<caption>Tableau 2, 3 et 4 : Fold change d'expressions des gènes indiqués et leur classification.</caption>

<table><caption>Tableau 2 : Metastable vs HMEC C</caption><thead><tr><th>Gene</th><th>Fold change Metastable</th><th>Category</th></tr></thead><tbody><tr><td>ALDH1A1</td><td>0.021876286</td><td>Aure</td></tr><tr><td>KYNU</td><td>492.9534964</td><td>Aure</td></tr><tr><td>CDKN1A</td><td>1.633786086</td><td>Cell Stress</td></tr><tr><td>DDIT4</td><td>1.527850107</td><td>Cell Stress</td></tr><tr><td>MDM2</td><td>7.192619777</td><td>Cell Stress</td></tr><tr><td>NFE2L2</td><td>2.429505265</td><td>Cell Stress</td></tr><tr><td>NQO1</td><td>5.803248273</td><td>Cell Stress</td></tr><tr><td>SOD1</td><td>1.718730797</td><td>Cell Stress</td></tr><tr><td>SOD2</td><td>2.920356399</td><td>Cell Stress</td></tr><tr><td>TP53</td><td>4.039793737</td><td>Cell Stress</td></tr><tr><td>WIGI</td><td>2.935078898</td><td>Cell Stress</td></tr><tr><td>ABCA1</td><td>6.418744775</td><td>Cholesterol Metabolism</td></tr><tr><td>ABCG1</td><td>1.248294099</td><td>Cholesterol Metabolism</td></tr><tr><td>FDFT1</td><td>5.884906289</td><td>Cholesterol Metabolism</td></tr><tr><td>HMOCR</td><td>3.194280066</td><td>Cholesterol Metabolism</td></tr><tr><td>SOLE</td><td>3.517653678</td><td>Cholesterol Metabolism</td></tr><tr><td>SQSTM</td><td>3.179694302</td><td>Cholesterol Metabolism</td></tr><tr><td>SREBF2</td><td>2.79060942</td><td>Cholesterol Metabolism</td></tr><tr><td>ACTA2</td><td>4.209748489</td><td>EMT</td></tr><tr><td>CD10</td><td>0.742437139</td><td>EMT</td></tr><tr><td>CD44</td><td>8.221206533</td><td>EMT</td></tr><tr><td>CDH2</td><td>2.562189114</td><td>EMT</td></tr><tr><td>FOXC1</td><td>0.820280559</td><td>EMT</td></tr><tr><td>ITGA5</td><td>4.168699529</td><td>EMT</td></tr><tr><td>SNAIL1</td><td>0.518666064</td><td>EMT</td></tr><tr><td>SNAIL2</td><td>1.958755487</td><td>EMT</td></tr><tr><td>TWIST1</td><td>26.00064428</td><td>EMT</td></tr><tr><td>VIM</td><td>14.52890244</td><td>EMT</td></tr><tr><td>ZEB1</td><td>132.8184459</td><td>EMT</td></tr><tr><td>ZEB2</td><td>56.94235915</td><td>EMT</td></tr><tr><td>DNER</td><td>22.950850991</td><td>EMT</td></tr><tr><td>CD24</td><td>0.021438895</td><td>EP differentiation</td></tr><tr><td>CDH1</td><td>0.008166578</td><td>EP differentiation</td></tr><tr><td>CDH3</td><td>0.085560562</td><td>EP differentiation</td></tr><tr><td>CLDN1</td><td>0.114934149</td><td>EP differentiation</td></tr><tr><td>CLDN3</td><td>0.160227258</td><td>EP differentiation</td></tr><tr><td>CLDN4</td><td>0.048724194</td><td>EP differentiation</td></tr><tr><td>CLDN7</td><td>0.037796162</td><td>EP differentiation</td></tr><tr><td>EpCam</td><td>0.213455382</td><td>EP differentiation</td></tr><tr><td>EREB3</td><td>0.049690847</td><td>EP differentiation</td></tr><tr><td>FOXA1</td><td>0.112613963</td><td>EP differentiation</td></tr><tr><td>GATA3</td><td>2.313038714</td><td>EP differentiation</td></tr><tr><td>ITGA6</td><td>0.736802166</td><td>EP differentiation</td></tr><tr><td>ITGB4</td><td>0.417741087</td><td>EP differentiation</td></tr><tr><td>KLK6</td><td>0.014229376</td><td>EP differentiation</td></tr><tr><td>KRT14</td><td>0.119062976</td><td>EP differentiation</td></tr><tr><td>KRT16</td><td>0.162926207</td><td>EP differentiation</td></tr><tr><td>KRT17</td><td>0.096391917</td><td>EP differentiation</td></tr><tr><td>KRT19</td><td>0.079559231</td><td>EP differentiation</td></tr><tr><td>KRT3</td><td>0.531736269</td><td>EP differentiation</td></tr><tr><td>KRT6a</td><td>0.146928744</td><td>EP differentiation</td></tr><tr><td>KRT7</td><td>0.549943866</td><td>EP differentiation</td></tr><tr><td>KRT8</td><td>0.123678749</td><td>EP differentiation</td></tr><tr><td>MUC1</td><td>4.295588901</td><td>EP differentiation</td></tr><tr><td>OVOL1</td><td>0.174414565</td><td>EP differentiation</td></tr><tr><td>OVOL2</td><td>4.049738059</td><td>EP differentiation</td></tr><tr><td>ATF4</td><td>2.458034094</td><td>ER Stress</td></tr><tr><td>CHAC1</td><td>0.68960937</td><td>ER Stress</td></tr><tr><td>HERPUD</td><td>0.124349294</td><td>ER Stress</td></tr><tr><td>HSPA5</td><td>1.509358724</td><td>ER Stress</td></tr><tr><td>HYOU1</td><td>0.896481433</td><td>ER Stress</td></tr><tr><td>PDIA4</td><td>2.848627687</td><td>ER Stress</td></tr><tr><td>PDIA6</td><td>9.755772926</td><td>ER Stress</td></tr><tr><td>TRIB3</td><td>1.859944956</td><td>ER Stress</td></tr><tr><td>GFPT2</td><td>3.219818924</td><td>Glycoly Branch</td></tr><tr><td>HK1</td><td>31.44797748</td><td>Glycoly Branch</td></tr><tr><td>HK2</td><td>4.468453337</td><td>Glycoly Branch</td></tr><tr><td>LDHA</td><td>1.894111836</td><td>Glycoly Branch</td></tr><tr><td>LDHB</td><td>0.598052818</td><td>Glycoly Branch</td></tr><tr><td>PDK4</td><td>20.37392578</td><td>Glycoly Branch</td></tr><tr><td>SLC2A3</td><td>2.083693724</td><td>Glycoly Branch</td></tr><tr><td>AIMF2</td><td>7.23761665</td><td>Iron Ferroptosis</td></tr><tr><td>FTH1</td><td>2.310032436</td><td>Iron Ferroptosis</td></tr><tr><td>FTL</td><td>2.742543509</td><td>Iron Ferroptosis</td></tr><tr><td>GCH1</td><td>115.4920597</td><td>Iron Ferroptosis</td></tr><tr><td>HMOX</td><td>1.894111836</td><td>Iron Ferroptosis</td></tr><tr><td>LCN2</td><td>0.024804014</td><td>Iron Ferroptosis</td></tr><tr><td>SLC7A11</td><td>17.96393916</td><td>Iron Ferroptosis</td></tr><tr><td>ACSL1</td><td>7.340638471</td><td>Lipid Metabolism</td></tr><tr><td>ACSL4</td><td>1.87393451</td><td>Lipid Metabolism</td></tr><tr><td>AGPAT4</td><td>1.124335262</td><td>Lipid Metabolism</td></tr><tr><td>FABP4</td><td>56.29861934</td><td>Lipid Metabolism</td></tr><tr><td>FASN</td><td>10.63277899</td><td>Lipid Metabolism</td></tr><tr><td>LDLR</td><td>1.842336366</td><td>Lipid Metabolism</td></tr><tr><td>PPARA</td><td>7.717209848</td><td>Lipid Metabolism</td></tr><tr><td>PPARG</td><td>1.860086269</td><td>Lipid Metabolism</td></tr><tr><td>SCD</td><td>2.103200241</td><td>Lipid Metabolism</td></tr><tr><td>SCD5</td><td>1.036091212</td><td>Lipid Metabolism</td></tr><tr><td>SREBF1</td><td>18.82075049</td><td>Stem cell</td></tr><tr><td>FOXO1</td><td>2.157462946</td><td>Stem cell</td></tr><tr><td>OCT4</td><td>2.345793341</td><td>Stem cell</td></tr><tr><td>TCF4</td><td>2.718058413</td><td>Stem cell</td></tr><tr><td>TOPO2A</td><td>5.940755491</td><td>Stem cell</td></tr></tbody></table>

<table><caption>Tableau 3 : Prog Lum vs Metastable</caption><thead><tr><th>Gene</th><th>Fold Change Prog Myo</th><th>Category</th></tr></thead><tbody><tr><td>ALDH1A1</td><td>3.495836964</td><td>Aure</td></tr><tr><td>ALDH1A3</td><td>0.684511731</td><td>Aure</td></tr><tr><td>KYNU</td><td>1.815145394</td><td>Aure</td></tr><tr><td>LCN2</td><td>144.7824231</td><td>Aure</td></tr><tr><td>CDKN1A</td><td>0.409310006</td><td>Cell Stress</td></tr><tr><td>MDM2</td><td>0.428116177</td><td>Cell Stress</td></tr><tr><td>NFE2L2</td><td>0.632684512</td><td>Cell Stress</td></tr><tr><td>NQO1</td><td>0.31473831</td><td>Cell Stress</td></tr><tr><td>SOD1</td><td>0.093488452</td><td>Cell Stress</td></tr><tr><td>SOD2</td><td>0.62231376</td><td>Cell Stress</td></tr><tr><td>ABCA1</td><td>0.60787234</td><td>Cholesterol Metabolism</td></tr><tr><td>FDFT1</td><td>0.242188439</td><td>Cholesterol Metabolism</td></tr><tr><td>HMOCR</td><td>0.398501618</td><td>Cholesterol Metabolism</td></tr><tr><td>SOLE</td><td>0.372587550</td><td>Cholesterol Metabolism</td></tr><tr><td>SQSTM</td><td>0.29441022</td><td>Cholesterol Metabolism</td></tr><tr><td>SREBF2</td><td>0.16210689</td><td>Cholesterol Metabolism</td></tr><tr><td>CD24</td><td>23.85164103</td><td>Differentiation</td></tr><tr><td>CD55</td><td>1.702418695</td><td>Differentiation</td></tr><tr><td>CDH1</td><td>4.884510116</td><td>Differentiation</td></tr><tr><td>CDH1</td><td>2.669572120</td><td>Differentiation</td></tr><tr><td>CLDN1</td><td>0.715349</td><td>Differentiation</td></tr><tr><td>CLDN3</td><td>5.43806127</td><td>Differentiation</td></tr><tr><td>GATA3</td><td>0.859497461</td><td>Differentiation</td></tr><tr><td>KLK6</td><td>0.626419773</td><td>Differentiation</td></tr><tr><td>KRT14</td><td>1.72151053</td><td>Differentiation</td></tr><tr><td>KRT16</td><td>1.021090202</td><td>Differentiation</td></tr><tr><td>KRT5</td><td>4.60932847</td><td>Differentiation</td></tr><tr><td>KRT6a</td><td>0.73644845</td><td>Differentiation</td></tr><tr><td>OVOL2</td><td>0.22647875</td><td>Differentiation</td></tr><tr><td>CD44</td><td>0.696791226</td><td>EMT</td></tr><tr><td>NDBR</td><td>1.154153972</td><td>EMT</td></tr><tr><td>FOXC1</td><td>1.174053153</td><td>EMT</td></tr><tr><td>SNAIL</td><td>5.387793159</td><td>EMT</td></tr><tr><td>SNAIL1</td><td>3.82625874</td><td>EMT</td></tr><tr><td>TWIST1</td><td>0.882625776</td><td>EMT</td></tr><tr><td>ZEB1</td><td>0.50282498</td><td>EMT</td></tr><tr><td>ZEB2</td><td>0.27617374</td><td>EMT</td></tr><tr><td>ATF4</td><td>0.501342488</td><td>ER Stress</td></tr><tr><td>CHAC</td><td>0.23443444</td><td>ER Stress</td></tr><tr><td>DDIT3</td><td>1.565934313</td><td>ER Stress</td></tr><tr><td>DDIT4</td><td>1.5019785</td><td>ER Stress</td></tr><tr><td>HERPUD</td><td>0.14964534</td><td>ER Stress</td></tr><tr><td>HSPA5</td><td>1.143784109</td><td>ER Stress</td></tr><tr><td>HYOU1</td><td>0.687435221</td><td>ER Stress</td></tr><tr><td>PDIA4</td><td>0.66505822</td><td>ER Stress</td></tr><tr><td>PDIA6</td><td>1.40915307</td><td>ER Stress</td></tr><tr><td>TRIB3</td><td>0.71243795</td><td>ER Stress</td></tr><tr><td>GPT2</td><td>0.05842851</td><td>Glycoly Branch</td></tr><tr><td>HK1</td><td>9.45616665</td><td>ER Stress</td></tr><tr><td>HK2</td><td>0.10628915</td><td>Glycoly Branch</td></tr><tr><td>LDHA</td><td>1.539029102</td><td>Glycoly Branch</td></tr><tr><td>LDHB</td><td>1.518504671</td><td>Glycoly Branch</td></tr><tr><td>PDK4</td><td>0.05059365</td><td>Glycoly Branch</td></tr><tr><td>SLC2A3</td><td>1.269683385</td><td>Glycoly Branch</td></tr><tr><td>AIFM2</td><td>0.69790466</td><td>Iron Ferroptosis</td></tr><tr><td>FTH1</td><td>0.698782286</td><td>Iron Ferroptosis</td></tr><tr><td>FTL</td><td>0.197903953</td><td>Iron Ferroptosis</td></tr><tr><td>GCH1</td><td>0.00044591</td><td>Iron Ferroptosis</td></tr><tr><td>HMOX</td><td>0.69261588</td><td>Iron Ferroptosis</td></tr><tr><td>SLC7A11</td><td>0.10444444</td><td>Iron Ferroptosis</td></tr><tr><td>ACSL1</td><td>0.211762558</td><td>Lipid Metabolism</td></tr><tr><td>ACSL4</td><td>0.685982749</td><td>Lipid Metabolism</td></tr><tr><td>AGPAT4</td><td>1.3382977</td><td>Lipid Metabolism</td></tr><tr><td>FASN</td><td>11.8235295</td><td>Lipid Metabolism</td></tr><tr><td>LDLR</td><td>0.37993545</td><td>Lipid Metabolism</td></tr><tr><td>PPARA</td><td>0.165890638</td><td>Lipid Metabolism</td></tr><tr><td>PPARG</td><td>0.64998149</td><td>Lipid Metabolism</td></tr><tr><td>SCD</td><td>0.148879816</td><td>Lipid Metabolism</td></tr><tr><td>SCD5</td><td>0.616603758</td><td>Lipid Metabolism</td></tr><tr><td>SRAN1</td><td>1.110168457</td><td>Lipid Metabolism</td></tr><tr><td>ELF3</td><td>1.70922447</td><td>Lipid Differentiation</td></tr><tr><td>ELF5</td><td>160.805050</td><td>Lipid Differentiation</td></tr><tr><td>EpCam</td><td>0.82221342</td><td>Lipid Differentiation</td></tr><tr><td>EREB3</td><td>0.28988181</td><td>Lipid Differentiation</td></tr><tr><td>ESR1</td><td>16.76717214</td><td>Lipid Differentiation</td></tr><tr><td>FOXA1</td><td>0.823881614</td><td>Lipid Differentiation</td></tr><tr><td>KRT19</td><td>2.399783914</td><td>Lipid Differentiation</td></tr><tr><td>KRT7</td><td>1.38978118</td><td>Lipid Differentiation</td></tr><tr><td>KRT8</td><td>0.884454583</td><td>Lipid Differentiation</td></tr><tr><td>MUC1</td><td>0.114684231</td><td>Lipid Differentiation</td></tr><tr><td>OVOL1</td><td>0.104033434</td><td>Lipid Differentiation</td></tr><tr><td>PRLR</td><td>0.219509623</td><td>Lipid Differentiation</td></tr><tr><td>ACTA2</td><td>2.731505691</td><td>Lipid Differentiation</td></tr><tr><td>BGN</td><td>0.60590783</td><td>Lipid Differentiation</td></tr><tr><td>CALD1</td><td>1.57803101</td><td>Lipid Differentiation</td></tr><tr><td>CALR</td><td>1.63483342</td><td>Lipid Differentiation</td></tr><tr><td>CD10</td><td>2.14258694</td><td>Lipid Differentiation</td></tr><tr><td>CD82</td><td>3.6921034</td><td>Lipid Differentiation</td></tr><tr><td>CNN1</td><td>104.0033434</td><td>Lipid Differentiation</td></tr><tr><td>COL1A1</td><td>39.3229843</td><td>Lipid Differentiation</td></tr><tr><td>COL4A1</td><td>3.10485999</td><td>Lipid Differentiation</td></tr><tr><td>COL4A2</td><td>3.18012835</td><td>Lipid Differentiation</td></tr><tr><td>COL5A1</td><td>2.811892652</td><td>Lipid Differentiation</td></tr><tr><td>COL6A1</td><td>1.685908663</td><td>Lipid Differentiation</td></tr><tr><td>DN3</td><td>1.094034842</td><td>Lipid Differentiation</td></tr><tr><td>FABP5</td><td>2.25165573</td><td>Lipid Differentiation</td></tr><tr><td>FN1</td><td>0.722471754</td><td>Lipid Differentiation</td></tr><tr><td>ITGA5</td><td>2.303872661</td><td>Lipid Differentiation</td></tr><tr><td>KRT15</td><td>2.69996218</td><td>Lipid Differentiation</td></tr><tr><td>KRT17</td><td>2.96657862</td><td>Lipid Differentiation</td></tr><tr><td>OXTR</td><td>7.46576926</td><td>Lipid Differentiation</td></tr><tr><td>POUCER</td><td>3.80738564</td><td>Lipid Differentiation</td></tr><tr><td>SERPINB5</td><td>3.92288457</td><td>Lipid Differentiation</td></tr><tr><td>TAGLN</td><td>1.668973282</td><td>Lipid Differentiation</td></tr><tr><td>Tap63</td><td>2.10122313</td><td>Lipid Differentiation</td></tr><tr><td>Vim</td><td>2.25982042</td><td>Lipid Differentiation</td></tr><tr><td>BMI</td><td>0.43740920</td><td>Stem cell</td></tr><tr><td>FOXO1</td><td>0.181524785</td><td>Stem cell</td></tr><tr><td>KLK4</td><td>0.44145431</td><td>Stem cell</td></tr><tr><td>NANOG</td><td>0.138289204</td><td>Stem cell</td></tr><tr><td>Oct4</td><td>0.967818375</td><td>Stem cell</td></tr><tr><td>TCF4</td><td>1.75401055</td><td>Stem cell</td></tr><tr><td>TOPO2A</td><td>1.48952278</td><td>Stem cell</td></tr></tbody></table>

