Model-based design of RNA hybridization networks implemented in living cells

[EN] Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermo-dynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations.The Consejo Superior de Investigaciones Cientificas (CSIC) Intramural [grant number 201440I017]; the Ministerio de Economia, Industria y Competitividad (MINECO)/FEDER [grant number BFU2015-66894-P]; and the AXA Research Fund Postdoctoral fellowship to G.R. The predoctoral fellowship [grant number AP2012-3751, MECD] to E.M. The Ministerio de Economia, Industria y Competitividad (MINECO) [grant numbers BIO2014-54269-R, AGL2013-49919-EXP] to J.A.D. The 7th Framework Programme [grant numbers 610730 (EVO-PROG), 613745 (PROMYS)]; the Horizon 2020 Marie Sklodowska-Curie [grant number 642738 (MetaRNA)]; the Engineering and Physical Sciences Research Council (EPSRC) and the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M017982/1 (WISB centre)]; and the School of Life Sciences (U. Warwick) [startup allocation] to A.J. Funding for open access charge: EPSRC/BBSRC [BB/M017982/1 to A.J.].Rodrigo, G.; Prakash, S.; Shen, S.; Majer, E.; Daros Arnau, JA.; Jaramillo, A. (2017). Model-based design of RNA hybridization networks implemented in living cells. Nucleic Acids Research. 45(16):9797-9808. https://doi.org/10.1093/nar/gkx698S979798084516Ausländer, S., Ausländer, D., Müller, M., Wieland, M., & Fussenegger, M. (2012). Programmable single-cell mammalian biocomputers. Nature, 487(7405), 123-127. doi:10.1038/nature11149Friedland, A. E., Lu, T. K., Wang, X., Shi, D., Church, G., & Collins, J. J. (2009). Synthetic Gene Networks That Count. Science, 324(5931), 1199-1202. doi:10.1126/science.1172005Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., … Voigt, C. A. (2016). Genetic circuit design automation. Science, 352(6281), aac7341-aac7341. doi:10.1126/science.aac7341Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold Switches: De-Novo-Designed Regulators of Gene Expression. Cell, 159(4), 925-939. doi:10.1016/j.cell.2014.10.002Dirks, R. M., & Pierce, N. A. (2004). From The Cover: Triggered amplification by hybridization chain reaction. Proceedings of the National Academy of Sciences, 101(43), 15275-15278. doi:10.1073/pnas.0407024101Chappell, J., Takahashi, M. K., & Lucks, J. B. (2015). Creating small transcription activating RNAs. Nature Chemical Biology, 11(3), 214-220. doi:10.1038/nchembio.1737Isaacs, F. J., Dwyer, D. J., Ding, C., Pervouchine, D. D., Cantor, C. R., & Collins, J. J. (2004). Engineered riboregulators enable post-transcriptional control of gene expression. Nature Biotechnology, 22(7), 841-847. doi:10.1038/nbt986Qi, L., Lucks, J. B., Liu, C. C., Mutalik, V. K., & Arkin, A. P. (2012). Engineering naturally occurring trans -acting non-coding RNAs to sense molecular signals. Nucleic Acids Research, 40(12), 5775-5786. doi:10.1093/nar/gks168Desai, S. K., & Gallivan, J. P. (2004). Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. Journal of the American Chemical Society, 126(41), 13247-13254. doi:10.1021/ja048634jWachsmuth, M., Findeiss, S., Weissheimer, N., Stadler, P. F., & Morl, M. (2012). De novo design of a synthetic riboswitch that regulates transcription termination. Nucleic Acids Research, 41(4), 2541-2551. doi:10.1093/nar/gks1330Wieland, M., & Hartig, J. S. (2008). Improved Aptazyme Design and In Vivo Screening Enable Riboswitching in Bacteria. Angewandte Chemie International Edition, 47(14), 2604-2607. doi:10.1002/anie.200703700Carothers, J. M., Goler, J. A., Juminaga, D., & Keasling, J. D. (2011). Model-Driven Engineering of RNA Devices to Quantitatively Program Gene Expression. Science, 334(6063), 1716-1719. doi:10.1126/science.1212209Hochrein, L. M., Schwarzkopf, M., Shahgholi, M., Yin, P., & Pierce, N. A. (2013). Conditional Dicer Substrate Formation via Shape and Sequence Transduction with Small Conditional RNAs. Journal of the American Chemical Society, 135(46), 17322-17330. doi:10.1021/ja404676xRodrigo, G., Landrain, T. E., Majer, E., Daròs, J.