvitz, M.; Guo, L.; Therrien, J.; Laprise, C.; Kaufman, J.S.; Abrahamowicz, M.; Marelli, A.J. Exposure to Low-Dose Ionizing Radiation from Cardiac Procedures and Malignancy Risk in Adults with Congenital Heart Disease. *Circulation* **2018**, *137*, 1334–1345. [CrossRef]

14. Abalo, K.D.; Malekzadeh-Milani, S.; Hascoet, S.; Dreuil, S.; Feuillet, T.; Cohen, S.; Dauphin, C.; Filippo, S.D.; Douchin, S.; Godart, F.; et al. Exposure to low-dose ionising radiation from cardiac catheterisation and risk of cancer: The COCCINELLE study cohort profile. *BMJ. Open* **2021**, *11*, e048576. [CrossRef]

15. Lang, N.N.; Walker, N.L. Adult Congenital Heart Disease and Radiation Exposure: The Malignant Price of Cardiac Care. *Circulation* **2018**, *137*, 1346–1348. [CrossRef]

16. Foffa, I.; Cresci, M.; Andreassi, M.G. Health risk and biological effects of cardiac ionising imaging: From epidemiology to genes. *Int. J. Environ. Res. Public Health* **2009**, *6*, 1882–1893. [CrossRef]

17. Pernot, E.; Hall, J.; Baatout, S.; Benotmane, M.A.; Blanchardon, E.; Bouffler, S.; El Saghire, H.; Gomolka, M.; Guertler, A.; Harms-Ringdahl, M.; et al. Ionizing radiation biomarkers for potential use in epidemiological studies. *Mutat. Res.* **2012**, *751*, 258–286. [CrossRef]

18. Averbeck, D.; Candeias, S.; Chandna, S.; Foray, N.; Friedl, A.A.; Haghdoost, S.; Jeggo, P.A.; Lumniczky, K.; Paris, F.; Quintens, R.; et al. Establishing mechanisms affecting the individual response to ionizing radiation. *Int. J. Radiat. Biol.* **2020**, *96*, 297–323. [CrossRef]

19. Haghdoost, S.; Czene, S.; Naslund, I.; Skog, S.; Harms-Ringdahl, M. Extracellular 8-oxo-dG as a sensitive parameter for oxidative stress in vivo and in vitro. *Free Radic. Res.* **2005**, *39*, 153–162. [CrossRef]

20. Haghdoost, S.; Svoboda, P.; Naslund, I.; Harms-Ringdahl, M.; Tilikides, A.; Skog, S. Can 8-oxo-dG be used as a predictor for individual radiosensitivity? *Int. J. Radiat. Oncol. Biol. Phys.* **2001**, *50*, 405–410. [CrossRef]

21. Valavanidis, A.; Vlachogianni, T.; Fiotakis, C. 8-hydroxy-2'-deoxyguanosine (8-OHdG): A critical biomarker of oxidative stress and carcinogenesis. *J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev.* **2009**, *27*, 120–139. [CrossRef] [PubMed]

22. Ebrahimian, T.; Le Gallic, C.; Stefani, J.; Dublineau, I.; Yentrapalli, R.; Harms-Ringdahl, M.; Haghdoost, S. Chronic Gamma-Irradiation Induces a Dose-Rate-Dependent Pro-inflammatory Response and Associated Loss of Function in Human Umbilical Vein Endothelial Cells. *Radiat. Res.* **2015**, *183*, 447–454. [CrossRef] [PubMed]

23. Halle, M.; Gabrielsen, A.; Paulsson-Berne, G.; Gahm, C.; Agardh, H.E.; Farnebo, F.; Tornvall, P. Sustained inflammation due to nuclear factor-kappa B activation in irradiated human arteries. *J. Am. Coll. Cardiol.* **2010**, *55*, 1227–1236. [CrossRef] [PubMed]

24. Ma, H.; Zhou, Z.; Wei, S.; Liu, Z.; Pooley, K.A.; Dunning, A.M.; Svenson, U.; Roos, G.; Hosgood, H.D.; Shen, M.; et al. Shortened telomere length is associated with increased risk of cancer: A meta-analysis. *PLoS ONE* **2011**, *6*, e20466. [CrossRef]
25. Vecoli, C.; Borghini, A.; Foffa, I.; Ait-Ali, L.; Picano, E.; Andreassi, M.G. Leukocyte telomere shortening in grown-up patients with congenital heart disease. *Int. J. Cardiol.* **2016**, *204*, 17–22. [CrossRef]

26. Borghini, A.; Vecoli, C.; Piccaluga, E.; Guagliumi, G.; Picano, E.; Andreassi, M.G. Increased mitochondrial DNA4977-bp deletion in catheterization laboratory workers with long-term low-dose exposure to ionizing radiation. *Eur. J. Prev. Cardiol.* **2019**, *26*, 976–984. [CrossRef]

27. Hu, L.; Yao, X.; Shen, Y. Altered mitochondrial DNA copy number contributes to human cancer risk: Evidence from an updated meta-analysis. *Sci. Rep.* **2016**, *6*, 35859. [CrossRef]

28. Kam, W.W.; Banati, R.B. Effects of ionizing radiation on mitochondria. *Free Radic. Biol. Med.* **2013**, *65*, 607–619. [CrossRef]

29. Campa, D.; Barrdahl, M.; Santoro, A.; Severi, G.; Baglietto, L.; Omichessan, H.; Tumino, R.; Bueno-de-Mesquita, H.B.A.; Peeters, P.H.; Weiderpass, E.; et al. Mitochondrial DNA copy number variation, leukocyte telomere length, and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. *Breast Cancer Res.* **2018**, *20*, 29. [CrossRef]

30. Assarsson, E.; Lundberg, M.; Holmquist, G.; Björkesten, J.; Thorsen, S.B.; Ekman, D.; Eriksson, A.; Rennel Dickens, E.; Ohlsson, S.; Edfeldt, G.; et al. Homogenous 96-plex pea immunoassay exhibiting high sensitivity, specificity, and excellent scalability. *PLoS ONE* **2014**, *9*, e95192. [CrossRef]

31. Troncale, S.; Barbet, A.; Coulibaly, L.; Henry, E.; He, B.; Barillot, E.; Dubois, T.; Hupé, P.; de Koning, L. NormaCurve: A SuperCurve-based method that simultaneously quantifies and normalizes reverse phase protein array data. *PLoS ONE* **2012**, *7*, e38686. [CrossRef]

32. Borghini, A.; Vecoli, C.; Mercuri, A.; Carpeggiani, C.; Piccaluga, E.; Guagliumi, G.; Picano, E.; Andreassi, M.G. Low-Dose Exposure to Ionizing Radiation Deregulates the Brain-Specific MicroRNA-134 in Interventional Cardiologists. *Circulation* **2017**, *136*, 2516–2518. [CrossRef]

33. Harbron, R.W.; Thierry-Chef, I.; Pearce, M.S.; Bernier, M.O.; Dreuil, S.; Rage, E.; Andreassi, M.G.; Picano, E.; Dreger, S.; Zeeb, H.; et al. The HARMONIC project: Study design for the assessment of radiation doses and associated cancer risks following cardiac fluoroscopy in childhood. *J. Radiol. Prot.* **2020**, *40*, 1074.

