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Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions
Open accessAbstract
Suppressive drug interactions, in which one antibiotic can actually help bacterial cells to grow faster in the presence of another, occur between protein and DNA synthesis inhibitors. Here, we show that this suppression results from nonoptimal regulation of ribosomal genes in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. To test whether ribosomal gene expression under DNA stress is nonoptimal for growth rate, we sequentially deleted up to six of the seven ribosomal RNA operons. These synthetic manipulations of ribosomal gene expression correct the protein-DNA imbalance, lead to improved survival and growth, and completely remove the suppressive drug interaction. A simple mathematical model explains the nonoptimal regulation in different nutrient environments. These results reveal the genetic mechanism underlying an important class of suppressive drug interactions.
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“This difference is important to consider when performing experiments that depend on the number of cellular divisions or cells present, such as cellular communication [12, 13] and antibiotic susceptibility [14–17] and biofilms [18].”
From Estimating microbial population data from optical density (2022) · cites this paper as [15]
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Cites
A Common Mechanism of Cellular Death Induced by Bactericidal Antibiotics
Kohanski, M. A., Dwyer, D. J., Hayete, B., Lawrence, C. A., & Collins, J. J. (2007) · Cell · doi:10.1016/j.cell.2007.06.049 · Cited by 2,982
Bacterial Persistence as a Phenotypic Switch
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., & Leibler, S. (2004) · Science · doi:10.1126/science.1099390 · Cited by 2,952
Self-citationAntibiotics in Laboratory Medicine.
Lorian, V. (1980) · Annals of Internal Medicine · doi:10.7326/0003-4819-93-4-653_4 · Cited by 2,809
THE TOXICITY OF POISONS APPLIED JOINTLY1
BLISS, C. I. (1939) · Annals of Applied Biology · doi:10.1111/j.1744-7348.1939.tb06990.x · Cited by 2,419
Escherichia coli and Salmonella.
Neidhart, F. C. (1996) · Cited by 1,577
Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments
Kussell, E. L., & Leibler, S. (2005) · Science · doi:10.1126/science.1114383 · Cited by 1,357
Aminoglycoside antibiotics induce bacterial biofilm formation
Hoffman, L. R., D’Argenio, D. A., MacCoss, M. J., Zhang, Z., Jones, R. A., & Miller, S. I. (2005) · Nature · doi:10.1038/nature03912 · Cited by 1,313
Efflux-mediated antimicrobial resistance
Poole, K. (2005) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dki171 · Cited by 1,082
(1953) · PubMed · Cited by 942
Modulation of Chemical Composition and Other Parameters of the Cell by Growth Rate
Bremer, H. J. (1999) · Cited by 909
Cited by
Sorted by citations
Items 101–200 of 227
3D microfluidic gradient generator for combination antimicrobial susceptibility testing
Sweet, E. C., Yang, B., Chen, J., Vickerman, R., Lin, Y., Long, A., Jacobs, E., Wu, T., Mercier, C., Jew, R., et al. (2020) · Microsystems & Nanoengineering · doi:10.1038/s41378-020-00200-7 · Cited by 50
Chubiz, L. M., Glekas, G. D., & Rao, C. V. (2012) · Journal of Bacteriology · doi:10.1128/jb.00680-12 · Cited by 46
Stability of β-lactam antibiotics in bacterial growth media
Brouwers, R., Vass, H., Dawson, A. J., Squires, T., Tavaddod, S., & Allen, R. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236198 · Cited by 48
Geisinger, E., Vargas-Cuebas, G. G., Mortman, N. J., Syal, S., Dai, Y., Wainwright, E. L., Lazinski, D. W., Wood, S. J., Zhu, Z., Anthony, J. S., et al. (2019) · mBio · doi:10.1128/mbio.01127-19 · Cited by 44
Park, B., Whichard, Z. L., & Corey, S. J. (2012) · Cancer Letters · doi:10.1016/j.canlet.2012.01.039 · Cited by 44
Wang, Y., Du, B., & Wu, G. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.139378 · Cited by 45