<table><caption>Tableau 4 : Prog Myo vs Metastable</caption><thead><tr><th>Gene</th><th>Fold Change Prog Myo</th><th>Category</th></tr></thead><tbody><tr><td>ALDH1A1</td><td>3.495836964</td><td>Aure</td></tr><tr><td>ALDH1A3</td><td>0.684511731</td><td>Aure</td></tr><tr><td>KYNU</td><td>1.815145394</td><td>Aure</td></tr><tr><td>LCN2</td><td>144.7824231</td><td>Aure</td></tr><tr><td>CDKN1A</td><td>0.409310006</td><td>Cell Stress</td></tr><tr><td>MDM2</td><td>0.428116177</td><td>Cell Stress</td></tr><tr><td>NFE2L2</td><td>0.632684512</td><td>Cell Stress</td></tr><tr><td>NQO1</td><td>0.31473831</td><td>Cell Stress</td></tr><tr><td>SOD1</td><td>0.093488452</td><td>Cell Stress</td></tr><tr><td>SOD2</td><td>0.62231376</td><td>Cell Stress</td></tr><tr><td>ABCA1</td><td>0.60787234</td><td>Cholesterol Metabolism</td></tr><tr><td>FDFT1</td><td>0.242188439</td><td>Cholesterol Metabolism</td></tr><tr><td>HMOCR</td><td>0.398501618</td><td>Cholesterol Metabolism</td></tr><tr><td>SOLE</td><td>0.372587550</td><td>Cholesterol Metabolism</td></tr><tr><td>SQSTM</td><td>0.29441022</td><td>Cholesterol Metabolism</td></tr><tr><td>SREBF2</td><td>0.16210689</td><td>Cholesterol Metabolism</td></tr><tr><td>CD24</td><td>23.85164103</td><td>Differentiation</td></tr><tr><td>CD55</td><td>1.702418695</td><td>Differentiation</td></tr><tr><td>CDH1</td><td>4.884510116</td><td>Differentiation</td></tr><tr><td>CDH3</td><td>2.669572120</td><td>Differentiation</td></tr><tr><td>CLDN1</td><td>0.715349</td><td>Differentiation</td></tr><tr><td>CLDN3</td><td>5.43806127</td><td>Differentiation</td></tr><tr><td>GATA3</td><td>0.859497461</td><td>Differentiation</td></tr><tr><td>KLK6</td><td>0.626419773</td><td>Differentiation</td></tr><tr><td>KRT14</td><td>1.72151053</td><td>Differentiation</td></tr><tr><td>KRT16</td><td>1.021090202</td><td>Differentiation</td></tr><tr><td>KRT5</td><td>4.60932847</td><td>Differentiation</td></tr><tr><td>KRT6a</td><td>0.73644845</td><td>Differentiation</td></tr><tr><td>OVOL2</td><td>0.22647875</td><td>Differentiation</td></tr><tr><td>CD44</td><td>0.696791226</td><td>EMT</td></tr><tr><td>NDBR</td><td>1.154153972</td><td>EMT</td></tr><tr><td>FOXC1</td><td>1.174053153</td><td>EMT</td></tr><tr><td>SNAIL</td><td>5.387793159</td><td>EMT</td></tr><tr><td>SNAIL1</td><td>3.82625874</td><td>EMT</td></tr><tr><td>TWIST1</td><td>0.882625776</td><td>EMT</td></tr><tr><td>ZEB1</td><td>0.50282498</td><td>EMT</td></tr><tr><td>ZEB2</td><td>0.27617374</td><td>EMT</td></tr><tr><td>ATF4</td><td>0.501342488</td><td>ER Stress</td></tr><tr><td>CHAC</td><td>0.23443444</td><td>ER Stress</td></tr><tr><td>DDIT3</td><td>1.565934313</td><td>ER Stress</td></tr><tr><td>DDIT4</td><td>1.5019785</td><td>ER Stress</td></tr><tr><td>HERPUD</td><td>0.14964534</td><td>ER Stress</td></tr><tr><td>HSPA5</td><td>1.143784109</td><td>ER Stress</td></tr><tr><td>HYOU1</td><td>0.687435221</td><td>ER Stress</td></tr><tr><td>PDIA4</td><td>0.66505822</td><td>ER Stress</td></tr><tr><td>PDIA6</td><td>1.40915307</td><td>ER Stress</td></tr><tr><td>TRIB3</td><td>0.71243795</td><td>ER Stress</td></tr><tr><td>GPT2</td><td>0.05842851</td><td>Glycoly Branch</td></tr><tr><td>HK1</td><td>9.45616665</td><td>ER Stress</td></tr><tr><td>HK2</td><td>0.10628915</td><td>Glycoly Branch</td></tr><tr><td>LDHA</td><td>1.539029102</td><td>Glycoly Branch</td></tr><tr><td>LDHB</td><td>1.518504671</td><td>Glycoly Branch</td></tr><tr><td>PDK4</td><td>0.05059365</td><td>Glycoly Branch</td></tr><tr><td>SLC2A3</td><td>1.269683385</td><td>Glycoly Branch</td></tr><tr><td>AIFM2</td><td>0.69790466</td><td>Iron Ferroptosis</td></tr><tr><td>FTH1</td><td>0.698782286</td><td>Iron Ferroptosis</td></tr><tr><td>FTL</td><td>0.197903953</td><td>Iron Ferroptosis</td></tr><tr><td>GCH1</td><td>0.00044591</td><td>Iron Ferroptosis</td></tr><tr><td>HMOX</td><td>0.69261588</td><td>Iron Ferroptosis</td></tr><tr><td>SLC7A11</td><td>0.10444444</td><td>Iron Ferroptosis</td></tr><tr><td>ACSL1</td><td>0.211762558</td><td>Lipid Metabolism</td></tr><tr><td>ACSL4</td><td>0.685982749</td><td>Lipid Metabolism</td></tr><tr><td>AGPAT4</td><td>1.3382977</td><td>Lipid Metabolism</td></tr><tr><td>FASN</td><td>11.8235295</td><td>Lipid Metabolism</td></tr><tr><td>LDLR</td><td>0.37993545</td><td>Lipid Metabolism</td></tr><tr><td>PPARA</td><td>0.165890638</td><td>Lipid Metabolism</td></tr><tr><td>PPARG</td><td>0.64998149</td><td>Lipid Metabolism</td></tr><tr><td>SCD</td><td>0.148879816</td><td>Lipid Metabolism</td></tr><tr><td>SCD5</td><td>0.616603758</td><td>Lipid Metabolism</td></tr><tr><td>SRAN1</td><td>1.110168457</td><td>Lipid Metabolism</td></tr><tr><td>ELF3</td><td>1.70922447</td><td>Lipid Differentiation</td></tr><tr><td>ELF5</td><td>160.805050</td><td>Lipid Differentiation</td></tr><tr><td>EpCam</td><td>0.82221342</td><td>Lipid Differentiation</td></tr><tr><td>EREB3</td><td>0.28988181</td><td>Lipid Differentiation</td></tr><tr><td>ESR1</td><td>16.76717214</td><td>Lipid Differentiation</td></tr><tr><td>FOXA1</td><td>0.823881614</td><td>Lipid Differentiation</td></tr><tr><td>KRT19</td><td>2.399783914</td><td>Lipid Differentiation</td></tr><tr><td>KRT7</td><td>1.38978118</td><td>Lipid Differentiation</td></tr><tr><td>KRT8</td><td>0.884454583</td><td>Lipid Differentiation</td></tr><tr><td>MUC1</td><td>0.114684231</td><td>Lipid Differentiation</td></tr><tr><td>OVOL1</td><td>0.104033434</td><td>Lipid Differentiation</td></tr><tr><td>PRLR</td><td>0.219509623</td><td>Lipid Differentiation</td></tr><tr><td>ACTA2</td><td>2.731505691</td><td>Lipid Differentiation</td></tr><tr><td>BGN</td><td>0.60590783</td><td>Lipid Differentiation</td></tr><tr><td>CALD1</td><td>1.57803101</td><td>Lipid Differentiation</td></tr><tr><td>CALR</td><td>1.63483342</td><td>Lipid Differentiation</td></tr><tr><td>CD10</td><td>2.14258694</td><td>Lipid Differentiation</td></tr><tr><td>CD82</td><td>3.6921034</td><td>Lipid Differentiation</td></tr><tr><td>CNN1</td><td>104.0033434</td><td>Lipid Differentiation</td></tr><tr><td>COL1A1</td><td>39.3229843</td><td>Lipid Differentiation</td></tr><tr><td>COL4A1</td><td>3.10485999</td><td>Lipid Differentiation</td></tr><tr><td>COL4A2</td><td>3.18012835</td><td>Lipid Differentiation</td></tr><tr><td>COL5A1</td><td>2.811892652</td><td>Lipid Differentiation</td></tr><tr><td>COL6A1</td><td>1.685908663</td><td>Lipid Differentiation</td></tr><tr><td>DN3</td><td>1.094034842</td><td>Lipid Differentiation</td></tr><tr><td>FABP5</td><td>2.25165573</td><td>Lipid Differentiation</td></tr><tr><td>FN1</td><td>0.722471754</td><td>Lipid Differentiation</td></tr><tr><td>ITGA5</td><td>2.303872661</td><td>Lipid Differentiation</td></tr><tr><td>KRT15</td><td>2.69996218</td><td>Lipid Differentiation</td></tr><tr><td>KRT17</td><td>2.96657862</td><td>Lipid Differentiation</td></tr><tr><td>OXTR</td><td>7.46576926</td><td>Lipid Differentiation</td></tr><tr><td>POUCER</td><td>3.80738564</td><td>Lipid Differentiation</td></tr><tr><td>SERPINB5</td><td>3.92288457</td><td>Lipid Differentiation</td></tr><tr><td>TAGLN</td><td>1.668973282</td><td>Lipid Differentiation</td></tr><tr><td>Tap63</td><td>2.10122313</td><td>Lipid Differentiation</td></tr><tr><td>Vim</td><td>2.25982042</td><td>Lipid Differentiation</td></tr><tr><td>BMI</td><td>0.43740920</td><td>Stem cell</td></tr><tr><td>FOXO1</td><td>0.181524785</td><td>Stem cell</td></tr><tr><td>KLK4</td><td>0.44145431</td><td>Stem cell</td></tr><tr><td>NANOG</td><td>0.138289204</td><td>Stem cell</td></tr><tr><td>Oct4</td><td>0.967818375</td><td>Stem cell</td></tr><tr><td>TCF4</td><td>1.75401055</td><td>Stem cell</td></tr><tr><td>TOPO2A</td><td>1.48952278</td><td>Stem cell</td></tr></tbody></table>
Résultat Contributeur n°1

<figure><img src="image_63.png" /></figure>

DiPhenyl Phosphate (DPP) modulateur du
métabolisme lipidique
Lors de ma thèse, j'ai eu l'occasion de travailler sur d'autres projets en lien avec le métabolisme lipidique et l'identité cellulaire. Ce projet se centre sur l'étude d'un perturbateur métabolique, le Diphénylphosphate (DPP) et a permis de publier un article dans *Environmental Health Perspectives*. Le DPP provient principalement de la dégradation de molécules utilisées comme retardateur de flamme. De ce fait on retrouve cette molécule de façon ubiquitaire dans l'environnement (l'atmosphère, l'eau, l'intérieur des maisons ...). Alors que l'ensemble des travaux sur la cytotoxicité des retardateurs de flamme sont basés sur les molécules primaires, il était donc intéressant de regarder l'effet de ces molécules après leur biodégradation, notamment le DPP. S. Selmi-Ruby, J. Marín-Sáez et A. Fildier ont donc analysé l'effet du DDP et de son principal précurseur, le Triphénylphosphate (TPP) lors d'exposition aiguë ou chronique sur un modèle animal. Ils ont par la suite effectué des analyses multi-omiques, des foies des souris traités au DPP. C'est lors de ces analyses que j'ai pu apporter une contribution dans ces travaux. Les conclusions apportées ont permis de mettre en évidence des modifications des métabolites contenant des noyaux aromatiques, notamment ceux contenant la fonction chimique de type indole associé au métabolisme du tryptophane. Ce résultat mettait en lumière une perturbation du récepteur AHR. En parallèle, des modifications métaboliques des lipides ont pu être observées, notamment une augmentation des lipides polyinsaturés. Alors qu'AHR est notamment connu pour son activité de détoxification d'agents toxiques pour la cellule, il a été démontré qu'il jouait aussi un rôle dans des processus embryonnaires. En effet, son activation par son ligand endogène, la Kynurénine, un métabolite dérivé du tryptophane permet un maintien du phénotype indifférencié des cellules souches ectodermiques²⁶⁰. Ainsi l'identité cellulaire peut être modulée par la régulation d'AHR. On retrouve ces modulations par AHR lors du développement tumoral sur les mécanismes d'autorenouvellement et de motilité cellulaire, qui comme je l'ai démontré dans l'introduction semblent être en lien avec la perturbation de l'identité cellulaire²⁶¹. Il a aussi été démontré que l'activation d'AHR est importante pour le développement du caractère souche des cellules cancéreuses mammaires²⁶². Dans les mécanismes retrouvés dans ce contexte tumorigénique, il est intéressant de constater l'existence d'une boucle d'activation entre les fibroblastes associés aux cellules cancéreuses (CAF) productrices de l'activateur endogène d'AHR, la kynurénine, et les cellules cancéreuses productrices d'eicosanoïdes²⁶³. En effet, une augmentation de l'activité de COX2 au sein des cellules cancéreuses, l'enzyme métabolisant l'acide arachidonique en prostaglandine E2, entraîne par sa sécrétion une augmentation du métabolisme du tryptophane et notamment la synthèse et la sécrétion de la kynurénine chez les CAF. Lorsqu'elle est présente dans le milieu, la kynurénine va alors pouvoir activer l'AHR des cellules cancéreuses modulant ainsi l'identité cellulaire. En lien avec nos travaux sur la corrélation entre fluidité membranaire et plasticité cellulaire, il est intéressant de constater qu'un enrichissement du métabolisme en PUFA ω6 permettrait le support d'un tel mécanisme moléculaire de communication cellulaire au niveau de la tumeur et qui serait lié à la mise en place d'un phénotype CCDD. Ceci permettrait de renforcer le rôle de la modulation du métabolisme lipidique pour l'acquisition du phénotype CCDD, se traduisant par une augmentation de la fluidité membranaire.

De plus, des travaux supplémentaires effectués au sein de l'équipe du CRCL ont permis de mettre en évidence que le DPP permettait de potentialiser plusieurs mécanismes lors de la mise en place d'un processus tumoral par traitement par un agent chimique le 3-méthylcholenthrène.
Ces travaux préliminaires mettent en évidence que la combinaison DPP et 3-méthylcholenthrène permettent d'augmenter le nombre d'hyperplasies atypique et sont associés au développement de régions métaplasiques. Ces résultats suggèrent la mise en place d'une perturbation de l'identité cellulaire par le DPP, probablement dû à une régulation d'AHR. En prenant en considération l'ensemble de ces résultats, il est intéressant d'émettre l'hypothèse que la modulation d'AHR par le DPP, augmenterait le métabolisme des lipides particulièrement celui des PUFA jouant notamment un rôle sur la mise en place d'une communication intercellulaire au niveau de la tumeur. Finalement, ce changement métabolique permettrait dans cette hypothèse de soutenir les mécanismes liés à la perturbation de l'identité cellulaire et par conséquent potentialiser le processus tumoral, de l'initiation jusqu'au processus métastatique. Cette hypothèse mettrait une nouvelle fois en lien la perte de l'identité cellulaire et l'augmentation de la fluidité membranaire.
# In Vivo Characterization of the Toxicological Properties of DPhP, One of the Main Degradation Products of Aryl Phosphate Esters

Samia Selmi-Ruby,<sup>1*</sup> Jesús Marín-Sáez,<sup>2*</sup> Aurélie Fildier,<sup>3*</sup> Audrey Buleté,<sup>3</sup> Myriam Abdallah,<sup>1</sup> Jessica Garcia,<sup>4</sup> Julie Deverchère,<sup>1</sup> Loïc Spinner,<sup>3</sup> Barbara Giroud,<sup>3</sup> Sébastien Ibanez,<sup>5</sup> Thierry Granjon,<sup>5</sup> Claire Bardel,<sup>6,7</sup> Alain Puisieux,<sup>1</sup> Béatrice Fervers,<sup>1</sup> Emmanuelle Vulliet,<sup>3</sup> Léa Payen,<sup>1,4</sup> and Arnaud M. Vigneron<sup>1</sup>

**BACKGROUND:** Aryl phosphate esters (APEs) are widely used and commonly present in the environment. Health hazards associated with these compounds remain largely unknown and the effects of diphenyl phosphate (DPhP), one of their most frequent derivatives, are poorly characterized.

**OBJECTIVE:** Our aim was to investigate whether DPhP per se may represent a more relevant marker of exposure to APEs than direct assessment of their concentration and determine its potential deleterious biological effects in chronically exposed mice.

**METHODS:** Conventional animals (FVB mice) were acutely or chronically exposed to relevant doses of DPhP or to triphenyl phosphate (TPhP), one of its main precursors. Both molecules were measured in blood and other tissues by liquid chromatography-mass spectrometry (LC-MS). Effects of chronic DPhP exposure were addressed through liver multi-omics analysis to determine the corresponding metabolic profile. Deep statistical exploration was performed to extract correlated information, guiding further physiological analyses.

**RESULTS:** Multi-omics analysis confirmed the existence of biological effects of DPhP, even at a very low dose of 0.1mg/mL in drinking water. Chemical structural homology and pathway mapping demonstrated a clear reduction of the fatty acid catabolic processes centered on acylcarnitine and mitochondrial β-oxidation in mice exposed to DPhP in comparison with those treated with vehicle. An interesting finding was that in mice exposed to DPhP, mRNA, expression of genes involved in lipid catabolic processes and regulated by peroxisome proliferator-activated receptor alpha (PPARα) was lower than that in vehicle-treated mice. Immunohistochemistry analysis showed a specific down-regulation of HMGCS2, a kernel target gene of PPARα. Overall, DPhP absorption disrupted body weight-gain processes.

**CONCLUSIONS:** Our results suggest that in mice, the effects of chronic exposure to DPhP, even at a low dose, are not negligible. Fatty acid metabolism in the liver is essential for controlling fast and feast periods, with adverse consequences on the overall physiology. Therefore, the impact of DPhP on circulating fat, cardiovascular pathologies and metabolic disease incidence deserves, in light of our results, further investigations. https://doi.org/10.1289/EHP6826

## Introduction

Diphenyl phosphate (DPhP) has been used as a main biomarker for assessing exposure to aryl phosphate esters (APEs), especially triphenyl phosphate (TPhP), a molecule suspected of presenting human health hazards. However, DPhP can be produced from degradation of several APEs, including ethylhexyl diphenyl phosphate (EHDPhP) or the resorcinol bis(diphenyl phosphate) (RDP) (Ballesteros-Gómez et al. 2015a, 2015b) and tert-butylphenyl diphenyl phosphate (BPDP) (Heitkamp et al. 1985). Moreover, DPhP itself is largely present in the environment worldwide

(Björnsdotter et al. 2018; Fu et al. 2017; Li et al. 2019b; Wang and Kannan 2018; Wang et al. 2019), either owing to its spontaneous/microorganism production from known APEs (Fu et al. 2017; Su et al. 2016), or to its direct use in industry (Mitchell et al. 2019). Most APEs are used as flame retardants. They are added to consumer products and raw materials to delay combustion and meet flammability standards (e.g., Fire initiation and growth, Fire containment, and Non-combustibility test standards), such as the ISO/TC92 Fire Safety code, and the TC89 Fire Hazard Testing code existing in Europe. Moreover, unlike other flame retardants, TPhP and EHDPhP are also largely used as plasticizers and lubricants in hydraulic fluids, rubber, paints, textile coatings, food packaging, and PVC, drastically increasing their presence in the environment. These compounds are not usually covalently linked to plastic materials and can easily leach into the environment (Jamarani et al. 2018).

High vapor pressure of TPhP is also likely to facilitate its release in the air once it is freed from its original material (Meeker et al. 2013). It is not surprising that TPhP and EHDPhP have thus been shown to be ubiquitous components of the human indoor environment where their sources and exposure pathways are quite diverse and heterogeneous with regard to other flame retardants. Indeed, TPhP and EHDPhP quantification in food (Li et al. 2019b; Wang and Kannan 2018), house dust (Björnsdotter et al. 2018; Meeker and Stapleton 2010; Meeker et al. 2013), water (Li et al. 2019a), or air (Wei et al. 2015) has systematically demonstrated their presence in these different matrices, raising awareness on the safety of these compounds. A study characterizing the direct

<footnote n="1"><sup>1</sup>INSERM 1052, CNRS 5286, Centre Léon Bérard, Cancer Research Center of Lyon, Equipe Labellisée Ligue contre le Cancer, Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France</footnote>

<footnote n="2"><sup>2</sup>Department of Chemistry and Physics, Analytical Chemistry Area, University of Almería, Research Centre for Agricultural and Food Biotechnology (BITAL), Agrifood Campus of International Excellence, Almería, Spain</footnote>

<footnote n="3"><sup>3</sup>CNRS, Institut des Sciences Analytiques, UMR 5280, Université de Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France</footnote>

<footnote n="4"><sup>4</sup>Hospices Civils de Lyon, Centre Hospitalier Lyon-Sud, Biochemistry, Pharmacotoxicology, and Molecular Biology Department, Université de Lyon, Université Claude Bernard Lyon 1, Pierre Bénite, France</footnote>

<footnote n="5"><sup>5</sup>CNRS, Molecular and Supramolecular Chemistry and Biochemistry Institute ICBMS UMR 5246, Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France</footnote>

<footnote n="6"><sup>6</sup>Department of Biostatistics, Hospices Civils de Lyon, Lyon, France</footnote>

<footnote n="7"><sup>7</sup>CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, Université de Lyon, Université Lyon 1, Villeurbanne, France</footnote>

<footnote>*These authors contribute equally to this work.</footnote>

<footnote>Address correspondence to Léa Payen, Centre Léon Bérard, Bat Cheney D, 28 rue Laënnec, 69373 Lyon CEDEX 08, France. Telephone: 04 69 85 60 85. Email: lea.payen@univ-lyon1.fr; or Arnaud M. Vigneron, Centre Léon Bérard, Bat Cheney D, 28 rue Laënnec, 69373 Lyon CEDEX 08, France. Telephone: 04 69 85 60 85. Email: arnaud.vigneron@lyon.unicancer.fr</footnote>

<footnote>Supplemental Material is available online (https://doi.org/10.1289/EHP6826).</footnote>

<footnote>The authors declare they have no actual or potential competing financial interests.</footnote>

<footnote>Received 24 January 2020; Revised 17 November 2020; Accepted 18 November 2020; Published 9 December 2020.</footnote>

<footnote>**Note to readers with disabilities:** EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehponline@niehs.nih.gov. Our staff will work with you to assess and meet your accessibility needs within 3 working days.</footnote>
biological effects of DPhP and their relationship to TPhP exposure thus appeared to be of particular relevance to better define the effects and mechanisms of action associated with exposure to APEs in a more comprehensive way.

Historically, DPhP was believed to be produced from TPhP in the liver by oxidase and aryl esterase (Sasaki et al. 1984). However, recent analyses obtained from *in vitro* cultured hepatocytes revealed that the main metabolites derived from TPhP were hydroxylated and glucuronated forms of TPhP (Su et al. 2014). An important finding is that these metabolites and their equivalents for EHDPhP have recently been detected in human urine samples (Zhao et al. 2019), although DPhP remained the most abundant metabolite in these samples. This finding may indicate either that degradation of APEs does not primarily occur in the human liver or that APEs are rapidly degraded and absorbed as DPhP by the environment. Finally, the presence of DPhP in the environment could be directly due to its importance as a catalyst in polymerization processes.

In line with these hypotheses, a recent study showed that serum hydrolase significantly contributed to TPhP and EHDPhP clearance and production of DPhP (Van den Eede et al. 2016). Bacteria present in the environment and the microbiota may participate in this type of transformation because bacterial metabolism has been shown to fully degrade TPhP, with DPhP initially being the main metabolite released into the biofilm (Hou et al. 2019). Similarly, microsomal preparation of human skin demonstrated the ability of carboxylesterases to efficiently generate DPhP from TPhP (Abdallah et al. 2019), indicating that the likelihood of TPhP reaching subcutaneous fat and blood through this route of exposure is very low. Rapid detection of DPhP in urine samples of women exposed to TPhP through nail polish tends to confirm this hypothesis (Mendelsohn et al. 2016). Finally, DPhP concentrations in the environment were reported to be strongly correlated with TPhP levels present in the same environment (Björnsdotter et al. 2018), hence raising concerns about these potentially hazardous molecules for human health.

The complexity of the routes of exposure described above can cast doubts as to the relevance of *in vitro* and *in vivo* studies describing the toxicities associated with APEs such as TPhP. For instance, very high doses of TPhP administered via oral gavage (300 mg/kg/d) in adult mice (Chen et al. 2015) or through direct subcutaneous injection (~200 µg/kg/d) in embryos/pups (Wang et al. 2018) may artificially expose the organism to an irrelevant dose of TPhP and its hydroxylated forms, thus misrepresenting the most common route of DPhP exposure, i.e., through the environment. These types of protocols have mainly led to the conclusion that TPhP has an obesogenic endocrine-disrupting activity. These findings were reinforced *in vitro* by studies showing that high doses (10–100 µM) of TPhP disturb the activity of peroxisome proliferator-activated receptor gamma (PPARγ) (Belcher et al. 2014; Tung et al. 2017), or by the ability of TPhP to enhance the lipogenic activity of the thyroid hormone on isolated chicken embryo hepatocytes (Su et al. 2014). Of note, these doses were highly toxic for mammalian cells, casting doubts on the relevance of these results for human physiology. In addition, cell-based transactivation assays somehow failed to confirm agonistic or antagonistic activities of TPhP on either PPAR or TR nuclear receptors (Kojima et al. 2013). Moreover, a treatment combining four APEs administered at lower individual doses of 1 mg/kg/d decreased the body weight (BW) gain of these animals instead of increasing it. Similarly, recent reports indicated that exposure to DPhP and TPhP could disrupt the metabolism in opposite manners (Wang et al. 2018).

Based on these controversial findings, we herein present a large study conducted in mice, in which the routes and doses of exposure

to DPhP could mimic those found in humans. Environmentally- and human-relevant doses of DPhP were established from an extensive review of the literature estimating the human mean exposure doses from the environment (Li et al. 2019b; Wei et al. 2015) and the known correlation between environmental measurements and biological fluid concentrations (Björnsdotter et al. 2018; Meeker et al. 2013; Pouech et al. 2015; Wei et al. 2015; Zhao et al. 2019). To validate our choice of using DPhP rather than TPhP or another APE in our toxicity study, we first analyzed DPhP concentrations in blood of mice treated with various doses of both molecules via different routes of exposure. We hypothesized that humans are more likely to be continuously/chronically exposed to TPhP and DPhP owing to the presence of TPhP in the air and dust, rather than temporarily/acutely exposed through nutrition. We have thus analyzed the effects of continuous absorption of these two molecules through drinking water via kinetic measurement of their transformation in mice (bioaccumulation and tissue distribution), in comparison with other acute modes of administration such as oral gavage or tail-vein injection. We then present the data reporting the bioaccumulation and distribution of these molecules in mice. Finally, because our aim was to analyze the biological consequences of a relevant DPhP exposure, we have conducted dualomics analyses combining metabolomics and transcriptomic measurement on tissue extracts obtained from independent experiments, as well as a histological validation.

## Methods

### Reagents and Chemicals

Diphenyl phosphate, triphenyl phosphate, diphenyl phosphate-D₁₀ and triphenyl phosphate-D₁₅ were purchased from Merck with a purity higher than 98%. The confirmation standards of carnitine, acetylcar carnitine, palmitoylcarnitine, lauroylcarnitine, decanoylcarnitine, hexanoylcarnitine, stearic acid, oleic acid and linoleic acid, 7-ketocholesterol, 3β-hydroxy-5-cholestene-7-one, Hypoxanthine, Ethyl oleate, cis-11-Eicosenoic acid, cis-11,14-Eicosadienoic acid, (R)-3-hydroxybutyric acid, Taurocholic acid, Taurodeoxycholate, Taurochenodeoxycholate, Taurooursodeoxycholic acid were obtained from Merck, all of them with a purity higher than 97%. Corn oil for DPhP and TPhP resuspension and animal exposure was also obtained from Merck.

Acetonitrile and heptane of LC-MS quality grade and ammonium formate were purchased from BioSolve. Water and formic acid quality grade optima LC-MS were purchased from Fisher Scientific.

### Animal Experiments and Animal Care

Female FVB mice (5 wk old; 15.2–18.7 g) from Charles River Laboratories were used for all experiments. Animals were housed (from 4 wk) in the ANICAN (Center de Recherche en Cancérologie de Lyon) animal facilities accredited by the French Ministry of Agriculture. Food and water were provided *ad libitum* (lights on: between 8 pm and 8 am; temperature: 22°C ± 1°C; humidity: 55% ± 10%). Food was obtained from Safe (150-SP-25) and contained low levels of phytoestrogens and isoflavones (no soybean and alfalfa).