-A., & Jaramillo, A. (2013). Full Design Automation of Multi-State RNA Devices to Program Gene Expression Using Energy-Based Optimization. PLoS Computational Biology, 9(8), e1003172. doi:10.1371/journal.pcbi.1003172Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., & Schuster, P. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte f�r Chemie Chemical Monthly, 125(2), 167-188. doi:10.1007/bf00818163Mathews, D. H., Disney, M. D., Childs, J. L., Schroeder, S. J., Zuker, M., & Turner, D. H. (2004). Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proceedings of the National Academy of Sciences, 101(19), 7287-7292. doi:10.1073/pnas.0401799101Dirks, R. M., Bois, J. S., Schaeffer, J. M., Winfree, E., & Pierce, N. A. (2007). Thermodynamic Analysis of Interacting Nucleic Acid Strands. SIAM Review, 49(1), 65-88. doi:10.1137/060651100Wright, P. R., Georg, J., Mann, M., Sorescu, D. A., Richter, A. S., Lott, S., … Backofen, R. (2014). CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains. Nucleic Acids Research, 42(W1), W119-W123. doi:10.1093/nar/gku359Adleman, L. (1994). Molecular computation of solutions to combinatorial problems. Science, 266(5187), 1021-1024. doi:10.1126/science.7973651Seelig, G., Soloveichik, D., Zhang, D. Y., & Winfree, E. (2006). Enzyme-Free Nucleic Acid Logic Circuits. Science, 314(5805), 1585-1588. doi:10.1126/science.1132493Yin, P., Choi, H. M. T., Calvert, C. R., & Pierce, N. A. (2008). Programming biomolecular self-assembly pathways. Nature, 451(7176), 318-322. doi:10.1038/nature06451Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Hersch, G. L., Baker, T. A., & Sauer, R. T. (2004). SspB delivery of substrates for ClpXP proteolysis probed by the design of improved degradation tags. Proceedings of the National Academy of Sciences, 101(33), 12136-12141. doi:10.1073/pnas.0404733101Lutz, R. (1997). Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 25(6), 1203-1210. doi:10.1093/nar/25.6.1203Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676-682. doi:10.1038/nmeth.2019Laidler, K. J., & King, M. C. (1983). Development of transition-state theory. The Journal of Physical Chemistry, 87(15), 2657-2664. doi:10.1021/j100238a002Rodrigo, G., Majer, E., Prakash, S., Daròs, J.-A., Jaramillo, A., & Poyatos, J. F. (2015). Exploring the Dynamics and Mutational Landscape of Riboregulation with a Minimal Synthetic Circuit in Living Cells. Biophysical Journal, 109(5), 1070-1076. doi:10.1016/j.bpj.2015.07.021Srinivas, N., Ouldridge, T. E., Šulc, P., Schaeffer, J. M., Yurke, B., Louis, A. A., … Winfree, E. (2013). On the biophysics and kinetics of toehold-mediated DNA strand displacement. Nucleic Acids Research, 41(22), 10641-10658. doi:10.1093/nar/gkt801Culler, S. J., Hoff, K. G., & Smolke, C. D. (2010). Reprogramming Cellular Behavior with RNA Controllers Responsive to Endogenous Proteins. Science, 330(6008), 1251-1255. doi:10.1126/science.1192128Benenson, Y., Paz-Elizur, T., Adar, R., Keinan, E., Livneh, Z., & Shapiro, E. (2001). Programmable and autonomous computing machine made of biomolecules. Nature, 414(6862), 430-434. doi:10.1038/3510653