Bacterial responses to antibiotics and their combinations
Mitosch, K., & Bollenbach, T. (2014) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12190 · Cited by 39
Self-citationBershtein, S., Choi, J., Bhattacharyya, S., Budnik, B., & Shakhnovich, E. I. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.03.051 · Cited by 41
Resolution of Gene Regulatory Conflicts Caused by Combinations of Antibiotics
Bollenbach, T., & Kishony, R. (2011) · Molecular Cell · doi:10.1016/j.molcel.2011.04.016 · Cited by 41
Self-citationMinimal exposure of lipid II cycle intermediates triggers cell wall antibiotic resistance
Piepenbreier, H., Diehl, A., & Fritz, G. (2019) · Nature Communications · doi:10.1038/s41467-019-10673-4 · Cited by 42
CombiANT: Antibiotic interaction testing made easy
Fatsis-Kavalopoulos, N., Roemhild, R., Tang, P.-C., Kreuger, J., & Andersson, D. I. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000856 · Cited by 39
Gjini, E., & Wood, K. B. (2021) · eLife · doi:10.7554/elife.64851 · Cited by 34
Diaz, J. E. L., Ahsen, M. E., Schaffter, T., Chen, X., Realubit, R. B., Karan, C., Califano, A., Losic, B., & Stolovitzky, G. A. (2020) · eLife · doi:10.7554/elife.52707 · Cited by 38
Prediction of drug cocktail effects when the number of measurements is limited
Zimmer, A., Tendler, A., Katzir, I., Mayo, A., & Alon, U. S. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2002518 · Cited by 39
Zuleta, I. A., Aranda-Díaz, A., Li, H., & El‐Samad, H. (2014) · Nature Methods · doi:10.1038/nmeth.2879 · Cited by 37
Optimization and control in bacterial Lag phase
Schultz, D., & Kishony, R. (2013) · BMC Biology · doi:10.1186/1741-7007-11-120 · Cited by 46
Self-citationPredicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics
Greulich, P., Doležal, J., Scott, M., Evans, M. R., & Allen, R. J. (2017) · Physical Biology · doi:10.1088/1478-3975/aa8001 · Cited by 34
Improved network performance via antagonism: From synthetic rescues to multi‐drug combinations
Motter, A. E. (2010) · BioEssays · doi:10.1002/bies.200900128 · Cited by 36
Antibiotic interactions shape short-term evolution of resistance in E. faecalis
Dean, Z., Maltas, J., & Wood, K. B. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008278 · Cited by 35
Martínez, A. K., Gordon, E., Sengupta, A., Shirole, N. H., Klepacki, D., Martínez‐Garriga, B., Brown, L. M., Benedik, M. J., Yanofsky, C., Mankin, A. S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt923 · Cited by 37
Bruggeman, F. J., Teusink, B., & Steuer, R. (2023) · BioEssays · doi:10.1002/bies.202300015 · Cited by 33
Combinatorial code governing cellular responses to complex stimuli
Cappuccio, A., Zollinger, R., Schenk, M., Walczak, A. M., Servant, N., Barillot, E., Hupé, P., Modlin, R. L., & Soumelis, V. (2015) · Nature Communications · doi:10.1038/ncomms7847 · Cited by 35
Machine learning to design antimicrobial combination therapies: Promises and pitfalls
Cantrell, J. M., Chung, C. H., & Chandrasekaran, S. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.04.006 · Cited by 32
Evolution of Drug Resistance in Bacteria
Waclaw, B. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-32189-9_5 · Cited by 34
Update on Feline Hemoplasmosis
Barker, E. (2019) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2019.02.009 · Cited by 32
Gaurav, A., Gupta, V., Shrivastava, S. K., & Pathania, R. (2021) · Communications Biology · doi:10.1038/s42003-021-02074-5 · Cited by 32
Dong, C., Li, L.-X., Cui, Z.-H., Chen, S.-W., Xiong, Y. Q., Lu, J.-Q., Liao, X., Gao, Y., Sun, J. G., & Liu, Y. (2017) · Frontiers in Pharmacology · doi:10.3389/fphar.2017.00553 · Cited by 32
When more is less: Emergent suppressive interactions in three-drug combinations
Beppler, C., Tekin, E., White, C., Mao, Z., Miller, J. H., Damoiseaux, R. D., Savage, V. M., & Yeh, P. J. (2017) · BMC Microbiology · doi:10.1186/s12866-017-1017-3 · Cited by 28
Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing
Bren, A., Glass, D. S., Kohanim, Y. K., Mayo, A., & Alon, U. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312651120 · Cited by 25
Large-Scale Identification and Analysis of Suppressive Drug Interactions
Cokol, M., Weinstein, Z., Yılancıoğlu, K., Tasan, M., Doak, A. K., Cansever, D., Mutlu, B., Li, S., Rodriguez‐Esteban, R., Akhmedov, M., et al. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.02.012 · Cited by 27
Ubiquitous selection for mecA in community-associated MRSA across diverse chemical environments
Snitser, O., Russ, D., Stone, L. K., Wang, K. K., Sharir, H., Kozer, N., Cohen, G., Barr, H. M., & Kishony, R. (2020) · Nature Communications · doi:10.1038/s41467-020-19825-3 · Cited by 27
Self-citationA Magic Spot in Genome Maintenance
Rasouly, A., Pani, B., & Nudler, E. (2016) · Trends in Genetics · doi:10.1016/j.tig.2016.11.002 · Cited by 28
Bioenergetic stress potentiates antimicrobial resistance and persistence
Li, B., Srivastava, S., Shaikh, M., Mereddy, G., Garcia, M. R., Chiles, E. N., Shah, A., Ofori-Anyinam, B., Chu, T., Cheney, N. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60302-6 · Cited by 28
Mathematical models to study the biology of pathogens and the infectious diseases they cause
Xavier, J. B., Monk, J. M., Poudel, S., Norsigian, C. J., Sastry, A. V., Liao, C., Bento, J., Suchard, M. A., Arrieta‐Ortiz, M. L., Peterson, E. J. R., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104079 · Cited by 27
Technologies for High-Throughput Identification of Antibiotic Mechanism of Action
da Cunha, B. R., Zoio, P., Fonseca, L. P., & Calado, C. R. C. (2021) · Antibiotics · doi:10.3390/antibiotics10050565 · Cited by 24
Bacterial Growth Curve Measurements with a Multimode Microplate Reader
Rogers, A., Bullard, K. R., Dod, A. C., & Wang, Y. (2022) · BIO-PROTOCOL · doi:10.21769/bioprotoc.4410 · Cited by 24
Structural basis of Cfr-mediated antimicrobial resistance and mechanisms to evade it
Aleksandrova, E. V., Wu, K. J. Y., Tresco, B. I. C., Syroegin, E. A., Killeavy, E. E., Balasanyants, S. M., Svetlov, M. S., Gregory, S. T., Atkinson, G. C., Myers, A. G., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-023-01525-w · Cited by 26
Pairwise interactions and the battle against combinatorics in multidrug therapies
Wood, K. B. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612365113 · Cited by 22
Chemical Genomic Approaches to Study Model Microbes
Barker, C. A., Farha, M. A., & Brown, E. D. (2010) · Chemistry & Biology · doi:10.1016/j.chembiol.2010.05.010 · Cited by 23
Growth‐mediated negative feedback shapes quantitative antibiotic response
Angermayr, S. A., Pang, T. Y., Chevereau, G., Mitosch, K., Lercher, M. J., & Bollenbach, T. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110490 · Cited by 21
Self-citationAntibiotic resistance: a physicist’s view
Allen, R. J., & Waclaw, B. (2016) · Physical Biology · doi:10.1088/1478-3975/13/4/045001 · Cited by 20
Livestock manure and antibiotics alter extracellular enzyme activity
McBride, S. G., Wepking, C., Hedin, M. L., Thompson, R. C., Barrett, J., & Strickland, M. S. (2020) · Applied Soil Ecology · doi:10.1016/j.apsoil.2020.103667 · Cited by 20
Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis
Krueger, A., Munck, C., Dantas, G. A., Church, G. M., Galagan, J. E., Lehár, J., & Sommer, M. O. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147651 · Cited by 19
Ribosome Provisioning Activates a Bistable Switch Coupled to Fast Exit from Stationary Phase
Remigi, P., Ferguson, G. C., McConnell, E., Monte, S. D., Rogers, D. W., & Rainey, P. B. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz041 · Cited by 19
Resource Competition May Lead to Effective Treatment of Antibiotic Resistant Infections
Gomes, A. L. C., Galagan, J. E., & Segrè, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080775 · Cited by 20
Hemoglobin and hematocrit: an analysis of clinical accuracy. Case study of the anemic patient.