Four procedures were used to treat animals with DPhP and TPhP. Minimal and maximal relevant dose were estimated as indicated in the introduction and from preliminary analyses measuring the quantity of TPhP and DPhP found in mouse biological fluids from a wide range of tested concentrations (0.01 µg to 100 µg). Our aim here was to obtain, from environmentally relevant dose, blood DPhP concentrations close to those found in humans. Three procedures mimicked acute exposure either
through vein-tail injection (TPhP = 0.1, 1, 10, or 100 µg and DPhP = 0.1 or 1 µg), oral gavage (TPhP = 0.1, 1, 10, or 100 µg, and DPhP = 0.1 or 1 µg), or drinking water [TPhP and DPhP = 0.1 mg/L (C1), 1 mg/L (C2), and 10 mg/L (C3)]. Experiments were stopped 1 h after treatment, except for that involving drinking water, where mice were euthanized after an overnight exposure. The amount of DPhP ingested through drinking water was higher than the amount ingested through the other routes, though it was spread over a longer period of time. The administered concentrations via drinking water were retained because they provided results that were similar to the acute oral gavage or vein-tail injection. Finally, one procedure mimicked chronic exposure, because mice were continuously treated from 5 to 17 wk through drinking water (DPhP = C1, C2, C3). Here, drinking water containing DPhP or the vehicle was changed twice a week until the end of the treatment. The concentrations administered were not adapted to the weight gain nor to the amount of water ingested/d/mouse. During the course of the experiment, drinking water consumption was verified at the group level and fluctuated from 4–5 mL to 6–7 mL per mouse between the start and the end of the experiment. We thus calculated a mean consumption per day and per mouse of 0.4–0.7 µg, 4–7 µg, and 40–70 µg of DPhP with the three different concentrations of DPhP used in drinking water along the duration of the experiments. As reported in the results section, BW gain was slightly different between the treated groups; therefore exposure by weight during the experiment increased by an average of 13% with C3 and was reduced by 4% and 7% in C2 and C1, respectively.

At least four animals were used in each group for acute exposure for each series of experiments described (five for oral gavage). For chronic exposure, 2 independent experiments were carried out with 10 animals per group. Metabolomics and transcriptomic analyses were performed on these separate and independent experiments, reinforcing the strength of the correlations observed. Between groups, animals were randomized according to their weight at the time of reception measured with an electronic balance and acclimated during 1 wk before the start of experiments.

For all exposures, DPhP and TPhP were first solubilized in an emulsion containing water and corn oil (Merck) (1:1 v/v) at 10 mg/L. These molecules were then diluted in water to obtain the indicated quantity in 100 µL for oral gavage and vein-tail injection and the indicated concentration in drinking water. Control animals were always given the same amount of diluted corn oil in water (5 µL in 100 µL of water for acute exposure and 500 µL in 1 L of drinking water for chronic exposure).

For all conditions, blood was collected by cardiac puncture using a syringe prefilled with heparin (arterial blood collection syringe, BD Preset) after mice were anesthetized with a mix of lethal dose of ketamine (100 mg/kg) and xylazine (10 mg/kg). All mice were finally euthanized by cervical dislocation. Blood was immediately frozen in liquid nitrogen and store at -80°C before metabolite extraction. Liver, mammary glands, and visceral fat were also harvested immediately after cervical dislocation. Gall bladder was first removed from the liver. All tissues were briefly rinsed twice in a bath of NaCl solution (0.9%) and once in distilled water to remove blood and bile. Organs were then immediately frozen in liquid nitrogen and store at -80°C before metabolite extraction.

Animal experiments were performed in compliance with French and European regulations on the protection of animals used for scientific purposes (EC Directive 2010/63/EU and French Decree 2013-118). They were approved by the Ethics Committee and authorized by the French Ministry of Research (APAFIS#3,680-2016010509529577v5).

## Sample Preparation for Targeted Analysis and Metabolomics

Three different extraction methods were used for the four matrices, i.e., a solid-liquid extraction for the liver, a liquid-liquid microextraction for whole blood, and a derived-QuEChERS for visceral fat and mammary glands. For each extraction, an internal standard solution of DPP-d10 and TPP-d15 was used. The same liver extraction was performed for targeted APE measurement and metabolomics analyses.

A mass of 20 mg of liver was extracted with 1 mL of acetonitrile and 0.5 mL of heptane, assisted by three zirconium balls. After centrifugation, the heptane was discarded, 750 µL of acetonitrile were transferred to a vial, and a second extraction with 1 mL of acetonitrile was conducted. Finally, all the extracts (total of 1.5 mL of acetonitrile) were pooled, split into two portions of 750 µL, evaporated at 35°C during approximately 90 min, and stored at -20°C. The samples were reconstituted with 75 µL of water/acetonitrile 90:10 (v/v), prior to UHPLC-HRMS analysis reverse phase chromatography, with 75 µL of acetonitrile/water 95:5 (v/v) for UHPLC-HRMS analysis HILIC chromatography or with 150 µL MeOH for targeted UHPLC-MS/MS analysis. For metabolomics, the extraction was conducted from eight samples of each administered concentration to obtain suitable, reliable, and reproducible statistical results.

A volume of 250 µL of whole blood was extracted with 950 µL of ACN, assisted by six zirconium beads and microtube homogenization for cell lysis. After centrifugation, 500 µL of supernatant was recovered. This extraction was repeated twice with recovery volumes of 1 mL of supernatant. The combined extract was evaporated to dryness under a stream of nitrogen at 40°C and reconstituted with 500 µL of distilled mono(2-ethyl-5-oxohexyl) phthalate (MeOH) for targeted UHPLC-MS/MS analysis.

A mass of 25 mg of visceral fat or mammary glands was introduced into a centrifugation tube containing three zirconium balls. For the QuEChERS extraction, 500 mL of water, 500 µL of ACN, 100 µL of heptane, 100 mg of sodium acetate, and 400 mg of magnesium sulfate were added into the tube. After a vigorous manual agitation and centrifugation, the heptane phase was removed, and 400 µL of supernatant ACN phase was recovered, evaporated to dryness under a stream of nitrogen at ambient, and reconstituted with 125 µL of distilled MeOH for targeted UHPLC-MS/MS analysis.

## UHPLC-ESI-MS/MS Analysis

For targeted analysis, separation was carried out using a 1200 HPLC system from Agilent Technologies. The chosen column was a Kinetex XB C18 column (50×2.1 mm ID, 1.7 µm porosity, 100 Å) from Phenomenex. The mobile phase was composed of 0.01% formic acid (FA) and 0.1 mM AmAc in distilled pure water (solution A) and 1 mM AmAc in distilled MeOH (solution B) with a gradient from 5% to 100% (solution B) in 2 min. The columns were maintained at 60°C during the analysis. The chromatographic system was coupled to a triple-stage 5500 QTRAP® from ABSciex with electrospray ionization interface (ESI). The multiple reaction mode monitoring was used for the identification and quantification of DPhP and TPhP (limit of detection, LOD<sub>DPhP</sub> = 0.3 ng/mL, LOQ<sub>DPhP</sub> = 0.5 ng/mL, LOD<sub>TPhP</sub> = 0.7 ng/mL, LOQ<sub>TPhP</sub> = 1 ng/mL). The Analyst software (version 1.6.2) was used for instrument control, data acquisition, and data processing.

For metabolomics, separation was carried out using an UltiMate 3000 UHPLC system (Thermo Scientific). The chosen column was a Luna Omega polar C₁₈ (100×2.1 mm, 1.6 µm particle size) (Phenomenex), because it was reported to have a greater affinity for
polar compounds, to detect and quantify a larger number of metabo-
lites. A nucleodur HILIC column (100 × 2.1 mm, 3 µm particle size)
(Macherey Nagel) was also used to confirm the identified compounds
or to detect other compounds that the C₁₈ column did not highlight
due to their different polar affinities. The columns were maintained at
30°C during the analysis.

With the C₁₈ column the mobile phase was water/acetonitrile 90/10 (v/v) 5 mM ammonium formate and 0.01% formic acid (solution A) and acetonitrile 5 mM ammonium formate and 0.01% formic acid (solution B). The gradient elution had a flow rate of 0.3 mL/min, and it started at 100% of solution A and was maintained for 1 min. The percentage of solution A then decreased until reaching 0% within 10 min. The gradient was held at this percentage for 4 min prior to finally returning to 100% of A and held for 3 min to condition the column for the next injection. The total running time was 18 min. The injection volume was 5 µL.

For the HILIC column, the mobile phase was water 5 mM ammonium formate and 0.01% formic acid (solution A) and acetonitrile:water 95/5 (v/v) 5 mM ammonium formate and 0.01% formic acid (solution B). The elution gradient had a flow rate of 0.4 mL/min and started with 95% of B and was held for 2 min. It was then decreased to 70% in 7 min and 50% in another 2 min. The percentage was held for 4 min to return to the initial percentage in 0.1 min and equilibrated during 10 min. The total running time was 25 min. The injection volume was 5 µL.

The chromatographic system was coupled to a QTOF mass spectrometer (Maxis Plus, Bruker Daltonics®) with electrospray ionization interface (ESI) operating in positive and negative mode. The following settings were used: capillary voltage of 3600 V, end plate offset of 500 V, nebulizer pressure of 3 bar (N2), drying gas of 9 L/min (N2), and drying temperature of 200°C. A solution of sodium formate and acetate (10 mM) clusters was used for external calibration at the beginning of each run. The analysis was performed in a full scan over the mass range of 50–1,000 daltons (Da) with a scan rate of 1 Hz. Moreover, the analysis was carried out in profile mode with the following transfer parameters: funnel 1 RF of 200 Vpp, multipole RF of 50 Vpp, quadrupole energy of 5 eV, collision energy of 7 eV, stepping basic, and a pre-pulse storage of 5 ms. The instrument resolution was estimated at 21,244 (full width at half maximum, FWHM) at m/z = 415.211.

All samples and quality controls (QCs) were injected in the
MS mode. QC was prepared by mixing 5 µL of each sample and
was injected every 13 samples, so as to represent a ratio sample/
QC of 10%. The data-dependent acquisition (DDA) mode was
conducted twice on the QC to obtain MS/MS spectra. It was per-
formed with a cycle time of 3 s and a spectral rate between 2 Hz
and 16 Hz in order to record low and high intensity precursors.
The samples were analyzed randomly to ensure the representa-
tiveness of the results.

The software used for instrument control and to acquire data in
positive and negative mode for both chromatographic platforms were
OTOF control 4.1, Hystar 4.1 (Bruker Daltonics®), and Data Analysis®
4.4. Preprocessing data (including bucketing, filtering, scaling, and
data normalization) and alignment were performed with the algorithm
Time aligned Region complete eXtraction 3D (T-Rex 3D) of
Metaboscape 4.0 (Bruker Daltonics®). The principal parameters used
to create the bucket tables were: Intensity threshold = 5,000 counts;
minimum peak length = 7 spectra; perform MS/MS import,
group by collision energy; retention time window (min) = 0.4–15;
mass window (m/z) = 60–1,000; electron ion collider (EIC) = 0.8;
Ions = H+, Na+, K+, NH<sub>4</sub>+, -H<sub>2</sub>O+H+ in positive mode and
Ions = H<sup>-</sup>, 2H<sup>-</sup>, 2H<sup>-</sup>+Na<sup>+</sup>, Cl<sup>-</sup>, H<sup>-</sup>+HCOOH. The bucket tables
contained information regarding retention time, ion m/z ratio, neutral
mass, detected ions, MS/MS spectrum, and relative peak intensity of
features in each sample.

Untargeted Analysis and Annotation Workflow

Multivariate procedure used data of four analyses for all samples
and the QCs. Features present simultaneously in QCs and in at
least two samples of each group of animals were conserved
(7,744 out of 11,168). Data retrieved from each type of metabolo-
mics analysis (HILIC and C18 in both negative and positive
mode) were combined and used to perform an unsupervised
principal component analysis using the R package ade4
(R Development Core Team). The first four components were
tested for sample discrimination and measuring the quality of
our analytical procedure.

To determine relevant features for the analysis and sample dis-
crimination, we calculated adjusted *p*-value (false discovery rate,
FDR) with the Benjamini-Hochberg method. Fold change and
*p*-value between groups were used to build volcano plot and repre-
sented which features were significantly different between group
(FDR at least <0.05) using the R package EnhanceVolcano (Blighe
2018).

For compound annotations, we selected putative compounds based on a mass deviation inferior to 5 ppm. The discovered formulas were from different databases (analyte DB, ChEBI, ChemSpider, PubChem, HMDB, LIPID MAPS®). This process was done using the compound crawler tool, included in MetaboScape 4.0 and the search tools associated to HMDB and LipidMaps website. Priority was given to the compounds arising from biological databases and previously found and quantified in human blood, urine, and feces (Data obtained from HMDB, ChEBI, and LIPID MAPS®). All features with putative annotations and MS/MS spectra were then tested for confirmation (see next section). The selection of conserved features was initially carried out at this step. These features and all those putatively annotated where no MS/MS spectra were available were then manually curated through physical and chemical properties of annotated metabolites. XLogP3, LogD at pH=3.0, Charge at pH=3.0, Topological surface area and molar mass were taken into consideration. From these data, features with a putative metabolite annotation but a discordant retention time were excluded. When possible, this concordant retention time was verified and cross-validated with both ionization modes (retention time difference <0.01 for a specific chromatography) and eventually one of the feature was excluded from the analysis. Also, we performed the same type of comparison between C18 and HILIC systems (same *m/z* with concordant RT specific to each chromatographic system) for putative metabolites annotated in both analyses. Moreover, metabolites from a same chemical series (e.g., acylcarnitine, fatty acid) were re-analyzed together to verify the right order of elution according to these properties by using those confirmed by MS/MS as beacons (Figure S1). Finally, when doubts remained between several potential annotations, chemical functions present on the molecules were also used to predict the preferential ionisation mode and exclude unlikely metabolites. Compounds with a carboxylic acid function were for example considered to be preferentially ionized in negative mode, whereas amine, alcohol, and ketone were more likely to be ionized in positive mode.

Metabolite Validation by MS/MS Spectra and Standard

Some of the annotated masses had MS/MS spectra. These experi-
mental spectra were compared to theoretical compounds with the
same masses (difference <5 ppm) in silico fragmented using
MetFrag (MetaboScape) but also to a bank of experimental spectra
available in MetFrag. Tested compounds were retrieved preferen-
tially from HMDB, CHEBI, and LIPID MAPS® databases, or from
PubChem when the number of potential candidates was inferior to
three with the other databases. All potential compounds tested were scored by comparing their *in silico/bank* spectrum to our experimental data and by measuring their internal coverage. An example of *in silico* fragmentation is presented in Figure S2. Annotation was considered validated when its corresponding compounds had the highest score or was in the set of compounds presenting one the highest scores not significantly different [Score > (1.85/2) with score computed from spectral similarity and percentage of internal coverage]. In this case, other parameters were used to finish the validation (presence in biological fluid, RT, preferred ionization mode). Annotations verified by MS/MS validation that were however not sufficient to firmly invalidate other possibilities have been indicated. Finally, to confirm these results, the analytical standards of some compounds with a logical retention time in both columns were purchased. Samples were spiked with these analytical standards and were injected in the same sequence as the unspiked samples in DDA mode with selection of the accurate mass (precursor ion) of the suspected compounds. Then, the extracted-ion chromatogram (EIC) and the MS/MS spectra of the suspected compounds for spiked and unspiked samples were compared. All standard validations (MS/MS and RT comparison) are reported in the Table S1.

## ChemRICH, MetaMapp and Hierarchical Clustering Analysis

KEGG ID, PubChem ID, SMILE, and InchiKeys of annotated metabolites were retrieved from public databases (PubChem and KEGG) and used to run ChemRICH and MetaMapp algorithms as indicated in the former publication of these statistical tools (Barretina et al. 2012; Barupal and Fiehn 2017). Adjusted *p*-value and fold enrichment were obtained as indicated previously from the signal intensities associated with the multiple injections of each group of samples. When several features were associated with a same metabolite, we conserved the one with the lowest adjusted *p*-value. ChemRICH analyses were then obtained directly from the ChemRICH interface website, whereas MetaMapp results were loaded in the cytoscape software to represent the network with an organic layout. Presented enrichment plot used Chemical enrichment statistics calculated by the Kolmogorov-Smirnov test. Cluster names were retrieved in an unbiased way from ChemRICH results tables. A couple of clusters could not be directly named by ChemRICH and were in this case curated manually (named through chemical similarity and pathway mapping). All tables associated with these analyses are available in the Table S9-S11.

For hierarchical clustering, Spearman rank correlation and city-block distance were used, respectively, for metabolites and samples sorting. Cluster3 and Java Tree View software were used to generate the clustering and the heat map/tree associated to the result of this algorithm. A table listing the results is available in Table S12.

## Sample Preparation for Transcriptomic Analyses and Next-Generation Sequencing (NGS)

**RNA extraction for tissue.** Total RNA was extracted and purified using the RNeasy Mini Kit (Qiagen # 74106) from mouse liver. Furthermore, the RNeasy procedure enriches RNA species >200 nt and excludes 5S rRNA, tRNAs, or other low molecular weight RNAs. RNA was isolated on the silica membrane in trusted RNeasy spin columns, with binding capacities of 100 µg of RNA, according to the supplier's recommendations (Qiagen).

**RNAseq library sequencing and analysis.** For the preparation of the NGS RNA library, RNA concentration was measured using the GE NanoView Spectrophotometer (Biochrom US). The quality of RNA samples was analyzed using the RNA 6000 Pico

Kit running on the 2100 BioAnalyzer (Agilent). Total RNA was diluted in a final volume of 50 µL for a total input of 1 µg. Only the RNA pools with a RIN score higher than 7 were used in the NGS library preparation prior to sequencing.

mRNAs arising from the pooled livers of 10 animals tested in each group were isolated using the NEBNext Poly(A) mRNA Magnetic Isolation Module from 1 µg of total RNA. Three analytical preparations were performed at this step. The isolation procedure was based on the selection of mRNA using oligo dT beads directed against polyA tails of intact mRNA. Second, the NGS libraries were created from mRNA isolated using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England BioLabs). Final libraries were sequenced on an Illumina NextSeq 500 on a high output flow cell with 2×75 bp paired-end read lengths.

The sequencing reads were obtained after demultiplexing the raw sequencing data using bcl2fastq v2.19.1.403 (version v2.15.0 for NextSeq™ 500 and HiSeq® X Systems; Illumina), after having validated the quality controls of each sample using the FastQC version 0.11.5 software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The alignment files were generated with STAR version 2.5.2b (University of Birmingham) in the 2-pass mode. We used the GRCm38, version M16 (Ensembl 91) as reference. This mode is known to improve the detection of more reads mapping novel splice junctions.

## Gene Ontology and Statistical Analyses of NGS

Genes were filtered through their obtained RPKM. Only genes sufficiently expressed in control conditions were conserved for further analyses (RPKM >0.2). Principal component analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) were performed on the R platform, using ROPLS package and 3D plots for visualization of the results. Gene ontology analyses were performed by using the Eigen values associated with the most significant axis used to construct PCA and OPLS-DA. With these methods, all conditions, namely the three different DPhP concentrations and the vehicle, were thus taken into consideration and simultaneously compared. Threshold for retained Eigen values used to select genes in these analyses are indicated directly in the figures. Functional enrichment analysis (STRING© Consortium 2019) was then performed by sorting the selected genes according to their Eigen values. A volcano plot (R package EnhanceVolcano Plot) was used to represent the discriminating functions for the studied axis through the enrichment score and the false discovery rate generated by the STRINGv11 algorithm. Only significant functions were retrieved.

Alternatively, paired analyses were performed using each concentration of DPhP against the vehicle. In this case, retained genes were selected through the fold change between both conditions. Gene ontology analysis was then performed identically with Functional Enrichment Analysis (STRING© Consortium 2019).

Finally, in a complementary approach, density of the protein networks associated with discriminating functions and encoded by the genes selected with these different approaches were measured through protein-protein association networks (STRING© Consortium 2019). High number of nodes and edges indicate that large functional complexes are disturbed through multiple genetic regulations induced by DPhP exposure, reinforcing the probability of that function being disturbed by this condition. We used high-confidence settings to retain the experimentally validated interactions. A protein-protein interaction enrichment *p*-value was calculated against an identical number of random proteins [PPI enrichment *p*-value (Szklarczyk et al. 2019)].
## Bipartite View of Enrichment Network

Network Analysis server (McGill University) was used through the List Enrichment function. Functions belonging to the KEGG database were used and only those with a *p*-value <0.03 were conserved (Zhou et al. 2019). Metabolic pathways and carbon metabolism functions were removed due to their very high coverage of the genome. The bipartite view was then selected, and an auto-layout was applied. Clustering between functions was then enhanced through the Force Atlas tool. Clusters were then manually highlighted and default encoding of the functions through their *p*-value was converted into a continuous green-red scale.

## Immunohistochemistry Analyses

For histological examination, liver samples were fixed in 10% buffered formalin and embedded in paraffin. 4-µm-thick tissue sections of formalin-fixed, paraffin-embedded tissue were prepared according to conventional procedures. Sections were then stained with hematoxylin and eosin.

Immunohistochemistry (IHC) was performed on an automated immunostainer (Ventana Discovery XT, Roche) using Omnimap DAB Kit according to the manufacturer's instructions. Sections were incubated for 60 min at 37°C with the following antibodies: Anti-Perilipin2 (AtlasAntibodies-HPA016607), Anti-Hmgcs2 (Santa Cruz-sc-376,092), Anti-PPARalpha (Abnova-MAB12349), Anti-Pck1 (AtlasAntibodies-HPA006507), Anti-FBP1 (Abcam-ab109020) (all diluted at 1:100). An antirabbit/mouse - HRP was applied on sections during 16 min at 37°C. Staining was visualized with DAB solution with 3,3-diaminobenzidine as a chromogenic substrate. Sections were counterstained with Gill's hematoxylin and finally scanned with panoramic scan II (3D Histech) at 20×. Scoring was performed by three independent investigators using a staining scale ranging from 0 to 10. The means were then calculated and encoded as follows. 0-2= no staining to very low staining (-), 2-4=low staining (+), 4-7= intermediate staining (++), 7-10=high staining (+++).

## Statistical Test and Accession Numbers

Statistical tests were performed as indicated in the caption of the figure. Briefly, for simple comparison between two experimental groups, Student's *t*-test was used. For more complex statistical procedures, test already integrated in the previously described algorithms were conserved without any modification.

Metabolomics data have been deposited to the EMBL-EBI MetaboLights database (doi: 10.1093/nar/gkz1019; PMID: 31691833) with the identifier MTBLS2038. The complete data set can be accessed at https://www.ebi.ac.uk/metabolights/MTBLS2038.

Microarray analyses have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are accessible through GEO accession number GSE158265.

## Results

### *Correlation Analysis between TPhP/DPhP Exposure and Their Level in Blood and Liver*

When 0.1 or 1 µg DPhP was injected directly into vein-tail or administered by oral gavage, DPhP was readily quantified after 1 h in a dose-dependent manner in whole blood (Figure 1A and 1B). Conversely, in animals exposed to these same concentrations of TPhP, this molecule could not be detected in their blood (LOD<sub>TPhP</sub> = 0.7 ng/mL). After administration of 10 µg or 100 µg TPhP, TPhP was only quantified above the LOD in the blood of

two animals at 2.33 ng/mL and 10.20 ng/mL, exposed to 100 µg following intravenous injection and oral gavage, respectively. In all other animals (18 out of 20 animals), TPhP remained undetected. To determine whether TPhP transformation into DPhP was the reason for the lack of detection of TPhP in the bloodstream, we also quantified DPhP in these same experiments. DPhP could not be detected in the blood of animals treated with 0.1 or 1 µg of TPhP (LOD<sub>DPhP</sub> = 0.3 ng/mL, LOQ<sub>DPhP</sub> = 0.5 ng/mL). At the highest doses of TPhP (10 and 100 µg), we were able to quantify DPhP in the blood of animals, but in a level comparable to that obtained after exposure to DPhP a hundred times lower (Figure 1A, 1B). Therefore, the large majority of injected or force-fed TPhP seemed not to have been metabolized into DPhP in the animals.