Cyclodepsipeptide Toxin Promotes the Degradation of Hsp90 Client Proteins through Chaperone-Mediated Autophagy

Promoting the degradation of Hsp90 client proteins by inhibiting Hsp90, an important protein chaperone, has been shown to be a promising new anticancer strategy. In this study, we show that an oxazoline analogue of apratoxin A (oz-apraA), a cyclodepsipeptide isolated from a marine cyanobacterium, promotes the degradation of Hsp90 clients through chaperone-mediated autophagy (CMA). We identify a KFERQ-like motif as a conserved pentapeptide sequence in the kinase domain of epidermal growth factor receptor (EGFR) necessary for recognition as a CMA substrate. Mutation of this motif prevents EGFR degradation by CMA and promotes the degradation of EGFR through the proteasomal pathway in oz-apraA–treated cells. Oz-apraA binds to Hsc70/Hsp70. We propose that apratoxin A inhibits Hsp90 function by stabilizing the interaction of Hsp90 client proteins with Hsc70/Hsp70 and thus prevents their interactions with Hsp90. Our study provides the first examples for the ability of CMA to mediate degradation of membrane receptors and cross talks of CMA and proteasomal degradation mechanisms

Spermidine and resveratrol induce autophagy by distinct pathways converging on the acetylproteome

The acetylase inhibitor spermidine and the sirtuin-1 activator resveratrol disrupt the antagonistic network of acetylases and deacetylases that regulate autophagy

