Specific modifications of colchicine followed by synthesis of its analogues have been tested in vitro with the objective of lowering colchicine toxicity. Our previous studies have clearly shown the anticancer potential of double-modified colchicine derivatives in C-7 and C-10 positions. Here, a series of novel triple-modified colchicine derivatives is reported. They have been obtained following a four-step strategy. In vitro cytotoxicity of these compounds has been evaluated against four human tumor cell lines . Additionally, the mode of binding of the synthesized compounds was evaluated in silico using molecular docking to a 3D structure of β-tubulin based on crystallographic data from the Protein Data Bank and homology methodology. Binding free energy estimates, binding poses, and MlogP values of the compounds were obtained. All triple-modified colchicine derivatives were shown to be active at nanomolar concentrations against three of the investigated cancer cell lines . Four of them also showed higher potency against tumor cells over normal cells as confirmed by their high selectivity index values. A vast majority of the synthesized derivatives exhibited several times higher cytotoxicity than colchicine, doxorubicin, and cisplatin. In our previous studies, we prepared double-modified, in the C-7 and C-10 positions, colchicine derivatives and evaluated their cytotoxicity + calcd. 500, found 500, [M + 2 + Na]+ calcd. 502, found 502, [2M + 2 + Na]+ calcd. 979, found 979, [2M + Na]+ calcd. 977, found 977, [2M + 4 + Na]+ calcd. 981, found 981.A mixture of N-bromosuccinimide  and 2  in MeOH/water , the sodium methanethiolate  was added. The mixture was stirred in at RT for 72 h. Reaction time was determined by TLC. After that time the reaction mixture was quenched by the addition of water (150 mL). The whole mixture was extracted four times with CH2Cl2, and the combined organic layers were dried over MgSO4, filtered, and evaporated under reduced pressure. The residue was purified by CombiFlash® (hexane/EtOAc (1/1), then EtOAc/MeOH, increasing concentration gradient) to give 3  δ 7.68 , 7.42 , 7.26 , 7.08 , 4.61–4.52 , 3.99 , 3.97 , 3.63 , 3.27 , 2.45 , 2.25 , 2.01 , 1.85  ppm. 13C-NMR  δ 182.4, 170.0, 159.2, 151.2, 151.0, 150.4, 146.6, 137.4, 134.8, 133.4, 130.2, 128.1, 126.3, 113.5, 61.6, 61.5, 61.0, 52.2, 34.5, 29.0, 22.9, 15.2 ppm. FT-IR (KBr pellet): 3267, 2930, 1659, 1603, 1559, 1462, 1410, 1347, 1138, 1074, 1053, 1014 cm−1. ESI-MS (m/z): [M + H]+ calcd 494, found 494, [M + 2 + H]+ 496, found 496, [M + Na]+ calcd 516, found 516, [M + 2 + Na]+ calcd 518, found 518, [2M + H]+ calcd 989, found 989, [2M + 2 + H]+ calcd 991, found 991, [2M + Na]+ calcd 1011, found 1011, [2M + 2 + Na]+ calcd 1013, found 1013.To a mixture of 4  in MeOH (3 mL), the 2 N HCl solution (5 mL) was added. The mixture was stirred at 90 °C for 72 h. Reaction time was determined by TLC. After that time the reaction mixture was quenched by the addition of water (100 mL). The whole mixture was extracted four times with CH2Cl2, and the combined organic layers were dried over MgSO4, filtered, and evaporated under reduced pressure. The residue was purified by CombiFlash®  to give 4 (MW = 452.4 g/mol) as amorphous brownish solid with yield 80% (366 mg) + calcd. 474, found 474, [M + 2 + Na]+ calcd. 476, found 476.Compound 4  was prep(366 mg) . 1H-NMR 5–12 were obtained directly from compound 4. To a solution of compound 4  in tetrahydrofuran  cooled to the 0 °C temperature, the following compounds were added: Et3N , and triphosgene . The mixture was first stirred at 0 °C temperature for 20 min and then for the next 20 min at RT. After that time respective alcohol (11 mmol) was added and the mixture was stirred at RT for the next 48 h. Reaction time was determined by TLC. The solution was filtered to remove triethylamine hydrochloride. The THF was evaporated and the residue was quenched by the addition of CH2Cl2 (100 mL) and was washed sequentially with a solution of HCl(aq) (0.5 M) and then with water. The organic layer was evaporated to dryness under reduced pressure and purified by CombiFlash®  to give respective compounds as amorphous yellow solids (5–12).Compounds 1H-NMR  δ 7.43 , 7.23 , 7.07 , 6.55 , 4.38–4.27 , 4.05 , 3.96 , 3.95 , 3.67–3.63 , 3.59 , 3.29–3.22 , 2.45 , 2.33–2.21 , 1.83  ppm. 13C-NMR  δ 182.4, 159.2, 156.0, 151.2, 150.6, 150.3, 146.5, 137.0, 134.7, 133.5, 130.0, 128.5, 126.2, 113.5, 66.9, 61.4, 61.4, 61.1, 61.0, 53.7, 34.9, 29.0, 15.1 ppm. FT-IR (KBr pellet): 3295, 2936, 1719, 1607, 1547, 1463, 1410, 1348, 1324, 1288, 1249, 1154, 1141, 1083, 1062, 1020 cm−1. ESI-MS (m/z): [M + H]+ calcd. 540, found 540, [M + 2 + H]+ calcd. 542, found 542, [M + Na]+ calcd. 562, found 562, [M + 2 + Na]+ calcd. 564, found 564.Amorphous yellowish brown solid, yield 55 mg, 46%, MW = 539.1 g/mol . 1H-NMR 1H-NMR  δ 7.32 , 7.20 , 7.03 , 5.60 , 4.37–4.27 , 3.97 , 3.95 , 3.60 , 3.59 , 3.25 , 2.43 , 2.25 , 1.76–1.66  ppm. 13C-NMR  δ 182.3, 159.3, 156.0, 151.2, 150.4, 149.9, 146.6, 136.6, 134.5, 133.4, 130.1, 128.5, 125.8, 113.5, 61.5, 61.4, 61.0, 53.6, 52.3, 35.2, 29.0, 15.2 ppm. FT-IR (KBr pellet): 3297, 2932, 1725, 1608, 1551, 1463, 1410, 1348, 1323, 1289, 1248, 1197, 1153, 1020 cm−1. ESI-MS (m/z): [M + H]+ calcd. for 510, found 510, [M + 2 + H]+ calcd. 512, found 512, [M + Na]+ calcd. 534, found 534, [M + 2 + Na]+ calcd. 536, found 536.Amorphous yellowish brown solid, yield 47 mg, 42%, MW = 509.1 g/mol . 1H-NMR 1H-NMR  δ 7.32 , 7.20 , 7.03 , 5.35 , 4.37–4.28 , 4.02 , 3.98 , 3.96 , 3.94–3.90 , 3.61 , 3.27 , 2.44 , 2.30–2.24 , 1.72–1.65 , 1.19  ppm. 13C-NMR  δ 182.3, 159.2, 155.5, 151.1, 150.4, 149.9, 146.6, 136.6, 134.5, 133.4, 130.09, 128.5, 125.8, 113.5, 61.5, 61.4, 61.2, 61.0, 53.4, 35.3, 29.0, 15.2, 14.4 ppm. FT-IR (KBr pellet): 3303, 2935, 1716, 1608, 1550, 1460, 1406, 1346, 1322, 1248, 1150, 1084, 1020 cm−1. ESI-MS (m/z): [M + H]+ calcd. for 524, found 524, [M + 2 + H]+ calcd. 526, found 526, [M + Na]+ calcd. 546, found 546, [M + 2 + Na]+ calcd. 548, found 548.Amorphous yellowish brown solid, yield 35 mg, 30%, MW = 524.4 g/mol . 1H-NMR 1H-NMR  δ 7.37 , 7.22 , 7.06 , 6.18 , 4.48 , 4.38–4.29 , 4.17–4.06 , 3.98 , 3.96 , 3.59 , 3.29 , 2.44 , 2.38–2.23 , 1.82  ppm. 13C-NMR  δ 182.3, 159.6, 153.5, 151.3, 150.4, 149.4, 146.7, 136.5, 134.8, 133.3, 129.9, 128.4, 126.0, 124.2, 121.4, 113.6, 61.5, 61.2, 61.2, 61.0, 60.8, 54.0, 35.1, 28.9, 15.2 ppm. 19F-NMR  δ −74.8 ppm. FT-IR (KBr pellet): 3221, 2938, 1735, 1610, 1543, 1464, 1411, 1349, 1325, 1283, 1244, 1161, 1100, 1081, 1021 cm−1. ESI-MS (m/z): [M + H]+ calcd. for 578, found 578, [M + 2 + H]+ calcd. 580, found 580, [M + Na]+ calcd. 600, found 600, [M + 2 + Na]+ calcd. 602, found 602.Amorphous yellowish solid, yield 49 mg, 38%, MW = 578.4 g/mol . 1H-NMR 1H-NMR  δ 7.33 , 7.21 , 7.04 , 5.39 , 4.33 , 3.98 , 3.96 , 3.95–3.90 , 3.61 , 3.27 , 2.44 , 2.27 , 1.69 , 1.63–1.52 , 0.89  ppm. 13C-NMR  δ 182.3, 159.2, 155.6, 151.1, 150.4, 149.9, 146.6, 136.6, 134.5, 133.4, 130.1, 128.5, 125.8, 113.5, 66.9, 61.5, 61.4, 61.0, 53.4, 35.3, 29.0, 22.1, 15.2, 10.2 ppm. FT-IR (KBr pellet): 3300, 2937, 1717, 1608, 1549, 1463, 1410, 1348, 1324, 1288, 1245, 1153, 1083, 1020 cm−1. ESI-MS (m/z): [M + Na]+ calcd. 560, found 560, [M + 2 + Na]+ calcd. 562, found 562.Amorphous yellowish brown solid, yield 57 mg, 48%, MW = 538.5 g/mol . 1H-NMR 1H-NMR  δ 7.29 , 7.19 , 7.02 , 5.15 , 4.77 , 4.36–4.26 , 3.97 , 3.95 , 3.60 , 3.29–3.23 , 2.43 , 2.25 , 1.71–1.62 , 1.16  ppm. 13C-NMR  δ 182.4, 159.2, 155.0, 151.1, 150.4, 149.9, 146.6, 136.6, 134.5, 133.4, 130.1, 128.5, 125.8, 113.5, 68.7, 61.5, 61.4, 61.0, 53.3, 35.4, 29.0 22.1, 22.1, 15.2. FT-IR (KBr pellet): 3328, 2936, 1715, 1609, 1550, 1464, 1411, 1348, 1323, 1286, 1245, 1153, 1111, 1084, 1021 cm−1. ESI-MS (m/z): [M + Na]+ calcd. 560, found 560, [M + 2 + Na]+ calcd. 562, found 562.Amorphous yellowish brown solid, yield 50 mg, 42%. MW = 538.5 g/mol . 1H-NMR 1H-NMR  δ 7.33 , 7.21 , 7.04 , 5.40 , 4.38–4.27 , 3.98 , 3.96 , 3.94–3.90 , 3.61 , 3.27 , 2.44 , 2.27 , 1.71 , 1.56 , 1.32 , 0.93–0.86  ppm. 13C-NMR  δ 182.3, 159.2, 155.6, 151.1, 150.4, 149.9, 146.6, 136.6, 134.5, 133.4, 130.1, 128.5, 125.8, 113.5, 65.1, 61.5, 61.4, 61.0, 53.4, 35.3, 30.8, 28.9, 19.0, 15.2, 13.7 ppm. FT-IR (KBr pellet): 3288, 2935, 1717, 1608, 1548, 1463, 1410, 1347, 1325, 1246, 1154, 1083, 1020 cm−1. ESI-MS (m/z): [M + H]+ calcd. for 552, found 552, [M + 2 + H]+ calcd. 554, found 554, [M + Na]+ calcd. 574, found 574, [M + 2 + Na]+ calcd. 576, found 576.Amorphous brownish solid, yield 54 mg, 44%. MW = 551.1 g/mol . 1H-NMR 1H-NMR  δ 7.36 , 7.22 , 7.06 , 5.97 , 4.35–4.20 , 4.13–4.06 , 3.98 , 3.96 , 3.78–3.70 , 3.65 , 3.62–3.57 , 3.27 , 2.44 , 2.27 , 1.80–1.70  ppm. 13C-NMR  δ 182.4, 159.3, 155.4, 151.2, 150.4, 150.2, 146.6, 136.8, 134.7, 133.4, 130.0, 128.3, 126.1, 113.5, 72.4, 69.1, 64.2, 61.5, 61.4, 61.0, 53.7, 34.9, 28.9, 15.2 ppm. FT-IR (KBr pellet): 3285, 2936, 1718, 1607, 1546, 1463, 1410, 1348, 1324, 1249, 1137, 1081, 1020 cm−1. ESI-MS (m/z): [M + Na]+ calcd. 