34. De Saint-Hubert, M.; Verbeek, N.; Baumer, C.; Esser, J.; Wulff, J.; Nabha, R.; Van Hoey, O.; Dabin, J.; Stuckmann, F.; Vasi, F.; et al. Validation of a Monte Carlo Framework for Out-of-Field Dose Calculations in Proton Therapy. *Front. Oncol.* **2022**, *12*, 882489. [CrossRef]

35. De Saint-Hubert, M.; Suesselbeck, F.; Vasi, F.; Stuckmann, F.; Rodriguez, M.; Dabin, J.; Timmermann, B.; Thierry-Chef, I.; Schneider, U.; Brualla, L. Experimental Validation of an Analytical Program and a Monte Carlo Simulation for the Computation of the Far Out-of-Field Dose in External Beam Photon Therapy Applied to Pediatric Patients. *Front. Oncol.* **2022**, *12*, 882506. [CrossRef]

36. Ding, G.X.; Alaei, P.; Curran, B.; Flynn, R.; Gossman, M.; Mackie, T.R.; Miften, M.; Morin, R.; Xu, X.G.; Zhu, T.C. Image guidance doses delivered during radiotherapy: Quantification, management, and reduction: Report of the AAPM Therapy Physics Committee Task Group 180. *Med. Phys.* **2018**, *45*, e84–e99. [CrossRef]

37. McFadden, S.L.; Hughes, C.M.; Mooney, R.B.; Winder, R.J. An analysis of radiation dose reduction in paediatric interventional cardiology by altering frame rate and use of the anti-scatter grid. *J. Radiol. Prot.* **2013**, *33*, 433–443. [CrossRef]

38. Reisz, J.A.; Bansal, N.; Qian, J.; Zhao, W.; Furdui, C.M. Effects of ionizing radiation on biological molecules-mechanisms of damage and emerging methods of detection. *Antioxid. Redox. Signal.* **2014**, *21*, 260–292. [CrossRef]

39. Hall, J.; Jeggo, P.A.; West, C.; Gomolka, M.; Quintens, R.; Badie, C.; Laurent, O.; Aerts, A.; Anastasov, N.; Azimzadeh, O.; et al. Ionizing radiation biomarkers in epidemiological studies—An update. *Mutat. Res. Rev. Mutat. Res.* **2017**, *771*, 59–84. [CrossRef]

40. Pfaffe, T.; Cooper-White, J.; Beyerlein, P.; Kostner, K.; Punyadeera, C. Diagnostic potential of saliva: Current state and future applications. *Clin. Chem.* **2011**, *57*, 675–687. [CrossRef]

41. Michalke, B.; Rossbach, B.; Goen, T.; Schaferhenrich, A.; Scherer, G. Saliva as a matrix for human biomonitoring in occupational and environmental medicine. *Int. Arch. Occup. Environ. Health* **2015**, *88*, 1–44. [CrossRef] [PubMed]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
<figure><img src="image_22.png" /></figure>

Article

# The miRNA Content of Bone Marrow-Derived Extracellular Vesicles Contributes to Protein Pathway Alterations Involved in Ionising Radiation-Induced Bystander Responses

Ilona Barbara Csordás <fnref n="1" />,<fnref n="2" />,† ID, Eric Andreas Rutten <fnref n="3" />,†, Tünde Szatmári <fnref n="1" />, Prabal Subedi <fnref n="4" />,<fnref n="5" /> ID, Lourdes Cruz-Garcia <fnref n="3" />, Dávid Kis <fnref n="1" />,<fnref n="2" />, Bálint Jezsó <fnref n="6" />,<fnref n="7" /> ID, Christine von Toerne <fnref n="8" />, Martina Forgács <fnref n="1" />, Géza Sáfrány <fnref n="1" />, Soile Tapio <fnref n="4" />, Christophe Badie <fnref n="3" /> ID and Katalin Lumniczky <fnref n="1" />,* ID

<figure><img src="image_23.png" /></figure>

check for
updates

**Citation:** Csordás, I.B.; Rutten, E.A.; Szatmári, T.; Subedi, P.; Cruz-Garcia, L.; Kis, D.; Jezsó, B.; Toerne, C.v.; Forgács, M.; Sáfrány, G.; et al. The miRNA Content of Bone Marrow-Derived Extracellular Vesicles Contributes to Protein Pathway Alterations Involved in Ionising Radiation-Induced Bystander Responses. *Int. J. Mol. Sci.* **2023**, *24*, 8607. https://doi.org/10.3390/ijms24108607

Academic Editor: François Chevalier

Received: 19 April 2023

Revised: 4 May 2023

Accepted: 7 May 2023

Published: 11 May 2023

<figure><img src="image_24.png" /></figure>

**Copyright:** © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).

**Abstract:** Extracellular vesicles (EVs), through their cargo, are important mediators of bystander responses in the irradiated bone marrow (BM). MiRNAs carried by EVs can potentially alter cellular pathways in EV-recipient cells by regulating their protein content. Using the CBA/Ca mouse model, we characterised the miRNA content of BM-derived EVs from mice irradiated with 0.1 Gy or 3 Gy using an nCounter analysis system. We also analysed proteomic changes in BM cells either directly irradiated or treated with EVs derived from the BM of irradiated mice. Our aim was to identify key cellular processes in the EV-acceptor cells regulated by miRNAs. The irradiation of BM cells with 0.1 Gy led to protein alterations involved in oxidative stress and immune and inflammatory processes. Oxidative stress-related pathways were also present in BM cells treated with EVs isolated from 0.1 Gy-irradiated mice, indicating the propagation of oxidative stress in a bystander manner. The irradiation of BM cells with 3 Gy led to protein pathway alterations involved in the DNA damage response, metabolism, cell death and immune and inflammatory processes. The majority of these pathways were also altered in BM cells treated with EVs from mice irradiated with 3 Gy. Certain pathways (cell cycle, acute and chronic myeloid leukaemia) regulated by miRNAs differentially expressed in EVs isolated from mice irradiated with 3 Gy overlapped with protein pathway alterations in BM cells treated with 3 Gy EVs. Six miRNAs were involved in these common pathways interacting with 11 proteins, suggesting the involvement of miRNAs in the EV-mediated bystander processes. In conclusion, we characterised proteomic changes in directly irradiated and EV-treated BM cells, identified processes transmitted in a bystander manner and suggested miRNA and protein candidates potentially involved in the regulation of these bystander processes.

**Keywords:** bone marrow; ionising radiation; extracellular vesicles; miRNA content; proteome; pathway analysis; bystander effects

<footnote n="1">¹ Unit of Radiation Medicine, Department of Radiobiology and Radiohygiene, National Public Health Centre, 1097 Budapest, Hungary; csordas.ilona@nnk.gov.hu (I.B.C.); szatmari.tunde@nnk.gov.hu (T.S.); kisd@osski.hu (D.K.); forgacs.martina@nnk.gov.hu (M.F.); safrany.geza@nnk.gov.hu (G.S.)</footnote>

<footnote n="2">² Doctoral School of Pathological Sciences, Semmelweis University, 1085 Budapest, Hungary</footnote>

<footnote n="3">³ Centre for Radiation, Chemical and Environmental Hazards, UK Health Security Agency, Chilton, Didcot OX11 0RQ, UK; eric.andreasrutten@ukhsa.gov.uk (E.A.R.); lourdes.cruzgarcia@ukhsa.gov.uk (L.C.-G.); christophe.badie@ukhsa.gov.uk (C.B.)</footnote>

<footnote n="4">⁴ Helmholtz Zentrum München, German Research Center for Environmental Health GmbH (HMGU), 80939 München, Germany; psubedi@bfs.de (P.S.)</footnote>

<footnote n="5">⁵ Federal Office for Radiation Protection (BfS), 85764 Oberschleissheim, Germany</footnote>

<footnote n="6">⁶ Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, 1053 Budapest, Hungary; jezso.balint@tkt.elte.hu</footnote>

<footnote n="7">⁷ Research Centre for Natural Sciences, Institute of Enzymology, 1117 Budapest, Hungary</footnote>

<footnote n="8">⁸ Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH (HMGU), 80939 München, Germany; vontoerne@helmholtz-muenchen.de</footnote>

<footnote>* Correspondence: lumniczky.katalin@nnk.gov.hu or lumniczky.katalin@osski.hu; Tel.: +36-30-5549308</footnote>

<footnote>† These authors contributed equally to this work.</footnote>
# 1. Introduction

Extracellular vesicles (EVs) are a heterogeneous class of nanoscale particles excreted by most cell types both under physiological and pathological conditions and under stress, and they serve primarily as intercellular communication vectors [1]. EVs include apoptotic bodies, microvesicles and exosomes, which are distinguished not primarily by size but by the method of biogenesis [2]. They shuttle several different kinds of cargo between cells, including lipids, proteins, DNA (DNA), mRNA, long non-coding RNA (lncRNA), and miRNA [3].