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression

[EN] Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA-RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.EVOPROG [FP7-ICT-610730]; PROMYS [FP7-KBBE-613745 to A.J.]; Ministerio de Economia y Competitividad, Spain [BIO2011-26741 to J.-A.D.]; PRES Paris Sud grant (S.S.); EMBO long-term fellowship co-funded by Marie Curie actions [ALTF-1177-2011 A.J., G.R.]; AXA research fund; Ministerio de Educacion, Cultura y Deporte, Spain [AP2012-3751 to E.M.]. Funding for open access charge: EVOPROG [FP7-ICT-610730]; PROMYS [FP7-KBBE-613745].Shen, S.; Rodrigo Tarrega, G.; Prakash, S.; Majer, E.; Landrain, T.; Kirov, B.; Daros Arnau, JA.... (2015). Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression. Nucleic Acids Research. 43(10):5158-5170. https://doi.org/10.1093/nar/gkv287S515851704310Ulrich, L. E., Koonin, E. V., & Zhulin, I. B. (2005). One-component systems dominate signal transduction in prokaryotes. Trends in Microbiology, 13(2), 52-56. doi:10.1016/j.tim.2004.12.006Kiel, C., Yus, E., & Serrano, L. (2010). Engineering Signal Transduction Pathways. Cell, 140(1), 33-47. doi:10.1016/j.cell.2009.12.028Isaacs, F. J., Dwyer, D. J., & Collins, J. J. (2006). RNA synthetic biology. Nature Biotechnology, 24(5), 545-554. doi:10.1038/nbt1208Liang, J. C., Bloom, R. J., & Smolke, C. D. (2011). Engineering Biological Systems with Synthetic RNA Molecules. Molecular Cell, 43(6), 915-926. doi:10.1016/j.molcel.2011.08.023Dueber, J. E. (2003). Reprogramming Control of an Allosteric Signaling Switch Through Modular Recombination. Science, 301(5641), 1904-1908. doi:10.1126/science.1085945Sallee, N. A., Yeh, B. J., & Lim, W. A. (2007). Engineering Modular Protein Interaction Switches by Sequence Overlap. Journal of the American Chemical Society, 129(15), 4606-4611. doi:10.1021/ja0672728Rodrigo, G., Landrain, T. E., Shen, S., & Jaramillo, A. (2013). A new frontier in synthetic biology: automated design of small RNA devices in bacteria. Trends in Genetics, 29(9), 529-536. doi:10.1016/j.tig.2013.06.005Callura, J. M., Dwyer, D. J., Isaacs, F. J., Cantor, C. R., & Collins, J. J. (2010). Tracking, tuning, and terminating microbial physiology using synthetic riboregulators. Proceedings of the National Academy of Sciences, 107(36), 15898-15903. doi:10.1073/pnas.1009747107Callura, J. M., Cantor, C. R., & Collins, J. J. (2012). Genetic switchboard for synthetic biology applications. Proceedings of the National Academy of Sciences, 109(15), 5850-5855. doi:10.1073/pnas.1203808109Werstuck, G. (1998). Controlling Gene Expression in Living Cells Through Small Molecule-RNA Interactions. Science, 282(5387), 296-298. doi:10.1126/science.282.5387.296Wieland, M., & Hartig, J. S. (2008). Improved Aptazyme Design and In Vivo Screening Enable Riboswitching in Bacteria. Angewandte Chemie International Edition, 47(14), 2604-2607. doi:10.1002/anie.200703700Win, M. N., & Smolke, C. D. (2007). A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proceedings of the National Academy of Sciences, 104(36), 14283-14288. doi:10.1073/pnas.0703961104Klauser, B., & Hartig, J. S. (2013). An engineered small RNA-mediated genetic switch based on a ribozyme expression platform. Nucleic Acids Research, 41(10), 5542-5552. doi:10.1093/nar/gkt253Bayer, T. S., & Smolke, C. D. (2005). Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nature Biotechnology, 23(3), 337-343. doi:10.1038/nbt1069Qi, L., Lucks, J. B., Liu, C. C., Mutalik, V. K., & Arkin, A. P. (2012). Engineering naturally occurring trans -acting non-coding RNAs to sense molecular signals. Nucleic Acids Research, 40(12), 5775-5786. doi:10.1093/nar/gks168Looger, L. L., Dwyer, M. A., Smith, J. J., & Hellinga, H. W. (2003). Computational design of receptor and sensor proteins with novel functions. Nature, 423(6936), 185-190. doi:10.1038/nature01556Kortemme, T., & Baker, D. (2004). Computational design of protein–protein interactions. Current Opinion in Chemical Biology, 8(1), 91-97. doi:10.1016/j.cbpa.2003.12.008Rodrigo, G., Landrain, T. E., & Jaramillo, A. (2012). De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells. Proceedings of the National Academy of Sciences, 109(38), 15271-15276. doi:10.1073/pnas.1203831109Isaacs, F. J., Dwyer, D. J., Ding, C., Pervouchine, D. D., Cantor, C. R., & Collins, J. J. (2004). Engineered riboregulators enable post-transcriptional control of gene expression. Nature Biotechnology, 22(7), 841-847. doi:10.1038/nbt986Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., & Schuster, P. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte f�r Chemie Chemical Monthly, 125(2), 167-188. doi:10.1007/bf00818163Pédelacq, J.