606, found 606, [M + 2 + Na]+ calcd. 608, found 608.Amorphous orangish solid, yield 54 mg, 42%. MW = 584.5 g/mol . 1H-NMR 2–12): human lung adenocarcinoma (A549), human breast adenocarcinoma (MCF-7), human colon adenocarcinoma cell lines sensitive and resistant to doxorubicin (LoVo) and (LoVo/DX) respectively, and also normal murine embryonic fibroblast cell line (BALB/3T3). The BALB/3T3 cell line was purchased from the American Type Culture Collection , A549 and MCF-7 cell lines—from European Collection of Authenticated Cell Cultures . The LoVo cell line was purchased from the American Type Culture Collection , and LoVo/DX by courtesy of Prof. E. Borowski . All the cell lines are maintained in the Institute of Immunology and Experimental Therapy (IIET), Wroclaw, Poland. The human lung adenocarcinoma cell line was cultured in a mixture of OptiMEM and RPMI 1640 (1:1) medium , supplemented with 5% foetal bovine serum  and 2 mM l-glutamine . Human breast adenocarcinoma cell line was cultured in mixture of Eagle’s medium , supplemented with 10% fetal bovine serum, 2 mM l-glutamine, 8 μg/mL insulin, and 1% amino-acids . Human colon adenocarcinoma cell lines were cultured in mixture of OptiMEM and RPMI 1640 (1:1) medium , supplemented with 5% fetal bovine serum , 2 mM l-glutamine, 1 mM sodium pyruvate  and 10 μg/100 mL doxorubicin for LoVo/DX . Murine embryonic fibroblast cells were cultured in Dulbecco medium , supplemented with 10% foetal bovine serum  and 2 mM glutamine . All culture media contained antibiotics: 100 U/mL penicillin and 100 μg/mL streptomycin . All cell lines were cultured during the entire experiment in a humid atmosphere at 37 °C and 5% CO2. Cells were tested for mycoplasma contamination by mycoplasma detection kit for conventional PCR: Venor GeM Classic  and negative results was obtained. The procedure was repeated every year or in the case of less frequently used lines: after thawing.Four human cancer cell lines and one murine normal cell line were used to evaluate antiproliferative activity of colchicine and its derivatives  and doxorubicin . Additionally, all cell lines were exposed to DMSO (solvent used for tested compounds)  at concentrations corresponding to these present in tested agents’ dilutions. After 72 h sulforhodamine B assay (SRB) was performed [Twenty-four h before adding the tested compounds, all cell lines were seeded in 96-well plates  in appropriate media with 10erformed .After 72 h of incubation with the tested compounds, cells were fixed in situ by gently adding 50 μL per well of cold 50% trichloroacetic acid TCA  and were incubated at 4 °C for one hour. Following, wells were washed four times with water and air dried. Next, 50 μL of 0.1% solution of sulforhodamine B  in 1% acetic acid  were added to each well and plates were incubated at room temperature for 0.5 h. After incubation time, unbound dye was removed by washing plates four times with 1% acetic acid whereas stain bound to cells was solubilized with 10 mM Tris base . Absorbance of each solution was read at Synergy H4 Hybrid Multi-Mode Microplate Reader  at the 540 nm wavelength.50  ± standard deviation. IC50 values were calculated in Cheburator 0.4, Dmitry Nevozhay software  for each experiment [50 for a given compound calculated for resistant cell line to that measured for its parental drug sensitive cell line , we obtained its sequence from UniProt (ID: Q13509). We used the tubulin structure 1SA0.pdb as a template to construct the homology model for βІ tubulin using MOE2015. We then docked the colchicine library to the protein using the Autodock4 program  under fl5–12). As shown in 2), 4-bromothiocolchicine (3), and 4-bromo-N-deacetylthiocolchicine (4) were prepared according to previously described methods [4 became the starting material for the synthesis of triple-modified colchicine derivatives. Compounds 5–12 were readily available from 4 by treatment with triphosgene as an activating reagent in the presence of triethylamine and the excess of respective alcohol or glycol in dry THF with yields from 30% to 48%. The structures of all products 2–12 were determined using the ESI-MS, FT-IR, 1H and 13C-NMR methods. Spectra are shown in In the present paper, we report the synthesis and spectroscopic analysis of a series of eight novel triple-modified colchicine derivatives , shifts of the signal for the carbamate carbon atom were observed in the range 153.5–156.0 ppm.In the 5–12), three other colchicine derivatives (2–4), and starting material (1) were evaluated for their in vitro antiproliferative effect on normal and cancerous cells. Each compound was tested on four human cancer cell lines, including one cell line displaying various levels of drug resistance, namely human lung adenocarcinoma (A549), human breast adenocarcinoma (MCF-7), human colon adenocarcinoma cell line (LoVo) and doxorubicin-resistant subline (LoVo/DX). The antiproliferative effect was also studied on the normal murine embryonic fibroblast cell line (BALB/3T3) for better evaluation of cytotoxic activity of the compounds studied. The mean values IC50 ± SD of the tested compounds are summarized in The eight triple-modified colchicine derivatives (2–12) showed higher antiproliferative activity against the A549 cell line than the commonly used cytostatic agents—doxorubicin (IC50 = 0.258 μM) and cisplatin (IC50 = 6.367 μM), as well as 1 itself (IC50 = 0.125 μM). The triple-modified colchicine derivatives (5–12) exhibited very significant activity with IC50 values ranging from 0.010 to 0.095 μM. As many as five of the compounds tested, including three of the triple-modified derivatives , exhibited better activity against the MCF-7 cell line than 1 (IC50 = 0.054 μM). All of the colchicine derivatives (2–12) as well as 1 showed higher antiproliferative activity against the MCF-7 cell line than doxorubicin (IC50 = 0.386 μM) and cisplatin (IC50 = 10.70 μM). As many as nine of the colchicine derivatives (except 4 and 12) showed better activity against the LoVo cell line, with IC50 values in the range of 0.007–0.085 μM, than 1 (IC50 = 0.108 μM) and doxorubicin (IC50 = 0.092 μM). All of the colchicine derivatives (2–12) as well as 1 showed higher antiproliferative activity against the LoVo cell line than cisplatin (IC50 = 4.37 μM).All of the colchicine derivatives (2–12) with IC50 values in the range of 0.050–1.550 μM as well as 1 (IC50 = 1.69 μM) showed higher antiproliferative activity against the LoVo/DX cell line than doxorubicin (IC50 = 4.75 μM) and cisplatin (IC50 = 5.70 μM). The calculated values of RI clearly confirmed that only compounds 10 and 11  are able to efficiently overcome the drug resistance of the LoVo/DX cell line simultaneously showing very high antiproliferative activity .The data presented in 6–9 (except SI of 8 for the MCF-7 cell line). These values were much higher than the SI values of commonly used drugs, such as doxorubicin and cisplatin. High SI values result from large differences between the cytotoxicity against cancer and normal cells and this means that cancer cells will be killed at a higher rate than normal ones. Moreover, compounds 6–9 showed very high antiproliferative activity against A549, MCF-7, and LoVo cell lines, which is expressed by very low IC50 values , and 0.007–0.018 for A549, MCF-7, and LoVo, respectively).The values of the selectivity index (SI) were calculated to evaluate the toxicity of compounds against normal cells. The SI values calculated for A549, MCF-7, and LoVo cell lines were especially high (SI ≥ 4.3) for compounds βІ tubulin, one of the subunits of microtubules in the cytoskeleton of eukaryotic cells, is a well-known and well-studied target for chemotherapeutic drugs selected to inhibit the growth and proliferation of cancer cells. The computational evaluation of binding energies between drug candidates and βІ tubulin performed by docking is a fast and inexpensive prediction method to investigate and rank the ability to arrest cancer cell proliferation by new compounds, which inhibit microtubule assembly. Here, the binding energies of new colchicine derivatives were calculated by docking the referred compounds to the colchicine-binding pocket of βІ tubulin.A combination of different computational methods was used to explore ligand-tubulin interactions. The ligand structures were first energy-minimized, then fully optimized based on the RHF/cc-pVDZ level of theory implemented in the software package GAMESS-US, version 2010-10-01. Since there is no crystal structure for human βІ tubulin (UniProt ID: P07437) available in the Protein Data Bank (PDB), the bovine tubulin structure 1SA0.pdb was used as a template to construct the homology model for human βІ tubulin using the software package MOE2015. Autodock4 program under flexible ligand and rigid receptor conditions was also used to dock the small library of investigated colchicine derivatives to the target protein structure see . AutoDoc11, 7, 6, and 3 show the