Keen, M. L. (1998) · PubMed · Cited by 19
Wlodarski, M., Mancini, L., Raciti, B., Sclavi, B., Lagomarsino, M. C., & Cicuta, P. (2020) · iScience · doi:10.1016/j.isci.2020.101560 · Cited by 18
Within-species variability of antibiotic interactions in Gram-negative bacteria
Tang, P.-C., Sánchez‐Hevia, D. L., Westhoff, S., Fatsis-Kavalopoulos, N., & Andersson, D. I. (2024) · mBio · doi:10.1128/mbio.00196-24 · Cited by 17
Pharmacogenomic Strategies Against Microbial Resistance: From Bright to Bleak to Innovative
Dandekar, T., & Dandekar, G. (2010) · Pharmacogenomics · doi:10.2217/pgs.10.18 · Cited by 18
A Mig-14-like protein (PA5003) affects antimicrobial peptide recognition in Pseudomonas aeruginosa
Jochumsen, N., Liu, Y., Molin, S., & Folkesson, A. (2011) · Microbiology · doi:10.1099/mic.0.049445-0 · Cited by 17
Cokol, M. (2013) · Current Drug Discovery Technologies · doi:10.2174/1570163811310020003 · Cited by 15
Modeling the Overproduction of Ribosomes when Antibacterial Drugs Act on Cells
Maitra, A., & Dill, K. A. (2016) · Biophysical Journal · doi:10.1016/j.bpj.2015.12.016 · Cited by 15
Campbell, E. M., & Chao, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086971 · Cited by 14
Gene Expression Tradeoffs Determine Bacterial Survival and Adaptation to Antibiotic Stress
Kratz, J. C., & Banerjee, S. (2024) · PRX Life · doi:10.1103/prxlife.2.013010 · Cited by 13
Bazurto, J. V., Riazi, S., D’Alton, S., Deatherage, D. E., Bruger, E. L., Barrick, J. E., & Marx, C. J. (2021) · Microorganisms · doi:10.3390/microorganisms9020347 · Cited by 14
Selection inversion: a probable tool against antibiotic resistance
Baloch, Z., Aslam, B., Muzammil, S., Khurshid, M., Rasool, M. H., & Ma, K. (2018) · Infection and Drug Resistance · doi:10.2147/idr.s176759 · Cited by 13
Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community
Adamowicz, E. M., & Harcombe, W. R. (2020) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00465-20 · Cited by 13
Principle of System Balance for Drug Interactions
Xavier, J. B., & Sander, C. (2010) · New England Journal of Medicine · doi:10.1056/nejmcibr1001270 · Cited by 13
Ribosome-binding antibiotics increase bacterial longevity and growth efficiency
Wood, E., Schulenburg, H., Rosenstiel, P. C., Bergmiller, T., Ankrett, D. N., Gudelj, I., & Beardmore, R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221507120 · Cited by 11
Shaferman, M., Gencel, M., Alon, N., Alasad, K., Rotblat, B., Serohijos, A. W. R., Alfonta, L., & Bershtein, S. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad123 · Cited by 9
Smith, R. P., Doiron, A., Muzquiz, R., Fortoul, M. C., Haas, M. F., Abraham, T., Quinn, R. J., Barraza, I., Chowdhury, K., & Nemzer, L. R. (2019) · Environmental Microbiology · doi:10.1111/1462-2920.14796 · Cited by 10
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering
Prescott, T. P., Lang, M., & Papachristodoulou, A. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004235 · Cited by 10
Applications of Coarse-Grained Models in Metabolic Engineering
Doan, D. T., Hoang, M. D., Heins, A., & Kremling, A. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.806213 · Cited by 10
Raatz, M., & Traulsen, A. (2023) · Evolution · doi:10.1093/evolut/qpad042 · Cited by 12
Environmental Antibiotic Resistance Associated with Land Application of Biosolids
McLain, J. E., Rock, C. M., & Gerba, C. P. (2017) · doi:10.1002/9781119192428.ch13 · Cited by 9