Apart from its cytotoxic and mutagenic effects, ionising radiation (IR) induces cellular stress and activates intercellular signalling mechanisms through which radiation damage can be transmitted in a bystander manner to cells not directly irradiated. This process is called the radiation-induced bystander effect, and it is a major mechanism through which tissue and systemic responses are elicited after local radiation damage [4–6]. Signals transmitted by chemokines, cytokines, small metabolites and various danger signal molecules released in the extracellular space play important roles in radiation-induced bystander responses. Given the complexity of bystander effects, it is less probable that single molecules mediate this response. EVs, with their complex cargo, are major candidates for mediating radiation-induced bystander responses, and several in vitro and in vivo studies actually proved the role of EVs in this process [7–10].

The role of bystander signalling is especially relevant in organs in which intercellular communication is a major element of proper organ functioning. Within the bone marrow (BM), interactions among the different stem and progenitor cells as well as the BM stroma are indispensable for normal haematopoiesis. In addition, BM is a particularly radiosensitive organ prone to the development of both IR-induced acute deterministic effects (acute BM damage) and late stochastic effects (radiation-induced leukaemia). EVs are known to play a major role in the communication with the BM microenvironment, being involved in the regulation of stem cell renewal, differentiation, proliferation and mobilisation [11,12].

Previously, we developed an in vivo model to study the role of EVs in mediating IR-induced bystander responses by injecting BM-derived EVs from irradiated into naïve, non-irradiated mice. We investigated local effects in the BM [9,10] and systemic effects in the blood and spleen [7,10,13]. In these studies, we showed that BM-derived EVs (1) induced changes in the pool of several of the BM haematopoietic and spleen immune cell subpopulations which mimicked direct irradiation effects; (2) induced DNA damage and apoptosis similarly to direct irradiation; (3) led to changes in several plasma protein levels involved in the inflammation and immune response, which were very similar to the effects observed after direct irradiation and (4) led to an increased oxidative stress. We also demonstrated that only EVs originating from the BM of acutely irradiated mice were able to initiate bystander responses, and these responses were long-lasting [9]. In the present study, we used a mouse model prone to radiation-induced leukaemia. We characterised IR-induced changes in the miRNA content of EVs and miRNA-regulated pathways as well as the proteome and related pathways of BM cells (BMC) treated with EVs from irradiated mice. By comparing pathways regulated by the differentially expressed miRNAs in the EVs with protein pathways altered in BMCs treated with EVs from irradiated mice, we identified miRNA candidates involved in bystander signalling in EV-recipient BMCs. The main steps of the workflow are presented in Figure 1.
<figure><img src="image_25.png" /></figure>

**Figure 1.** Schematic figure of the workflow. EV characterization is shown in Figure 2, EV uptake upon EV and BMC coculture is shown in Figure 5. Results of the miRNA expression analysis are shown in Figures 3 and 4 and Table 1. Summary of mass spectrometric results are presented in Figure 6. Results of miRNA and protein pathway analysis alongside with protein clustering results are shown in Figure 7. miRNA and protein interaction results are presented in Figures 8–10.

To simplify the terminology within the manuscript, we will use the following terms: EVs will refer to BM-derived EVs, since BM was the only source of EV isolation in the present study. Control EVs will refer to EVs isolated from the BM of control, sham-irradiated mice and, consecutively, 0.1 Gy and 3 Gy EVs will refer to EVs isolated from the BM of mice irradiated with either 0.1 Gy or 3 Gy.

**2. Results**

BM-derived EVs were isolated from the BM supernatant of total body-irradiated CBA/CA mice and characterised as described [14]. The electron microscopic characterisation of EV morphology and size indicated typical EV structures, while EV-specific proteins were identified by Western blotting (Figure 2A,B). The average particle size measured with TRPS was 150 nm, with no significant differences between EVs from control or irradiated mice (Figure 2C).
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /><figcaption>**Figure 2.** Characterisation of Bone Marrow-Derived Extracellular Vesicles. (A) Representative transmission electron microscopy images of extracellular vesicles isolated from the bone marrow of mice irradiated with the indicated doses of ionising radiation. (B) Representative Western blot analysis of whole cell lysates and extracellular vesicles isolated from the bone marrow of mice irradiated with the indicated doses of ionising radiation. Lane 1: protein ladder, lane 2: bone marrow whole cell lysate, lane 3–5: extracellular vesicle sample from control, nonirradiated, 0.1 Gy- and 3 Gy-irradiated mice. (C) Size of extracellular vesicle suspensions was examined by tunable resistance pulse sensing using an NP150 nanopore (measurement range: 70–420 nm). Mean values of extracellular vesicle size are shown, with bars representing standard deviations (SD). n = 3.</figcaption></figure>

2.1. *The miRNA Cargo of BM-Derived EVs Shows a Dose-Dependent Response to IR*

The total RNA extracted from control, 0.1 Gy and 3 Gy EVs from three independent experiments was run in an nCounter panel which included probes to identify 800 different murine miRNAs. Analysis performed with the DIANA Tools, mirExTra2.0 software indicated that the level of two miRNAs (mmu-miR-761 and mmu-miR-129-5p) was increased in 0.1 Gy EVs compared to in EVs from controls (Supplementary Table S1). Pathway analysis based on the KEGG database using the Diana Tools miR path software identified two significantly altered pathways (TGF-beta signalling pathway and Signalling pathways regulating pluripotency of stem cells) relevant for the radiation response in the BM (Supplementary Table S2).

In the 3 Gy EVs, 17 miRNAs were differentially expressed; two of them (mmu-miR-709 and mmu-miR-706) had decreased levels, while the others had increased levels (Supplementary Table S1). The two miRNAs with increased levels in 0.1 Gy EVs were also increased in 3 Gy EVs. Both miRNAs were present at higher levels in 3 Gy EVs than in 0.1 Gy EVs, suggesting dose-dependent alteration. No EV-derived miRNAs unique for low doses were identified. Pathway analysis revealed 34 significantly altered pathways potentially regulated by the 17 differentially expressed miRNAs in 3 Gy EVs (Supplementary Table S2). The majority of the pathways were linked to cancer (29%), signal transduction (26%) and cellular processes (15%). Pathways relevant for BM functioning (signalling pathways regulating pluripotency of stem cells, acute myeloid leukaemia, chronic myeloid leukaemia) were among the significantly altered pathways.