strongest binding energies of −9.25, −9.20, −8.99, and −8.60 kcal/mol, respectively. Note that two Met 259 and Lys 352 residues in the colchicine binding site of βІ tubulin interact with C-20 (sidechain donor) and oxygen of carbonyl (sidechain acceptor) on ring C of the new colchicine derivatives, respectively. The diagrams depicting these interactions can be found in Based on our in silico calculations, 50 values in a cell-based assay involves interactions of the tested compounds with all tubulin expressed by the cell and βІ tubulin isotype is not the only isotype of tubulin expressed, although it is expected to be one of the most abundant ones. Even though both normal and cancer cells in humans contain the same tubulin isotypes, their expression levels and distribution of tubulin isotypes in each of the cell lines are different. In particular, the most abundant isotype in most tumors is βІ isotype (TUBB) followed by, βІVb (TUBB4B), βІIa (TUBB2A), βV (TUBB6), and βІII (TUBB3), with 47%, 38%, 8.9%, 3.1%, and 2.2%, respectively, and then βІVa (TUBB4), βІIb (TUBB2B), and βVI (TUBB1) with levels below 0.5% of the total expressed β tubulins. Since The expression levels of tubulin isotypes in each individual cell lines are unique, the binding energies for each of these isotypes would differ and affect the overall response to cytotoxic agents and make the computational prediction fairly complex [The evaluation of IC complex . To quan50 and computational prediction for different β tubulin isotypes. To have a more realistic correlation coefficient, a logarithmic value of solubility of the compounds, the Moriguchi octanol-water partition coefficients (MLogP), were calculated using a software package called ADMET Predictor 8.0  and taken into account for predicting the biopharmaceutic properties like permeability and the understanding of transport mechanisms of the drugs in vivo [Following the docking simulations, in order to determine the level of correlation between experiment and computational simulations, we have calculated the linear regression coefficients between experimental values of IC in vivo . The par50 for LoVo and LoVo/DX cell lines, respectively, was the result of linear regression analysis with two independent variables, namely the compounds’ MlogP values and the other being the binding free energies of our compounds and the tubulin βІ isotype. These two cell lines have also a good correlation of 0.72 and 0.80 with the binding free energies of the compounds to the βІVb isotype, respectively. MlogP and the experimental IC50 values of 4-bromocolchicine based series are listed in A very good correlation of 0.66 and 0.84 involving log IC50 values, more accurate but also more time-consuming computational methods were applied to calculate the binding free energies between the novel colchicine derivatives and β tubulin isotypes. This analysis is described below.To further investigate the accuracy of the reported linear regressions between our computational results obtained using the docking method and the experimentally generated ICTUBA1A (UniProt ID: Q71U36) was chosen as a reference sequence for human α-tubulin whereas gene TUBB associated to βI isoform (UniProt ID: P07437) was chosen for human β-tubulin. Homology modeling was performed using MOE by setting the number of generated models to 10 and by selecting the final model based on MOE’s generalized born/volume integral (GB/VI) scoring function. As a template, the crystallographic structure of α-βIIB tubulin isotype complexed with colchicine (PDB ID: 1SA0) was used [The molecular operating environment (MOE) was used to build a 3D model for the α-βI tubulin heterodimer . Human twas used . During In order to equilibrate our model and get representative conformations, molecular dynamics (MDs) simulations were run using Amber14 . Amber’sClustering analysis of the last 30 ns of the generated MD trajectory was carried out using Amber’s CPPTRAJ program  to identDocking of 4-bromocolchicine derivatives was performed using the AutoDock Vina  program,The MM/PBSA technique was used to calculate the free energy associated with the binding of 4-bromocolchicine derivatives ,39. Thiseference . For eac50 for the LoVo and LoVo/DX cell lines is that very good correlation coefficients were found, namely 0.78 and 0.67, respectively. Also, reasonably good correlation of 0.65 and 0.54 was obtained with the binding free energies of βІ isotype for these two cell lines, respectively. These correlation coefficients are also consistent with the correlation coefficients calculated using the docking binding energies and these cell lines except for LoVo/DX and βІ which were lower but still acceptable. For the A549 cell line, the regression coefficients found were 0.62, 0.58, and 0.48 for the binding free energies with βІII, βІVb, and βІ isotypes, respectively. The reported regression coefficient for the A549 cell line was improved a little bit by using the more expensive method but the MM/PBSA method could not improve the correlation coefficient values for the MCF-7 and BALB/3T3 cell lines and they are still very low  as well as 4-bromocolchicine (2), 4-bromothiocolchine (3) and 4-bromo-N-deacetylthiocolcicine (4) and evaluated their biological activity according to the in vitro antiproliferative tests as well as in silico studies. Biological activity was evaluated on four human cancer cell lines and a normal murine embryonic fibroblast cell line. The results of our study have clearly showed that the cytotoxicity of almost all colchicine derivatives (2–12) is higher than the corresponding cytotoxicity of commonly used cytostatic agents—doxorubicin and cisplatin against A549, MCF-7, LoVo (except 4 and 12) and LoVo/DX cancer cell lines. The majority of the derivatives also exhibit a higher cytotoxicity than unmodified colchicine. Particularly noteworthy are the compounds 6–9, which show very high antiproliferative activity against A549, MCF-7 and LoVo cell lines, that is expressed by very low IC50 values at nanomolar concetrations  and 0.007–0.018 μM for A549, MCF-7 and LoVo, respectively). Moreover, compounds 6–9 were demonstrated to be less toxic to normal murine fibroblast cells than the currently used anticancer drugs, such as cisplatin and doxorubicin, which is confirmed by particularly high selectivity index (SI) values calculated for A549, MCF-7, and LoVo cell lines are . The toxicity is a major challenge in designing a potential colchicine-based drug candidate. High SI values result from large differences between the cytotoxicity against cancer and normal cells and this means that cancer cells exposed to the same concentration of the compound will be killed at a higher rate than normal ones, which might lead to a potential colchicine-based drug candidate.We synthesized a series of novel triple-modified 4-bromothiocolchicine derivatives  and oxygen of carbonyl (sidechain acceptor) on ring C of the new colchicine derivatives, respectively. In order to determine the level of correlation between experiment and computational simulations, we have calculated the linear regression coefficients between IC50 values and the binding free energies involving the compounds and tubulin isotypes as well as the MlogP coefficients for these compounds. A very good correlation of 0.66 and 0.84 with log IC50 for LoVo and LoVo/DX cell lines, respectively, has been found. For the A549 cell line, the regression coefficient found is 0.43, still acceptable, while its value for the MCF-7 and BALB/3T3 cell lines is very low. This may be explained by a number of additional effects taking place in living cells compared to the computational simulations that focus only on the binding mode of the compounds to the target. Specifically, off-target interactions involving efflux pumps with different affinities for the individual compounds may explain the observed partial correlation between IC50 values and binding free energies. Additionally, differences in the solubility values and membrane permeability may have to be accounted for when ranking the various compounds in biological assays and comparing them to computational predictions based on binding affinity alone. We have additionally supported these findings with calculations using two very accurate methods of calculating the binding energies of ligands to proteins, namely MM/PBSA and MM/GBSA. The results using all three in silico approaches have been found consistent.Based on our in silico calculations, In short, these results confirm that a suitable chemical modification of colchicine aimed for an improved binding affinity to human tubulin ßI or ßIVb isotypes is a promising approach to finding highly biologically active and less toxic compounds. Some of the obtained compounds are suitable candidates for further tests . The synthesis of new colchicine derivatives with diverse substituents is also a next step in developing structure-activity relationship (SAR) of colchicine-binding site inhibitors.