We further compared these results to a more continuous exposure to both molecules present in the drinking water of animals to mimic their chronic ingestion from swallowed dust. We used 3 concentrations of 0.1 mg/L (C1), 1 mg/L<sup>-1</sup> (C2) and 10 mg/L (C3), equivalent to 0.5 µg, 5 µg, and 50 µg of each molecule ingested overnight (active period for mice). These quantities were comparable to those used with other routes of exposure previously given through oral gavage or vein-tail injection. At the two highest concentrations, DPhP was still dose-dependently quantified in whole blood with lower concentrations measured than those obtained previously (Figure 1C). This finding was expected because the dose was now spread over a much longer period. Conversely, we did not record any measurable amount of TPhP in animals exposed to this same molecule through drinking water ([TPhP] < LOD). Most important, the amount of DPhP retrieved from the whole blood of animals exposed by this route to TPhP was not significantly different from those of animals treated with the vehicle (Figure 1D).

Because the liver is the first organ to process exogenous molecules absorbed from the digestive tract, we further determined whether these molecules were present in this organ using the same exposure doses via drinking water. DPhP was detected and quantified in a dose-dependent manner at all concentrations tested (Figure 1E). Conversely, neither TPhP nor DPhP were measured at a significant different amount in the liver of mice exposed to TPhP or the vehicle through drinking water [(TPhP) < LOD for all animals and Figure 1F].

### *Bioaccumulation and Distribution of DPhP in Chronically Exposed Mice*

Based on these analyses, we next focused on the consequences of chronic exposure to DPhP. Mice were exposed daily to similar doses (C1, C2, or C3) of DPhP in their drinking water over a 12-wk period. We analyzed four tissues, namely whole blood, the liver, visceral fat, and the mammary glands, potentially presenting a trismus for the molecule, either due to their chemical characteristics (presence of hydrophobic constituent for mammary gland and visceral fat) or to the route of exposure used for these experiments (whole blood and liver). In the blood, DPhP concentrations were significantly higher in all animals chronically exposed to DPhP in drinking water at 1 mg/mL and 10 mg/mL in comparison with control animals (Figure 2A). DPhP was also measured in the liver, visceral fat, and mammary glands at all tested concentrations in a dose-dependent manner (Figure 2B-D). These results confirmed the ability of chronically ingested DPhP to reach various tissues in mice.

### *Biological Effects of Exposure to DPhP through Metabolomics Analyses of the Liver*

Because DPhP was abundant in the liver of all treated animals, we focused our subsequent experiments on this organ to measure the possible biological consequences of the presence of DPhP in
<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

<figure><img src="image_69.png" /><figcaption>**Figure 1.** Acute exposure to DPhP and TPhP. (A–D) Four or 5 animals were exposed to the indicated doses of DPhP or TPhP via the indicated routes. After 1 h, DPhP was quantified from the whole blood of each animal. Concentrations obtained have been plotted on the box-and-whisker plot, indicating significant *p*-values between tested conditions and the vehicle (Student's *t*-test). (E–F) Identical experiment with DPhP quantification performed on liver extracts from each animal. (Midline = median, box limits = 1st and 3rd quartile, whiskers = largest, and smallest value within 1.5 × the interquartile range. ummary data in Table S25.) Note: LOD, limit of detection; LOQ, limit of quantification; ns, nonsignificant.</figcaption></figure>

this tissue. The metabolite profile of each group [untreated control (CTRL) or treated with three doses (C1, C2, or C3) of DPhP] was obtained with two chromatographic methods (C18 and HILIC) using two ionization modes for the mass spectrometry analyses of eluted metabolites.

Unsupervised PCA was first performed with features present simultaneously in QCs and samples (7,744 out of 11,168 by combining all acquisitions). The consistency of our analytical process was verified by confirming the spatial clustering of QCs on the first four principal components (PCs) (Figure 3A) and their intermediate position in comparison with real samples as expected from their preparation (see “Methods”). Acquired metabolic profiles discriminated the four groups of samples even for the lowest

concentration C1. An interesting finding was that samples corresponding to C2 and C3 were clearly not separated by the same PCs. PC1–2 strongly discriminated C2 from CTRL, whereas C3 was only separated from the CTRL using PC3–4. Therefore, two sets of metabolites were differently altered by DPhP according to concentration at which it was administered.

All features were further annotated at this stage through three procedures of validation (see methods). A total of 633 metabolites were conserved from this process (Tables S2–S8) with their level of validation and their co-occurrence in the different types of acquisitions indicated in Figure 3B,C and the Tables S2–S5. Volcano plot representations were then used to compare each treated group to control animals for each type of chromatography
A

Blood DPhP

<figure><img src="image_70.png" /></figure>

B

Liver DPhP

<figure><img src="image_71.png" /></figure>

C

Mammary Gland DPhP

<figure><img src="image_72.png" /></figure>

D

Visceral fat DPhP

<figure><img src="image_73.png" /><figcaption>**Figure 2.** Chronic exposure to DPhP. (A–D) Ten animals were chronically exposed to the indicated concentrations of DPhP for 12 wk through drinking water. DPhP was then quantified in the indicated biological tissue. Blood of each animal was analyzed separately, whereas analytical replicates were performed from pooled tissue extracts. Concentrations obtained have been plotted on the box-and-whisker plot indicating significant *p*-values between tested conditions and the vehicle (Student's *t*-test). (Midline = median, box limits = 1st and 3rd quartile, whiskers = largest, and smallest value within 1.5 × the interquartile range. Summary data in Table S26). Note: ns, nonsignificant.</figcaption></figure>

and ionization to capture potential features and metabolites present in several analyses and thus highly relevant (Figure 3D–G and S3A–SD). From this first analysis, we noted that a subset of acylcarnitines, key metabolites of fatty acid degradation, were significantly depleted in exposed animals at all concentrations when compared to those in vehicle-exposed animals. An important observation was that this effect could be observed with both types of chromatography confirming the robustness of the identified modification. From these first hits, we decided to perform an in-depth analysis combining our 633 annotated metabolites. Due to the heterogeneity of these metabolites and, for some of them, their nonappurtenance to the endogenous metabolism, we performed an initial comparison based on their chemical ontology that can be represented through a circular tree plot of annotated compounds (Figure 4A and S4A and SB). This type of analysis was shown to eliminate bias due to incomplete mapping and network size heterogeneity (Barupal and Fiehn 2017). Moreover, it is weakly sensitive to isomer confusion belonging to a similar chemical series, providing some flexibility for the analysis of highly similar metabolites (e.g., phospholipids) with incomplete validation. From this statistical analysis, an enrichment plot could be built, displaying the significance of the observed change for structural homologies, the size of the cluster, and the homogeneity of their variation (Figure 4B, Figure S4C and S4D, and Table S9–S11). In line with the multivariate analysis, the intermediate concentration C2 induced the largest number of altered chemical clusters. Among the different disturbed clusters, acylcarnitines were the most affected in liver extracts at all DPhP doses. Lower levels of ethyl

and choline-esterified fatty acids were also highlighted using this analysis. An important finding was that clusters associated with ketone bodies usually produced from fatty acid degradation also showed a uniform lower level of these ketone bodies after exposure to DPhP. Finally, multiple metabolic pools without clear functional connection with acylcarnitines were also altered.

Stress markers, such as *N*-acylethanolamides were homogeneously more concentrated in extracts from exposed animals. Conversely, sulphated phenol group, 3-oxosteroid and bile acids were more specifically disturbed in the C2 group of samples, whereas groups of metabolites such as those containing a naphthalene or an indole ring showed perturbations with opposite directions between C2 and C3. Next, to determine how these different clusters were organized around endogenous metabolic networks, we combined chemical and biochemical mapping in a joint analysis using the MetaMapp algorithm (Barupal et al. 2012) (Figure 4C and S4E and S4F). Strong connections were observed among the reduced concentration of acylcarnitine, ketone bodies, and the esterified fatty acids in treated animals. An important finding was that metabolites highlighted by the previous analyses and related to other fatty acid derivatives were globally weakly altered (most phospholipids, unsaturated fatty acids), even if their concentration was statistically significantly different. However, some exceptions could be noted for instance for the main lysophosphatidic acid (16:0/0:0). Other clusters of metabolites were more scattered in the network and less well connected. Subnetworks were however present, such as the steroids, clearly less concentrated in exposed animals, especially in the C2 group.
<figure><img src="image_74.png" /><figcaption>**Figure 3.** Hepatic metabolomics analysis of animals exposed to DPhP. (A) Principal Component Analysis (PCA) was performed to discriminate groups of samples based on the amount of retained ion *m/z* (see "Methods"). Explained variance with the four first axes has been indicated. Red diamond-shaped = CTRL, Black square = C1, orange small circle = C2, blue triangle = C3, green large circle = QC. (B) An UpSet plot was calculated reporting the number of metabolites annotated in each analytical procedure and the number of each type of co-occurrence between these procedures. (C) An Euler diagram was created reporting the number of annotated metabolites and their level of validation (expected RT and *m/z*, MS/MS, spiked chemical standard). (D–G) Volcano plots were created through observed median fold change and adjusted *p*-value algorithm, comparing the metabolite level for the C2 group used against control values for each type of indicated analytical platform. Most significant metabolites were annotated when possible and indicated directly on the plot. Cutoff for fold change/adjusted *p*-value = 4/0.002 for (D), 3/0.002 for (E), 2/0.05 for (F), and 2/0.05 for (G).</figcaption></figure>

Similarly, disturbed pools of homocyclic aromatic compounds usually corresponding to xenometabolites were also spatially clustered. Moreover, perturbation of indole-related molecules normally connected to tryptophan and nicotinamide metabolism was confirmed through this analysis.

We performed a hierarchical clustering of our annotated metabolites associated with their measured level in each condition, the aim being to combine on the same graph the results obtained with the different concentrations of DPhP used. It is interesting to note that this analysis performed with only 633 features clearly
<figure><img src="image_75.png" /><figcaption>**Figure 4.** Metabolic network analysis through structural identity and pathway mapping. (A) Data associated with 635 annotated metabolites were used to perform a Chemical Similarity Enrichment Analysis (ChemRICH). A Tanimoto chemical similarity mapping form a clustered circular similarity tree. Dark black lines indicate boundaries of clusters that were significantly different in exposed animals at the concentration C2 vs. control mice (p < 0.05). Higher metabolite levels in DPhP-exposed mice (compared with those exposed to vehicle) have been labeled as red nodes; lower levels have been marked in blue. Acylcarnitine Cluster label has also been indicated. (B) ChemRICH set enrichment statistics plot for the same metabolites as (A), extracted from exposed animals with the C2 concentration of DPhP vs. control. Each node reflects a significantly altered chemical cluster of metabolites. Enrichment *p*-values have been obtained using the Kolmogorov-Smirnov test, and median XlogP3 is calculated from individual value associated to each metabolite present in the cluster. Node sizes represent the total number of metabolites in each set of clusters. Continuous scale color for the nodes shows the proportion of fully increased (red) or fully decreased (blue) family of compounds in exposed mice compared to control mice. Nodes with an intermediate color (pink to purple) have both increased and decreased metabolites. (C) MetaMapp visualization of the same metabolomics data highlighting the differential metabolic regulation and the organization of metabolic clusters based on KEGG reactant pair information and Tanimoto chemical similarity matrix. Only metabolites significantly different (adjusted *p*-value < 0.05) are used to build the network. Metabolite levels that were higher in exposed mice have been labeled as red nodes; those with lower levels have been marked in blue (continuous scale with white intermediate). *p*-Values are encoded in node size (lowest *p*-value = highest size). Cluster label has been indicated. Metabolites previously clustered together based on their structural similarity could now be separated according to their different pathway mapping. (D–F) Identical data obtained for the same 633 metabolites and the 4 groups of exposed and control animals were clustered hierarchically through their relative level (centroid linkage with Spearman correlation (metabolites) and city-block distance (samples). Features corresponding to a same metabolite present in different analytical procedure have been left separated, explaining why a metabolite can appear several times in the heat map. Yellow-blue encoding has been used to represent these metabolites according to their absolute amounts. Distance between levels of samples and metabolites have been shown as two tree plots. The complete heat map is presented in the Figure S5. Subclusters containing metabolites globally less present in exposed animal such as the acylcarnitines, (R)-3-hydroxybutyric acid have been highlighted and enlarged in the indicated left-hand panels. Similarly, a subcluster containing metabolites more present in the C1–3 groups such as N-acylethanolamide is presented in the right-hand panel.</figcaption></figure>
discriminated the three exposure conditions from the control, C2 group being the most different as observed with the PCA that was based on the entire data set of detected mass (Figure S5 and Table S12). Careful inspection of the obtained clusters confirmed lower concentrations of the acylcarnitine pool and the ketone bodies in exposed animals, even with the lowest dose of DPhP and without a clear dose-dependent response (Figure 4D and S5). The ethyl-esterified form of the most common fatty acids (palmitate, linolenate, oleate) were also present in this cluster. Furthermore, we noted the same variation for trans-2-hexenoic acid, a metabolite associated to the β-oxidation and the degradation of fatty acid. Conversely, a cluster highlighted a group of more concentrated metabolites in all treated animals (Figure 4E). As expected from previous analyses, N-acylethanolamides were present in this cluster as well as several polyunsaturated fatty acids (linolenic acid, linoleic acid, eicosadienoic acid). Also, several monoacylglycerols were more concentrated in liver extracts of exposed animals, most of them being polyunsaturated.

Finally, from the previous analysis, we looked for position of
homocyclic aromatic compounds and indole-derivative. These
compounds were more scattered in the hierarchical tree (Figure S5
and Table S12). However, sulphated derivatives were globally less
concentrated in exposed animals (phenol sulfate, *p*-cresol sulfate,
indoxyl sulfate). At the opposite, the picture for nonprocessed
forms of homocyclic aromatic compounds and indole-related
compounds was more complicated. Phenol and 1,4,5 naphthalene-
triol displayed for example a classic dose–response relationship
(Figure 4D). Several indole-related compounds behaved differ-
ently between low C1/C2 dose and high C3 dose where they
were markedly less concentrated (indole-3-carbinol, indole-3-
acetamide, indole-3-carboxaldehyde, quinolin-2-ol). At the
opposite, 5-hydroxyindole and indole-3-ylacetaldoxime were
homogeneously less concentrated in exposed animals in compar-
ison of the control.

In a final experiment, the robustness of observed differences was
assessed by comparing observed fold change between the different
analytical chromatography techniques. This analysis focused on
acylcarnitines because they were the most altered metabolites. A
strong correlation was evident between both chromatographic sys-
tems, globally for all annotated metabolites and more specifically
for acylcarnitine (Figure S6). An important finding is that the same
results were observed for metabolites connected to acylcarnitines
such as fatty acids and ketone bodies (Figure S7).

Biological Effects of DPhP Exposure through
Transcriptomic Analyses of the Liver

The second part of the dual-omics analyses was performed on four distinct batches of 10 animals untreated or treated chronically with one of the three DPhP concentration C1, C2, or C3 during 12 wk. mRNAs were extracted from pooled liver of each group and analyzed by NGS using four analytical replicates. Genes were then filtered, and those displaying a mean RPKM>0.2 in the control condition were conserved (Table S13). PCA was performed using the mean expression associated with the four experimental conditions. When the first axis was considered, the three treated conditions were significantly different from the control, the most discriminating treatment being associated with the intermediate C2 concentration of DPhP (Figure 5A, left panel). Interestingly, this pattern was correlated with the metabolic difference observed, because this concentration had the greatest effects. We noticed that 44% of the total variance was attributable to the first axis (Figure 5A, right panel), indicating that the genes with the highest Eigen values on this axis were the most relevant for the biological effects of DPhP. We consequently selected these most discriminating genes (Table

S14, Eigen value >0.3) and performed a gene ontology analysis using the STRING software (version 11). For the analysis, genes were ranked according to their Eigen value for generating a functional enrichment score/false discovery rate, then used to construct a volcano plot (Figure 5B and Table S15). Among the highly depleted processes in the exposed vs. control conditions, those related to lipid metabolism and more specifically to fatty acid oxidation represented the largest part of altered functions. However, we also noticed a significant inhibition of genetic responses associated with xenobiotic metabolism. Moreover, when we retrieved the gene list associated with the fatty acid catabolic process, and selected the most discriminating genes in our dataset (Eigen value >0.3), we confirmed that these genes encoded functional protein networks related to mitochondrial and peroxisomal fatty acid oxidation with a very high confidence rate (Figure 5C). Since fatty acid oxidation is strongly regulated by the PPAR transcription factor, we verified that PPAR signaling was also among the significant terms associated with our ranked gene list (Figure 5D). The network reconstructed from this last term demonstrated that PPARα target genes were at the heart of the dysregulation process.

Next, we verified for each treatment dose that the same trends
were observed by comparing their RPKM values to those of the
control. In this case, we directly used the calculated fold change
of genes with an RPKM value >0.2. Based on an identical
approach combining a volcano plot and a protein network recon-
struction for each analysis, we observed that genes related to fatty
acid oxidation and lipid catabolism were expressed at lower lev-
els in treated animals, independently of the doses used (Figure
S8A–C and S9A–C, Tables S16-S18). The enrichment scores of
the observed differences increased according to DPhP doses,
whereas the density of the protein network encoded by genes
altered and related to lipid catabolism was the highest with the in-
termediate concentration (edge and node numbers, see methods).

Finally, to exclude a possible artifact arising from poor analyti-
cal replicates, we performed a PCA and an OPLS-DA analysis with
the individual RPKM values of the 16 analytical samples (instead of
the mean expression for each group) and compared each treated
group with control animals. Adequate separation between replicates
was obtained by using the second, the fourth, and the fifth axis of the
PCA (Figure 6A). For each principal component, we retrieved the
Eigen values and performed a gene ontology analysis with these
scores (Tables S19-S21). Using the second axis in which C2 is the
most distant from the control, this analysis confirmed the lower
expression of genes controlling fatty acid catabolism in samples
arising from animals exposed to DPhP compared with those
exposed to vehicle (Figure 6B). An interesting result is that, by ana-
lyzing the two other components, we noticed that the lowest DPhP
concentration also led to the higher expression of genes related to
tryptophan and xenobiotic metabolism (Figure 6C).

Finally, we observed that the highest concentrations of DPhP
also induced a stronger expression of genes associated with the
synthesis of sterol-derived metabolites, indicating a more com-
plex alteration of lipid metabolism (Figure 6D). To improve sam-
ple clustering, we then tested an OPLS-DA all possible
combinations able to discriminate our four groups of replicates,
using the control replicates as negative controls. Four predictive
components were determined (Table S22), three of which were
plotted as indicated in the Figure 6E and confirmed the reliable
separation of the different groups of replicates. The predictive
component 3, classifying the samples in a dose-response manner
was then used to build a bipartite view of the enrichment network
associating the 2,000 most relevant genes to this component and
the KEGG function related to these genes (Figure 6F and Tables
S23, S24). The network highlighted two interconnected clusters
<figure><img src="image_76.png" /><figcaption>**Figure 5.** Transcriptomic analysis of livers belonging to animals exposed to DPhP. (A) Principal component analysis (PCA) was performed using pareto algorithm as an observatory method to discriminate group of sample replicates based on the amount of mRNA expression obtained from indicated animals, through reverse transcription and next generation sequencing (NGS). Considered genes and explained variance with the two first axes of the PCA have been indicated. (B) Gene ontology analysis of the most discriminant genes (Eigen value >0.3) used to build the first axis of the previous PCA performed through functional enrichment analysis (STRING Consortium 2019). Results have been presented as a volcano plot of the significant discriminating functions associated with this principal component. Term related to lipid oxidation (red square), lipid metabolic processes (blue triangle), and xenobiotics metabolism (yellow diamond) have been highlighted as indicated. The highest significant functions belonging to these terms have been listed in the right-hand panel. (Cutoff for fold change/adjusted *p*-value = 4/0.01). (C) Identical genes with a significant Eigen value (>0.3) and overlapping the indicated GO term were used to build a protein-protein interaction network (STRING© Consortium 2019) with a high level of confidence setting. Genes belonging to a particular organelle network have been highlighted in blue (mitochondria) and red (peroxisome). Numbers of edges and nodes have been indicated, as well as Protein-Protein-Interaction (PPI) enrichment (see “Methods”). (D) Identical genes with a significant Eigen value (>0.3) and overlapping the indicated GO term were used to build a protein-protein interaction network (STRING© Consortium 2019) with a high level of confidence. Genes belonging to a particular network of organelles have been highlighted in blue (PPARα specific target genes), green (PPARγ specific target genes) and red (any PPAR target gene). Number of edges and node numbers have been indicated, as well as PPI enrichment (see “Methods”).</figcaption></figure>
A

<figure><img src="image_77.png" /></figure>

B

<figure><img src="image_78.png" /></figure>

C

<figure><img src="image_79.png" /></figure>

D

<figure><img src="image_80.png" /></figure>

E

<figure><img src="image_81.png" /></figure>

F

<figure><img src="image_82.png" /><figcaption>**Figure 6.** Transcriptomic functions significantly associated with each group of exposed animals. (A) principal component analysis (PCA) was performed using pareto algorithm as an observatory method to discriminate individual sample replicates based on the amount of mRNA expression obtained from indicated animals, through reverse transcription and Next-Generation-Sequencing. Individual replicates could be grouped efficiently through the indicated PCA axis. (B–D) Gene ontology analysis of the most discriminating genes (Eigen value > 0.3) used to build the indicated axis of the previous PCA performed with STRING. Results have been presented as a volcano plot of the significant discriminating function for the indicated axis. Note that axis 2, axis 4, and axis 5, respectively, correspond to this order of gene expression, C2 < C3 < C1 < CTL, CTL = C2 = C3 < C1, and CTL < C1 = C2 < C3. Cutoff for fold change/p-value = 4/0.01 for (B), 2/0.05 for (C), and 2/0.05 for (D). (E) Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) analysis was performed to discriminate individual sample replicates based on the amount of mRNA expression obtained from indicated animals, through reverse transcription and Next-Generation-Sequencing, and in a supervised manner. A 3D dot plot has been presented showing the efficient replicate clustering and the discriminating power attributed to the first three predictors. (F) Using the predictor (x axis) that classified the samples in a classical dose-response, a bipartite view of enrichment network based on the 2,000 most discriminating genes is represented. Small dots and large dots represent individual genes and enriched functions (KEGG based), respectively. Genes or functions have been encoded by a green-red continuous scale according to their fold change or their *p*-value, respectively, as well as the direction (repressed-activated) of the regulation. Dot size of the functions represents the percentage of genes used and matching the full list associated to this function in the KEGG database. Three clusters of functions are highlighted: I = Lipid metabolism, II = Xenobiotics responses, III = mRNA metabolism and acute phase response. Note: CTL, control.</figcaption></figure>
of genetic programmes repressed in a dose-dependent manner by DPhP, namely fatty acid metabolism and the xenobiotic response. Conversely, one cluster was related to weakly activated functions belonging to the control of mRNA processes and the acute phase response. The fatty acid cluster encompassed highly significant programmes related to fatty acid oxidation, peroxisome, and PPAR transcriptional response (Figure 6F). Moreover, fatty acid biosynthetic processes such as fatty acid elongation also contributed to this large cluster, these latter functions being inhibited by DPhP treatment. Similarly, the xenobiotic cluster included several types of xenobiotic responses, such as those related to the cytochrome P450, the glucuronidation through the aldarate metabolism, or glutathione metabolism. Last, although not present directly in these clusters, we confirmed that the genetic program controlling the tryptophan metabolism was repressed by increasing doses of DPhP and established the existence of a genetic connection (Maob, Hadh, Aldh3a2, etc) between this response and the two previously mentioned repressed clusters.