Clustering analysis performed with the BRB array tool showed clear up- and down-
regulated clusters of miRNAs, which also included the miRNAs identified using Diana
tools software (Figure 3A,B). Dose correlation analysis identified 13 miRNAs that showed a
strong dose-response (Table 1), with correlation coefficients between 0.95 and 0.84.
<figure><img src="image_29.png" /><figcaption>**Figure 3.** Based on clustering and dose correlation analysis, miRNAs from the bone marrow-derived extracellular vesicles of mice irradiated with 0.1 Gy and 3 Gy correlate with the dose. Heatmap and cluster dendrogram showing (A) a cluster of upregulated miRNAs and (B) a cluster of downregulated miRNAs from bone marrow extracellular vesicles of mice irradiated with 0.1 Gy and 3 Gy. MiRNAs highlighted in red are those miRNAs that were identified by Diana tools mirExTra 2.0. as significantly up- or downregulated. Clustering and dose correlation analysis was performed with the BRB array tools.</figcaption></figure>
<table><caption>**Table 1.** MiRNAs correlating with the dose. Dose correlation analysis was performed with the BRB array tools. Correlations were considered at the nominal 0.001 level of the univariate test.</caption><thead><tr><th>Correlation Coefficient</th><th>Parametric p-Value</th><th>FDR</th><th>miRNA Name</th></tr></thead><tbody><tr><td>0.953</td><td>&lt;1 × 10<sup>-7</sup></td><td>&lt;1 × 10<sup>-7</sup></td><td>mmu-miR-323-5p</td></tr><tr><td>0.888</td><td>9.17 × 10<sup>-5</sup></td><td>0.00592</td><td>mmu-miR-1933-5p</td></tr><tr><td>0.888</td><td>9.17 × 10<sup>-5</sup></td><td>0.00592</td><td>mmu-miR-1961</td></tr><tr><td>0.888</td><td>9.17 × 10<sup>-5</sup></td><td>0.00592</td><td>mmu-miR-338-5p</td></tr><tr><td>0.888</td><td>9.17 × 10<sup>-5</sup></td><td>0.00592</td><td>mmu-miR-504</td></tr><tr><td>0.884</td><td>0.0001922</td><td>0.0103</td><td>mcmv-miR-m107-1-5p</td></tr><tr><td>0.875</td><td>0.0003089</td><td>0.0125</td><td>mmu-miR-290-5p</td></tr><tr><td>0.874</td><td>0.0003089</td><td>0.0125</td><td>mmu-miR-708</td></tr><tr><td>0.857</td><td>0.0005971</td><td>0.0161</td><td>mmu-miR-181c</td></tr><tr><td>-0.857</td><td>0.0005971</td><td>0.0161</td><td>mmu-miR-2146</td></tr><tr><td>0.859</td><td>0.0005971</td><td>0.0161</td><td>mmu-miR-467h +<br>mmu-miR-669d +<br>mmu-miR-669l</td></tr><tr><td>0.857</td><td>0.0005971</td><td>0.0161</td><td>mmu-miR-669j</td></tr><tr><td>-0.843</td><td>0.0009695</td><td>0.0222</td><td>mmu-miR-93</td></tr></tbody></table>

QRT-PCR measurements confirmed nCounter results for the downregulated miRNAs in the EVs identified by the DIANA Tools and mirExTra and also for most of the miRNAs correlating with the dose (based on BRB array analysis), though a dose-response relationship could not be identified for all miRNAs (Figure 4). Significant changes in miRNA levels were detected mostly after irradiation with 3 Gy (mmu-miR-669d-5p, mmu-miR-93-5p, mmu-miR-467h, mmu-miR-706, mmu-miR-1933-5p, mmu-miR-181c-5p, mmu-miR-338-5p), while 0.1 Gy induced significant changes only in two cases (mmu-miR-1933 and mmu-miR-34b-5p). Three miRNAs, which showed a positive dose correlation based on BRB array analysis, correlated negatively with the dose after qRT-PCR (mmu-miR-669d-5p, mmu-miR-504-5p and mmu-miR-467h). However, we have to point out that the nCounter system used the same ID for the identification of both mmu-miR-669d-5p and mmu-miR-467h, while individual miRNA-specific primers were used in the qRT-PCR reaction. Mmu-miR-34b-5p showed a positive dose correlation based on BRB array analysis, while qRT-PCR indicated an inverse dose relationship (decreased miRNA level after 0.1 Gy and enrichment after 3 Gy). For three miRNAs (miR-708-5p, mmu-miR-761 and miR-129-5p), we could not confirm any significant dose correlation with qRT-PCR (Figure 4).

## 2.2. Proteomic Changes in BMCs Treated with BM-Derived EVs from Irradiated Mice Indicate Partially Distinct Pathway Alterations to Directly Irradiated BMCs

Proteomic changes in BMCs treated with BM-derived EVs from irradiated mice have been compared to the proteome of directly irradiated BMCs. The ex vivo incubation of BMCs with fluorescently labelled EVs indicated that the rate of EV uptake was around 80%, with no significant difference in EV uptake if EVs originated from the BM of irradiated or control mice (Figure 5).
<figure><img src="image_30.png" /><figcaption>**Figure 4.** Ionising radiation influences the miRNA content of bone marrow-derived extracellular vesicles in irradiated mice. Extracellular vesicles were isolated from the bone marrow of control and irradiated mice, miRNAs were purified from the extracellular vesicles and the relative concentration of miRNAs was measured by qRT-PCR, as described in the Materials and Methods section. *n* = 3; Significant changes are indicated with \* *p* < 0.05 and \*\*\* *p* < 0.001.</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>**Figure 5.** Irradiation does not influence the extracellular vesicle uptake by bone marrow cells. Bone marrow cells from control, non-irradiated mice were co-cultured in vitro with fluorescently labelled bone marrow-derived extracellular vesicles isolated from control, 0.1 Gy- and 3 Gy-irradiated mice, and the uptake rate was determined by flow cytometry, as described in Materials and Methods. (A) Flow cytometry blots showing the representative uptake rates of extracellular vesicles by bone marrow cells. Green colour indicates cells with EV uptake, red colour indicates cells without EV uptake. (B) The percentage of bone marrow cells taking up extracellular vesicles. *n* = 3. Error bars represent standard deviation (SD).</figcaption></figure>
Proteomic changes were investigated in ex vivo cultured murine BMCs either non-irradiated (0 Gy or control), irradiated with 0.1 Gy or 3 Gy X-Rays or treated with BM-derived EVs isolated from mice irradiated with the same doses (0 Gy, 0.1 Gy or 3 Gy). A total of 3718 proteins were identified in the BMCs, among which 2707 proteins were qualified for further studies according to the selection criteria (FDR < 1% and identified with at least two unique peptides). Principal component analysis (PCA) and heat map clustering demonstrated that the 3 Gy group was the most different from all other experimental groups (Figure 6).

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

**Figure 6.** Irradiation of bone marrow cells with 3 Gy leads to a distinct proteomic pattern. (A) Principal component analysis was performed in Perseus software version 2.0.7.0. Proteome discoverer output with all proteins identified in all samples with more than two peptides was loaded. After log2 transformation, missing values were low-abundance-imputed according to program settings. Categorical annotations were added for experimental groups. Principal component analysis was performed using the standard settings of Perseus, displaying experimental groups. (B) Heatmap clustering. Proteins were characterised by hierarchical clustering (Euclidean distance algorithm and average distance method).
2.2.1. Proteomic Changes in Directly Irradiated BMCs

Irradiation with 0.1 Gy led to 142 deregulated (33 downregulated and 109 upregulated) proteins (Supplementary Table S3A, Figure 7A and Supplementary Figure S1A) compared to control samples. STRING clustering analysis indicated that the largest protein cluster included mitochondrial proteins involved in ATP synthesis and oxidative phosphorylation (Supplementary Table S7). Irradiation with 3 Gy led to 360 deregulated (236 downregulated and 124 upregulated) proteins in the BMCs (Supplementary Table S3B, Figure 7A and Supplementary Figure S1B) compared to control samples, which could be grouped into several clusters. One big cluster contained proteins involved in biological processes related to DNA repair, replication and DNA metabolism, stress response and metabolic pathways. A further cluster contained proteins involved in chromatin remodelling and histone proteins. Mitochondrial proteins involved in electron transport, oxidative phosphorylation and oxidation-reduction complexes constituted a third cluster (Supplementary Table S7). Seventy-two proteins were in common between BMCs irradiated with 0.1 Gy and 3 Gy. Changes in the expression of 35 proteins correlated with the dose, with 50% or higher difference in the protein abundance ratio between 0.1 Gy and 3 Gy samples (Figure 7A and Supplementary Table S3C; highlighted proteins indicate those correlating with the dose). Clustering analysis of the common proteins indicated that the largest protein clusters belonged to biological processes related to mitochondrial processes such as mitochondrial ATP synthesis and oxidative phosphorylation, as well as iron sulphur clusters (Figure 7B, Supplementary Table S7). The common proteins most significantly upregulated (highest abundance ratio and lowest *p* value) were putative transferase CAF17 homolog, mitochondrial (Iba57) and cysteine desulfurase mitochondrial (Nfs1) and also belonged to this cluster.