The structures of the new steroids were established mainly on the basis of extensive analysis of 1D and 2D NMR as well as HRESIMS data. Moreover, the absolute configurations of 1 were confirmed unambiguously by the single-crystal X-ray crystallography. Compounds 2 and 4–7 showed moderate antiproliferative effects selectively against 12 different cancer cell lines with IC50 values of around 5 μM. Compounds 2 and 6, potent RXRα binders with Kd values of 13.8 and 12.9 μM, respectively, could induce apoptosis by a Retinoid X Receptor (RXR)-α-dependent mechanism by regulating RXRα transcriptional expression and promoting the poly-ADP-ribose polymerase (PARP) cleavage. Moreover, they could inhibit proliferation by cell cycle arrest at the G0/G1 phase.Five new ergostanes, penicisteroids D−H ( RXRα), one of the most promising members of the nuclear receptor superfamily, plays an important role for the treatment of cancer, metabolic, and neurodegenerative diseases , three methyl singlets , three olefinic protons , and two oxymethine . The 13C and DEPT (Distortionless Enhancement by Polarization Transfer) spectra , and four non-protonated carbons (two sp2 and two sp3). Altogether, the 1D NMR spectroscopic data ; δC 172.3 , 21.7 ] and a 28-carbon skeleton. Since four olefinic carbons and one carboxyl moiety accounted for three unsaturation degrees, 1 should be a tetracyclic molecule. The assumption, along with the presence of four methyl doublets and two methyl singlets, suggested a steroid skeleton for 1.Compound spectrum  exhibite spectra  revealedpic data  indicateδH 3.38 m) via H-4 at δH (2.20 m) to the allyl proton H-6 , and subsequently to H-7 /H-8 (δH 1.48 m)/H-14 (δH 1.0 m)/H-15 (δH 2.30 m and 1.02 m)/H-16 /H-17 (δH 1.29 m)/H-20 (δH 2.53 m)/H-22 (δH 5.19 m), from H-3 via H-2 (δH 1.45 m) to H-1 , from H-8 via H-9 (δH 0.96 m) to H-11 (δH 1.57 m)/H-12 , and from H-20 to Me-21 . In addition, another fragment could be deduced by the COSY correlations from two methyl doublets of Me-26 and Me-27 via H-25 (δH 1.40 m) to H-24 (δH 1.76 m), while H-24 to H-23 (δH 5.19 m) and Me-28 (sp2 methine protons were overlapped at δH 5.19 (m), the above deduced two fragmentations could not be connected according to the COSY spectrum. However, it could be easily resolved by the HMBC (Heteronuclear Multiple-bond Correlation) cross peaks of H-20, H-24, Me-21, and Me-28  spectrum , a long nd Me-28 . Since tnd Me-28 . Further1 was established according to the coupling constants and NOESY (Nuclear Overhauser Effect) spectrum (δH 1.86)/H-2a (δH 1.45)/H-8 (δH 1.48), Me-18 to H-8/H-12a (δH 2.04), while H-3 to H-1b (δH 1.08)/H-2b (δH 1.77) (δH 0.96 m)/H-14 (δH 1.00 m)/H-15a (δH 1.02 m) and H-12a (δH 1.28 m)/H-17 (δH 1.29 m) make it impossible to establish its relative configuration. Fortunately, an orthorhombic crystal was obtained from MeOH. By the single X-ray diffraction analysis using Cu–Kα radiation . Howeverradiation, the absiation was indicative of its axial α-orientation and the small coupling constants of JH9,H11;H11,H12  pointed to its axial-orientation. This was confirmed by the NOESY correlations of H-9 to H-7/H-11, H-7 to H-14, and H-8 to Me-18/Me-19  via H-7  to H-8 (δH 1.94 m). By detailed analysis of the HSQC, COSY, HMBC, and NOESY  at the C-11 position in 3 was replaced by the methylene (δC 22.2) in 4, suggesting the absence of the hydroxyl group at C-11. This assumption was evidenced by the significant upshift of C-12 from δC 50.2 to δC 41.3 and C-9 from δC 48.4 to δC 45.4. Further confirmation was obtained by the COSY correlations  to H-11 (δH 1.42 m). Accordingly, 4 was defined as 16β-acetoxy-3β,5α,6β,7β-tetrahydroxyergost-22E-ene, and named penicisteroid G.The molecular formula of pic data  of 4 werelations  of H-12 5 was isolated as a white powder. The molecular formula of C30H50O5 was assigned according to the sodium adduct ion peak at m/z 513.3570 in its HRESIMS spectrum. Comparison of the 1H and 13C NMR spectra (5 and 4 showed they were very similar except that the oxygenated non-protonated carbon (δC 77.8) at the C-5 position in 4 was replaced by a methine moiety (δC 47.4) in 5. This was evidenced by the HMBC correlations (H 1.06) to C-1 (δC 39.7), C-5 (δC 47.4), C-9 (δC 53.9), and C-10 (δC 35.9). Thus, 5 was established as 16β-acetoxy-3β,6β,7β-trihydroxyergost-22E-ene, and named penicisteroid H.Compound  spectra  of 5 andelations  from H-16) , 2′1–19) were tested for cytotoxicity against 12 cancer cell lines of SHG-44, HepG2, A549, BIU-87, BEL-7402, ECA-109, Hela-S3, PANC-1, SW620, HcT116, MCF-7, and MB-231. They showed selectively cytotoxicity against A549, BIU-87, BEL-7402, ECA-109, Hela-S3, and PANC-1 cells. However, none exhibited antiproliferative effect against SW620, HcT116, MCF-7, and MB-231 cancer cell lines , using the dual-luciferase reporter gene assay. However, they did not display positive effects on Nur77. Instead, they could significantly decrease the transcriptional activation of RXRα induced by 9-cis , which inhibited RXRα expression, abrogated the apoptosis effect induced by compounds 2 and 6  under 20 °C. Ultraviolet spectra were detected by a UV8000 UV/Vis spectrophotometer . The HRESIMS spectra were measured by a Xevo G2 Q-TOF mass spectrometer . The NMR spectra were recorded on a 400 MHz spectrometer  using TMS as the internal standard. Reversed-phase HPLC was performed on a 1260 infinity instrument  equipped with the DAD detector. Purifications by column chromatography (CC) were performed on silica gel, Sephadex LH-20, and ODS. The TLC plates were visualized under UV light or by spraying with 10% HPenicillium granulatum MCCC 3A00475 was provided by the Marine Culture Collection of China (MCCC). The isolation and identification were described previously + .Penicisteroid D (2): amorphous powder; + .Penicisteroid E (3): white powder; + .Penicisteroid F (4): white powder; + .Penicisteroid G (5): white powder; + .Penicisteroid H . Space group P212121, a = 5.94350(10) Å, b = 11.98440(10) Å, c = 38.3459(4) Å, α = β = γ = 90º, V = 2731.35(6) Å3, Z = 4, Dcalcd = 1.111 mg/cm3; crystals size 0.15 × 0.12 × 0.05 mm3, µ = 0.533 mm−1, F (000) = 1008; A total of 18,704 reflections were collected in the range of 2.304 to 67.080°, of which 4726 independent reflections [R (int) = 0.0845] were used for analysis. The final R indices [I > 2sigma(I)] R1 = 0.0797, wR2 = 0. 2359. The absolute structure parameter was −0.06 (15). Crystallographic data of 2 have been deposited in the Cambridge Crystallographic Data Center (CCDC), with deposition number 1887656. Copies of the data can be obtained, free of charge, on application to CCDC, 12 Union Road, Cambridge CB21EZ, UK, (fax: +44(0)-1233-336033; email: deposit@ccdc.cam.ac.uk).Compound Cytotoxic activities of all steroids were conducted on human glioma cell line SHG-44, liver cancer cell lines HepG2, and 7402, non-small cell lung cancer cell line A549, bladder cancer cell line BIU-87, esophageal cancer cell line ECA-109, cervix cancer cell line Hela-S3, pancreatic cancer cell line PANC-1, colon carcinoma cell lines SW620 and HcT116, breast cancer cell lines MCF-7and MB-231 by MTT method as reported previously .The levels of cellular superoxide were assessed by using FITC Annexin V Apoptosis Detection Kit  according to the manufacturer’s instructions. Briefly, A549 cells treated with trypsin and resuspended in Annexin V binding buffer, then the cells were labelled by PI and FITC. At last, fluorescence was measured by flow cytometry using FITC-A .RXRα, ΔN197, Santa Cruz, 2145 Delaware Ave, CA, USA) in TBST for 1 h and then washed twice, probed with horseradish peroxide-linked anti-immunoglobulin (1:5000 dilution) for 1 h at room temperature. After three washes with TBST, immunoreactive products were visualized using enhanced chemiluminescence reagents and autoradiography.Cell lysates were boiled in sodium dodecyl sulfate (SDS) sample loading buffer, resolved by 10% SDS–polyacrylamide gel electrophoresis (SDS–PAGE) and transferred to nitrocellulose. The membranes were blocked in 5% milk in Tris-buffered saline and Tween 20 , 150 mM NaCl, and 0.05% Tween 20) for 1 h at room temperature. After washing twice with TBST, the membranes were incubated with appropriate primary antibodies . At last, fluorescence was measured by flow cytometry using PB450-A .Cells were transfected with the corresponding plasmids for 24 h and then treated with compounds for 12 h. Cells were lysed and luciferase relative activity was tested by the Dual-Luciferase Reporter Assay System according to the manufacturer’s instructions. Transfection efficiency was normalized to Renilla luciferase activity. Culture medium DMEM containing 0.5% DMSO was regarded as blank control. Celastrol (1 μM), UVI3003 , 9-cis  were used as positive controls.RXRα-LBD was cloned as an N-terminal histidine-tagged fusion protein in pET15b expression vector and overproduced in the Escherichia coli BL21 DE3 strain. Briefly, cells were harvested and sonicated, and the extract was incubated with the His60 Ni Superflow resin.The human RXRα-LBD protein (1 μM in 3 mL phosphate buffer) was measured with Agilent Technologies Cary Eclipse Fluorescence Spectrophotometer , and the fluorescence spectra were obtained from 300 nm to 500 nm. Compounds were added to the protein. After incubation for 30 s at RT, the incubation buffer was measured with the spectrophotometer. Data were processed and fitted to obtain the binding affinities using Origin .The t-test (two-sided) was used to test for significant differences between groups.Each experiment was performed three times. The values shown are the mean ± SD. Student’s Penicillium granulatum MCCC 3A00475. Compounds 2 and 4–7 showed moderate activity against the human lung cancer cell line A549. They could induce PARP cleavage and regulate RXRα transcriptional expression.In conclusion, five new and 27 known compounds were isolated from the deep-sea-derived fungus


Dear Editor,2, ibrutinib resistance often develops in MCL, even following initial positive responses, and DLBCL is frequently resistant to ibrutinib3. We and others have found that specific tumor suppressor gene defects are correlated with relapsed/refractory characteristics and cause poor clinical outcomes5. However, these specific tumor suppressors, including TP53 and CDKN2A, are not druggable, and indirectly targeting these tumor suppressor-mediated pathways has not resulted in high clinical response rates. Auranofin, a gold-containing compound that is FDA-approved for treatment of rheumatoid arthritis, is being repurposed as a potential anti-tumor drug against different refractory malignancies6. Currently, auranofin is being assessed in clinical trials for chronic lymphocytic leukemia, breast cancer, and lung cancer . Auranofin has a proven safety profile, making it an attractive compound for clinical trials7, as an estimated 70–90% of agents fail clinical trials, with safety concerns being a major cause of drug development discontinuation8. However, repurposing FDA-approved drugs with known safety in humans for new indications may circumvent these issues and offer a promising new area of investigation. Here, we demonstrate that auranofin targets thioredoxin reductase-1 (Txnrd1) to effectively induce DNA damage, reactive oxygen species (ROS) production, cell growth inhibition, and apoptosis in aggressive B-cell lymphomas, especially in TP53-mutated or PTEN-deleted lymphomas. First, auranofin has consistently shown to induce lethality in a panel of DLBCL and MCL cell lines. We treated 8 DLBCL cell lines—including three GCB-type cell lines  and five ABC-type cell lines —and six MCL cell lines  with auranofin in concentrations ranging from 0 to 5 μM for 72 h and tested cell viability using a luminescent assay. We found that auranofin was cytotoxic to both DLBCL  and diffuse large B-cell lymphoma (DLBCL) are aggressive non-Hodgkin lymphomas (NHL). Although the BTK inhibitor ibrutinib has offered markedly improved clinical outcomes after disease relapse from multiple prior therapiesBCL Fig.  and MCL BCL Fig.  in a dosnes Fig. . The JeKnes Fig. .Fig. 1AuTP53 mutation and PTEN loss, in TP53-mutated MCL cell lines Mino, Maver-1 . However, TP53 knockdown in two TP53 wild-type cell lines JVM-2 and OCI-Ly3 did not affect the changes of growth inhibition after auranofin treatment (data not shown), indicating that only mutated-TP53 sensitized cells to auranofin. Next, we specifically knocked down PTEN in JVM-2 . The result is consistent with the data in PTEN-lost Z-138 cells , which results in ROS accumulation and cell damage11. Therefore, we investigated the expression of both Txnrd1 and GPX1 in lymphoma cells, especially TP53-mutated or PTEN-deleted lymphoma cells, and explored their correlation with auranofin treatment. We found that all 14 tested aggressive lymphoma cell lines expressed Txnrd1 and GPX1, but the TP53-mutated cell line U2932 had very low GPX1 protein levels . However, GPX1 protein levels did not correlate with auranofin IC50 . The results indicate that auranofin-induced cytotoxicity to lymphoma cells is related to Txnrd1 protein levels but not to GPX1. To further address the correlation of auranofin to Txnrd1, Txnrd1 knockdown in Mino, Maver-1 .A recent publication shows that the main function of auranofin is to inhibit Txnrd1 in the cytoplasm and nucleus to induce ROS production, tumor cell growth inhibition, and apoptosisels Fig. . We usedr-1 Fig. , and OCIr-1 Fig.  caused cTP53 or PTEN wild-type/mutated/deleted cell lines with 0–1.2 μM auranofin for 24 h, and a dose-dependent apoptosis was observed in all of these cell lines. We observed that the TP53-mutated cell lines U2932 and JeKo-1 and the PTEN-lost cell line Z-138, were much more sensitive to auranofin treatment than the TP53 and PTEN wild-type cell line OCI-Ly10 . All mice were in good body condition during treatment, and there was no body weight difference between the vehicle and auranofin treatment cohorts .Next, to investigate how auranofin induces apoptosis, we treated the y10 Fig. . To valip < 0.0001). To probe the mechanism of auranofin-induced cell death, cells were then treated with auranofin and co-incubated with either 5 mM NAC or 20 μM Z-VAD-FMK, a pan-caspase inhibitor. Incubation with either NAC or Z-VAD-FMK abrogated auranofin-induced apoptosis . The results demonstrate that auranofin induced ROS- and caspase-dependent apoptosis. Furthermore, treatment with 1.2 μM or 2.5 μM auranofin reduced the mitochondrial membrane potential at 12 h . Since long-term exposure to high levels of ROS causes DNA damage, we then investigated the effects of auranofin on DNA damage response pathways. We found that auranofin dose-dependently activated CHK2 and γH2A.X, as well as an observed concomitant decrease of MDM2 in Z-138, Mino, OCI-Ly8, and OCI-Ly10, suggesting TP53 release and activation in these TP53-intact lymphoma cell lines  and that 5 mM NAC generally inhibited H2A.X phosphorylation . These results demonstrate that these TP53-intact lymphoma Z-138 cells are DNA damaged during ROS-dependent apoptosis induced by auranofin treatment, regardless of PTEN loss. Interestingly, TP53-mutated JeKo-1 cells had no DNA damage response (Fig. TP53-mutated JeKo-1 cells is not dependent on the DNA damage pathway activation.To elucidate the mechanism of action of auranofin in lymphoma cells, we first showed that auranofin completely inactivated Txnrd1 activity in vitro Fig. . Two-hounes Fig. . Next, inse Fig. , indicatTP53 wild-type and mutated lymphoma cells, we performed RPPA analysis on OCI-Ly8, OCI-Ly10, Mino, Z-138, U2932, and JeKo-1 cell lines. The top 30 most differentially expressed proteins were analyzed in two independent RPPA data analyses of DLBCL (Fig. TP53-mutated DLBCL and MCL cells have conspicuous changes of protein expression, which were totally different from TP53 wild-type cells. Especially, in TP53-mutated DLBCL cell line U2932, auranofin increased the expression levels of HSP70, histone H3, caspase-3, 7, p-H2A.X, LC3A, and SLC1A5, and decreased the expression of p-S6, ARID1A, MSH6, Wee1, eIF4G, PLK1, XPA, p-NDRG1, Cdc25C, mTOR, ATR, STAT3, ATM, and TRIM25 (Fig. TP53-mutated MCL cell line JeKo-1, auranofin increased the expression levels of PAR, histone H3, caspase-7, HSP27, and SLC1A5, and decreased the expression of HES1, p-Rb, and Weel (Fig. TP53-mutated DLBCL cell line U2932 and MCL cell line JeKo-1. SLC1A5 is a glutamine transporter12, and Wee1 regulates DNA damage checkpoints13. Auranofin may strongly induce metabolic stress, as evidenced by reducing the mitochondrial membrane potential and then increasing the expression of SLC1A5 as compensation for more nutrient supplements from glutaminolysis. In addition, auranofin increases the pro-autophagic protein LC3A and decreases the proteins for signaling activation of mTOR, STAT3, and the cell cycle in TP53-mutated DLBCL cell line U2932, indicating that auranofin may have more mechanisms for treating TP53-mutated DLBCL.To further investigate other pathways that auranofin targets in both BCL Fig.  and MCL BCL Fig. . We founM25 Fig. . In TP53eel Fig. . The comTP53-mutated or PTEN-deleted lymphomas. Our brief study points out that auranofin may be repurposed as an effective clinical option for TP53-mutated or PTEN-deleted refractory B-cell lymphoma.In summary, our study demonstrates that auranofin exerts its anti-lymphoma cytotoxic effects through ROS-based therapeutics by targeting Txnrd1. Auranofin induces DNA damage, cell growth inhibition, and ROS- and caspase-dependent apoptosis in aggressive B-cell lymphomas, and it especially shows more significant therapeutic effects on Supplemental Materials and Methods


In vitro models are very important in medicine and biology, because they provide an insight into cells' and microorganisms' behavior. Since these cells and microorganisms are isolated from their natural environment, these models may not completely or precisely predict the effects on the entire organism. Improvement in this area is secured by organ-on-a-chip development. The organ-on-a-chip assumes cells cultured in a microfluidic chip. The chip simulates bioactivities, mechanics and physiological behavior of organs or organ systems, generating artificial organs in that way. There are several cell lines used so far for each tested artificial organ. For lungs, mostly used cell lines are 16HBE, A549, Calu-3, NHBE, while mostly used cell lines for liver are HepG2, Hep 3B, TPH1, etc. In this paper, state of the art for lung and liver organ-on-a-chip is presented, together with the established in vitro testing on lung and liver cell lines, with the emphasis on Calu-3 (for lung cell lines) and Hep-G2 (for liver cell lines). Primary focus in this review is to discuss different researches on the topics of lung and liver cell line models, approaches in determining fate and transport, cell partitioning, cell growth and division, as well as cell dynamics, meaning toxicity and effects. The review is finalized with current research gaps and problems, stating potential future developments in the field. In vitro models are the starting point in biological and medical research. With scientific progress and emergence of different in vitro models, knowledge of the entire organism behavior is growing. Together with experimental in vitro models, computer in silico models are being developed. Different types of models are developed, at different scales—macro, meso, micro, nano, depending on whether they explain the behavior of the whole system , behavior at the level of molecular clusters  or behavior at the molecular level . Results obtained from in vitro and in silico models should be compared and verified. The final goal is to develop adequate in silico models, which reduce costs and time of experimental measurements and provide satisfactory results. However, first we need the results of in vitro models. In vitro models were used for many segments of the human organism—for blood-brain barrier  in a 25-year-old Caucasian male with lung adenocarcinoma , the liver is involved in more than 300 vital functions Angier, . When itin vitro models for lung and liver cell lines. After that, data processing and available models of lung and liver cell lines are discussed. The fourth section contains on-chip review related to the previously mentioned cell lines, and, finally, the conclusion section provides a summary of the state-of-the-art and critically observes possible future steps in this field.The following section presents Even though Calu-3 cells represent immortalized cells, they still possess many characteristics of primary airway cells and can be used for observation of transport, metabolism and testing novel medicament approach  and at liquid-liquid interface (L-L). A-L interface showed to be more natural in physiological way, forming pseudostratified columnar epithelium with more microvilli and secretory vesicles, showing higher TEER values and lower permeability of dextran. Longer time in culture significantly decreased dextran permeability and increased expression of drug transporter. Major conclusion from this investigation was that cell culture conditions as well as time in culture affect cell differentiation, barrier function, permeability values and drug transporter expression. All these should be standardized for Calu-3 cell line as an in vitro model for drug delivery system and lung diseases.Differentiated human bronchial epithelial cell culture systems were evaluated for asthma research —primary cells  and non-primary cells, cell lines . Mucociliary transport was measured and it was observed that the particle velocity was nearly identical to the one observed in healthy human lungs' airway. The conclusion is that airway-on-a-chip can be used as a representation of the human living healthy lung and further for asthma and chronic obstructive pulmonary disease (COPD). IL-13 has an important role in asthma evolving and it is sufficient and necessary for induction of allergic asthma in animal models. It participates in airway inflammation, goblet cell hyperplasia and mucus hypersecretion, as well as in creation of subepithelial fibrosis and airway hyper-responsiveness. Increase in the number of goblet cells was noticed after 8 days when small airway-on-a-chip was treated with IL-3, like in other in vitro models. Higher production of inflammatory cytokines G-CSF and GM-CSF into vascular channel and decrease in cilia beating frequency were also noticed. Pathogenic infections are major cause of COPD exacerbation in patients. Airways-on-a-chip epithelial cells were lined up with either normal or COPD epithelial cells and treated with the viral mimic poly (I:C) or lipopolysaccharide endotoxin (LPS). Bacterial wall derived component that simulates cytokine production and has been widely used for that purpose. M-CSF cytokine secretion was analyzed, because it promotes differentiation and survival of macrophages. On the other hand, production of the IL-8 was monitored, because IL-8 attracts neutrophils. Stimulation with poly(I:C) promoted secretion of the cytokines IP-10 and RANTES in both healthy and COPD chips. Besides IP-10, which is an excellent clinical marker, level of M-CSF was increased in poly(I:C) treated airway chip. For this reason, it could be used as a new biomarker for COPD exacerbations induced by respiratory viruses. The developed microfluidics models of human asthmatic and COPD airways can be further used for testing the efficacy of new experimental drugs and also for dissection of the mechanism of drug action at the molecular level.In the Wyss laboratoryCells from lung-on-chip micro device can be monitored with microimpedance tomography system, which can provide useful information about the cells and culture conditions  