## Liver Protein Expression and Physiological Analysis in Mice Treated Chronically with DPhP

To further investigate the observed abnormalities in the processes related to lipid catabolism and PPARα signaling, we performed a series of IHC analyses revolving around key enzymes of liver physiology, involved in fatty acid catabolism and ketone body formation. In addition, we stained the lipid droplets using Perilipin 2 staining, a protein surrounding these vesicles and correlated with their abundance (Straub et al. 2013). Furthermore, we analyzed enzymes controlling the gluconeogenesis to verify that an overall nonspecific dysregulation of liver metabolism did not appear following DPhP treatment (Figure 7A). Expression of phosphoenolpyruvate carboxykinase1 (PCK1), an important enzyme for gluconeogenesis was thus not lower and was even higher in some animals after DPhP exposure. Conversely, the expression of *Hmgcs2*, a kernel PPARα target gene, controlling the ketone bodies synthesis from fatty acid oxidation was strongly inhibited compared with control, even at the lowest dose of DPhP (Figure 7A). Therefore, only specific enzymes were altered after DPhP exposure. This inhibition occurred more specifically in the peri-portal area, where *Hmgcs* is normally more strongly expressed and active (Figure 7B and S10A). At the highest dose, 100% of animals presented this physiological alteration. Interestingly, this weaker expression of HMGCS2 was highly correlated with a lower amount of lipid droplets in animals exposed to 10 mg/mL of DPhP (Figure 7A-C and S10B). However, this was not the case for the lowest doses where the amount of Perilipin 2 staining was more intense upon exposure to DPhP, demonstrating here that lower levels of acylcarnitine in these conditions was not the consequence of a general depletion in lipid stores. Counterintuitively, PPARα staining was also uncorrelated with *Hmgcs2* expression because exposed animals displayed a stronger nuclear and cytosolic expression (Figure 7A).

Finally, because all of our results highlighted a disturbance in lipid metabolism, we collected the data obtained from animals used for the metabolomics and the transcriptomic analyses to construct the growth curve of these animals according to their overall weight gain. No significant difference was observed at the lowest doses, but a trend for weight loss was clearly evident in a dose-dependent manner (Figure 7D). Moreover, weight was significantly lowered in animals treated with the highest dose of DPhP.

## Discussion

Only few studies have been conducted to directly test the effect of DPhP, the most common APE derivative in human samples. Our results showed that at least in mice, DPhP levels in

biological fluids are unlikely to represent a surrogate of direct APE ingestion. We demonstrated that even through direct and acute exposure by IV or oral gavage, only a minor fraction of a parent APE such as TPhP were converted *in vivo* into DPhP. Moreover, during an overnight exposure to low TPhP concentrations in drinking water, conversion of TPhP into DPhP could no longer be measured. *In vivo* DPhP may thus be a surrogate of spontaneous degradation of APEs in the environment, suggesting that experimental procedures revolving around DPhP are likely more relevant for assessing APE toxicity.

Using a dual-omics analyses approach based on this strategy, our results demonstrated that in mice, DPhP exposure, even at doses close to the lowest doses encountered in the environment, disturbed important parameters associated with the control of liver metabolism. A substantial body of evidence consistently pointed toward a dysregulation of lipid catabolism. As a key result, we observed an important perturbation, in terms of intensity and distribution, of the acylcarnitine pools, the main metabolites associated with the degradation of fatty acids by β-oxidation. Moreover, additional modifications could be connected to this perturbation, reinforcing the likelihood that DPhP induced alteration of lipid metabolism. The most important of them was the simultaneous reduction of ketone bodies, especially (R)-3-hydroxybutyric acid, the final product of this synthetic pathway. Indeed, ketone bodies are typically produced from fatty acid oxidation during fasting (Grabacka et al. 2016), their diminution supporting a lower activity of this pathway. In addition, small dicarboxylic acids such as the trans-2-hexenedioic acid, known for a long time to be excreted in correlation with fatty acid oxidation activity (Tserng and Jin 1991), were also less concentrated in liver extracts of exposed animals. Finally, even if we could not confirm a significant modification in the main endogenously produced free fatty acids, we noted that acylcholine and ethyl-fatty acids, two of their esterified forms produced nonenzymatically from available fatty acids, were significantly depleted in liver extracts of exposed animals. Similarly, in phosphatidylcholine, the occurrence of even fatty acid chains produced endogenously was reduced in favor of odd and polyunsaturated carbon chain. These observations suggested that fatty acid production was also likely to be altered.

Aside from metabolic evidence, our results demonstrated that the genetic programs associated with the oxidation of fatty acids were also significantly repressed, even at the lowest DPhP concentration. PPARα target genes, such as *Hmgcs2* or *Cpt1a* were specifically less expressed in exposed animals. It is important to note that these genes are directly connected to the regulation of perturbed pool of metabolites, *Hmgcs2* controlling the synthesis of (R)-3-Hydroxybutyric acid (Grabacka et al. 2016), and *Cpt1a*, catalyzing acylcarnitine production and rate limiting for fatty acid oxidation (Foster 2012). Finally, reduced levels of fat in the liver and smaller BW could be observed in exposed animals, especially at the highest dose of DPhP. In line with this result, the transcriptional control of fatty acids synthesis/uptake was apparently also disturbed, especially at the highest dose of DPhP. Consistently, *Cd36*, *Scd1*, *Fads2*, all canonical target genes of PPARα involved in fatty acid biosynthesis, were found to be less expressed in exposed animals. Taken together, these results strongly pointed toward a significant dysregulation of lipid homeostasis upon exposure to DPhP and suggested an important role for PPARα activity in this perturbation.

PPARα is considered to be a key coordinator of fast-fed transition at the hepatic level with paradoxical effects (Dubois et al. 2017). PPARα is thus the main activator of fatty acid oxidation and ketogenesis during adaptation to long-term fasting (Janssen et al. 2015). However, it is now recognized that PPARα also acts in normal conditions to adapt metabolism to circadian feeding
<figure><img src="image_83.png" /><figcaption>**Figure 7.** Histological and physiological alterations induced by exposure to DPhP. (A) Liver sections at the indicated scale of mice exposed to the indicated concentration of DPhP or a vehicle were immunostained with the indicated antibodies. Star and hash symbols denote the centrolobular and the portal area of the liver, respectively. Inset represents a 10× magnification of the original image. (B) Histogram quantifying immunohistochemistry (IHC) scores associated with *Hmgcs* staining of five animals in each indicated group (representative of two independent experiments, 2×5 animals). Score associated with the centrilobular and the portal area were dissociated as indicated. (-) = no to very low staining, (+) = low staining, (++) = intermediate staining, (+++) = intense staining (see “Methods”). (C) Histogram quantifying IHC scores associated with Perilipin 2 staining of five animals in each indicated group (representative of two independent experiments, 2×5 animals). Score associated with the intermediate zone and the portal area were dissociated as indicated (no staining was present in the zone contiguous to the centrilobular vein). (-) = no to very low staining, (+) = low staining, (++) = intermediate staining, (+++) = intense staining (see methods). (D) Box-and-whisker plot quantifying the body weight of 10 animals representative of two independent experiments (2×5 animals), after 8 wk of DPhP exposure at the indicated concentrations or with a vehicle. (Midline = median, box limits = 1st and 3rd quartile, whiskers = largest and smallest value within 1.5 × the interquartile range; summary data in Table S27). Outliers and significant *p*-values have been indicated (Student's *t*-test, *p* < 0.05 compared to vehicle).</figcaption></figure>
(Gachon et al. 2011). Therefore, PPARα controls on the one hand de novo lipid synthesis and fatty acid uptake during feeding periods, supplying store droplets, and on the other hand activates triglyceride and cholesteryl-ester lysis and oxidation of released lipids between meals (Chen and Yang 2014; Gachon et al. 2011). Accordingly, our results showing a double perturbation of both mechanisms, fatty acid synthesis and degradation, by DPhP exposure were consistent with this known role of PPARα. In absence of any observed reduction of PPARα expression, our results match a model of inhibition of PPARα transcriptional activity upon exposure to DPhP. Conversely, our findings that lipid stores in the liver of animals exposed to low concentrations of DPhP are not depleted invalidated a model of low fatty acid oxidation by an overall lack of lipids and was consistent with a genetic perturbation of this metabolic pathway. Finally, the absence of a strong rise in lipid droplets in these same conditions was also coherent with an inhibition of PPARα activity, because an equilibrium may be found between the lack of fatty acid storage and a lower use of these fatty acids.

Interestingly, our study highlighted the perturbation of several pools of metabolites and genetic controls susceptible to alter this equilibrium, directly in the liver or involving a more complex interaction with other organs. Among them, N-acylethanolamide, significantly more represented in extracts of animals exposed to DPhP, are known lipid stress markers enzymatically produced and controlling PPARα activity (Piomelli 2013). Similarly, depletion of bile acids may participate in the control of lipid metabolism, either directly through a decrease in lipid absorption, or via the regulation of nuclear receptors such as FXR protein. Finally, we noticed the alteration of several metabolites, endogenous and exogenous, containing homocyclic aromatic rings and/or indole chemical functions associated with tryptophan metabolism. Several perturbed indole-containing compounds (indoxyl-sulfate, quinolin-2-ol) (Wikoff et al. 2009) and tryptophan derivatives (Venkateswaran et al. 2019; Yamamoto et al. 2019) were shown to be directly implicated in the regulation of AHR protein, an important xenobiotic receptor (Bock 2019b) involved in the elimination of aromatic xenobiotics. Moreover, even though DPhP does not contain the classical polycyclic structure associated with known ligands of these receptors, it possesses two aromatic rings susceptible of stacking with these structures and disturbing their interaction with AHR (Soshilov and Denison 2014). Importantly, we also noticed at the genetic level that exposure to DPhP disturbed xenobiotic response and tryptophan metabolism reinforcing the possibility that DPhP disturbed AHR activity in our experiments. Recent studies have suggested that AHR may be involved in fatty acid metabolism (Bock 2019a), independently of its role as a xenobiotic regulator but through the control of PPARα activity (Wang et al. 2011).

Overall, these regulations linked to exposure to DPhP may be highly relevant to human health with regards to long-term exposure. Of note, our study used relatively young animals and did not analyze long-term consequences of chronic exposure to DPhP. Therefore, it is difficult to predict the exact consequences of PPARα inhibition by DPhP on older animals. Moreover, it is likely that this type of perturbation intersects with other environmental constraints such as the type of diet susceptible to tilt the equilibrium controlled by PPARα in one direction or the other. Finally, the dose of DPhP exposure may strongly impact the final outcome of PPARα perturbation in an inverted U-shape type of dose-response, because we have seen in mice that the intermediate concentration had a stronger impact on liver metabolism than the highest. However, several data could support a role for PPARα inhibition by DPhP in the development of lipid disorders. The lack of PPARα activity leads for instance to a higher amount of plasma triglyceride and LDL (Schoonjans et al. 1996; Watts et al. 2003)

and is the target of fibrates, a therapeutic class of molecules extensively used to treat human metabolic syndromes. Moreover, transgenic mice with a liver-specific PPARα knockout were more sensitive to high-fat diet, displaying specific inflammation and hyperlipidemia at the hepatic level (Stec et al. 2019). Therefore, PPARα inhibition in individuals exposed to DPhP may contribute to the development of metabolic syndromes and their consequences, such as cardiovascular disease and even cancer.

## Strengths and Limitations

As described in the “Methods” section, we used pooled samples of livers for the omics analyses. This approach was chosen due to technical reasons with the aim of obtaining sufficient material for each group/concentration to perform the multiple analytical experiments used for the metabolomics analyses. The robustness of our analytical procedure and our metabolic coverage were strongly improved by this decision. We assumed that a divergent metabolite profile of one animal could overlap those of others pooled in the same extraction procedure. However, to compensate this limitation, we performed our transcriptomic analyses on a distinct batch of animals. A strong correlation and integration could be observed and built between both types of omics analysis. Therefore, it was extremely unlikely that observed differences in both omics analyses were the consequences of some animals presenting highly divergent biological responses. Moreover, immunohistochemistry (IHC) analyses were performed on separate animals and confirmed the homogeneous response to the DPhP treatment.

## Conclusion

Our results raised many questions on the use, the safety and the presence in the environment of APEs, most of which may expose humans to DPhP. We did not fully characterize the molecular mechanisms underlying the apparent alterations in lipid homeostasis and PPARα activities by these compounds because this was beyond the scope of our study. However, the known function of these factors and their association with the metabolic syndromes should constitute a sufficiently strong risk factor to measure more precisely the health hazards associated with their presence in the environment, by taking into consideration diet in future epidemiological studies. Moreover, the potential interaction of these compounds with known activators of AHR should be investigated with the aim of determining the possible existence of synergistic effects and to characterize the mechanisms, directly or indirectly, inhibiting PPARα activities.

## Acknowledgments

The authors would like to thank N. Gadot for IHC analyses, I. Puisieux for its support to design animal experiments, and B. Manship for critical reading of the manuscript. This study was financially supported by the Plan Cancer et Environnement 2015–2019 (Project PLASTOC-Plastic additives: Study of the Potential Risk for Cancer Development Through the Evaluation of Pharmacodynamics, Accumulation and Mechanisms in Endocrine Tissues). Our team is also certified by the Ligue nationale contre le cancer. Additionally, this work was supported by the Ligue contre le cancer (Comité du Rhône), the Association pour la Recherche contre le Cancer (Project No. SFI20121205395, PJA20141201758, PJA 20171206303), and the Bonus Qualité Recherche (BQR) Accueil of Lyon University.

S.S.R., M.A., and J.D. performed experiments on cell lines and animal models. S.S.R. coordinated animal experimentation and IHC analysis. J.G., C.B., and L.P. performed NGS and their bioinformatics analysis. A.M.V. performed statistical retreatment of the data. J.M.S., A.F., A.B., and E.V. designed and supervised
metabolomics analyses and raw data retreatment. J.M.S. and A.M.V. performed then their statistical interpretation. L.S. and B.G. performed supervised quantification of APEs. E.V. supervised these analyses. B.F., S.I., and T.G. helped to interpret the data. L.P. and A.M.V. conceived the project, designed experiments, interpreted data, and wrote the manuscript. All authors read and approved the final version of the manuscript.

## References

Abdallah MA, Nguyen KH, Moehring T, Harrad S. 2019. First insight into human ex- trahepatic metabolism of flame retardants: biotransformation of EH-TBB and Firemaster-550 components by human skin subcellular fractions. Chemosphere 227:1–8, PMID: 30981098, https://doi.org/10.1016/j.chemosphere.2019.04.017.

Ballesteros-Gómez A, Erratico CA, Eede NV, Ionas AC, Leonards PE, Covaci A. 2015a. In vitro metabolism of 2-ethylhexyldiphenyl phosphate (EHDPHP) by human liver microsomes. Toxicol Lett 232(1):203–212, PMID: 25448284, https://doi.org/10.1016/j. toxlet.2014.11.007.

Ballesteros-Gómez A, Van den Eede N, Covaci A. 2015b. In vitro human metabolism of the flame retardant resorcinol bis(diphenylphosphate) (RDP). Environ Sci Technol 49:3897–3904, PMID: 25692932, https://doi.org/10.1021/es505857e.

Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. 2012. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603–607, PMID: 22460905, https://doi.org/10. 1038/nature11003.

Barupal DK, Fiehn O. 2017. Chemical similarity enrichment analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci Rep 7(1):14567, PMID: 29109515, https://doi.org/10.1038/s41598-017-15231-w.

Barupal DK, Haldiya PK, Wohlgemuth G, Kind T, Kothari SL, Pinkerton KE, et al. 2012. MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity. BMC Bioinformatics 13:99, PMID: 22591066, https://doi.org/10.1186/1471-2105-13-99.

Belcher SM, Cookman CJ, Patisaul HB, Stapleton HM. 2014. In vitro assessment of human nuclear hormone receptor activity and cytotoxicity of the flame retardant mixture FM 550 and its triarylphosphate and brominated components. Toxicol Lett 228(2):93–102, PMID: 24786373, https://doi.org/10.1016/j.toxlet.2014.04.017.

Björnsdotter MK, Romera-García E, Borrull J, de Boer J, Rubio S, Ballesteros- Gómez A. 2018. Presence of diphenyl phosphate and aryl-phosphate flame retardants in indoor dust from different microenvironments in Spain and the Netherlands and estimation of human exposure. Environ Int 112:59–67, https://doi.org/10.1016/j.envint.2017.11.028.

Blighe K. 2018. Enhanced Volcano: publication-ready volcano plots with enhanced colouring and labeling. https://github.com/kevinblighe [accessed 12 June 2020].

Bock KW. 2019a. Aryl hydrocarbon receptor (AHR) functions in NAD⁺ metabolism, myelopoiesis and obesity. Biochem Pharmacol 163:128–132, PMID: 30779909, https://doi.org/10.1016/j.bcp.2019.02.021.

Bock KW. 2019b. Aryl hydrocarbon receptor (AHR): from selected human target genes and crosstalk with transcription factors to multiple AHR functions. Biochem Pharmacol 168:65–70, PMID: 31228464, https://doi.org/10.1016/j.bcp.2019.06.015.

Chen G, Jin Y, Wu Y, Liu L, Fu Z. 2015. Exposure of male mice to two kinds of or- ganophosphate flame retardants (OPFRs) induced oxidative stress and endo- crine disruption. Environ Toxicol Pharmacol 40(1):310–318, PMID: 26183808, https://doi.org/10.1016/j.etap.2015.06.021.

Chen L, Yang G. 2014. PPARs integrate the mammalian clock and energy metabo- lism. PPAR Res 2014:653017, PMID: 24693278, https://doi.org/10.1155/2014/653017.

Dubois V, Eeckhoute J, Lefebvre P, Staels B. 2017. Distinct but complementary contributions of PPAR isotypes to energy homeostasis. J Clin Invest 127(4):1202–1214, PMID: 28368286, https://doi.org/10.1172/JCI63967.

Foster DW. 2012. Malonyl-CoA: the regulator of fatty acid synthesis and oxidation. J Clin Invest 122(6):1958–1959, PMID: 22833869, https://doi.org/10.1172/jci63967.

Fu L, Du B, Wang F, Lam JCW, Zeng L, Zeng EY. 2017. Organophosphate triesters and diester degradation products in municipal sludge from wastewater treatment plants in China: spatial patterns and ecological implications. Environ Sci Technol 51(23):13614–13623, PMID: 29083881, https://doi.org/10.1021/acs.est.7b04106.

Gachon F, Leuenberger N, Claudel T, Gos P, Jouffe C, Fleury Olela F, et al. 2011. Proline- and acidic amino acid-rich basic leucine zipper proteins modulate peroxisome proliferator-activated receptor alpha (PPARalpha) activity. Proc Natl Acad Sci USA 108(12):4794–4799, PMID: 21383142, https://doi.org/10.1073/ pnas.1002862108.

Grabacka M, Pierzchalska M, Dean M, Reiss K. 2016. Regulation of ketone body metabolism and the role of PPARα. Int J Mol Sci 17(12):2093, PMID: 27983603, https://doi.org/10.3390/ijms17122093.

Heitkamp MA, Freeman JP, McMillan DC, Cerniglia CE. 1985. Fungal metabolism of tert-butylphenyl diphenyl phosphate. Appl Environ Microbiol 50(2):265–273, PMID: 4051482, https://doi.org/10.1128/AEM.50.2.265-273.1985.

Hou R, Luo X, Liu C, Zhou L, Wen J, Yuan Y. 2019. Enhanced degradation of tri- phenyl phosphate (TPHP) in bioelectrochemical systems: kinetics, pathway and degradation mechanisms. Environ Pollut 254(Part A):113040, PMID: 31421579, https://doi.org/10.1016/j.envpol.2019.113040.

Jamarani R, Erythropel HC, Nicell JA, Leask RL, Marić M. 2018. How green is your plasticizer? Polymers (Basel) 10(8):834, https://doi.org/10.3390/polym10080834.

Janssen AW, Betzel B, Stooen G, Berends FJ, Janssen IM, Peijnenburg AA, et al. 2015. The impact of PPARα activation on whole genome gene expression in human precision cut liver slices. BMC Genomics 16(1):760, https://doi.org/10. 1186/s12864-015-1969-3.

Kojima H, Takeuchi S, Itoh T, Iida M, Kobayashi S, Yoshida T. 2013. In vitro endo- crine disruption potential of organophosphate flame retardants via human nu- clear receptors. Toxicology 314(1):76–83, PMID: 24051214, https://doi.org/10. 1016/j.tox.2013.09.004.

Li Y, Chen R, He J, Ma H, Zhao F, Tao S, et al. 2019a. Triphenyl phosphate at envi- ronmental levels retarded ovary development and reduced egg production in Japanese medaka (Oryzias latipes). Environ Sci Technol 53(24):14709–14715, PMID: 31751126, https://doi.org/10.1021/acs.est.9b05669.

Li J, Zhao L, Letcher RJ, Zhang Y, Jian K, Zhang J, et al. 2019b. A review on or- ganophosphate ester (OPE) flame retardants and plasticizers in foodstuffs: lev- els, distribution, human dietary exposure, and future directions. Environ Int 127:35–51, PMID: 30901640, https://doi.org/10.1016/j.envint.2019.03.009.

Meeker JD, Cooper EM, Stapleton HM, Hauser R. 2013. Urinary metabolites of or- ganophosphate flame retardants: temporal variability and correlations with house dust concentrations. Environ Health Perspect 121(5):580–585, PMID: 23461877, https://doi.org/10.1289/ehp.1205907.

Meeker JD, Stapleton HM. 2010. House dust concentrations of organophosphate flame retardants in relation to hormone levels and semen quality parameters. Environ Health Perspect 118(3):318–323, PMID: 20194068, https://doi.org/10.1289/ ehp.0901332.

Mendelsohn E, Hagopian A, Hoffman K, Butt CM, Lorenzo A, Congleton J, et al. 2016. Nail polish as a source of exposure to triphenyl phosphate. Environ Int 86:45–51, PMID: 26485058, https://doi.org/10.1016/j.envint.2015.10.005.

Mitchell CA, Reddam A, Dasgupta S, Zhang S, Stapleton HM, Volz DC. 2019. Diphenyl phosphate-induced toxicity during embryonic development. Environ Sci Technol 53(7):3908–3916, PMID: 30864794, https://doi.org/10.1021/acs.est.8b07238.

Piomelli D. 2013. A fatty gut feeling. Trends Endocrinol Metab 24(7):332–341, PMID: 23567058, https://doi.org/10.1016/j.tem.2013.03.001.

Pouech C, Kiss A, Lafay F, Léonard D, Wiest L, Cren-Olivé C, et al. 2015. Human ex- posure assessment to a large set of polymer additives through the analysis of urine by solid phase extraction followed by ultra high performance liquid chro- matography coupled to tandem mass spectrometry. J Chromatogr A 1423:111– 123, PMID: 26554294, https://doi.org/10.1016/j.chroma.2015.10.091.

Sasaki K, Suzuki T, Takeda M, Uchiyama M. 1984. Metabolism of phosphoric acid triesters by rat liver homogenate. Bull Environ Contam Toxicol 33(3):281–288, PMID: 6478075, https://doi.org/10.1007/BF01625544.

Schoonjans K, Peinado-Onsurbe J, Lefebvre AM, Heyman RA, Briggs M, Deeb S, et al. 1996. PPARalpha and PPARgamma activators direct a distinct tissue-specific tran- scriptional response via a PPRE in the lipoprotein lipase gene. EMBO J 15(19):5336–5348, PMID: 8995578, https://doi.org/10.1002/j.1460-2075.1996.tb00918.x.

Soshilov AA, Denison MS. 2014. Ligand promiscuity of aryl hydrocarbon receptor agonists and antagonists revealed by site-directed mutagenesis. Mol Cell Biol 34(9):1707–1719, PMID: 24591650, https://doi.org/10.1128/MCB.01183-13.

Stec DE, Gordon DM, Hipp JA, Hong S, Mitchell ZL, Franco NR, et al. 2019. Loss of hepatic PPARα promotes inflammation and serum hyperlipidemia in diet- induced obesity. Am J Physiol Regul Integr Comp Physiol 317(5):R733–R745, https://doi.org/10.1152/ajpregul.00153.2019.

Straub BK, Gyoengyoesi B, Koenig M, Hashani M, Pawella LM, Herpel E, et al. 2013. Adipophilin/perilipin-2 as a lipid droplet-specific marker for metabolically active cells and diseases associated with metabolic dysregulation. Histopathology 62(4):617–631, PMID: 23347084, https://doi.org/10.1111/his.12038.

Su G, Crump D, Letcher RJ, Kennedy SW. 2014. Rapid in vitro metabolism of the flame retardant triphenyl phosphate and effects on cytotoxicity and mRNA expression in chicken embryonic hepatocytes. Environ Sci Technol 48(22):13511–13519, PMID: 25350880, https://doi.org/10.1021/es5039547.