Pletnev, P. I., Pupov, D., Pshanichnaya, L., Esyunina, D., Petushkov, I., Nesterchuk, M. V., Osterman, I. А., Rubtsova, M. P., Mardanov, A. V., Ravin, N. V., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa798 · Cited by 8
Hidden suppressive interactions are common in higher-order drug combinations
Lozano-Huntelman, N. A., Zhou, A., Tekin, E., Cruz‐Loya, M., Østman, B., Boyd, S. M., Savage, V. M., & Yeh, P. J. (2021) · iScience · doi:10.1016/j.isci.2021.102355 · Cited by 8
Suppressive Drug Interactions between Antifungals
de Vos, M. G. J., & Bollenbach, T. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.04.004 · Cited by 8
Self-citationExtreme Antagonism Arising from Gene-Environment Interactions
Wytock, T. P., Zhang, M., Jinich, A., Fiebig, A., Crosson, S., & Motter, A. E. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.09.038 · Cited by 7
Lozano-Huntelman, N. A., Bullivant, A., Chacon-Barahona, J. A., Valencia, A., Ida, N., Zhou, A., Kalhori, P., Bello, G., Xue, C., Boyd, S. M., et al. (2023) · Evolutionary Applications · doi:10.1111/eva.13608 · Cited by 7
Deletion of rRNA Operons of Sinorhizobium fredii Strain NGR234 and Impact on Symbiosis With Legumes
Cherni, A. E., & Perret, X. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00154 · Cited by 7
Browne, S., Bhatia, S., Sarkar, N., & Kaushik, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99866-6.00017-9 · Cited by 7
Delays in Fitness Adjustment Can Lead to Coexistence of Hierarchically Interacting Species
Bauer, M., & Frey, E. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.268101 · Cited by 6
Moran, J., & Wood, K. B. (2024) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-042924-110923 · Cited by 5
CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations
Kang, X., Liu, X., Zou, Q., Li, T., & Luo, X. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1608832 · Cited by 5
Wu, D., Yao, K.-S., Hermann, M., Chen, N., Huang, A., Ying, G., Smidt, H., & den Brink, P. J. V. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.141237 · Cited by 5
Qi, Q., Angermayr, S. A., & Bollenbach, T. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.760017 · Cited by 5
Self-citationModak, B., Rahman, M. S., Gazi, M. S., Uddin, M. A., & Rahman, T. (2015) · Stamford Journal of Microbiology · doi:10.3329/sjm.v3i1.22749 · Cited by 5
Delayed adaptation in stochastic metapopulation models
Bauer, M., & Frey, E. (2018) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/122/68002 · Cited by 5
Hydroxyurea Triggers Cellular Responses that Actively Cause Bacterial Cell Death
Bollenbach, T., & Kishony, R. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.11.027 · Cited by 5
Self-citationOjkic, N., Lilja, E., Direito, S., Dawson, A., Allen, R. J., & Waclaw, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/791145 · Cited by 6
Appearance of ciprofloxacin/chlortetracycline-resistant bacteria in waters of Québec City in Canada
Cuprys, A. K., Lecka, J., Proulx, F., Brar, S. K., & Drogui, P. (2019) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2019.04.012 · Cited by 4
Khan, M. S. (2019) · Springer eBooks · doi:10.1007/978-981-13-9871-1_19 · Cited by 4
Hamami, E., Huo, W., Neal, K., Neisewander, I., Shivangi, Freundlich, J. S., Geisinger, E., & Isberg, R. R. (2026) · mBio · doi:10.1128/mbio.00902-26 · Cited by 3
Is Catalytic Activity of Chaperones a Selectable Trait for the Emergence of Heat Shock Response?