-D., Cabantous, S., Tran, T., Terwilliger, T. C., & Waldo, G. S. (2005). Engineering and characterization of a superfolder green fluorescent protein. Nature Biotechnology, 24(1), 79-88. doi:10.1038/nbt1172Hersch, G. L., Baker, T. A., & Sauer, R. T. (2004). SspB delivery of substrates for ClpXP proteolysis probed by the design of improved degradation tags. Proceedings of the National Academy of Sciences, 101(33), 12136-12141. doi:10.1073/pnas.0404733101Rodrigo, G., Kirov, B., Shen, S., & Jaramillo, A. (2013). Theoretical and experimental analysis of the forced LacI-AraC oscillator with a minimal gene regulatory model. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(2), 025109. doi:10.1063/1.4809786Danino, T., Mondragón-Palomino, O., Tsimring, L., & Hasty, J. (2010). A synchronized quorum of genetic clocks. Nature, 463(7279), 326-330. doi:10.1038/nature08753Mathews, D. H., Sabina, J., Zuker, M., & Turner, D. H. (1999). Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of Molecular Biology, 288(5), 911-940. doi:10.1006/jmbi.1999.2700Paige, J. S., Nguyen-Duc, T., Song, W., & Jaffrey, S. R. (2012). Fluorescence Imaging of Cellular Metabolites with RNA. Science, 335(6073), 1194-1194. doi:10.1126/science.1218298Chen, X., & Ellington, A. D. (2009). Design Principles for Ligand-Sensing, Conformation-Switching Ribozymes. PLoS Computational Biology, 5(12), e1000620. doi:10.1371/journal.pcbi.1000620Quarta, G., Sin, K., & Schlick, T. (2012). Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function. PLoS Computational Biology, 8(2), e1002368. doi:10.1371/journal.pcbi.1002368Freeman, J. B., & Dale, R. (2012). Assessing bimodality to detect the presence of a dual cognitive process. Behavior Research Methods, 45(1), 83-97. doi:10.3758/s13428-012-0225-xWieland, M., Benz, A., Klauser, B., & Hartig, J. S. (2009). Artificial Ribozyme Switches Containing Natural Riboswitch Aptamer Domains. Angewandte Chemie International Edition, 48(15), 2715-2718. doi:10.1002/anie.200805311Penchovsky, R., & Breaker, R. R. (2005). Computational design and experimental validation of oligonucleotide-sensing allosteric ribozymes. Nature Biotechnology, 23(11), 1424-1433. doi:10.1038/nbt1155Chushak, Y., & Stone, M. O. (2009). In silico selection of RNA aptamers. Nucleic Acids Research, 37(12), e87-e87. doi:10.1093/nar/gkp408Bartel, D., & Szostak, J. (1993). Isolation of new ribozymes from a large pool of random sequences [see comment]. Science, 261(5127), 1411-1418. doi:10.1126/science.7690155Lutz, R. (1997). Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 25(6), 1203-1210. doi:10.1093/nar/25.6.1203Mutalik, V. K., Qi, L., Guimaraes, J. C., Lucks, J. B., & Arkin, A. P. (2012). Rationally designed families of orthogonal RNA regulators of translation. Nature Chemical Biology, 8(5), 447-454. doi:10.1038/nchembio.919Bennett, M. R., & Hasty, J. (2009). Microfluidic devices for measuring gene network dynamics in single cells. Nature Reviews Genetics, 10(9), 628-638. doi:10.1038/nrg2625Cookson, N. A., Mather, W. H., Danino, T., Mondragón‐Palomino, O., Williams, R. J., Tsimring, L. S., & Hasty, J. (2011). Queueing up for enzymatic processing: correlated signaling through coupled degradation. Molecular Systems Biology, 7(1), 561. doi:10.1038/msb.2011.94Hermann, T. (2000). Adaptive Recognition by Nucleic Acid Aptamers. Science, 287(5454), 820-825. doi:10.1126/science.287.5454.820Lou, C., Stanton, B., Chen, Y.-J., Munsky, B., & Voigt, C. A. (2012). Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nature Biotechnology, 30(11), 1137-1142. doi:10.1038/nbt.2401Qi, L., Haurwitz, R. E., Shao, W., Doudna, J. A., & Arkin, A. P. (2012). RNA processing enables predictable programming of gene expression. Nature Biotechnology, 30(10), 1002-1006. doi:10.1038/nbt.2355Liu, C. C., Qi, L., Lucks, J. B., Segall-Shapiro, T. H., Wang, D., Mutalik, V. K., & Arkin, A. P. (2012). An adaptor from translational to transcriptional control enables predictable assembly of complex regulation. Nature Methods, 9(11), 1088-1094. doi:10.1038/nmeth.2184Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013). Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression. Cell, 152(5), 1173-1183. doi:10.1016/j.cell.2013.02.022Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., … Qi, L. S. (2013). CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes. Cell, 154(2), 442-451. doi:10.1016/j.cell.2013.06.044Bashor, C. J., Horwitz, A. A., Peisajovich, S. G., & Lim, W. A. (2010). Rewiring Cells: Synthetic Biology as a Tool to Interrogate the Organizational Principles of Living Systems. Annual Review of Biophysics, 39(1), 515-537. doi:10.1146/annurev.biophys.050708.133652Yen, L., Svendsen, J., Lee, J.-S., Gray, J. T., Magnier, M., Baba, T., … Mulligan, R. C. (2004). Exogenous control of mammalian gene expression through modulation of RNA self-cleavage. Nature, 431(7007), 471-476. doi:10.1038/nature0284