Su G, Letcher RJ, Yu H. 2016. Organophosphate flame retardants and plasticizers in aqueous solution: pH-dependent hydrolysis, kinetics, and pathways. Environ Sci Technol 50(15):8103–8111, PMID: 27347783, https://doi.org/10.1021/acs.est.6b02187.

Szkarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. 2019. STRING v11: protein-protein association networks with increased cover- age, supporting functional discovery in genome-wide experimental data- sets. Nucleic Acids Res 47(D1):D607–D613, PMID: 30476243, https://doi.org/ 10.1093/nar/gky1131.

Tserng KY, Jin SJ. 1991. Metabolic origin of urinary 3-hydroxy dicarboxylic acids. Biochemistry 30(9):2508–2514, PMID: 2001377, https://doi.org/10.1021/ bi00223a030.
Tung EWY, Ahmed S, Peshdary V, Atlas E. 2017. Firemaster®550 and its components isopropylated triphenyl phosphate and triphenyl phosphate enhance adipogenesis and transcriptional activity of peroxisome proliferator activated receptor (Pparγ) on the adipocyte protein 2 (aP2) promoter. PLoS One 12(4):e0175855, PMID: 28437481, https://doi.org/10.1371/journal.pone.0175855.

Van den Eede N, Ballesteros-Gómez A, Neels H, Covaci A. 2016. Does biotransformation of aryl phosphate flame retardants in blood cast a new perspective on their debated biomarkers? Environ Sci Technol 50(22):12439-12445, PMID: 27766855, https://doi.org/10.1021/acs.est.6b03214.

Venkateswaran N, Lafita-Navarro MC, Hao YH, Kilgore JA, Perez-Castro L, Braverman J, et al. 2019. Myc promotes tryptophan uptake and metabolism by the kynurenine pathway in colon cancer. Genes Dev 33(17-18):1236-1251, PMID: 31416966, https://doi.org/10.1101/gad.327056.119.

Wang Y, Kannan K. 2018. Concentrations and dietary exposure to organophosphate esters in foodstuffs from Albany, New York, United States. J Agric Food Chem 66(51):13525-13532, PMID: 30525574, https://doi.org/10.1021/acs.jafc.8b06114.

Wang Y, Kannan P, Halden RU, Kannan K. 2019. A nationwide survey of 31 organophosphate esters in sewage sludge from the united states. Sci Total Environ 655:446-453, PMID: 30472646, https://doi.org/10.1016/j.scitotenv.2018.11.224.

Wang C, Xu CX, Krager SL, Bottum KM, Liao DF, Tischkau SA. 2011. Aryl hydrocarbon receptor deficiency enhances insulin sensitivity and reduces PPAR-α pathway activity in mice. Environ Health Perspect 119(12):1739-1744, PMID: 21849270, https://doi.org/10.1289/ehp.1103593.

Wang D, Zhu W, Chen L, Yan J, Teng M, Zhou Z. 2018. Neonatal triphenyl phosphate and its metabolite diphenyl phosphate exposure induce sex- and dose-

dependent metabolic disruptions in adult mice. Environ Pollut 237:10-17, PMID: 29466770, https://doi.org/10.1016/j.envpol.2018.01.047.

Watts GF, Barrett PH, Ji J, Serone AP, Chan DC, Croft KD, et al. 2003. Differential regulation of lipoprotein kinetics by atorvastatin and fenofibrate in subjects with the metabolic syndrome. Diabetes 52(3):803-811, PMID: 12606523, https://doi.org/10.2337/diabetes.52.3.803.

Wei GL, Li DQ, Zhuo MN, Liao YS, Xie ZY, Guo TL, et al. 2015. Organophosphorus flame retardants and plasticizers: sources, occurrence, toxicity and human exposure. Environ Pollut 196:29-46, PMID: 25290907, https://doi.org/10.1016/j.envpol.2014.09.012.

Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, et al. 2009. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci USA 106(10):3698-3703, PMID: 19234110, https://doi.org/10.1073/pnas.0812874106.

Yamamoto T, Hatabayashi K, Arita M, Yajima N, Takenaka C, Suzuki T, et al. 2019. Kynurenine signaling through the aryl hydrocarbon receptor maintains the undifferentiated state of human embryonic stem cells. Sci Signal 12(587): eaaw3306, PMID: 31239324, https://doi.org/10.1126/scisignal.aaw3306.

Zhao F, Kang Q, Zhang X, Liu J, Hu J. 2019. Urinary biomarkers for assessment of human exposure to monomeric aryl phosphate flame retardants. Environ Int 124:259-264, PMID: 30660026, https://doi.org/10.1016/j.envint.2019.01.022.

Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. 2019. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47(W1):W234-W241, PMID: 30931480, https://doi.org/10.1093/nar/gkz240.
Résultat Contributeur n°2

<figure><img src="image_84.png" /></figure>

Dosage lipidique des membranes par
marquage au Dioll
Le second projet dans lequel j'ai pu participer se porte sur la création d'un outil de quantification membranaire, ses travaux ont été portés par la Dr. Maniti et la Pr. Girard-Egrot de l'équipe GEMBAS à l'ICBMS. A l'heure d'aujourd'hui, la quantification lipidique des membranes nécessite une étape d'extraction lipidique puis une quantification colorimétrique par le dosage de Stewart. Cependant ces méthodes comportent plusieurs désavantages. La première est l'utilisation de solvant organique tel que le chloroforme. Le deuxième et la nécessité de manipuler des volumes de plusieurs millilitres. La troisième est la nécessité d'utiliser de la verrerie, notamment lors de la mesure par absorbance du ferrothiocyanate complexé avec les phospholipides. L'ensemble de ces défauts rendent périlleuse la quantification des membranes. L'équipe GEMBAS ont alors développé une mesure de quantification membranaire grâce à l'utilisation du Dioll. En effet, comme nous l'avons vu dans la partie résultat n°1, le Dioll possède plusieurs avantages pour son utilisation dans un tel contexte. La première est la quasi-absence de fluorescence dans un milieu aqueux, en comparaison à sa fluorescence lors de son insertion par affinité au sein des membranes. La deuxième est sa nature fluorescente et son intensité de fluorescence élevée, qui permettent la création d'un outil de mesure de fluorescence précis, à des concentrations faibles et des volumes faibles. Troisièmement, son affinité élevée avec les régions rigides et fluides et son rapport quantique des deux longueurs d'onde quasi similaires permet d'avoir une robustesse de la mesure sur des échantillons de composition différente et donc de fluidité différente. Enfin, l'utilisation d'une sonde comme outil de quantification permet d'éviter de passer l'étape d'extraction des lipides et donc de l'utilisation du chloroforme. Deux avantages ressortent d'un outil de mesure « *cholorform-free* ». La première est la diminution de l'utilisation de solvant organique qui n'est pas forcément présent dans l'ensemble des laboratoires, notamment dans le domaine de la biologie. La deuxième qui en lien avec l'intensité de fluorescence élevée de la sonde est la possibilité d'utiliser des plaques en plastique 96 puits et donc des lecteurs de plaques permettant ainsi un gain de temps par échantillon. Dans ces travaux ils ont pu mettre au point, l'utilisation du Dioll pour la création d'un outil de quantification de membrane, rapide, « *chloroform-free* », aussi robuste que le dosage Stewart que ce soit pour des systèmes membranaires entièrement lipidiques comme les liposomes, ou encore des systèmes membranaires protéo-lipidiques artificiels ou naturels qui sont respectivement des protéoliposomes et les vésicules extracellulaires (microvésicules et exosomes).
# A novel fluorescent method to quantify membranes in artificial and natural lipid and proteolipid vesicles

Meriem Chadli<fnref n="1" />‡, Samira Samba-Mossiamasson<fnref n="1" />‡, Julien Bompard<fnref n="1" />, Larissa Socrier<fnref n="1" />†, Sebastien Ibanez<fnref n="1" />, Thomas Guillot<fnref n="1" />, Leyre Brizuela-Madrid<fnref n="1" />, Arnaud Vigneron<fnref n="2" />, David Gueyrard<fnref n="1" />, Thierry Granjon<fnref n="1" />, Agnès Girard-Egrot<fnref n="1" /> and Ofelia Maniti<fnref n="1" />*

**ABSTRACT:** Quantification methods of membrane content in various samples are scarce and require lipid extraction in organic solvents, energetic conditions or costly instruments. Here, we present an easy-to-use method of membrane lipid quantification in artificial and natural vesicles based on the fluorescence of an in-house synthesized Laurdan-derivative, Dioll. This fluorophore has a very low fluorescence in aqueous buffers, but when it spontaneously inserts into lipid bilayers its fluorescence increases with a hyperbolic dependency on the vesicles or liposome concentration in the sample. Thus, a plain solution of POPC liposomes of known concentrations was used as standard to determine lipid membrane concentration in samples of diverse compositions and regardless of membrane order or charge. The method was successfully used to quantify membrane lipids in proteoliposome samples, exosomes and giant plasma membrane vesicles.

## Introduction

Membranes, as selective barriers between the inner and the outer compartments regulate a plethora of phenomena related to signalling, molecule entry into cells, metabolite exchange or membrane trafficking. Therefore, investigations in the field of membranes are exponentially increasing to-day. The complexity of biological membranes, as major components of living cells, and their numerous interactions with intra- and extracellular networks, make their direct investigation difficult. Therefore, biomimetic models have been developed with the aim of providing a simplified environment to study, among others, membrane-protein interactions. Liposomes 1–4, various supported or tethered bilayers 5–9, monolayers 10–12, bicelles 13,14 or nanodiscs15, to name only some, are largely used to mimic plasma and organelle membranes.

Among such models, liposomes are widely used to probe protein-lipid interactions, lipid organization and molecule penetration across membranes. They are also used as versatile assemblies in drug delivery systems, as they are non-toxic, bio-degradable, and large scale production is possible 4,16–19. They are also extensively used for the reconstitution of trans-membrane proteins, to form proteoliposomes, the lipid bilayer offering a native-like environment for membrane-spanning domains 20. For all these applications, liposomes undergo various preparation steps: extrusion, centrifugation, protein reconstitution, cell-free protein synthesis, molecule grafting, etc 2,21–24. It is therefore of interest to determine

lipid concentration after various processes and in different production batches.

To estimate liposome concentration, several methods are popular. Among them, colorimetric methods such as Bartlett 25 and Rouser 26 methods are based on the reaction between the inorganic phosphate obtained after liposome treatment with perchloric acid and ammonium molibdate to obtain phosphomolibdate. The addition of a reducing agent such as 1-amino-2-naphtol-4 sulfonic acid (Bartlett method) and ascorbic acid (Rouser method) leads to formation of a violet complex absorbing at 820-830 nm. Both methods are rather sensitive (limit of detection (LOD) and limit of quantification (LOQ) around 5 and 20 µg.ml⁻<fnref n="1" />, respectively). Alternatively, the Stewart method 27, probably the most widely used method for phospholipid determination, is based on the formation of complexes between the phosphate group of lipid polar heads and ammonium ferro-thiocyanate, which absorb at 485 nm. It is rather fast and easy to implement, but requires lipid extraction in organic solvent and the use of chloroform-resistant recipes and protection. LOD and LOQ are evaluated at 10 µg.ml⁻<fnref n="1" /> and 100 µg.ml⁻<fnref n="1" />, respectively.

Chromatographic methods of lipid separation, together with mass spectroscopy 28–30 or NMR 31,32 are also used for lipid detection and quantification, as one can obtain not only the lipid amount, but also the lipid class distribution. Yet, they are time consuming, rather expensive and not readily used in routine experiments. Among non-destructive methods we can

<footnote n="1">¹Institut de Chimie et Biochimie Moléculaires et Supramoléculaires, ICBMS UMR 5246, Univ Lyon, Université Lyon 1, CNRS, Lyon, France</footnote>

<footnote n="2">²Université de Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Cancer Research Center of Lyon, Equipe Labellisée Ligue contre le Cancer, Lyon, France; LabEx DEVweCAN, Université de Lyon, Lyon, France.</footnote>

<footnote>† present address Max Planck Society, Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany</footnote>

<footnote>and Georg-August-Universität Göttingen, Institute of Organic and Biomolecular Chemistry, Tammannstraße 2, 37077 Göttingen, Germany</footnote>

<footnote>*to whom correspondence should be addressed ofelia.maniti@univ-lyon1.fr</footnote>

<footnote>‡ these authors contributed equally</footnote>
cite the report from Elmer-Dixon and Bowler, 2017, which proposes an interesting quantification of cardiolipin-containing liposomes based on absorbance measurements and Mie diffusion using MatLab algorithms 33.

Given the number of studies dwelling on liposomes, proteoliposomes and on cell-derived vesicles, we propose a simple method to estimate vesicle concentration in aqueous solutions. This method is based on the fluorescence of an in-house synthesized Laurdan-derived fluorophore, Dioll (Patent pending EPO19306175.1, 1-(6-((2,3-dihydroxypropyl)(methylethyl)amino)naphthalen-2-yl)dodecan-1-one). This fluorophore, like its more illustrious analogue, Laurdan 34–36, is a membrane probe sensitive to the viscosity of its environment, used for the study of membrane fluidity and organization 37. Both Dioll and Laurdan have a very low fluorescence emission intensity in aqueous buffers, but the fluorescence intensity increases drastically when the fluorophore is inserted into a lipid bilayer. In this report, we analyzed the evolution of the fluorescence emission of Dioll with respect to the amount of phospholipid vesicles in the sample and found that it followed a hyperbolic function. Fitting the experimental results to a hyperbola permitted us to quantify lipids in various preparations.

The Dioll-based quantification method was found to be robust with respect to membrane fluidity, presence of charges, or cholesterol content, and was used to determine membrane content in proteoliposomes and in two types of natural vesicles, exosomes and giant plasma membrane vesicles (GPMV).

# Experimental section

## Materials

Lipids were purchased from Avanti Polar Lipids (Alabaster, Al). Dioll was produced as described elsewhere (patent pending EPO19306175.1). HEPES, PBS, NaCl, ferric chloride hexahydrate ($FeCl_3 \cdot (H_2O)_6$) and ammonium thiocyanate ($NH_4SCN$) were purchased from Sigma Aldrich (St Quentin Falavier, France). Ultrapure water was obtained with a Pure-LabOption Q7 system (Véolia Water STI, Decines, France).

## Liposome preparation

Liposomes were prepared using a classical freeze/thaw protocol. Briefly, the appropriate lipid mixture solubilized in chloroform was dried under vacuum, then solubilized with 1 ml buffer under strong agitation. Assays were done either in HEPES 20 mM pH 7.4 buffer 100 mM NaCl or in phosphate buffer saline (PBS). The liposome emulsion obtained was submitted to 6 freeze/thaw cycles (5 min in liquid nitrogen/10 min in water bath at 37°C, 1 minute strong agitation) to decrease the number of bilayers and obtain unilamellar vesicles. To obtain homogenous vesicles, the liposome solution was extruded 19 times through a 400 nm and 100 nm polycarbonate membrane successively using an extruder (Avanti Polar Lipids, Alabaster Al). Liposomes were routinely prepared at lipid concentrations between 1 and 10 mg·ml⁻¹ and subsequently diluted at the desired concentration.

## Dioll fluorescence-based liposome quantification

A POPC (1-palmitoyl-2-oleoyl-glycero-3-phosphocholine) liposome solution (100 µg·ml⁻¹) was used as standard. Increasing volumes of the standard solution were diluted with buffer in a 96 well plate (Nunc). The final volume per well was 200 µl and lipid concentrations between 0 and 80 µg·ml⁻¹ were used. Samples were prepared in parallel to a final volume of 200 µl per well.

A constant volume of Dioll solution in ethanol (5 µl) was added in each well to obtain a final Dioll concentration of 2 µM.

The volume of ethanol added represented 2.5 % of the final volume and had no effect on liposome integrity. The plate was incubated at room temperature for 10 min. Fluorescence emission spectra were recorded between 420 nm and 600 nm for an excitation wavelength of 390 nm with a TECAN InfiniteM200 plate reader (Tecan Group Ltd., Männedorf, Switzerland). Spectra were recorded at 21°C.

The surface under the spectrum was calculated for each condition using an Excel complementary function, IntegralP (www.fordom.free.fr), which allows calculating the integral of a function when discrete data sets are available. Maximum fluorescence intensity or surface under the curve values were plotted as a function of the lipid concentration to obtain the standard curve. They both showed a hyperbolic variation and the equation of the standard curve was obtained using a hyperbolic fit $y=ax/(x+b)$ (SigmaPlot). The calculation of the *a* and *b* coefficients allowed us to determine lipid concentrations in the samples. As various volumes were used for the assays, the concentration of the stock liposome solution was presented taking into account various dilution factors. Measurements were performed in triplicate.

For the estimation of the LOD and LOQ, the fluorescence spectra of 2 µM Dioll in aqueous buffer were recorded in the absence of liposomes and the surface under the curve was calculated to estimate the blank value on 5 independent measurements. LOD and LOQ were calculated as 3 times or 10 times the standard deviation of the blank, respectively.

Sensitivity was estimated from the slope of the linear fit for low liposome concentrations (up to 20 µg·ml⁻¹). As the fluorescence intensity is dependent on the instrument used, in this study we estimated that an increase of 100 A.U. in the fluorescence intensity obtained with TECAN InfiniteM200 was significant.

To conclude upon the repeatability of the method, the coefficient of variation (CV) was calculated as the percentage of the standard deviation on the mean of 5 independent measurements. Accuracy was deduced from the difference between the concentrations obtained with the Dioll-based method and a standard method of phospholipid quantification, in our case the Stewart method 27.

## Lipid quantification with Stewart Method

Lipids were extracted from samples with chloroform. In brief, aqueous samples were mixed with chloroform (10 times the volume of the sample) under vigorous shaking. The samples were then centrifuged at 300 x g for 10 min to accelerate phase separation (Multifuge 3 LR, Heraeus ThermoScientific). The chloroform (bottom) phase was recovered with a glass pipette.

A POPC standard solution was prepared at 100 µg·ml⁻¹ in chloroform. Increasing volumes of the standard solution were diluted with chloroform in glass centrifuge tubes to a final volume of 2 ml. Samples extracted in chloroform were prepared in parallel and diluted to 2 ml. 2 ml of Ferrothiocyanate solution (obtained by mixing 20.7 g of Ferric chloride hexahydrate and 30.4 g of ammonium thiocyanate in 1L of ultrapure water) was added to each tube. The solutions were vigorously mixed and then, centrifuged at 300 x g for 10 min (Multifuge 3 LR, Heraeus). The chloroform phase was recovered and the absorbance was measured at 485 nm in glass cuvettes with a Jasco V-730 spectrophotometer (Jasco, Lisses, France). For quantification, absorbance at 485 nm was plotted against the phospholipid concentration.

## Sample preparation
Proteoliposomes were a kind gift from Synthelis (Grenoble, France) and were prepared according to previously published protocols 38.

Exosomes were obtained by series of differential centrifugations adapted from 39. In brief, 120 ml of HMEC culture medium were collected after two days culture, and centrifuged at 2 000 x g at 4°C during 10 min (Multifuge 3LR Heraus™). Supernatants were collected and ultracentrifuged at 20 000 x g, 4°C during 30 min (Optima LE-80 Beckman™ with a fixed rotor angle 50.2 Ti Beckman™). A second ultracentrifugation was realized at 100 000 x g, 4 °C during 2 h. Pellets were resuspended with 1 mL of PBS buffer, pooled in the same tube and ultracentrifuged at 100 000 x g, 4 °C during 1 h. Finally, the pellet was resuspended in 200 µL of PBS buffer to obtain concentrated exosomes.

Giant Plasma Membrane Vesicles (GPMVs) were prepared according to previously described protocol 40, but adapted to PC-3 cell culture. In brief, PC-3 were cultured in RPMI-1640 medium with 10% FBS, 100 U/mL penicillin and 100 µg/mL streptomycin under a humidified atmosphere of 5% CO₂ at 37°C until cells reached a confluency of 80% in 25 cm² plastic flasks. Growth medium was removed and cell were incubated for 1h with 25 mM formaldehyde 2 mM DTT in PBS at room temperature under agitation. The supernatant containing GPMVs was recovered and the presence of GPMVs was checked with an optical microscope. For Dioll-based measurements, 200 µl of the GPMV preparation was incubated with 5 µl Dioll ethanol solution (2 µM final concentration) and fluorescence spectra were recorded as described above. The lipid concentration was determined using POPC liposomes as standard.

## Results

To probe the dependency of Dioll fluorescence upon liposome content, increasing concentrations of POPC liposomes were incubated with a constant concentration of 2 µM Dioll. Fluorescence spectra were recorded and plotted in Figure 1A. Fluorescence intensity increased progressively with lipid amount, but seemed to reach saturation at concentrations over 50 µg·ml⁻¹. The fluorescence intensity emission maximum was obtained at 490 nm, which corresponded to lipid membranes in liquid disordered state, as expected for POPC liposomes at 21°C (Tm -4°C). Fluorescence intensity at 490 nm as a function of the lipid concentration in the well (Figure 1B) showed a hyperbolic evolution. Yet, for polarity-sensitive probes, like Dioll, the position of the maximum fluorescence emission largely depends on the order state of the lipid membrane. This characteristic, which makes them valuable for determining the order state of the membrane, is an inconvenient when sample fluidity does not match that of the standard solution. Therefore, to overcome this inconvenient, the fluorescence intensity parameter was replaced by the surface under the peak, which does not depend on the fluidity state of the membrane 34,35. The surface under the peak was estimated by integrating the spectra obtained between 420 and 560 nm. When plotted as a function of the liposome concentration, the surface under the peak also fitted a hyperbolic function (Figure 1C). Therefore, the hyperbolic dependency between the surface under the experimental fluorescence emission spectra and the liposome concentration in the well can be used as a calibration curve to measure liposome concentration in various samples.

The LOD, the lowest concentration that can be detected but not quantified, and the LOQ, the lowest concentration that can be quantified with a reasonable degree of confidence, of the calibration curve were determined from the standard deviation of the blank solution (2 µM Dioll in buffer) (Figure

1D). The LOD, which corresponded to the lipid concentration obtained at 3 times the standard deviation of the blank, was equal to 0.4 µg·ml⁻¹, whereas the LOQ, which corresponded to 10 times the standard deviation of the blank, was equal to a lipid concentration of 1.2 µg·ml⁻¹. These limits are rather low and make this method interesting for measuring low lipid concentrations, when small or diluted sample volumes are available.

The sensitivity of the detection method is defined as the dependency between the instrumental response and the variation in the concentration of the compound of interest. It is instrument-dependent. In our conditions, the slope of the linear part equaled to 10 000 A.U. per concentration unit (µg·ml⁻¹). That means that, considering that the sensitivity limit of TECAN InfiniteM200 used in this study was 100 A.U., one can detect variations in liposome concentration of the sample as low as 1 ng·ml⁻¹.

The repeatability of the method, which represents the determination of how close values are to each other under identical experimental conditions, was assessed by determination of the concentration of a POPC liposome solution prepared independently of the standard solution. A concentration of 914 ± 56 µg·ml⁻¹ was obtained with 5 replicates, which gave a repeatability of 6%. To assess the accuracy of the new quantification method, the same POPC liposome solution was measured using the largely used Stewart method. A difference of 1.2% was obtained between mean values obtained with the two methods. Results were therefore consistent and opened the possibility to routinely use the Dioll as a probe to measure membrane amount in various samples, without lipid extraction and in a rather short time (less than 30 min).

<figure><img src="image_85.png" /><figcaption>Figure 1. Quantification of POPC liposomes using the Dioll method: A. Fluorescence emission spectra of Dioll inserted in the lipid bilayers (λ<sub>ex</sub> 390 nm) - 2 µM Dioll was added to increasing liposome concentrations. B. Maximum fluorescence intensity (λ<sub>max</sub> 490 nm) as function of the lipid concentration – experimental data was fitted to a hyperbolic function. C. Surface under the peak as a function of the lipid concentration and hyperbolic fit of the data (full line). Data sets are representative of at least 3 independent measurements. D. Evaluation of the efficiency of the Dioll method: LOD, LOQ and sensitivity were evaluated as described in material and methods section. Repeatability was determined with a POPC liposome preparation different of the</figcaption></figure>
standard on 5 independent measurements. Accuracy was determined with respect to phospholipid quantification by Stewart method.