Based on pathway enrichment analysis using the PathDIP tool, deregulated proteins in BMCs irradiated with 0.1 Gy could be associated with 107 altered pathways (Supplementary Table S4A and Figure 7C). The vast majority of the deregulated pathways were associated with immune processes (15%), signal transduction (11%) as well as cancer (9%) and metabolism (9%, including oxidative phosphorylation and pyruvate metabolism); nevertheless, cell growth and death-related pathways were also deregulated (Figure 7D). In the BMCs irradiated with 3 Gy, the 360 altered proteins could be associated with 87 altered pathways (Supplementary Table S4B and Figure 7C). The highest number of altered pathways were related to metabolism (18%) and genetic information processing (17%). One-third of the pathways within genetic information processing were related to DNA damage and repair. Cancer-related pathways represented 10% of the total pathway alterations, including acute myeloid leukaemia. Immune-related and signal transduction pathways were also present but in lower percentages compared to BMCs irradiated with 0.1 Gy (Figure 7D).

Sixty-three pathways were in common between BMCs irradiated with 0.1 Gy and 3 Gy (Supplementary Table S4C and Figure 7C). Common pathways were related to metabolism (14%), cancer (11%), genetic information processing (11%), signal transduction (10%), the immune system (8%) and cell growth and death (Figure 7E).
<figure><img src="image_35.png" /><figcaption>**Figure 7.** Significantly deregulated proteins and associated pathways in directly irradiated and extracellular vesicles-treated bone marrow cells. (A) Venn Diagram analysis of significantly deregulated proteins identified across all four treatment groups. (B) Common clusters formed by significantly deregulated proteins, as performed by STRING clustering analysis. (C) Venn diagram analysis of significant protein pathways. (D) Pathway distribution in the different treatment groups. (E) Pathway comparison between treatment groups. Pathway annotations and enrichment analysis were performed with the pathDIP database, integrating data from the KEGG database. BM: bone marrow; EV: extracellular vesicles.</figcaption></figure>

### 2.2.2. Proteomic Changes in EV-Treated BMCs

The treatment of BMCs with 0.1 Gy EVs led to 106 deregulated (33 downregulated and 73 upregulated) proteins (Supplementary Table S5A, Figure 7A and Supplementary Figure S1C) compared to BMCs treated with control EVs, which could be clustered into several biological functions, out of which mitochondria-related biological processes (ATP synthesis, electron transport, oxidation-reduction), stress response, signal transduction and immune processes represented the largest clusters (Supplementary Table S7). Based on pathway enrichment analysis, the 106 proteins could be associated with 15 pathways (Supplementary Table S6A and Figure 7D), the majority of which were metabolism-related (Figure 7D). Treatment with 3 Gy EVs led to 63 deregulated (29 downregulated and 34 upregulated) proteins (Supplementary Table S5B, Figure 7A and Supplementary Figure S1D) compared to BMCs treated with control EVs. STRING clustering analysis indicated fewer protein clusters than
in the other treatment groups, the largest clusters belonging to nuclear and nucleosomal proteins involved in chromatin organisation, the regulation of histone methylation and DNA binding as well as spliceosomal complex assembly, mRNA splicing and ribonucleoprotein complex assembly. Protein clusters involved in immune processes and metabolic processes were also present (Supplementary Table S7). The 63 deregulated proteins could be associated with 117 pathways (Supplementary Table S6B and Figure 7C). The most highly represented altered pathways were related to cancer (20%), signal transduction (16%) and the immune system (12%). Further altered pathways were endocrine system-related, genetic information processing including replication and repair and cellular processes including cell growth and death (Figure 7D).

Twenty-four proteins (Supplementary Table S5C and Figure 7A) and four pathways (Supplementary Table S6C and Figure 7C) were in common in BMCs treated with 0.1 Gy EVs and 3 Gy EVs, though common pathways could not be linked to either irradiation- or cancer- or BM damage-related processes (Figure 7E).

### 2.2.3. Comparison of Proteomic Changes between Directly Irradiated and EV-Treated BMCs

Next, we identified deregulated common proteins and pathways between directly irradiated and EV-treated BMCs. Eighteen proteins were common in BMCs irradiated with 0.1 Gy and treated with 0.1 Gy EVs, out of which eight (Carbonyl reductase NADPH1 (Cbr1), Cytochrome b-c1 complex subunit 6, mitochondrial (Uqcrh), RUN and FYVE domain-containing protein 1 (Rufy1), N-acetylglucosamine-1-phosphodiester alpha-N-acetylglucosaminidase (Nagpa), Mitochondrial 2-oxodicarboxylate carrier (Slc25a21), Thioredoxin domain-containing protein 5 (Txndc5), Enoyl-[acyl-carrier-protein] reductase, mitochondrial (Mecr) and Mitochondrial import inner membrane translocase subunit Tim8A (Timm8a1)) could be associated with redox and mitochondrial processes (Supplementary Table S8A and Figure 7A). Seven pathways were in common (Supplementary Table S8A and Figure 7C), out of which oxidative phosphorylation was the most significant and also the only relevant for IR-related processes.

Twenty-six common proteins and 52 common pathways could be identified between BMCs directly irradiated and treated with 3 Gy EVs (Supplementary Table S8B and Figure 7A). The 26 common proteins could not be grouped in big clusters; nevertheless, 3 common proteins (CD48 antigen (CD48), C-type lectin domain family 1 member B (Clec1b) and Integrin alpha-IIb (Itga2b)) were involved in platelet aggregation and activation, wound healing, cell adhesion and migration, 2 common proteins (p21-activated protein kinase-interacting protein 1 (Pak1ip1) and U4/U6 small nuclear ribonucleoprotein Prp31 (Prpf31)) were involved in spliceosomal complex assembly, mRNA splicing and ribonucleoprotein complex assembly, 2 proteins (Carbonyl reductase NADPH1 (Cbr1) and Carbonyl reductase NADPH2 (Cbr2)) were involved in NADPH activity and 2 other mitochondrial matrix proteins (Electron transfer flavoprotein-ubiquinone oxidoreductase, mitochondrial (Etfdh) and Propionyl-CoA carboxylase alpha chain, mitochondrial (Pcca)) were involved in propionyl-CoA carboxylase activity (Figure 7B). Pathway enrichment analysis indicated that out of the 52 common pathways, 17% were cancer-related, including acute myeloid leukaemia, 13% were signal transduction-related, 12% were related to genetic information processing, including replication and repair, 10% were linked to cell growth and death and 8% were linked to the immune system (Figure 7C,E).

### 2.3. Protein Pathways Regulated by EV-Containing miRNAs from Irradiated Mice Partially Overlap with Protein Pathways Altered in the EV-Treated BMCs

EV-derived miRNAs can potentially alter cellular pathways in EV-recipient cells by regulating their protein content. In order to identify those EV-derived miRNAs that are most probably involved in regulating cellular processes in the EV-recipient cells, we analysed overlaps in protein pathways predicted to be altered by EV-transmitted miRNAs and altered protein pathways based on proteomic data.
In 0.1 Gy EVs, two miRNAs had altered expression levels, which belonged to three pathways. BMCs treated with 0.1 Gy EVs led to 106 deregulated (up- or downregulated) proteins that could be linked to 15 pathways (Supplementary Tables S2 and S6A). The two pathway lists had no common elements; therefore, we assumed that EV-containing miRNAs after low-dose irradiation are not the major regulators of protein pathways in EV-acceptor BMCs.