substrates in order to use it for hepatocyte culture in membrane-based bioreactors  is the most common reason for delays in the clinical phase in drug development and even withdrawal of an approved drug (Bies et al., All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Elucidating mechanisms in oncogenes and epigenetic modifiers are needed to gain insights into the etiology and treatment of cancer, regulation of oncogene by chromatin modifiers at post-transcriptional level is critical and remains unclear. We have investigated the role of GINS4 in NSCLC.The expression of chromatin modifier lymphoid-specific helicase (LSH) and GINS4 was assessed in tumor and normal tissue from 79 patients with NSCLC with clinical characteristics. HBE, A549, H358, and H522, PC9, 95C and 95D were cultured after overexpression or silencing of GIAT4RA. Cell proliferation assay, cell migration and invasion assays, plate colony formation assay, immunofluorescence assay, Operetta® high-content screening and analysis, Western blot analysis and Co-Immunoprecipitation (Co-IP) assay, RNA immunoprecipitation assay and tumor growth assay was used to address the potential interplay of between GINS4 and LSH, and the functional of GINS4.GINS4 is highly expressed in lung cancer cells and tissues, and GINS4 expression is not association with clinical risk factors, but linked with clinical stage and lymphatic metastasis status. Higher expression of GINS4 poorly linked with overall survival in lung adenocarcinomas. Furthermore, GINS4 promoted many characteristics of tumorigenesis including cell growth, clonal formation, migration and invasion, epithelial–mesenchymal transition, tumor sphere and tumor growth in vivo. Interestingly, our results demonstrated that LSH increases GINS4 expression through binding to 3’UTR region of GINS4 and stabilizing its mRNA levels. Finally, LSH overexpression rescues GINS4 knockdown-induced features.GINS4 facilitates lung cancer progression by promoting key characteristics of tumor potential, and LSH epigenetically interacts with and stabilizes GINS4 transcripts.The online version of this article (10.1186/s13046-019-1276-y) contains supplementary material, which is available to authorized users. Lung cancer is classified into small cell lung cancer and non-small cell lung cancer (NSCLC), including adenocarcinomas (ADC) and squamous cell carcinoma (SCC), which account for 80–85% of all lung cancer cases . Lung caChromatin modifiers display widespread interactions with RNA transcripts, and regulation of the length of the 3′UTR (3′-untranslated region) is suggested to be associated with the ability of chromatin modifiers to interact with both chromatin and mRNA transcripts –12. SWI/Drosophila [Go, Ichi, Nii, and San  complex subunit 4 (GINS4) is a member of GINS family of proteins that are essential for the initiation of DNA replication in yeast and Xenopus egg extracts , 28. GINosophila , suggestIn this study, we examined the physiological role of GINS4 in lung cancer progression and their potential epigenetic mechanisms. We found that LSH increased GINS4 expression by stabilizing its mRNA level post-transcriptionally.2. All the cell lines yielded negative result for mycoplasma contamination. All the cell lines were passaged < 10 times after their initial revival from frozen stocks and were authenticated by performing short tandem repeat profiling before their use.Normal lung cell lines, HBE (ATCC: CRL-2741™) were purchased from the ATCC. The lung cancer cell lines A549 (ATCC: CCL-185™), H358 (ATCC: CRL-5807™), and H522 (ATCC: CRL-5810™) were obtained from the ATCC. The lung cancer cell lines PC9, 95C and 95D were obtained from the Cancer Research Institute of Central South University. A549 cells were maintained in DME/F12 1:1(Hyclone), 293 T cells were maintained in DMEM (Gibco), and the other cells were maintained in RPMI 1640 (Gibco). All media were supplemented with 10% (v/v) FBS, and all the cells were maintained at 37 °C in an atmosphere of 5% COGINS4 were purchased from Vigene Biosciences .Actinomycin D, MG132, and CHX were purchased from Selleck . Vectors overexpressing truncated FLAG–LSH fragments were generated by cloning cDNAs encoding these fragments into pLVX-EF1α-IRES-Puro vector  by using restriction enzymes EcoRI and BamHI (Takara). Lentiviral vectors expressing GINS4, and LSH and a non-targeting control vector were purchased from Genechem . All the plasmid vectors were verified by performing sequencing.Lentiviral shRNA vectors targeting human Western blotting analysis was performed as described previously . PrimaryLung cancer tissue samples, which were validated by pathologist Dr. Desheng Xiao , were obtained from the Department of Pathology of Xiangya Hospital. A lung cancer tissue array was purchased from Pantomics . IHC analysis of paraffin-embedded tissue samples obtained from patients with lung cancer was performed as described previously .qRT-PCR was performed as described previously , 31. Pri7 cells were harvested by trypsinization and resuspended in 2 mL of PBS. The cell lysate was pelleted by centrifugation at 4 °C and 500×g for 15 min. The cell lysate was resuspended in 1 mL of RIP buffer, split into three fractions , and then centrifuged at 4 °C and 13,000 rpm for 10 min. Antibodies against normal mouse IgG , normal rabbit IgG , and anti-FLAG M2 Magnetic Beads  were added to the supernatant and incubated overnight at 4 °C with gentle rotation. Next, 40 μL of protein A/G beads were added and the mixture was incubated at 4 °C for an additional hour. The beads were pelleted at 2500 rpm for 30 s, washed three times with 500 μL of RIP buffer and one time with PBS, and then resuspended in 1 mL of RNAiso Plus. The total RNA (input control) and RNA precipitated with the isotype control (IgG) for each antibody were assayed simultaneously with all test samples. The coprecipitated RNAs were detected by qRT-PCR for GINS4.RNA immunoprecipitation assay was performed as described previously , a totalThese assays were performed as described previously , 31. Det− 2 Plus Media Supplement , B-27 Supplement , 20 μg/ml EGF , and 10 μg/ml basic FGF  to support the growth of undifferentiated oncospheres. Cells were incubated in a CO2 incubator for 1–2 weeks, and the numbers of oncosphere cells were counted under a microscope.These assays were performed as described previously , 34. CelTo test the regulation of LSH on the GINS4 3′UTR, we constructed a pMiR-REPORT luciferase vector expressing the 3′UTR of GINS4. Luciferase levels were normalized to those of a non-responsive vector expressing Renilla luciferase.http://www.hnsja.com) and housed in dedicated pathogen-free barrier facilities. The mice were injected with the indicated cells in the mammary fat pad. Injected mice were imaged from both the dorsal and ventral sides every 3 days. Data were analyzed using Student’s t-test; a p value< 0.05 was considered significant.The xenograft tumor formation assay was essentially performed as previously described , 32–35. All experiments performed in this study were repeated at least three times. Results of western blotting analysis are representative of three independent experiments. All experiments, except experiments involving nude mice, were repeated at least three times.p value of < 0.05 was considered statistically significant. *, **, and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.Results are expressed as mean ± SD or SEM, as indicated. A two-tailed Student’s t-test was used for performing intergroup comparisons. A GINS4 mRNA expression using RNA sequencing [We previously found that LSH upregulated quencing . Here weFinally, multivariate analysis showed that the expression level of GINS4 was independent of clinical risk factors such as gender, smoking, tumor differentiation and tumor size, but linked with clinical stages and lymphatic metastasis Table . Kaplan–in cis. Consistent with our previous findings, we found that LSH was highly expressed in lung cancer tissues  and LSH   also showed that GIINS4 overexpression significantly increased the tumor volume, tumor size, and tumor weight   Fig. h. Togeth6GINS4- knockdown H1299 cells into nude mice and found that GINS4 knockdown significantly decreased the volume, size and weight of tumors derived from these cells


Our results demonstrate that p53 functional states are associated with distinct ALDH isoform transcriptomic signatures. Moreover, tumor ALDH profiling may provide insight on which ALDH isoform to target in high AVS HNSCC tumors to deplete the CIC population.p53 and aldehyde dehydrogenase (ALDH) have been implicated in key tumorigenesis processes including cancer initiating cell (CIC) maintenance; however, the relationship between these two mediators remains poorly defined. In this study, ALDH isoform expression diversity was revealed in CICs with disparate p53 functional states: gain of function, high risk p53 mutation (p53HRmut) and wildtype p53 (p53WT) inactivated by the human papillomavirus 16 (HPV16) E6 oncogene. Interrogation of head and neck squamous cell carcinoma (HNSCC) cell lines and patient tumors showed that HPV16+/p53WT cases have higher ALDH variance score (AVS), a measure of tumor ALDH isoform expression diversity, compared to HPV−/p53HRmut cases (p = 0.03). AVS and several individual ALDH isoforms were associated with prognosis in HPV16+/p53WT HNSCC but not in HPV−/p53HRmut HNSCC. Knockdown of the dominant ALDH isoform in high AVS HNSCC depleted the CIC pool  Nanog, a core embryonic stem cell transcription factor, known to modulate normal stem cell and CIC pluripotency is negatively regulated by p53WT4. In addition, CD44 and CD133, two well-recognized markers for CICs, are repressed by p53WT through direct promoter occupancy6. A logical extension of these studies is that loss of p53WT function will likely result in CIC expansion through multiple mechanisms to drive cancer promotion and progression. In contrast to p53WT, literature on p53HRmut in this space is scant. A recent paper reported that p53HRmut is more efficient than p53WT deficiency to facilitate somatic cell reprogramming; however, these p53HRmut reprogrammed cells exhibit genetic instability and have a higher potential for malignant transformation with tumor initiating properties7.A frequent genetic event in the tumorigenesis cascade is aberrant p53 function, either loss of wildtype p53 (p53WT) function or gain of function, high-risk p53 mutation (p53HRmut). p53WT controls normal stem cell homeostasis and evidence exists to support p53WT dysfunction in cancer initiating cell (CIC) expansion and maintenance10. However, depending on the cell line or anatomical site, there is now compelling evidence that other ALDH isoforms may contribute to or be primarily responsible for elevated ALDH activity in CICs15. A relationship between p53 and ALDH was revealed when p53HRmut was shown to act as a transcription factor to drive ALDH1A1 transcription to confer a novel gain of function effect on CIC maintenance and/or expansion16. However, the influence of p53 functional states on the ALDH gene family in the context CICs and cancer, in general, remains poorly understood.Aldehyde dehydrogenase (ALDH) is a superfamily consisting of 19 evolutionarily conserved isoforms and its activity is used as a functional assay to identify normal stem cells and CICs. Based on initial work, it was generally believed that ALDH1A1 is indispensable for high ALDH activity to maintain the CIC poolHead and neck squamous cell carcinomas (HNSCCs) are cancers from various anatomical site in the head and neck region and can be subdivided based on two major etiologic factors: smoking/alcohol use and high-risk human papillomavirus (HPV), in particular HPV16. HPV− HNSCCs are driven by smoking and alcohol use, and often harbor p53 genomic alterations, mostly gain of function, missense mutations. In contrast, HPV + HNSCCs have p53WT that is inactivated by the HPV oncogene, E6. These two distinct etiologies offer an unique opportunity to explore the relationship between p53 and ALDH without the use of genetic engineering approaches. Using HPV− and HPV16+ cell lines and patient tumors, we showed that p53 functional states have differential ALDH isoform expression diversity at the CIC and bulk tumor levels. HPV16+/p53WT tumors tend to display a dominant ALDH isoform expression pattern with enrichment of a particular ALDH family member. Interestingly, targeting the dominant ALDH isoform in high AVS HNSCC cell lines resulted in robust depletion of the CIC population. Our work demonstrates that p53 functional states are associated with distinct ALDH isoform transcriptomic signatures and suggests that tumor ALDH profiling may identify the isoform responsible for CIC maintenance in high AVS HNSCC.17. The HPV− HSNCC cell lines have distinct p53 states; CAL27 has gain of function, high risk mutant p53 (H193L), SCC25 has loss of function p53 due to the deletion of two base pairs in codon 209, and UMSCC74A has wildtype p5318. ALDH activity measured using the ALDEFLUOR assay is a well-established methodology to identify and quantitate CICs in hematologic and solid malignancies. ALDHhigh CIC fraction varied across our panel of HNSCC cell lines  and ALDHlow (bottom 5%) populations to determine the ALDH isoform expression signature in CICs from these cell lines. As shown in Fig. high CICs from HPV16+/p53WT HNSCC displayed a dominant ALDH isoform expression signature; ALDH1A3 was enriched in UMSCC47 and UPCI-SCC090 CICs, whereas, ALDH2 was the main isoform enriched in UD-SCC2 CICs. Similarly, in SCC25, a HPV− HNSCC cell line with truncated, loss of function p53, ALDHhigh CICs were enriched for a single ALDH isoform, ALDH1A3. In contrast, high ALDH isoform expression diversity was shown in ALDHhigh CICs from HPV−/p53HRmut CAL27. HPV−/p53WT UMSCC74A CICs represent an intermediate group and had moderate ALDH isoform expression diversity.The HPV16+ HNSCC cell lines, UD-SCC2, UMSCC47, and UPCI-SCC090, have wildtype p53 that is inactivated by HPV16E6Our results using HNSCC cell lines suggest that HPV status and p53 functional states may drive distinct ALDH expression signatures. In order to extend these findings from cell lines to patient tumors, we developed a mathematical algorithm, ALDH variance score (AVS), as an approach to quantitate the ALDH isoform expression variance in individual samples. HPV16+/p53WT HNSCC cell lines had AVS >100; 109.6 for UD-SCC2, 313.1 for UMSCC47, and 137.4 for UPCI-SCC090. HPV− SCC25 expresses truncated, loss of function p53 and thus, has an inactivated p53 functional state analogous to HPV16-driven inactivation of p53WT. Similar to HPV16+/p53WT cell lines, SCC25 had a high AVS of 175.7. In comparison, HPV−/p53HRmut CAL27 HNSCC cell line had a low AVS of 28.4. Next, the AVS algorithm was applied to the HPV16+ and HPV− cohorts from the TCGA HNSCC dataset. The difference in AVS was unremarkable between HPV16+ and HPV− tumors  in this TCGA HNSCC cohort . This is consistent with a previous report showing that HPV16+/p53WT HNSCCs have a higher intrinsic CIC frequency than HPV−/p53HRmut HNSCC20 and thus, supports the potential utility of mRNAsi to infer relative CIC frequency from global transcriptome in patient tumors.Direct quantitation of the CIC pool in individual TCGA HNSCC tumors is not feasible so we used the PanCancer Atlas stemness index (mRNAsi)ort Fig. . MoreoveSince HPV16+/p53WT patients have superior overall survival (OS) and disease-specific survival (DSS) compared to HPV−/p53HRmut patients .We hypothesized that high AVS is indicative of a homogenous CIC pool and thus, targeting the dominant ALDH isoform may result in contraction of the CIC population. Tetracycline-inducible ALDH1A3 shRNA polyclonal UMSCC47 and SCC25 cells were generated to determine if the dominant ALDH isoform is indispensable for CIC maintenance in representative HPV16+ and HPV− HNSCC with high AVS 22 than HPV−/p53HRmut tumors  HPV16 preferentially infects basal cells in the squamous epithelium and these undifferentiated, isogenic cells are likely to be the cell of origin for HPV16+/p53WT tumors, (b) HPV16+/p53WT tumors have higher CIC frequency23. In addition to aldehyde metabolism, ALDHs are involved in a plethora of cellular processes which influence tumorigenesis, including retinoic acid (RA) synthesis and signaling, ultraviolet light absorption, hydroxyl radical scavenging, and antioxidant activity25. Multiple groups have investigated and shown select ALDH isoforms, in particular ALDH1 members, as prognostic biomarkers in a spectrum of solid malignancies28. We assessed the entire ALDH family and found a select number of isoforms, ALDH1A3, ALDH2, ALDH3B1, ALDH7A1, and ALDH18A1, to be associated with survival in HPV16+/p53WT HNSCC. ALDH2 has tumor suppressive actions and polymorphisms in this gene is associated with increased risk to a number of alcohol-related cancers29. The pro-tumorigenic role of ALDH1A3 in cancer is well-described and its utility as a prognostic biomarker is beginning to emerge30. Scant literature exists to link ALDH3B1, ALDH7A1, and ALDH18A1 to cancer, although, these studies provide initial evidence that these isoforms favor tumorigenesis33. Additional work is needed to improve our understanding of these poorly studied ALDH isoforms in tumorigenesis, stemness, and treatment relapse in HPV16-driven malignancies.The ALDH superfamily consists of 19 evolutionarily conserved isoforms recognized to oxidize aldehydes to carboxylic acidsAVS was developed as a quantitative tool to capture ALDH isoform expression diversity from bulk tumor transcriptomic datasets. An initial concern was that AVS may just be capturing global genomic and transcriptomic differences. However, this potential issue was mitigated based on our analyses showing that AVS was not correlated with aneuploidy score or MATH . UMSCC74A and UMSCC47 cell lines were obtained from Thomas Carey, University of Michigan. UPCI:SCC090 and UD-SCC2 cell lines were provided by Susanne Gollin, University of Pittsburgh and Henning Bier, Heinrich-Heine University, Dusseldorf, Germany. CAL27, UD-SCC2, UMSCC47, UMSCC74A, and UPCI:SCC090 were grown in Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum, 100 µg/ml streptomycin, and 100 U/ml penicillin. SCC25 cells were grown in a 1:1 mixture of Ham’s F-12 and DMEM supplemented with 10% FBS, 0.4 µg/mL hydrocortisone, 100 µg/ml streptomycin, and 100 U/ml penicillin. Cell lines were maintained in a humidified atmosphere of 5% COCells were extracted for total RNA using the TRIzol reagent  or TaqMan PreAmp Cells-to-CT kit (ThermoFisher Scientific). mRNA expression of ALDH isoforms was determined using the Applied Biosystems 7900HT Fast Real-Time PCR System with validated, pre-designed TaqMan primers (ThermoFisher Scientific). The expression of ALDH isoforms were normalized to glyceraldehyde 3-phosphate dehydrogenase (GADPH) using the ∆∆Ct method.http://cancergenome.hih.gov/. HPV status for the TCGA HNSCC cohort were determined by PCR from the biospecimen center Nationwide Children’s Hospital, and downloaded from the auxiliary file of the clinical TCGA HNSCC dataset from the GDC data portal. All HPV16+ and HPV− cases from the TCGA HNSCC cohort were identified and analyzed. Mutational profiles of individual patients were obtained from the updated Pan-Cancer dataset. p53 missense mutations were classified as high risk using the evolutionary action score of p53 (EAp53) threshold of >7534. Pre-processed legacy RNA-seq data were downloaded from NCI Genomic Data Commons using TCGAbiolinks package in R and gene level GC-content normalization was performed36. RNA-Seq by Expectation Maximization (RSEM) of individual ALDH isoforms was converted to percent of total ALDH RSEM by dividing the RSEM for each ALDH isoform by the sum of RSEM from all ALDH isoforms. For each sample, the deviation of each ALDH isoform from the mean percentage was calculated and squared to avoid negative values. ALDH variance score (AVS) was then calculated as an average of the squared deviations using the formula:Clinical data from the TCGA HNSCC dataset were downloaded from the Genomics Data Commons data portal/TCGA Research Network: 19. Briefly, the entire RNA expression gene set was evaluated and each gene was weighted based on stemness features. The stemness of each sample was scored accordingly and subsequently mapped to the  range.The PanCancer Atlas published a set of stemness indices based on mRNA expression (mRNAsi) by machine-learning scoring the oncogenic dedifferentiation of TCGA tumor samplesThree targeting sequences to knockdown ALDH1A3 were designed, synthesized, and cloned into the inducible tetracycline-on pLV-RNAi  vector. Sequence 1: 5′ AAAAGGTCAAGTTCACCAAGATATTGGATCCAATATCTTGGTGAACTTGACC 3′; Sequence 2: 5′ AAAAGCAGAGAACTAGGTGAATATTGGATCCAATATTCACCTAGTTCTCTGC 3′ Sequence 3: 5′AAAAGCAGGTCTACTCTGAGTTTGTTTGGATCCAAACAAACTCAGAGTAGACCTGC 3′. Cells were transduced with pLV-RNAi/shRNA-ALDH1A3 and polyclonal cell populations were collected. Preliminary studies demonstrated that sequence 1 resulted in the most efficient knockdown of ALDH1A3. All experiments in this study were performed using sequence 1 and cells were stimulated with doxycycline at 1 µg/ml.Whole cell lysates were mixed with Laemmli loading buffer, boiled, separated by SDS–PAGE, and transferred to a nitrocellulose membrane. Subsequently, immunoblot analyses were performed using antibodies specific to ALDH1A3  or GADPH  and processed using the Pierce Fast Western Blot Kit (ThermoFisher Scientific).6 cells) and incubated for 45 mins at 37 °C. For each experiment, an aliquot of cells was exposed to diethylaminobenzaldehyde (50 mM), an ALDH inhibitor, to serve as the negative control. The sorting gate was established using the negative control as the baseline. Cell suspensions were centrifuged at 300 g for 5 mins at 4 °C and re-suspended in 0.5 mL ALDEFLUOR assay buffer for analysis. Fluorescence-activated cell sorting analyses were performed using BD FACS Calibur  at The Ohio State University Comprehensive Cancer Center Analytical Cytometry Core.Cells were suspended in ALDEFLUOR assay  buffer containing ALDH substrate . Tumorsphere formation efficiency was calculated by dividing the number of tumorspheres (≥50 µm in diameter) formed in 7 d by the initial number of cells seeded. Tumorsphere diameter was measured using the NIS-Elements software .ad libitum. Tumor incidence was monitored for 49 days following tumor cell implantation. Cancer initiating cell frequency was calculated using the L-Calc program . Sample size estimate was not performed for this experiment. All animals were included in our analysis. Investigative team was not blinded to the group allocation during the experiment and when assessing the endpoint of tumor incidence. Animal experiments were conducted in compliance with ethical regulations and under an approved protocol from The Ohio State University.Inducible shRNA-ALDH1A3 UMSCC47 cells were suspended in 50:50 DMEM:Matrigel and implanted subcutaneously into the flank of 6–8 week old, female NOD/SCID mice. Subsequently, for each cell dilution, mice were randomly assigned to two treatment arms; control diet (n = 5) or doxycycline (200 mg/kg)-containing diet (n = 5) All analyses were performed in R. Fisher’s exact, Spearman, Student’s t, or Wilcoxon tests were used when appropriate to evaluate the association between categorical variables. Five-year OS and DSS plots were generated by the Kaplan-Meier method and log-rank test was used to compare the plots. Cox proportional hazards model was used for univariate analysis for individual ALDH isoforms as a continuous variable. All tests were two-tailed and p-values < 0.05 were considered significant.Supplementary Information.