Robustness of the Dioll method was checked with respect to various parameters. One major point of interest was whether the membrane fluidity, which largely influences Dioll fluorescence, had an impact on the Dioll-based quantification method. To this purpose, liposomes constituted of DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) and DPPC (1,2-dipalmitoyl-glycero-3-phosphocholine) were prepared (Table 1).

<table><caption>Table 1: Liposome preparations used and corresponding gel-to-liquid crystalline phase transition temperatures (Tm).</caption><thead><tr><th>Liposome preparation</th><th>Lipid</th><th>Acyl chain composition</th><th>Schematic structure</th><th>Ti</th></tr></thead><tbody><tr><td>POPC</td><td>1-palmitoyl-2-oleoyl-glycero-3-phosphocholine</td><td>16:0-18:1 PC</td><td></td><td>-4°</td></tr><tr><td>DMPC</td><td>1,2-dimyristoyl-sn-glycero-3-phosphocholine</td><td>14:0 PC</td><td></td><td>24</td></tr><tr><td>DPPC</td><td>1,2-dipalmitoyl-glycero-3-phosphocholine</td><td>16:0 PC</td><td></td><td>41</td></tr></tbody></table>

In our experimental conditions (21°C), DMPC, with a gel-to-liquid crystalline transition temperature (Tm) of 24°C, is expected to be in an ordered state, but close to the liquid ordered-liquid disordered transition, and DPPC (Tm, 41°C) in an ordered configuration. Independently, a standard liposome solution was prepared for each lipid to obtain the corresponding calibration curves. DMPC-inserted Dioll fluorescence spectra (Figure 2A) showed a maximum fluorescence emission at 440 nm consistent with lipid membranes in an ordered state, with a contribution at 490 nm corresponding to the fluid phase.

Although it is difficult to calculate the proportion of each state, the fluorescence emission spectra indicated that, in our experimental conditions, DMPC liposomes make a good model of membranes in which two phases coexist. When inserted in DPPC liposomes, Dioll maximum fluorescence emission was obtained at 440 nm, which is consistent with a membrane in gel/ordered state (Figure 2B). For both DMPC and DPPC liposomes, the surface under the peak estimated as described above gave hyperbolic evolutions (Figure 2C and D). The equations obtained by fitting the experimental data to a hyperbola allowed us to estimate concentration of liposomes in various samples.

To check the influence of the membrane fluidity on the quantification method, POPC, DMPC and DPPC liposomes were prepared and their concentration was determined using either POPC, DMPC or DPPC calibration curves, or by the Stewart method with POPC in chloroform solution as standard. Results are presented in figure 2E. The concentration of the POPC solution measured using either POPC, DMPC or DPPC calibration curves was consistent with that obtained by the Stewart method. Similar results were obtained for DMPC and DPPC liposomes quantified using Dioll-based calibration curves or by Stewart method. Therefore, the surface under the peak parameter as a function of the liposome concentration can successfully replace the fluorescence intensity to measure sample concentration, whatever the membrane fluidity state. This is particularly interesting as membrane fluidity in samples of interest is usually unknown.

<figure><img src="image_86.png" /><figcaption>Figure 2. Robustness of Dioll quantification method upon variations of lipid bilayer fluidity. Fluorescence emission spectra of Dioll (2 µM) inserted in DMPC (A) or DPPC (B) liposomes ($\lambda_{ex}$ 390 nm). Surface under the peak calculated from spectra from figure A and B as a function of DMPC (C) or DPPC (D) liposome concentration. Cross calculations of concentrations of POPC, DMPC or DPPC test solutions using POPC (white bars), DMPC (light grey), DMPC (grey) standard solutions or by Stewart method (black bars) (E). Error bars represent standard deviation obtained with 3 independent measurements.</figcaption></figure>

Cholesterol is one major component of membranes and also a major modulator of membrane order 41. Depending on cell type and membrane, the proportion of cholesterol can vary between 10 and 30 % of lipid total mass. Liposomes containing a mixture of POPC and cholesterol where prepared and, the concentration was measured using Dioll method and a stock solution of POPC liposomes as standard. The concentration obtained for 10, 20 and 30 % cholesterol correlated with the concentration of phospholipids obtained using the Stewart method (Figure 3A), and was not influenced by the cholesterol content in the sample.

<figure><img src="image_87.png" /><figcaption>Figure 3. Cholesterol and charge effect on liposome quantification with Dioll. Quantification of POPC liposomes containing (A) cholesterol and (B) POPS using the Dioll method with POPC liposomes as standard (white bars) or by Stewart method (black bars). Error bars are SD obtained with 3 independent measurements.</figcaption></figure>
Membranes contain non negligible amounts of negatively charged lipids with precise and fundamental functions. It is thus of interest to check the robustness of the Dioll-based quantification method towards negatively charged lipids. As an example, we have chosen to prepare liposomes containing 50 % POPC and 50 % POPS (1-palmitoyl-2-oleoyl-glycero-3-phosphoserine). The concentration of the prepared liposome solution was estimated either by the Dioll-based method, with POPC liposomes of known concentration as standard curve, or by the Stewart method with a POPC chloroform solution as standard (Figure 3B). The two methods gave consistent results.

Liposomes provide an adequate environment that preserves transmembrane protein structure and function. Therefore, they are largely used to produce various transmembrane proteins in a native-like environment by different strategies. Whatever the production strategy, it is important that, after various preparation and purification steps, one easily estimates the proteoliposome amount in the sample without additional extraction steps.

The transmembrane protein used as a model in this study was the C-X-C motif chemokine receptor 4 (CXCR4), a 7 transmembrane domains G protein-coupled receptor (GPCR). CXCR4 was obtained with a cell-free expression system allowing a one-step synthesis reaction of the protein with concomitant insertion in a lipid bilayer through the addition of liposomes to the reaction mix (Synthelis). The following lipid composition DOPC/DOPE/DMPA/Chol (31:17:20:32 %mol) was used to prepare liposomes and the functionality of CXCR4 was tested using T22, a synthetic antagonist specific to this receptor 38,42. To probe the use of the Dioll-based fluorescence quantification method with proteoliposomes, we quantified lipid concentration in six different protein production batches (Figure 4). Transmembrane proteins are known to modify membrane fluidity towards a more disordered conformation. This was the case for CXCR4, which increased the 490 nm contribution on the Dioll fluorescence emission spectrum, which corresponds to a membrane in a more fluid state (data not shown). As described above, the surface under the curve parameter allowed us to overcome the modifications in membrane fluidity and in the shape of the spectrum. Indeed, membrane lipid concentration in the sample obtained by Dioll method correlated well with phospholipid membrane amount obtained by Stewart method, after protein precipitation with acetone and lipid extraction with chloroform. Hence, the Dioll-based quantification of membranes turns out to be a fast and easy to use method of proteoliposome quantification.

<figure><img src="image_88.png" /><figcaption>Figure 4: Proteoliposomes quantification using the Dioll (white bar) and Stewart (black bar) methods. Proteoliposomes were obtained following previously described protocols 38. Six different samples were tested. Error bars are SD obtained with 3 independent experiments.</figcaption></figure>

To summarize, the use of the surface under the curve proved to be a robust parameter for the Dioll-based method to quantify

membrane lipids with respect to the order state of the membrane. By using a simple standard such as plain liposome solution and a constant fluorophore concentration, one can determine membrane amount in samples of different compositions: DMPC, DPPC, cholesterol-containing liposomes, negatively charged liposomes or protein-containing liposomes.

The challenge was now to measure membrane lipid concentration in various natural vesicles, such as extracellular vesicles in general, and exosomes in particular. The number of studies on exosomes is various physiological and pathological conditions is exponentially growing. Indeed, exosomes are small vesicles (50 to 150 nm) secreted by cells, specific of their origin cell and which can be extracted from numerous body fluids (blood, urine...). They are therefore of interest for studies of pathologies, but also for the development of non-invasive diagnostic tools, notably in oncology 43. Measuring exosome concentration rapidly, with small sample volumes, in a non-destructive manner is of growing interest. Therefore, we adapted the Dioll-based quantification method to this type of vesicles. A standard curve based on POPC liposomes of known concentration in the presence of a constant Dioll concentration (2 µM) was recorded as the surface under the curve vs. liposome concentration in the well, as described above. Exosome samples were incubated with the same Dioll concentration as the standard solution, and the surface under the curve was determined. Vesicle concentration was obtained using a hyperbolic fit of the standard curve and a value of 98 ± 10 µg.ml⁻¹ was calculated, which correlated with phospholipid determination obtained using Stewart method (102 ± 10 µg.ml⁻¹) (Figure 5).

<figure><img src="image_89.png" /><figcaption>Figure 5: Quantification of exosomes using the Dioll (white bar) and Stewart (black bar) methods. POPC liposomes were used as standards in both cases. Error bars are SD obtained with 3 independent measurements.</figcaption></figure>

A second type of vesicles used in this study was obtained from living cells by plasma membrane budding. GPMVs are derived from biological cells, by chemically induced plasma membrane vesiculation (here with 25 mM formaldehyde, 2 mM DTT in PBS) or ‘blebbing’ 40.

This type of vesicles is largely used to analyze phase coexistence in systems with realistic biological membrane compositions 40,44,45 or peptide penetration across membranes 46,47. Given the raising interest in the study of plasma membrane heterogeneities, we determined the amount of membranes in the GPMVs samples. As expected from literature data 40,44, GPMVs obtained by this method show simple, low-curvature geometries of giant unilamellar vesicles and appear free of cytoskeletal constraints (Figure 6A). Using Dioll fluorescence, a lipid concentration of 84 ± 1 µg.ml⁻¹ was obtained from an estimated PC-3 cell population of 5 million. Using the Stewart method a good correlation was observed with an estimated phospholipid sample concentration of 87 ± 1 µg.ml⁻¹ (Figure 6B).
<figure><img src="image_90.png" /></figure>

Figure 6: Evaluation of the Dioll method using GPMVs. (A) GPMVs observed with an optical microscope. (B) Quantification of GPMVs with the Dioll (white bar) and Stewart (black bar) methods. GPMVs were obtained by incubation of PC-3 cells with a 2 mM DTT/ 25 mM formaldehyde solution. Dioll quantification was performed using POPC liposomes as standard. Regarding the Stewart method, lipids were extracted with chloroform and quantified as described in the experimental section. Error bars are SD obtained with 3 independent measurements.

Discussion

Quantification methods of membrane lipid content in various samples are scarce and require either lengthy protocols involving lipid extraction, costly instruments, or energetic conditions.

Here, we present a new, rapid and easy to use method of mem-
brane lipid quantification in artificial and natural vesicles based
on the fluorescence of a Laurdan derivative, Dioll 1-(6-((2,3-di-
hydroxypropyl)(methyl)amino)naphthalen-2-yl)dodecan-1-
one). This fluorophore has a very low fluorescence in aqueous
buffers, but when it spontaneously inserts into lipid bilayers its
fluorescence drastically increases. We checked therefore
whether this was a regular increase by incubating a constant
amount of fluorophore with increasing amounts of liposomes
(Figure 1). A hyperbolic dependency existed, not only between
the maximum fluorescence intensity, but also between the sur-
face under the fluorescence emission spectra and the lipid
amount in the sample. This parameter, does not depend on the
membrane fluidity and may serve as a universal parameter to
quantify membranes.

To check this hypothesis, 3 types of liposomes were prepared
with lipids with different ordering degrees at room temperature
(23°C): POPC (fluid state), DMPC (phase transition) and DPPC
(gel state). The amount of liposomes was quantified using as
standards either liposome solutions of the same nature, or lipo-
some solutions of different fluidity. Quantification results (Fig-
ure 2) show that similar results were obtained whatever the com-

position of the standard solution. Moreover, the lipid concentra-
tions in the samples correlated well with those obtained by Stew-
art method after lipid extraction in chloroform. Thus, a plain so-
lution of POPC liposomes of known concentration may serve as
standard for samples of diverse lipid composition and diverse
membrane states.

Detection and quantification limits of the POPC standard curve attained values as low as 0.4 and 1.2 µg.ml⁻¹, respectively, comparable with those of Bartlett and Stewart methods, which allows one to quantify low amounts of lipids in biological samples. Although a fluorophore is inserted into the bilayer, this method is not destructive and liposomes, proteoliposomes or diverse vesicles may be used for further applications.

The sensitivity of the method was good up to 40 µg.ml⁻¹ (Figure 1 and 2). Above this value, the intensity of fluorescence reaches a plateau and samples require dilution. The sensitivity of the method depends on the instrumental response to the variation in the analyte concentration, therefore it is instrument-dependent. In our conditions, by using an instrument such as TECAN InfiniteM200, we were able to detect variations in the liposome concentrations in the range of 1 ng.ml⁻¹.

Robustness of the detection method was checked with respect to the presence of cholesterol (Figure 3A) and negatively charged lipids (Figure 3B). A good correlation was obtained with phospholipid quantification by Stewart method. Therefore, this method was used for quantification of more complex vesicles, such as proteoliposomes (Figure 4), and natural vesicles, such as exosomes (Figure 5) and GPMVs (Figure 6). If we consider the insertion mode of its analogue, Laurdan, Dioll is expected to fit between lipids at the level of the glycerol backbone 48. Therefore, the fluorescence intensity is dependent on the amount of phospholipid membranes. Cholesterol and other components, like proteins in proteoliposomes, or glycolipids in natural vesicles, largely influence membrane fluidity and, consequently, the shape of the spectrum, but did not influence fluorescence yield in terms of surface under the curve. Therefore, systematic correlation was obtained between membrane content determined by Dioll and phospholipid concentration determined by Stewart method.

To conclude, Dioll fluorescence can be used as a parameter to
measure the amount of phospholipid membranes in complex
samples without solvent extraction and that, by using simple lip-
osome compositions as standards. The detection method, based
on fluorescence, is fast, simple to use, sensitive and requires
standard lab equipment.

**Acknowledgments:** We acknowledge funding from CNRS (pre-
maturation program) and the Technology Transfer Accelerator
Office Lyon-Saint-Etienne *Pulsalys*. We thank Synthelis (Gre-
noble, Fr) for kind gift of proteoliposomes.

References

(1) Sezgin, E.; Schwille, P. Model Membrane Platforms to Study Protein-Membrane Interactions. *Mol. Membr. Biol.* **2012**, *29* (5), 144-154. https://doi.org/10.3109/09687688.2012.700490.

(2) Shen, H.-H.; Lithgow, T.; Martin, L. Reconstitution of Membrane Proteins into Model Membranes: Seeking Better Ways to Retain Protein Activities. *Int. J. Mol. Sci.* **2013**, *14* (1), 1589–1607. https://doi.org/10.3390/ijms14011589.

(3) Pick, H.; Alves, A. C.; Vogel, H. Single-Vesicle Assays Using Liposomes and Cell-Derived Vesicles: From Modeling Complex Membrane Processes to Synthetic Biology and Biomedical Applications. *Chem. Rev.* **2018**, *118* (18), 8598–8654. https://doi.org/10.1021/acs.chemrev.7b00777.

(4) Torchilin, V. P. Multifunctional, Stimuli-Sensitive Nanoparticulate Systems for Drug Delivery. *Nat. Rev. Drug Discov.* **2014**, *13* (11), 813–827. https://doi.org/10.1038/nrd4333.

(5) Tamm, L. K.; McConnell, H. M. Supported Phospholipid Bilayers. *Biophys. J.* **1985**. https://doi.org/10.1016/S0006-3495(85)83882-0.

(6) Sackmann, E.; Tanaka, M. Supported Membranes on Soft Polymer Cushions: Fabrication, Characterization and Applications. *Trends Biotechnol.* **2000**, *18* (2), 58–64. https://doi.org/10.1016/S0167-7799(99)01412-2.

(7) Sackmann, E. Supported Membranes: Scientific and Practical Applications. *Science* (80–). **1996**, *271* (5245), 43–48. https://doi.org/10.1126/science.271.5245.43.

(8) Richter, R. P.; Bérat, R.; Brisson, A. R. Formation of Solid-Supported Lipid Bilayers: An Integrated View. *Langmuir* **2006**, *22* (8), 3497–3505. https://doi.org/10.1021/la052687c.

(9) Rebaud, S.; Maniti, O.; Girard-Egrot, A. P. Tethered Bilayer Lipid Membranes (TBLMs): Interest and Applications for Biological Membrane Investigations. *Biochimie* **2014**, *107* (Part A), 135–142. https://doi.org/10.1016/j.biochi.2014.06.021.

(10) Elderdfi, M.; Sikorski, A. F. Langmuir-Monolayer Methodologies for Characterizing Protein-Lipid Interactions. *Chemistry and*
*Physics of Lipids*. 2018, pp 61–72. https://doi.org/10.1016/j.chemphyslip.2018.01.008.

(11) Daear, W.; Mahadeo, M.; Prenner, E. J. Applications of Brewster Angle Microscopy from Biological Materials to Biological Systems. *Biochim. Biophys. Acta - Biomembr.* **2017**, *1859* (10), 1749–1766. https://doi.org/10.1016/j.bbamem.2017.06.016.

(12) Brezesinski, G.; Möhwald, H. Langmuir Monolayers to Study Interactions at Model Membrane Surfaces. *Adv. Colloid Interface Sci.* **2003**, *100–102* (SUPPL.), 563–584. https://doi.org/10.1016/S0001-8686(02)00071-4.

(13) Poulos, S.; Morgan, J. L. W.; Zimmer, J.; Faham, S. Bicelles Coming of Age: An Empirical Approach to Bicelle Crystallization. *Methods Enzymol.* **2015**, *557*, 393–416. https://doi.org/10.1016/bs.mie.2014.12.024.

(14) Durr, U. H. N.; Gildenberg, M.; Ramamoorthy, A. The Magic of Bicelles Lights up Membrane Protein Structure. *Chem. Rev.* **2012**, *112* (11), 6054–6074. https://doi.org/10.1021/cr300061w.

(15) Borch, J.; Hamann, T. The Nanodisc: A Novel Tool for Membrane Protein Studies. *Biol. Chem.* **2009**, *390* (8), 805–814. https://doi.org/10.1515/BC.2009.091.

(16) Allen, T. M.; Cullis, P. R. Liposomal Drug Delivery Systems: From Concept to Clinical Applications. *Adv. Drug Deliv. Rev.* **2013**, *65* (1), 36–48. https://doi.org/10.1016/jaddr.2012.09.037.

(17) Samad, A.; Aqil, Y. S. and M. Liposomal Drug Delivery Systems: An Update Review. *Current Drug Delivery*. 2007, pp 297–305. https://doi.org/http://dx.doi.org/10.2174/156720107782151269.

(18) Ahmed, K. S.; Hussein, S. A.; Ali, A. H.; Korma, S. A.; Lipeng, Q.; Jinghua, C. Liposome: Composition, Characterisation, Preparation, and Recent Innovation in Clinical Applications. *J. Drug Target.* **2018**, 1–20. https://doi.org/10.1080/1061186X.2018.1527337.

(19) Shah, S.; Dhawan, V.; Holm, R.; Nagarsenker, M. S.; Perrie, Y. Liposomes: Advancements and Innovation in the Manufacturing Process. *Adv. Drug Deliv. Rev.* **2020**. https://doi.org/https://doi.org/10.1016/j addr.2020.07.002.

(20) Kuhn, A.; Haase, M.; Leptihn, S. Assisted and Unassisted Protein Insertion into Liposomes. *Biophys. J.* **2017**, *113* (6), 1187–1193. https://doi.org/10.1016/j.bpj.2017.03.027.

(21) Rigaud, J.-L.; Pitard, B.; Levy, D. Reconstitution of Membrane Proteins into Liposomes: Application to Energy-Transducing Membrane Proteins. *Biochim. Biophys. Acta - Bioenerg.* **1995**, *1231* (3), 223–246. https://doi.org/10.1016/0005-2728(95)00091-V.

(22) Soranzo, T.; Martin, D. K.; Lenormand, J.-L.; Watkins, E. B. Coupling Neutron Reflectivity with Cell-Free Protein Synthesis to Probe Membrane Protein Structure in Supported Bilayers. *Sci. Rep.* **2017**, *7* (1), 3399. https://doi.org/10.1038/s41598-017-03472-8.

(23) Mus-Veteau, I.; Demange, P.; Zito, F. Membrane Protein Production for Structural Analysis. In *Membrane Proteins Production for Structural Analysis*; 2014; pp 3–44. https://doi.org/10.1007/978-1-4939-0662-8_1.

(24) Carlson, E. D.; Gan, R.; Hodgman, C. E.; Jewett, M. C. Cell-Free Protein Synthesis: Applications Come of Age. *Biotechnol. Adv.* **2012**, *30* (5), 1185–1194. https://doi.org/10.1016/j.biotechadv.2011.09.016.

(25) Bartlett, R.; Clinic, S.; Jolla, L. Phosphorus Assay in Column. *J. Biol. Chem.* **1959**, *234* (3), 466–448.

(26) G, R.; S, F.; A, Y. Two Dimensional Thin Layer Chromatographic Separation of Polar Lipids and Determination of Phospholipids by Phosphorus Analysis of Spots. *Lipids* **1970**, *5*, 494–496. https://doi.org/10.4218/etrij.17.0116.0074.

(27) Stewart, J. C. M. Colorimetric Determination of Phospholipids with Ammonium Ferrothiocyanate. *Anal. Biochem.* **1980**, *104* (1), 10–14. https://doi.org/10.1016/0003-2697(80)90269-9.

(28) Brouwers, J. F. Liquid Chromatographic-Mass Spectrometric Analysis of Phospholipids. Chromatography, Ionization and Quantification. *Biochim. Biophys. Acta - Mol. Cell Biol. Lipids* **2011**, *1811* (11), 763–775. https://doi.org/10.1016/j.bbalip.2011.08.001.

(29) Ayciriex, S.; Regazzetti, A.; Gaudin, M.; Prost, E.; Dargère, D.; Massicot, F.; Auzeil, N.; Laprévote, O. Development of a Novel Method for Quantification of Sterols and Oxysterols by UPLC-ESI-HRMS: Application to a Neuroinflammation Rat Model. *Anal. Bioanal. Chem.* **2012**. https://doi.org/10.1007/s00216-012-6396-6.

(30) Roces, C. B.; Kastner, E.; Stone, P.; Lowry, D.; Perrie, Y. Rapid Quantification and Validation of Lipid Concentrations within Liposomes. *Pharmaceutics* **2016**. https://doi.org/10.3390/pharmaceutics8030029.

(31) Han, Y.; Wen, Q.; Chen, Z.; Li, P. Review of Methods Used for Microalgal Lipid-Content Analysis. In *Energy Procedia*; 2011. https://doi.org/10.1016/j.egypro.2011.10.124.

(32) Hein, R.; Uzundal, C. B.; Hennig, A. Simple and Rapid Quantification of Phospholipids for Supramolecular Membrane Transport Assays. *Org. Biomol. Chem.* **2016**, *14* (7), 2182–2185. https://doi.org/10.1039/c5ob02480c.

(33) Elmer-Dixon, M. M.; Bowler, B. E. Rapid Quantification of Cardiolipin and DOPC Lipid and Vesicle Concentration. *Anal. Biochem.* **2017**, *520*, 58–61. https://doi.org/10.1016/j.ab.2016.12.024.

(34) Parasassi, T.; Gratton, E. Membrane Lipid Domains and Dynamics as Detected by Laurdan Fluorescence. *J. Fluoresc.* **1995**, *5* (1), 59–69. https://doi.org/10.1007/BF00718783.

(35) Parasassi, T.; Di Stefano, M.; Loiero, M.; Ravagnan, G.; Gratton, E. Influence of Cholesterol on Phospholipid Bilayers Phase Domains as Detected by Laurdan Fluorescence. *Biophys. J.* **1994**, *66* (1), 120–132. https://doi.org/10.1016/S0006-3495(94)80763-5.

(36) Parasassi, T.; Conti, F.; Gratton, E. Time-Resolved Fluorescence Emission Spectra of Laurdan in Phospholipid Vesicles by Multifrequency Phase and Modulation Fluorometry. *Cell. Mol. Biol.* **1986**, *32* (1), 103–108.