The 17 miRNAs differentially expressed in 3 Gy EVs regulated 34 pathways. BMCs treated with 3 Gy EVs resulted in 63 (up- or downregulated) proteins which could be grouped in 117 pathways. Seventeen pathways were in common between miRNAs and proteins, and the vast majority of common pathways were cancer-related (59%) (Supplementary Table S9 and Figure 8). These findings suggested that miRNAs from 3 Gy EVs might have important roles in regulating protein pathways in EV-acceptor BMCs.

<figure><img src="image_36.png" /><figcaption>**Figure 8.** Common pathways between differentially expressed miRNAs from bone marrow-derived extracellular vesicles from mice irradiated with 3 Gy and deregulated proteins in bone marrow cells treated with bone marrow-derived extracellular vesicles of mice irradiated with 3 Gy. (A) Number of pathways; (B) Distribution of KEGG pathway classes.</figcaption></figure>

In order to better characterise the interactions between EV-derived miRNAs and deregulated cellular proteins in the EV-acceptor BMCs, direct miRNA-protein interactions were analysed using the TARBASE database (for validated interactions) and the miRDB database (for predicted interactions). Out of the 17 miRNAs differentially expressed in the 3 Gy EVs, we identified those that could interact with at least one of the 63 deregulated proteins in the BMCs treated with 3 Gy EVs and found that 11 miRNAs could interact directly with 19 proteins. Nevertheless, only interactions between 6 miRNAs and 11 proteins were involved in the 17 altered pathways that were common between deregulated miRNAs in 3 Gy EVs and deregulated proteins in BMCs treated with 3 Gy EVs (Figure 9 and Supplementary Table S10). Alterations in the level of two highly significantly upregulated proteins ((Histone H2A.V (H2afv) and Cysteine protease ATG4B (Atg4b)) in the samples treated with 3 Gy EVs (for Atg4b *p* = 4.51 × 10⁻¹⁶ and for H2afv *p* = 3.63 × 10⁻⁵) and participating in validated interactions with miRNAs were investigated by Western blot analysis as well in order to confirm the proteomic data. The level of both
proteins increased in the BMCs treated with 3 Gy EVs; nevertheless, changes were only significant for H2afv (Figure 10).

<figure><img src="image_37.png" /></figure>

**Figure 9.** MiRNA–protein interactions involved in the common pathways between differentially expressed miRNAs from bone marrow-derived extracellular vesicles from mice irradiated with 3 Gy and deregulated proteins in bone marrow cells treated with bone marrow-derived extracellular vesicles of mice irradiated with 3 Gy. Only those miRNA and protein interactions are presented where both the miRNA and the protein could be linked to certain common pathways. MiRNA–protein interactions were searched for in the Tarbase (validated interactions) and miRDB databases (predicted interactions). Colour blocks indicate an identical number of proteins involved in a particular pathway.

<figure><img src="image_38.png" /></figure>

**Figure 10.** Western blot validation of deregulated proteins in bone marrow cells treated with bone marrow-derived extracellular vesicles from irradiated mice. A total of 40 µg of protein was loaded on the gel and hybridised to either anti-Atg4b or anti-H2afv or β-actin (loading control), as described in Materials and Methods. Quantification was performed with ImageJ software. Relative fold changes compared to bone marrow cells treated with extracellular vesicles from non-irradiated mice are shown in (A) for H2afv and (B) for Atg4b. (C) Representative blot image. n = 3. Error bars represent standard deviations (SD). Significant changes are indicated with \* *p* < 0.05.
### 3. Discussion

In our previous studies, we reported that BM-derived EVs played an important role in transmitting IR-induced bystander effects in the BM [9,10,13]. Using the C57Bl/6 mouse model, we demonstrated that EVs isolated from the BM supernatant of irradiated mice, when injected systemically into naïve animals, induced changes in cell viability and cell subpopulation distribution in the BM and periphery that were similar to those for mice directly irradiated. We also highlighted the potential role of EV-derived miRNAs in mediating bystander effects. The aim of the present study was to identify those miRNAs in the BM-derived EVs of irradiated mice, which might interfere with cellular processes in the EV-acceptor cells. The CBA/CA mouse was used because this radiosensitive strain is prone to develop acute myeloid leukaemia exclusively upon irradiation [15].

Although blood is the most frequently used source of EVs for biological studies [16], blood-derived EVs are not optimal for characterising EV-mediated communication within a tissue microenvironment, since blood-derived EVs represent a mixture of EVs released from various parts of the body and, thus, they are not representative for a particular tissue microenvironment. Therefore, our study was performed using BM-derived EVs isolated from BM supernatant, in which EVs released by various BM cells are present in a concentrated form.

EVs carry a complex cargo comprising various proteins [17], different types of RNAs, various non-coding RNA types such as miRNAs [10,18], circular RNAs [19,20], long non-coding RNAs [21,22] as well as DNA of various sources such as genomic, mitochondrial [23], extrachromosomal [24], small metabolites [25] and possibly other biological structures present in the cells releasing the EVs. Out of these proteins and miRNAs are the best characterised molecules due to their role in regulating cellular processes in the EV-acceptor cells.

According to our results, the number of differentially expressed miRNAs in the EVs isolated from mice irradiated with low (0.1 Gy) or high (3 Gy) doses was low. In the EVs isolated from 0.1 Gy-irradiated mice, only two differentially expressed miRNAs were detected; nevertheless, they regulated important pathways involved in either signal transduction (TGF-β signalling pathway) or normal BM functioning (signalling pathways regulating the pluripotency of stem cells). Seventeen miRNAs were differentially expressed in EVs isolated from mice irradiated with 3 Gy, regulating mostly cancer-related pathways (including acute myeloid leukaemia) and multiple pathways relevant for either BM functioning (Wnt signalling pathway, signalling pathways regulating the pluripotency of stem cells) or for the cellular response to IR (Hippo signalling, cell cycle, TGF-β signalling, FoxO signalling, MAPK signalling, Erb signalling), suggesting the potential involvement of these miRNAs in mediating IR-induced bystander responses. The findings that all of the differentially expressed miRNAs of 0.1 Gy EVs were present in 3 Gy EVs and the expression level of several miRNAs in the EVs correlated with the applied dose suggest a dose-dependent regulation of miRNA packaging in EVs. Previous studies identified cellular and circulating miRNAs with various degrees of correlation with ionising radiation dose [26,27], but we are not aware of reports regarding the dose-dependent or dose-correlating expression of miRNAs in EVs.

Comparing our current results with the study of Szatmári et al., in which differentially expressed miRNAs in BM-derived EVs were investigated in C57Bl/6 mice irradiated with either 0.1 Gy or 2 Gy [10], only two miRNAs (miR709 and miR761) were in common in the EVs irradiated with high doses. Nevertheless, the number of common pathways regulated by the differentially expressed miRNAs in the two studies was largely overlapping. The two pathways regulated by miRNAs differentially expressed in the 0.1 Gy EVs (TGF-β signalling pathway and signalling pathways regulating the pluripotency of stem cells) and 80% of pathways regulated by the miRNAs from 3 Gy EVs were also present in the study by Szatmári et al.