Allocation of resources to competing processes of growth, maintenance, or reproduction is arguably a key process driving the physiology of life history trade‐offs and has been shown to affect immune defenses, the evolution of aging, and the evolutionary ecology of offspring quality. Here, we develop a framework to investigate the evolutionary consequences of physiological dynamics by developing theory linking reproductive cell dynamics and components of fitness associated with costly resource allocation decisions to broader life history consequences. We scale these reproductive cell allocation decisions to population‐level survival and fecundity using a life history approach and explore the effects of investment in reproduction or tissue‐specific repair (somatic or reproductive) on the force of selection, reproductive effort, and resource allocation decisions. At the cellular level, we show that investment in protecting reproductive cells increases fitness when reproductive cell maturation rate is high or reproductive cell death is high. At the population level, life history fitness measures show that cellular protection increases reproductive value by differential investment in somatic or reproductive cells and the optimal allocation of resources to reproduction is moulded by this level of investment. Our model provides a framework to understand the evolutionary consequences of physiological processes underlying trade‐offs and highlights the insights to be gained from considering fitness at multiple levels, from cell dynamics through to population growth. Geometric frameworks rely on fixed effects approaches  whereas dynamics involve processes with a critical temporal axis. Physiology changes through time. For example, Sudyka, Casasole, Rutkowska, and Cichoń  argue thHere, we develop a theoretical approach to link the dynamics of a physiological process (cellular protection) to its effects on life history evolution through survival and reproduction. As outlined, life history theory provides a logical framework in which to develop predictions on allocation decisions and their evolutionary consequences. By decomposing an organism's life history into distinct stages, we can investigate the consequences of resource allocation decisions to reproductive cell production and/or cell maintenance on survival and fecundity schedules. This allows appropriate life history metrics for ecological and evolutionary success to be derived. In our framework, an organism allocates resources to either somatic or reproductive tissues and we investigate how these allocation decisions scale to influence life history evolution.This approach to linking dynamics across scales of organization has implications for appropriately assessing fitness: it is important not to conflate a component of fitness with the ultimate success of a life history strategy. Determining fitness at different scales provides details on the processes of selection, and we investigate this here by linking both reproductive cell allocation measures and life history measures of fitness. We begin by defining the reproductive cell dynamics and a component of fitness before exploring the full life history consequences of allocation decisions using the Euler–Lotka equation. In contrast to previous models on the implication of cellular damage accumulation and repair  that can be allocated with proportion q to maintenance and related functions or (1-q) to reproductive cell production  allocated to cell maintenance, a proportion a is allocated to protecting or repairing somatic cells, b to protecting or repairing reproductive cells, and the remainder (1-a-b) to other cellular functions .To investigate changes in reproductive cells, we consider a limiting resource (tions or -q to repV0) into functional reproductive cells (Vn) at a constant rate γ0 and that cellular protection (at rate qbR) can increase the baseline survival  of precursor reproductive cells  of functional reproductive cells. >0 and μn(μ0(1-bqR)+γ0)>0 (This equilibrium state is stable when R)+γ0)>0 .2.2r)  Table .l(x) is survival to age x and m(x) is the reproduction at age x (Table sj) is defined as the product of the probability of surviving from age x to x+1:The discrete version of the Euler–Lotka equation is as follows:x and x + 1:The age‐specific survival probabilities are determined by the instantaneous death rate between age x, f(R), and the costs of reproduction, a function of the number of precursor reproductive cells, g(V0). More explicitly, the age‐specific survival probabilities are determined from:(f(R(x)), is a diminishing function (1aqR(x)), g(V0) is ψV0(x), a is the investment in protection of somatic cells (a+b≤1) and ψ is a scaling factor linking precursor reproductive cell numbers to costs on survival.Here we define the instantaneous death rate between age classes as costs associated with both the loss of somatic cell function, which is a function of resource allocation at age x is defined as the expected number of functional reproductive cells at age x (from Equation V0(x) is the expected number of precursor cells at age, x given by V0(x)=(1-q)R(x)γ0+μ0(1-bqR(x)).Reproduction at age 2.3r) under different life history parameters associated with providing protection to somatic (a) or reproductive (b) cells, proportion of resource allocated between production of reproductive cells and repair/maintenance of existing cells (q) and the pattern of resource availabilities at age x . Specifically, we consider resource declines which are linear, accelerating , or decelerating (1-exp(-x)).Model analysis proceeds by defining and investigating the component of fitness associated with the reproductive cell dynamics (cellular fitness). This only provides a partial understanding of the evolution of cellular protection as it fails to account for survival differences across the life history. To address this and link the cellular dynamics to life histories, we explore the effects of cellular protection on life histories by investigating the force of selection and reproductive value under both increasing (positive rates of increase) and declining (negative rates of increase) population dynamics  can be determined from Equations  and low maturation rates (long development times) have fitness benefits (λ>0). When cell death rates are high  for a range of maturation rates. This occurs through the multiplicative effects of resource availability and resource allocation (bqR) that offset high levels of intrinsic precursor cell mortality.Reproductive cell fitness (quations  and 2 anquations :(11)λ=(bwithin life histories). We then extend these results to investigate how lifetime fitness varies across life histories, when cellular protection varies.In the next sections, we link these findings to investigate how investment in cellular protection affects life histories. We begin by deriving consequences of cellular protection on the force of selection and reproductive value  ratio between the net reproductive rate and the mean age of reproduction  is as follows:The force of selection on protection of somatic cells (the level of exp(-rx). The denominator is a measure of the expected age of reproduction again scaled by exp(-rx) and also the level of protection to somatic cells (a), resources allocated to cellular maintenance, (q) and the amount of resource available at age x. A limiting case  is as follows:η=μn(γ0+μ0(1-bqR(x)). In this expression, the net reproductive rate is scaled by a non‐linear function associated with the abundance of precursor reproductive cells (V0) while the expected age of reproduction is scaled by the precursor reproductive cell maturation (γ0), functional reproductive cell mortality (μn), and the abundance of precursor cells (V0) (The change in lifetime fitness with respect to changes in the amount of protection provided to precursor reproductive cells (the level of lls (V0) .q).Limiting cases  show tha3.2.2Reproductive value Fisher, , 1982, ta) and precursor reproductive cell protection (b)  Figure . As resoAge‐dependent patterns of reproductive value depend on the underlying population (increasing or decreasing) and physiological (high or low reproductive cell development) dynamics. For given physiologies  as a function of resources (R) allocated to cellular maintenance (q)) changes as the allocation to protection of somatic (a) and reproductive cells (b) changes.Predicted fitness (lifetime fitness (a) rather than to protection of reproductive cells (b) as this depends on the survival function (x is the product of exp-1aqR(x) and exp(-ψV0(x)). The function exp-1aqR(x) is the survival costs of protecting somatic cells, and this leads to very low survival as a→0. In contrast, as a→1 (and b→0), survival is maximized (and hence fitness is maximized) as a function of resources allocated to cellular maintenance (q)  .a + b) > 0), lifetime fitness is always higher when the amount of protection to somatic cells (a) is greater than the amount of protection to reproductive cells (b) , lifetime fitness peaks at intermediate levels of resource allocation to cellular maintenance (q). When protection of somatic cells is greater than reproductive cells (a > b), lifetime fitness, as noted, is higher and peaks at high levels of resource allocation to cellular maintenance (q). When protection of somatic cells is less than reproductive cells (a < b), lifetime fitness, as noted, is lower and peaks at low levels of resource allocation to cellular maintenance (q).Overall, across different life history scenarios of resource uses and physiological demand  > 0)  affects both the quantitative and qualitative patterns in lifetime fitness under resource allocation to cellular maintenance (q). Increasing physiological demand decreases lifetime fitness when somatic cell protection is greater than reproductive cell protection (a>b), increases lifetime fitness when reproductive cell protection is greater than somatic cell protection (a<b) and shifts the peak in lifetime fitness when the amount of protection to somatic and reproductive cells is equivalent (a=b). Increasing physiological demand drives life history strategies toward an equivalent protection allocation strategy (toward a=b) potentially through stabilizing selection on nonextreme values: physiology is constraining fitness.In contrast, physiological demand to maintain other cellular functions  depending on how resources are allocated and how resources decline as individuals get older.Resource acquisition and allocation are central in determining optimal life history strategies. That phenotypes have multiple manifestations through differences in underlying physiologies has been thoroughly appreciated . We show here that as development times and schedules change, this alters the levels of precursor reproductive cell mortality and hence affects both survival and fecundity schedules. Investment in somatic cell maintenance (high a) over reproductive cell protection (low b) leads to higher reproductive values in declining compared to increasing populations. Under varying reproductive cell development, we would predict that with slow development of reproductive cells, investing in protecting reproductive cells would maximize reproductive value, and hence lifetime fitness. Conversely, fast development favors investing less in protecting precursor reproductive cells and, instead, maintaining high somatic cell survival. However, it is likely that these effects are not independent of other life history processes. For instance, Buttermer, Abele, and Costantini  dynamics. Hoogendyk and Estabrook  highlighstantini  have sugstantini .We consider fitness at multiple levels—cellular, individual, population—and find complex patterns for resource allocation strategies at each level of organization. Our results highlight the utility of such an approach for measuring fitness, as any single individual component of fitness may not provide the complete overview of life history evolution. In considering the Euler–Lotka measure of lifetime (population‐level) fitness, we take into account the effects of investment in cellular protection on both the survival and reproductive contributions to fitness. This allows us to consider complex relationships between resource input, allocation and mortality or fecundity: for example, survival has a hump‐shaped relationship for a fixed resource allocation strategy as it is a function of both somatic damage (which decreases with increasing resources) and costs of reproduction . While empirical studies do not always calculate age‐specific schedules of both survival and reproduction, we believe that these data would provide an appropriate population‐level measure of fitness to allow the development of a comprehensive understanding of the life history consequences of resource allocation and physiological trade‐offs.In summary, we present a theoretical framework to investigate the evolutionary consequences of investment in cellular defense, by linking the dynamics of reproductive cell production and death to population‐level fitness, which incorporates age‐specific schedules of survival and fecundity. Our approach raises a number of insights about how patterns of resource acquisition, allocation and mitigation of damage in somatic or reproductive tissue can generate contrasting life history strategies.This framework emphasizes possibilities for future empirical and theoretical work. Future empirical studies should consider the physiological mechanisms underlying resource allocation at a cellular level, as well as measuring fitness in a way that takes account of survival and fecundity across the whole life history of an organism. Future theoretical models can build on our framework to consider trade‐offs amongst life history parameters, including growth as well as reproduction and repair, and other schedules of how resource input changes with age. More generally, the framework we develop provides a novel way in which to link physiological mechanism and dynamics to components of life history, fitness, and adaptation.We declare we have no conflict of interests.Both authors conceived the study. Model development and analysis was undertaken by MBB and both authors contributed to writing the manuscript.