(37) Bompard, J.; Rosso, A.; Brizuela, L.; Mebarek, S.; Blum, L. J.; Trunfio-Sfarghiu, A.-M.; Lollo, G.; Granjon, T.; Girard-Egrot, A.; Maniti, O. Membrane Fluidity as a New Means to Selectively Target Cancer Cells with Fusogenic Lipid Carriers. *Langmuir* **2020**, *36* (19), 5134–5144. https://doi.org/10.1021/acs.langmuir.0c00262.

(38) Chadli, M.; Rebaud, S.; Maniti, O.; Tillier, B.; Cortès, S.; Girard-Egrot, A. New Tethered Phospholipid Bilayers Integrating Functional G-Protein-Coupled Receptor Membrane Proteins. *Langmuir* **2017**, *33* (39), 10385–10401. https://doi.org/10.1021/acs.langmuir.7b01636.

(39) Théry, C.; Amigorena, S.; Raposo, G.; Clayton, A. Isolation and Characterization of Exosomes from Cell Culture Supernatants and Biological Fluids. *Curr. Protoc. cell Biol.* **2006**, *30* (1), 3.22.1-3.22.29. https://doi.org/10.1002/0471143030.cb0322s30.

(40) Baumgart, T.; Hammond, A. T.; Sengupta, P.; Hess, S. T.; Holowka, D. A.; Baird, B. A.; Webb, W. W. Large-Scale Fluid/Fluid Phase Separation of Proteins and Lipids in Giant Plasma Membrane Vesicles. *Proc. Natl. Acad. Sci. U. S. A.* **2007**, *104* (9), 3165–3170. https://doi.org/10.1073/pnas.0611357104.

(41) Yeagle, P. L. Cholesterol and the Cell Membrane. *BBA - Reviews on Biomembranes*. 1985. https://doi.org/10.1016/0304-4157(85)90011-5.

(42) Chadli, M.; Maniti, O.; Marquette, C.; Tillier, B.; Cortès, S.; Girard-Egrot, A. A New Functional Membrane Protein Microarray Based on Tethered Phospholipid Bilayers. *Analyst* **2018**, *143* (9), 2165–2173. https://doi.org/10.1039/c8an00260f.
(43) Colombo, M.; Raposo, G.; Théry, C. Biogenesis, Secretion, and Intercellular Interactions of Exosomes and Other Extracellular Vesicles. *Annu. Rev. Cell Dev. Biol.* **2014**. https://doi.org/10.1146/annurev-cellbio-101512-122326.

(44) Baumgart, T.; Hess, S. T.; Webb, W. W. Imaging Coexisting Fluid Domains in Biomembrane Models Coupling Curvature and Line Tension. *Nature* **2003**, *425* (6960), 821–824. https://doi.org/10.1038/nature02013.

(45) Lingwood, D.; Ries, J.; Schwille, P.; Simons, K. Plasma Membranes Are Poised for Activation of Raft Phase Coalescence at Physiological Temperature. *Proc. Natl. Acad. Sci. U. S. A.* **2008**, *105* (29), 10005–10010. https://doi.org/10.1073/pnas.0804374105.

(46) Maniti, O.; Piao, H. R.; Ayala-Sanmartin, J. Basic Cell Penetrating Peptides Induce Plasma Membrane Positive Curvature, Lipid Domain Separation and Protein Redistribution. *Int. J. Biochem. Cell Biol.* **2014**, *50* (1), 73–81. https://doi.org/10.1016/j.biocel.2014.02.017.

(47) Maniti, O.; Blanchard, E.; Trugnan, G.; Lamazire, A.; Ayala-Sanmartin, J. Metabolic Energy-Independent Mechanism of Internalization for the Cell Penetrating Peptide Penetratin. *Int. J. Biochem. Cell Biol.* **2012**, *44* (6), 869–875. https://doi.org/10.1016/j.biocel.2012.02.010.

(48) Jurkiewicz, P.; Cwiklik, L.; Jungwirth, P.; Hof, M. Lipid Hydration and Mobility: An Interplay between Fluorescence Solvent Relaxation Experiments and Molecular Dynamics Simulations. *Biochimie*. 2012. https://doi.org/10.1016/j.biochi.2011.06.027.

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I- Optimisation de la mesure de la fluidité membranaire par la sonde Dioll :

Lors de ma thèse, j'ai pu montrer à partir de modèles in vitro que les modulations métaboliques présentes lors de l'acquisition d'un phénotype plastique, que l'on retrouve lors de la mise en place de mécanismes de dédifférenciations, peuvent être suivies grâce à la mesure de la fluidité membranaire. La stratégie envisagée pour effectuer ces mesures de fluidité membranaire et le développement d'outils de diagnostic basés sur le suivi de ce paramètre physico-chimique des membranes cellulaires mettent en œuvre l'utilisation de sondes fluorescentes ratiométriques sensibles à l'hydratation des membranes<sup>245,265</sup>. Or, par leur nature, les sondes préexistantes, telles que le Laurdan, possèdent des désavantages pour une utilisation sur des modèles biologiques (marquage des gouttelettes lipidiques, faible intensité, agrégation en milieu aqueux, rapport quantique dépendant de l'environnement et donc différent entre les deux longueurs d'onde d'émissions). Afin de pallier ces défauts, des travaux d'une thèse antérieure au sein de l'équipe ont été consacrés à la création de sondes analogues du Laurdan<sup>260</sup>. L'une des sondes obtenues, nommée Dioll, possède des caractéristiques intéressantes sur des modèles de membranes biomimétiques. En effet, le Dioll possédant une structure moins hydrophobe que celle du Laurdan, permet une meilleure solubilisation tout en gardant la capacité de suivre la fluidité membranaire, avec en plus une meilleure résolution des variations de fluidité. Ma première partie de thèse a donc été consacrée à la validation de cette sonde pour le suivi de la fluidité membranaire des cellules. Mes différents travaux ont révélé plusieurs avantages du Dioll comparativement aux autres sondes ratiométrique utilisées jusqu'à présent dans la littérature, palliant une grande partie de leurs inconvénients cités plus haut. La première est la distribution du Dioll dans l'ensemble des membranes cellulaires ou du moins dans les membranes principales de la cellule. En effet, le co-marquage du Dioll avec des sondes spécifiques de certaines organelles (mitochondrie, réticulum endoplasmique, noyau, lysosome) montre le marquage de leur membrane par le Dioll. Même si ces résultats sous-entendent que sa distribution doit s'effectuer dans l'ensemble des membranes, il serait intéressant de les confirmer par des co-marquages avec des sondes spécifiques du trafic vésiculaire, l'appareil de Golgi ou encore du peroxysome. Lors de nos observations, nous avons pu obtenir une qualité d'image encore jamais atteinte, permettant de mesurer pour la première fois *in cellulo* la fluidité membranaire des différentes membranes. Ces résultats mettent clairement en évidence un niveau de fluidité membranaire en adéquation avec la composition des membranes décrite dans la littérature, avec dans l'ordre de fluidité croissante la membrane plasmique, le RE, la mitochondrie et le noyau<sup>172</sup>. Ces mesurent ont été rendues possible, grâce à une meilleure discrimination des domaines fluides par le Dioll, mais aussi grâce au fait que le rendement quantique du Dioll reste élevé même dans les régions désordonnées. De plus, nous avons pu montrer lors de nos travaux qu'il était possible de mesurer la fluidité membranaire lors d'un marquage au Dioll à l'aide de différents appareils. Pour la mesure sur cellule entière nous avons des mesures efficaces à partir de la spectrofluorimétrie, la cytométrie en flux et la micrsocopie
confoalce. Cependant pour des mesures sur des membranes spécifiques d'une cellule, seule la microscopie confocale est utilisable.

L'ensemble de ces avancées techniques ouvrent de nouvelles perspectives notamment sur le suivi de modulations dynamiques de la fluidité membranaire dans un contexte sain ou pathologique. Le Dioll n'étant pas cytotoxique pour les cellules, il est donc possible de suivre une cinétiquement l'évolution la fluidité membranaire. Cette caractéristique du Dioll permettrait dans des conditions normales de mieux comprendre les mécanismes sous-jacents de l'homéostasie des membranes et de la réponse cellulaire aux variations de fluidités. Il pourrait de même être intéressant de comprendre par imagerie de cellules vivantes comment la cellule répond à des modifications nutritives notamment en variant l'apport lipidique. Des travaux antérieurs, utilisant le c-Laurdan, ont pu mettre en évidence qu'un import exogène en DHA induisait une augmentation de la fluidité membranaire de la cellule. En réponse à cette augmentation de fluidité, la cellule retrouve une homéostasie de ses membranes en augmentant la synthèse de cholestérol grâce à l'activation de SREBP2<sup>255</sup>. L'une des questions qui reste en suspens est le mécanisme permettant son activation lors de l'augmentation de la fluidité membranaire. Alors que ces résultats sous-entendent une sensibilité de l'activation de SREBP2 à la modulation de la fluidité membranaire, il serait intéressant d'imaginer un protocole de comarquage du Dioll avec une protéine SREBP2 de fusion couplée à une protéine fluorescente (par exemple iRFP). Une telle expérimentation permettrait ainsi de suivre en direct la cinétique d'activation du SREBP2 couplé à la modulation du GP. Si ce dernier est activé dans des régions au GP spécifique, ceci sous-entendrait que l'activation de SREBP2 serait sensible à un contexte particulier de fluidité membranaire du RE. J'ai pris pour exemple l'activation de SREBP2, cependant l'activation d'autres protéines dans un contexte de modifications de compositions lipidiques reste encore mal comprise<sup>266-269</sup>. En plus de l'analyse des mécanismes associés aux modulations de la fluidité membranaire au sein des cellules saines, l'utilisation du Dioll dans un contexte pathologique pourrait être utilisée pour comprendre les maladies associées à un déséquilibre métabolique qui comme dans le cas dudiabète 2 ou encore dans les maladies cardiovasculaires peut impacter la composition membranaire des cellules. Il serait donc intéressant de comprendre dans quelle mesure ces modulations métaboliques impacteraient les membranes cellulaires et les mécanismes associées à cette dérégulation<sup>270-272</sup>. Tout comme les pathologies prédemment citées, les maladies neurodégénératives sont aussi associées à des dérégulations du métabolisme lipidique pouvant donc se traduire par des modifications de la fluidité membranaire. Je prendrai deux exemples :

Le premier, la maladie d'Alzheimer qui est une pathologie associée à l'activité des bêta et gamma sécrétases formant des peptides β-amyloïdes. Ces peptides vont par leurs agrégations produire des plaques peptidiques qui déstabilisent l'activité des neurones. Du fait de l'activité des sécrétases au niveau de la membrane, il est justifiable de se demander si ces dernières seraient susceptibles d'être modulées par des variations de leur environnement membranaire. Il serait donc intéressant à partir du Dioll de regarder s'il existe des conditions dans lesquelles le niveau de fluidité membranaire induirait une activité pathologique de ces enzymes, d'autant plus que cette maladie est aussi associée à une modulation du métabolisme des lipides<sup>273,274</sup>.
Je terminerai avec mon second exemple qui est la maladie de Huntington. Cette pathologie est associée à une altération du métabolisme des lipides, tout particulièrement celui du cholestérol, se traduisant par sa diminution au niveau cellulaire. Ce métabolisme réduit du cholestérol couplé à son faible renouvellement au niveau du système nerveux a pour conséquence des troubles neurodégénératifs<sup>275</sup>. Dans ce contexte, des molécules analogues au cholestérol ont été utilisées comme traitement. Dans ces travaux, l'analyse de l'activité de la molécule a été effectuée par un suivi de la fluidité membranaire des mitochondries après leur purification<sup>276</sup>.

En regardant ces résultats, deux avantages ressortent à l'utilisation du Dioll :

Premièrement, dans un but du suivi de l'activité d'une molécule active, le Dioll permettrait d'effectuer des mesures *in cellulo* gardant ainsi les organelles dans un environnement cellulaire et évitant des biais lors de la purification.

Deuxièmement, sachant que le Dioll marque l'intégralité des membranes, il est tout à fait possible d'imaginer la création d'un système de « drug discovery » haut débit dans lequel le marquage des cellules au Dioll suivi de mesures au lecteur de plaque permettrait de discriminer des molécules permettant la réacquisition d'une homéostasie membranaire normale.

Mes travaux de thèses ont aussi pu mettre en lumière le marquage des gouttelettes lipidiques par le Laurdan et le c-Laurdan, mais pas par le Dioll. La conséquence du marquage des gouttelettes lipidiques dans les papiers antérieurs est la focalisation systématique des modulations de la fluidité membranaire au niveau de la membrane plasmique, laissant de côté celle des endomembranes<sup>251,252</sup>. Ce marquage des gouttelettes lipidiques peut être l'une des explications de la difficulté à effectuer des mesures répétables sur cellules entières lors de l'utilisation du Laurdan au sein de l'équipe. En effet, la quantité de gouttelettes étant fortement dépendante des variations nutritives, des changements au niveau de la culture cellulaire peuvent entraîner des variations de quantité de gouttelettes lipidiques et donc modifier le GP mesuré de la cellule d'une expérimentation à l'autre. Ainsi, la spécificité du Dioll à marquer uniquement les lipides membranaires est un avantage majeur pour éviter des biais lors de la mesure de la fluidité membranaire. Alors qu'il est assez intuitif, de par sa structure pauvre en région polaire, que le Laurdan ait la capacité de s'insérer au sein des gouttelettes lipidiques, il est beaucoup plus difficile de comprendre pourquoi le c-Laurdan se retrouve aussi au sein des gouttelettes lipidiques. L'une des explications pourrait venir de sa fonction carboxylique au niveau de sa région polaire. Il serait alors intéressant de voir si le c-laurdan par sa structure peut servir de substrat pour l'acylation des TAG et des ester de cholestérol, permettant alors son incorporation au sein des gouttelettes lipidiques.

Enfin comme je l'ai démontré lors de cette thèse, par son importante intensité de fluorescence (7 fois plus intense que le Laurdan incorporé dans les mêmes liposomes), il a été possible, gâce au Dioll de suivre la fluidité membranaire des vésicules sécrétées via l'utilisation d'un cytomètre en flux de type spectral. Ces travaux ouvrent plusieurs perspectives notamment une meilleure discrimination des microvésicules et des exosomes par leur fluidité membranaire. En
effet, ces vésicules auraient respectivement une fluidité membranaire rigide et fluide due à leur
origine, le « shedding » de la membrane plasmique et les corps multivésiculaires du système
endosomale. Le couplage du Dioll avec la technologie de trieur par cytométrie en flux spectrale
développé récemment par l'entreprise Cyteck® permettrait alors, non seulement de discriminer
les différentes vésicules extracellulaires mais également de les trier par leur niveau de fluidité
membranaire, qui semble être un marqueur de discrimination plus relevant que la taille et les
marqueurs protéiques (des expériences sont en cours en collaboration avec l'entreprise
Cytek®). Parallèlement, l'analyse protéomique de ces vésicules permettrait alors de décrire les
EV afin de comprendre l'origine de ces vésicules qui restent encore mal comprises en
comparant celles aux membranes rigides à celle aux membranes fluides. Je reviendrai sur les
EV à la fin de la discussion dans la partie création d'outils de diagnostic.

Nous venons de voir certains avantages du Dioll tout particulièrement en ce qui concerne la
mesure de la fluidité membranaire au sein des cellules vivantes, J'aimerais cependant souligner
deux limites à l'utilisation du Dioll. La première est l'impossibilité de marquer les organismes
comportant une paroi. En effet, par sa structure riche en molécules polaires, elle doit exercer
une barrière physico-chimique au Dioll, qui par sa structure apolaire empêche son incorporation
au sein des membranes lipidiques. En effet, lors de ma thèse, nous avons pu remarquer
l'incapacité du Dioll à marquer les levures, limitant ainsi l'utilisation du Dioll sur l'analyse de
cellules d'organismes de type animal multicellulaires. Enfin, la deuxième limite est sa
fluorescence dans le bleu qui ne permet pas encore l'analyse dans les tissus de la fluidité
membranaire. En effet, les basses longueurs d'onde ont un pouvoir pénétrant tissulaire très
faible en comparaison des hautes longueurs d'onde.

## II- Fluidité membranaire paramètre du suivi de la plasticité cellulaire :

Ainsi lors de cette première partie de thèse nous avons pu mettre en évidence l'intérêt d'utiliser
le Dioll afin de suivre la fluidité membranaire des cellules. Comme je l'ai souligné dans
l'introduction de ce manuscrit, quelques indices semblaient indiquer que cette propriété
biophysique puisse être associée au développement tumoral et à l'agressivité des tumeurs même
si le critère expliquant cette corrélation n'était absolument pas défini. Plus précisément, il était
bien décrit que l'anabolisme des lipides était généralement augmenté, l'oléate étant considéré
comme un acide gras particulièrement abondant dans les cellules cancéreuses permettant de
maintenir les propriétés biophysiques de leurs membranes. Ce type d'observation était
cependant difficile à concilier avec une propriété très spécifique des tumeurs puisque de façon
générale les mécanismes de prolifération pathologique, mais aussi physiologique, sont associés
avec une plus grande production de lipide. Intuitivement, le caractère mésenchymateux de
certains carcinomes constituait aussi un autre indice d'un lien entre fluidité membranaire et
agressivité des tumeurs, ce caractère étant important dans les processus de migration et
d'invasion tissulaire. En effet, le processus de migration nécessite une certaine déformabilité
cellulaire, et une plus grande fluidité membranaire pouvait sembler un élément intéressant à
considérer dans ce contexte. En réalité, cette déformabilité est majoritairement soutenue par des
mécanismes moléculaires associés au cytosquelette plus qu'à des modifications locales de
fluidité qui n'impacte que faiblement la capacité de la membrane à changer sa position ou sa courbure à l'échelle de la cellule.

Finalement, nous avons conceptualisé un nouveau lien entre la fluidité membranaire et une propriété fondamentale des cellules cancéreuses, complètement originales et de primes abords tout à fait inattendus de par l'éloignement apparent des mécanismes moléculaires impliqués dans ces deux propriétés. Très récemment, la plasticité cellulaire c'est-à-dire la capacité d'une cellule à bouleverser son identité est apparue comme une nouvelle propriété permettant de répondre à de nombreux problèmes conceptuels sur la compréhension des mécanismes du développement tumoral et les différents niveaux d'agressivité des tumeurs. Notamment, il était tout à fait déroutant d'observer que l'élimination des cellules les plus tumorigéniques ne permettait généralement pas d'éradiquer les tumeurs, cette population étant reconstituée à partir de cellules cancéreuses initialement moins agressives. La plasticité cellulaire et la dynamique phénotypique qu'elle permet sont alors apparues comme une des propriétés déterminantes de l'agressivité tumorale. Malheureusement, cette propriété, essentiellement basée sur des mécanismes épigénétiques, est difficile à mesurer et ne constituerait pas a priori un élément pouvant être mis en lumière par la fluidité des membranes. Quelques éléments, que j'ai indiqués dans l'introduction, nous ont cependant guidés dans cette direction et encouragés à explorer ce lien. Pour rappel, la sensibilité à la ferroptose et quelques modulations du métabolisme des PUFA et du cholestérol dans des cellules cancéreuses à l'identité particulièrement altérée étaient des éléments intéressants qui pouvaient apparaître comme des indices de corrélation entre ces dérégulations épigénétiques et une plus grande fluidité des membranes.

En suivant cette piste, nous avons pu établir des preuves de concept assez solide du lien existant entre ces deux paramètres, excluant notamment le rôle du caractère mésenchymateux. L'analyse de la sensibilité de la ferroptose dans différentes lignées cancéreuses mammaires se voyait beaucoup plus marquée au sein des lignées Basal-like, que celles Luminal et Claudin-low. Cette analyse est intéressante, car les cellules Basal-like proviennent des tumeurs les plus agressives avec pour origine des cellules ayant subies des mécanismes de dédifférenciations induisant une altération profonde de leur identité cellulaire. Remarquablement, cette sensibilité à la ferroptose était associée à une fluidité membranaire plus importante au sein des lignées Basal-like. À l'inverse, les lignées Luminal qui elles ont gardé leur identité cellulaire d'origine, étaient moins sensibles à la ferroptose et arboraient une fluidité membranaire moindre. De façon étonnante, les lignées Claudin-low, qui a pour origine des progéniteurs très précoces, possédaient une sensibilité faible à la ferroptose corrélée à une faible fluidité membranaire. Comme je l'ai expliqué dans la discussion des résultats, la plasticité cellulaire élevée de la cellule d'origine de ce type de tumeur pourrait faire intervenir un mécanisme très transitoire permettant alors à la cellule d'acquérir rapidement une nouvelle identité cellulaire. Dans un second temps, nous avons généré un modèle *in vitro* dans lequel nous pouvions moduler l'identité cellulaire des cellules HMEC jusqu'à l'obtention de cellules aux caractéristiques progéniteurs bipotents et maintenues dans un état métastable. Ces cellules avaient la faculté de rentrer dans une voie de différenciation luminale et myoépithéliale et de façon contrôlée grâce à la régulation de l'environnement hormono-nutritif. Ce modèle dans lequel il était possible de faire varier le niveau de plasticité cellulaire nous a permis de démontrer que l'augmentation de
la fluidité membranaire était une nouvelle fois associée à l'acquisition d'une plus grande plasticité cellulaire. À l'inverse l'entrée de ces cellules dans un programme de différenciation était systématiquement corrélée à une perte de fluidité membranaire. De plus, comme nous allons le voir, la signature du transcriptome des cellules métastables est fortement liée à celle des cellules cancéreuses dynamiquement dédifférenciées décrites dans la littérature. Ces résultats qui portent en grande partie sur des modèles in vitro indiquent que la mesure de la fluidité membranaire est en adéquation avec les mécanismes que j'ai décrits dans l'introduction. Ainsi, l'acquisition d'un phénotype de types cellule cancéreuse dynamiquement dédifférenciée est associée à une augmentation de la fluidité des membranes. Cependant, comme indiqué dans la discussion des résultats n°2, nous n'avons pas encore effectué de liens entre fluidité membranaire et expérimentation in vivo. Il est donc à partir de notre modèle in vitro difficile de dissocier la fluidité membranaire de nos cellules plastiques telles que les métastables et les conditions de cultures, notamment la nécessité d'un enrichissement du milieu en PUFA pour l'acquisition et le maintien de la plasticité cellulaire. Sans revenir sur l'ensemble de ce qui a déjà été dit, les futures approches in vivo au sein de l'équipe permettront de valider s'il y a bien une corrélation entre fluidité membranaire et plasticité cellulaire et si dans notre modèle les cellules métastables acquièrent une capacité tumorigénique avec un fort potentiel d'adaptation tissulaire.

À partir de ces résultats, essentiellement corrélatifs, j'aimerais maintenant mettre en perspectives les données que nous avons obtenues avec des éléments de la littérature permettant de proposer une intégration mécanistique du contrôle de la fluidité des membranes et des processus associés à une plus grande plasticité épigénétique telle que celle retrouvée dans les cellules cancéreuses les plus agressives. Cet effort constituera d'autre part les bases conceptuelles permettant d'établir de nouvelles stratégies expérimentales nécessaires à la poursuite de ce projet et à la démonstration définitive du lien entre fluidité membranaire et plasticité cellulaire.

## Métabolisme, réponse antioxydante et stress du RE au cœur de l'acquisition d'un phénotype plastique :

Je commencerai en première partie par investiguer le rôle probablement essentiel de NRF2 à l'acquisition d'un phénotype plastique, que ce soit dans notre modèle ou dans les cellules CCDD. Nous verrons dans un second temps les liens existant entre l'activation de NRF2, la plasticité et l'induction d'une réponse au stress du réticulum endoplasmique. Je m'attarderai ensuite sur les modulations nutritives potentiellement impliquées dans l'acquisition de ces phénotypes, aussi bien dans nos conditions expérimentales que dans un processus de développement tumoral *in situ*. Enfin j'apporterai des éléments sur les liens entre les perturbations métaboliques associés à la plasticité cellulaire des cellules cancéreuses et les mécanismes de contrôle de la fluidité membranaire. Je terminerai par une hypothèse tout à fait conceptuelle sur une raison thermodynamique permettant de comprendre les racines profondes du lien fluidité membranaire-plasticité cellulaire.