We are not aware of any other publication investigating differentially expressed miRNAs in BM-derived EVs after IR. Comparing IR-induced differences in the miRNA
content of EVs from different biological sources is difficult, since the miRNA content of the EVs largely reflects the miRNA panel of the EV-donor cells. Nevertheless, the majority of the EVs released by the BM should be present in the blood as well, although in a much lower yield. Therefore, we compared our current data with another study by Szatmári et al., in which they investigated differentially expressed miRNAs in EVs isolated from the blood of C57Bl/6 mice irradiated with either 0.1 Gy or 2 Gy [13]. Although identical miRNAs were not detected, several pathways relevant for cancer (such as acute myeloid leukaemia, chronic myeloid leukaemia, mTOR signalling) or signal transduction processes altered by IR (such as FoxO signalling, Hippo signalling, MAPK signalling, TGF-beta signalling) were in common. Moertl et al. investigated the effect of IR on the miRNA content of human peripheral blood mononuclear cell-derived EVs 72 h after irradiation with 2 Gy or 6 Gy. Similar to our data, they identified a limited number of differentially expressed miRNAs, and only the upregulated miRNA-338 was identical with the ones reported in our current study. MiRNA-regulated pathways related to the cell cycle, cell death and proliferation as well as DNA repair were also reported as altered [28]. The proteome of BMCs irradiated with 0.1 Gy revealed protein clusters involved in mitochondria-related processes, most notably ATP synthesis and oxidative phosphorylation, supporting that low-dose irradiation induces oxidative stress, as reported in other studies as well [29–31]. Pathway enrichment analysis indicated that immune system-related signalling pathways (such as NF-κB, T cell receptor, B cell receptor, Toll-like receptor, IL-17) and pathways related to immune cell differentiation and function were abundant, supporting the immune modulatory effect of low-dose irradiation, as reported [32–35].

The irradiation of the BMCs with 3 Gy led to a much higher number of deregulated proteins, clustered around biological processes relevant for cellular responses to high-dose radiation damage, such as DNA-related processes (repair, replication, DNA and nucleotide metabolism), stress response, chromatin remodelling, the regulation of transcription and mRNA maturation as well as several mitochondrial processes reflecting oxidative stress. Pathway enrichment analysis largely supported protein clustering, since the most significantly altered pathways were metabolism and genetic information processing, out of which one-third was related to DNA damage and repair (such as homologous recombination, DNA replication, mismatch repair, nucleotide excision repair). Metabolic pathways affected nucleotide metabolism, carbohydrate metabolism as well as oxidative phosphorylation. These data are in agreement with previous studies performed in various biological models indicating that high-dose irradiation leads to oxidative stress and the activation of DNA damage response pathways [36–39].

Approx. 50% of the common proteins between BMCs irradiated with 0.1 Gy and 3 Gy correlated with the dose, and these proteins clustered around the oxidative stress response, MAPK and NF-κB signalling and apoptosis, indicating that the severity of radiation-induced oxidative stress, inflammation and apoptosis is dose-dependent. Similar findings were reported by others as well [40–42]. Pathway enrichment analysis indicated 63 common pathways basically supporting protein clustering. Nevertheless, several important differences in pathway alterations between the two doses were also noted. Pathway alterations indicating radiation-induced DNA damage and pathways related to nucleotide metabolism were only present in BMCs irradiated with 3 Gy and were completely absent in low-dose-irradiated samples, indicating that low-dose irradiation does not activate DNA damage response pathways. These findings support previous reports with the same conclusion [43,44]. On the other hand, immune system-related and signal transduction pathways were less abundant in BMCs irradiated with high dose compared to low dose. While apoptosis was a common pathway for both doses, senescence-related pathways were only present after high-dose irradiation. Leukaemia-related pathway alterations were only present in BMCs irradiated with 3 Gy, which strongly supports previous data showing an increased incidence of myeloid leukaemia in the CBA/Ca mouse strain after irradiation with 3 Gy [15]. Altogether, commonalities and differences in the pathways between low- and high dose-irradiated BMCs highlight the major differences in the biological responses
to the two doses. Systemic responses are markedly present after low-dose irradiation (increased number of immune system-related pathways), while mechanisms related to DNA damage repair (repair and replication-related pathways, metabolic pathways related to nucleotide metabolism) predominate in cellular responses to high-dose irradiation.

In order to study EV-induced changes in the protein profile of BMCs, first, we investi-
gated whether the fact that EVs originate from irradiated mice influences their uptake rate
by BMCs. Since we found no significant differences in the uptake rate, we concluded that
changes in the protein pathways of BMCs treated with EVs were due to differences in the
EV cargo and not due to differences in the number of internalised EV particles.

Proteomic changes in BMCs treated with 0.1 Gy EVs were clustered around mitochon-
drial processes involved in oxidative phosphorylation and were involved in 15 pathways,
out of which 5 were metabolism-related such as oxidative phosphorylation and various
components of lipid metabolism. The rest of the pathways were of less relevance for BM
functioning and/or the response to IR. BMCs treated with 3 Gy EVs had 63 deregulated
proteins that did not form big clusters except for a cluster of nuclear proteins involved in
processes such as chromatin remodelling, histone methylation and DNA binding. Other
smaller clusters were involved in immunological and metabolic processes. Pathway en-
richment analysis indicated that, by far, the most highly represented group of pathways
was cancer, followed by signal transduction and the immune response. Among cancer
pathways, acute myeloid leukaemia- and chronic myeloid leukaemia-related pathways
were significantly altered, indicating the potential role of EVs in leukemogenesis [45,46].
An additional important group of pathways was replication and repair, indicating that
EVs could either transmit DNA damage into EV-recipient BMCs or signals leading to the
activation of DNA damage response. This was also reflected in the alterations of certain
signal transduction pathways interacting with the DNA damage response such as mTOR,
PI3K-Akt, Wnt, cAMP, and NF-κB signalling pathways. Alterations in metabolism-related
pathways were much less represented compared to either directly irradiated BMCs or
BMCs treated with 0.1 Gy EVs. The comparison of common proteins and pathways be-
tween BMCs treated with 0.1 Gy EV and 3 Gy EV indicated that mostly proteins involved
in energy metabolism and mitochondrial processes were in common; however, these were
not reflected in identical pathway alterations.

Protein and pathway overlap between directly irradiated and EV-treated BMCs is a potential way to identify radiation-induced bystander signalling mechanisms transferred by EVs in the BM. As mentioned above, both direct irradiation with 0.1 Gy and the treatment of BMCs with 0.1 Gy EVs led to the deregulation of several mitochondrial proteins and protein pathways involved in energy metabolism. As such, 8 out of the 18 common deregulated proteins were involved in mitochondrial and redox-related processes, and the only common pathway that could be related to the IR response was oxidative phosphorylation. These data indicate that oxidative stress after the low-dose direct irradiation of BMCs can be transferred by EVs to non-irradiated BMCs in a bystander manner. EV-induced oxidative stress has been reported by other groups as well [47–49].

While we could not identify strong synergies between BMCs directly irradiated with 3 Gy or treated with 3 Gy EVs at the level of individual proteins, several altered pathways highly relevant for the IR response were identical between the two groups based on pathway enrichment analysis. Most importantly, all three pathways related to DNA replication and repair that were altered in BMCs treated with 3 Gy EVs were also altered in the directly irradiated cells, indicating that DNA damage response-related signals can be transmitted by EVs in a bystander manner. In addition, several of the signalling pathways related to DNA damage (PI3K-Akt, NF-κB) as well as cell death pathways characteristic of high-dose irradiation (apoptosis, senescence) were also in common between directly irradiated and EV-treated BMCs. The acute myeloid leukaemia pathway was present both in the directly irradiated and EV-treated BMCs. The number of cancer-related pathways was much higher in BMCs treated with 3 Gy EVs compared to their directly irradiated counterparts (23 vs. 9). Nevertheless, the majority of these pathways were related to cancer in specific
organs other than BM, which might be due to signals originating from EVs present in the BM but released by cells from other organs, reaching the BM through the circulation.