Çetinbaş, M., & Shakhnovich, E. I. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2014.11.3468 · Cited by 4
Serbanescu, D., Ojkic, N., & Banerjee, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.08.982728 · Cited by 7
Snoeyenbos-West, O., Guerrero, C. R., Valencia, M., & Carini, P. (2024) · Microbiology Spectrum · doi:10.1128/spectrum.03650-23 · Cited by 3
Inhibition of bacterial growth by antibiotics: a minimal model
Ledoux, B., & Lacoste, D. (2025) · Physical Biology · doi:10.1088/1478-3975/ae1343 · Cited by 3
Kireewan, S., & Suanpairintr, N. (2017) · The Thai Journal of Veterinary Medicine · doi:10.56808/2985-1130.2825 · Cited by 3
Geisinger, E., Vargas-Cuebas, G. G., Mortman, N. J., Syal, S., Wainwright, E. L., Lazinski, D. W., Wood, S., Zhu, Z., Anthony, J. S., van Opijnen, T., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/442681 · Cited by 3
Bottacin, G., Raach, B., Fröhlich, L., Künnecke, J., Kaczmarczyk, A., Tejada-Arranz, A., Ugolini, G. S., Stocker, R., Jenal, U., Bumann, D., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2604163123 · Cited by 2
Ilyas, U., du Toit, L. J., & McDonald, M. R. (2024) · PhytoFrontiers™ · doi:10.1094/phytofr-09-24-0105-fi · Cited by 2
Broughton, J., Fraisse, A., & Karoui, M. E. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00162-w · Cited by 7
Incidence, Complications and Novel Treatment Strategies: Joint Arthroplasty
Simpson, H. (2022) · Springer eBooks · doi:10.1007/978-3-030-83251-3_8 · Cited by 2
High-throughput profiling of drug interactions in Gram-positive bacteriaPreprint
Cacace, E., Kim, V., Knopp, M. R., Tietgen, M., Brauer-Nikonow, A., İnecik, K., Mateus, A., Milanese, A., Mårli, M. T., Mitosch, K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.23.521747 · Cited by 2
Broughton, J., Fraisse, A., & Karoui, M. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.18.590101 · Cited by 2
Kačániová, M., Haščík, P., Ban, Z., Bianchi, A., Saad, R. B., Hsouna, A. B., Terentjeva, M., Elizondo‐Luévano, J. H., Čmiková, N., & Garzoli, S. (2025) · Flavour and Fragrance Journal · doi:10.1002/ffj.3870 · Cited by 1
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Metadata
- Authors
- Bollenbach, T., Quan, S., Chait, R., & Kishony, R.
- Affiliations
- Harvard University (US)
- Stanford University (US)
- Corresponding author
- Kishony, Roy
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 139
- Issue
- 4
- Pages
- 707-718
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC2838386
- OpenAlex
- W2145744900
- Cited by
- 227
- References
- 79
- Citations by year
- 2026: 15 · 2025: 10 · 2024: 11 · 2023: 9 · 2022: 12 · 2021: 8 · 2020: 19 · 2019: 12 · 2018: 14 · 2017: 15 · 2016: 16 · 2015: 19 · 2014: 16 · 2013: 19 · 2012: 10
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1390 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM081617)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- antibiotic synergy, ribosomal RNA operon, growth rate, Escherichia coli, transcriptional regulation, protein synthesis inhibitors, DNA synthesis inhibitors
- MeSH terms
- Anti-Infective Agents, DNA, Drug Interactions, Escherichia coli, Ribosomes, Protein Biosynthesis, Nucleic Acid Synthesis Inhibitors
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