Acquisition by cancer cells of a plethora of resistance-conferring genetic alterations greatly limits the clinical utility of most anti- cancer drugs.

Acquisition by cancer cells of a plethora of resistance-conferring genetic alterations greatly limits the clinical utility of most anti- cancer drugs. Therefore, there is a need to improve the effective- ness of treatment before mutational-acquired resistance prevails. Relapse is driven by a small subpopulation of residual or â â drug-tolerantâ â cells, which are traditionally called â â minimal residual diseaseâ â (MRD), that remain viable upon drug exposure. Recent in vitro findings have indicated that the emergence of these per- sisters is unlikely due to mutational mechanisms. A non-mutually exclusive scenario proposes that the drug-tolerant phenotype is transiently acquired by a small pro- portion of cancer cells through non-mutational mechanisms. To gain insights into the biology of MRD, we applied single-cell RNA sequencing to malignant melanoma BRAF mutated cells, and we identified a subpopulation of melanoma cells is tolerant to targeted therapy via metabolic reprogramming. Cancer cells were known to reprogram their metabolic profiles geared toward glycolysis, despite sufficient oxygen available to support oxidative phosphorylation (OXPHOS), a phenomenon known as the Warburg effect. We found that melanoma MRD can switch their metabolic program from glycolysis towards mitochondrial OXPHOS alimented by fatty acid oxidation (FAO), thereby renders the melanoma MRD highly sensitive to FAO inhibition in vitro and in mouse tumor models. This MRD-directed metabolic reprogramming suggests a more clever treatment combination regimen to fight against cancer resistance.Non UBCUnreviewedAuthor affiliation: Institut Gustave Roussy VillejuifPostdoctora

Cell plasticity in cancer cell populations

International audienceIn this review, we propose a recension of biological observations on plasticity in cancer cell populations and discuss theoretical considerations about their mechanisms

Emerging role of mRNA epitranscriptomic regulation in chemoresistant cancer cells

Cancer persister cells remain a significant barrier to effective anti-cancer therapy. We found that melanoma persister cells undergo a reversible reprogramming of mRNA translation. A subset of mRNAs, harboring N6-methyladenosine in their 5'-untranslated regions, is translationally up-regulated in an eIF4A-dependent manner. Targeting eIF4A prevents the emergence of resistant clones

Emerging role of mRNA epitranscriptomic regulation in chemoresistant cancer cells

Cancer persister cells remain a significant barrier to effective anti-cancer therapy. We found that melanoma persister cells undergo a reversible reprogramming of mRNA translation. A subset of mRNAs, harboring N6-methyladenosine in their 5ʹ-untranslated regions, is translationally up-regulated in an eIF4A-dependent manner. Targeting eIF4A prevents the emergence of resistant clones

Persistent Cancer Cells: The Deadly Survivors

International audienc