Altogether, common protein and pathway alterations between directly irradiated and EV-treated BMCs indicated that cellular and molecular processes highly relevant for cellular responses to IR damage could be transmitted by EVs in a bystander manner. However, several important differences in the pathways could also be seen, as summarised in Supplementary Figure S2.

Next, we were curious to see to what extent differentially expressed miRNAs of EVs isolated from irradiated mice were responsible for the transmission of bystander responses. Since the number of differentially expressed miRNAs in 0.1 Gy EVs was very low (only two miRNAs involved in three pathways, out of which one was unrelated to either IR or BM), we assumed that miRNAs from 0.1 Gy EVs do not play a major role in IR-induced bystander effects. On the other hand, the 17 differentially expressed miRNAs from 3 Gy EVs regulated several pathways relevant for IR-induced damage in the BM which overlapped with protein pathways identified in BMCs treated with 3 Gy EVs. Such common pathways with relevance in IR-induced BM damage were the cell cycle, pathways in cancer, transcription misregulation in cancer and acute and chronic myeloid leukaemia. Based on direct miRNA-protein interaction studies, 6 miRNAs and 11 proteins were identified that could directly interact and were involved in the mentioned pathways (Figure 9). This raises the possibility that alterations in the abovementioned protein pathways in the BMCs treated with 3 Gy EVs could be attributed to the regulatory role of the six EV-derived miRNAs. Further studies are needed to confirm the direct miRNA-protein interactions and their impact on the cell cycle and BM malignancies.

While several other pathways relevant for IR response were altered in the BMCs treated with 3 Gy EVs, the majority of these pathways could not be linked to specific miRNAs transmitted by 3 Gy EVs. This suggests that proteomic changes in BMCs treated with 3 Gy EVs might be only in small part regulated by the EV-derived miRNAs, highlighting the role of other molecules within the EVs able to transmit IR-induced bystander effects.

# 4. Materials and Methods

## 4.1. *Mouse Model and Irradiation*

CBA/Ca mice were used in all experiments. Mice were kept and investigated in accordance with the guidelines and all applicable sections of the 2011 CLVIII Hungarian law about animal protection and welfare and the European 2010/63/EU directives and regulations. All animal studies were approved, and permission was issued by the Budapest and Pest County Administration Office Food Chain Safety and Animal Health Board (ethical permission: PE/EA/392-7/2017).

Nine- to twelve-week-old male mice randomly selected from at least five different litters were either sham-irradiated (0 Gy, controls) or total-body-irradiated with 0.1 Gy and 3 Gy X-rays using an X-RAD 225/XLi X-ray source (Precision X-ray, North Branford, CT, USA). The mice were sacrificed 24 h after irradiation by using a 100 mg/kg (0.25 mL/kg) intraperitoneal (i.p.) injection of sodium pentobarbital.

## 4.2. *Isolation of BMCs and Co-Culture of BM-Derived EVs with BMCs*

Twenty-four hours after irradiation, the BMs were isolated from the femur and tibia of mice by flushing out the tissue from the diaphyses of the bones and suspended in PBS. A BM single-cell suspension was made by the mechanical disaggregation of the tissue; then, the cells were filtered by a 40 µm mesh filter. The cells were pelleted by centrifugation at 500× g and 4 °C for 10 min. The supernatant was removed and used for BM-derived EV isolation, as described below. Cell pellets were used for proteomic analysis, as described below.

BMCs from control (0 Gy or sham-irradiated) mice were prepared as described above and were incubated with EVs isolated from the BM supernatant of either control (0 Gy), 0.1 Gy- or 3 Gy-irradiated mice. Cell pellets were resuspended in RPMI1640 (Lonza
Bioscience, Verviers, Belgium) containing 10% foetal bovine serum (FBS) (Euroclone S.p.a., Pero(MI), Italy). Live BMCs were determined by trypan blue exclusion, and 20 × 10⁶ BMCs per sample were incubated with EVs isolated from the supernatant of 100 × 10⁶ BMCs for 3 h at 37 °C and 5% CO₂. BMCs from directly irradiated mice were processed in a similar manner, without adding EVs. After the three-hour incubation time, 20 × 10⁶ cells per sample were pelleted at 500× g and 4 °C, and cell pellets were snap-frozen in liquid nitrogen and kept at -70 °C until proteomic analysis.

Part of the EVs and BMCs were used to trace the EV uptake. EVs were isolated from the BM supernatant of 0 Gy-, 0.1 Gy- or 3 Gy-irradiated mice, as described above. Isolated EVs were stained with a cell permeant nucleic acid stain that is selective for RNA (SYTO® RNASelect™ Green Fluorescent Cell Stain Invitrogen, Waltham, MA, USA), following the manufacturer's protocol. Briefly, EVs were incubated with the dye (1 µM final concentration) at 37 °C in the dark for 20 min, and the excess of nonincorporated dye was removed using a GE Healthcare PD SpinTrap G-25 desalting column (GE Healthcare, Life Science, Chicago, IL, USA). Stained EVs were incubated with freshly isolated BMCs at a 1/5 ratio for 3 h at 37 °C; the non-internalised EVs were removed by centrifugation at 400× g for 10 min. As a control, PBS was stained and incubated with BMCs in the same way. The EV uptake was assessed by measuring the acquired fluorescence with a CytoFlex flow cytometer (Beckman Coulter, Brea, CA, USA). The results were analysed using the Cytexpert software version 2.3.0.84 (Beckman Coulter, Brea, CA, USA).

### 4.3. Isolation, Validation and Quantification of Mouse BM-Derived EVs

BM-derived EVs were isolated from the BMC supernatant of control and irradiated mice by pooling the supernatants of five mice/irradiation doses/experiments. EV isolation was conducted 24 h after irradiation using the ExoQuick-TC kit (System Biosciences, Palo Alto CA, USA), as described previously [10]. EVs were used for miRNA analysis and for co-culture with BMCs.

BM-derived EVs were investigated by transmission electron microscopy (TEM) as described [14]. The size distribution analysis of EVs was conducted by tunable resistance pulse sensing (TRPS) using an NP150 nanopore (measurement range: 70–420 nm) [9]. EV-specific protein markers were investigated by Western blot analysis, as detailed below.

### 4.4. RNA Extraction from BM-Derived EVs for miRNA Analysis

Total RNA extraction from BM-derived EVs was performed using the RNeasy mini extraction kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions. The MiRNA concentration was measured on a BioAnalyzer 2100 (Agilent, Santa Clara, CA, USA) according to the manufacturer's instructions. Briefly, the concentrations of the samples to be measured were standardised according to the total RNA concentration, as measured by NanoDrop (ThermoFisher, Waltham, MA, USA), between 20 ng/µL and 50 ng/µL depending on the available sample amount. These were then loaded onto the BioAnalyzer small RNA chip (Agilent, part number 5067-1548) and read by the BioAnalyzer 2100. The resultant miRNA concentration was calculated by multiplying the measured miRNA concentration by the dilution factor of the sample, yielding the miRNA concentration in the undiluted sample.

### 4.5. nCounter Analysis of BM-Derived EV miRNAs and Their Quantitative Validation by Real-Time Polymerase Chain Reaction (RT-PCR)

An nCounter Mouse v1.5 miRNA Expression Panel including 800 miRNAs (NanoString Technologies, Seattle, WA, USA) was used to analyse the levels of miRNA expression in EVs, according to the manufacturer's instructions. MiRNA expression was analysed in four biological replicates in each experimental group (0 Gy, 0.1 Gy and 3 Gy EV, respectively). Briefly, the miRNA samples prepared as described above were hybridised to a miRNA-specific probe, which in turn hybridises to a barcoded fluorescent reporter specific to that probe; the barcode identifies which miRNA it is attached to. These were