Members

Tufts University

Aldridge Lab Website

Department of Molecular Biology and Microbiology, Tufts University School of Medicine

Boston, Massachusetts USA

Member since 2020

Aldridge virtual group meeting- Bree Aldridge (2022)

Representative

Bree Aldridge, Ph.D. Principal Investigator and Associate Professor

Team

    • Dan Zhang
    • Hope D’Erasmo
    • Mariana Pereira Moraes
    • Ares Alivisatos
    • William Johnson
    • Trever Smith
    • Nhi Van
    • Tracy Washington
    • Joshua Whiteley

    About

    We are a multidisciplinary research team integrating quantitative measurement with computational modeling and analysis to create intuitive descriptions of complex cell biology. We focus our studies on (1) characterizing single-cell determinants of mycobacterial virulence and drug tolerance, (2) understanding how growth heterogeneity is controlled, and (3) engineering combination therapy.

    Role & Expertise

    In the TBDA, our work focuses on the design of optimized combination therapies for tuberculosis. We systematically measure the effects of drug combinations on M. tuberculosis to simultaneously explore the drug combination space for therapeutic potential and determine how lesion microenvironments influence bacterial response to specific drug regimens. In a second focus area, we combine fixed-cell imaging with machine learning to better understand drug pathways of action. Our goal is to develop and apply quantitative in vitro measurement with mathematical modeling to prioritize drug combinations for in vivo studies early in drug development.

    Links

    Aldridge Lab website

    References

    1. Larkins-Ford, J.; Degefu, Y. N.; Van, N.; Sokolov, A.; Aldridge, B. B. Design Principles to Assemble Drug Combinations for Effective Tuberculosis Therapy Using Interpretable Pairwise Drug Response Measurements. bioRxiv 2021, 2021.12.05.471248. https://pubmed.ncbi.nlm.nih.gov/36084643/
    2. Davis, K.; Greenstein, T.; Viau Colindres, R.; Aldridge, B. B. Leveraging Laboratory and Clinical Studies to Design Effective Antibiotic Combination Therapy. Current Opinion in Microbiology 2021, 64, 68–75. https://doi.org/10.1016/J.MIB.2021.09.006.
    3. Li, M.; Patel, H. V.; Cognetta, A. B.; Smith, T. C.; Mallick, I.; Cavalier, J.-F.; Previti, M. L.; Canaan, S.; Aldridge, B. B.; Cravatt, B. F.; Seeliger, J. C. Identification of Cell Wall Synthesis Inhibitors Active against Mycobacterium Tuberculosis by Competitive Activity-Based Protein Profiling. Cell Chemical Biology 2021. https://doi.org/10.1016/j.chembiol.2021.09.002.
    4. Larkins-Ford, J.; Greenstein, T.; Van, N.; Degefu, Y. N.; Olson, M. C.; Sokolov, A.; Aldridge, B. B. Systematic Measurement of Combination-Drug Landscapes to Predict in Vivo Treatment Outcomes for Tuberculosis. Cell Systems2021, 12 (11), 1046-1063.e7. https://doi.org/10.1016/j.cels.2021.08.004.
    5. Egbelowo, O.; Sarathy, J. P.; Gausi, K.; Zimmerman, M. D.; Wang, H.; Wijnant, G. J.; Kay, F.; Gengenbacher, M.; Van, N.; Degefu, Y.; Nacy, C.; Aldridge, B. B.; Carter, C. L.; Denti, P.; Dartois, V. Pharmacokinetics and Target Attainment of SQ109 in Plasma and Human-like Tuberculosis Lesions in Rabbits. Antimicrobial Agents and Chemotherapy 2021, 65 (9). https://doi.org/10.1128/AAC.00024-21.
    6. Van, N.; Degefu, Y. N.; Aldridge, B. B. Efficient Measurement of Drug Interactions with DiaMOND (Diagonal Measurement of N-Way Drug ); 2021; pp 703–713. https://doi.org/10.1007/978-1-0716-1460-0_30.
    7. Ii, T. C. S.; Pullen, K. M.; Olson, M. C.; McNellis, M. E.; Richardson, I.; Hu, S.; Larkins-Ford, J.; Wang, X.; Freundlich, J. S.; Ando, D. M.; Aldridge, B. B. Morphological Profiling of Tubercle Bacilli Identifies Drug Pathways of Action. Proceedings of the National Academy of Sciences of the United States of America 2020, 117 (31). https://doi.org/10.1073/pnas.2002738117.
    8. Ma, S.; Jaipalli, S.; Larkins-Ford, J.; Lohmiller, J.; Aldridge, B. B.; Sherman, D. R.; Chandrasekaran, S. Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis. mBio 2019, 10 (6). https://doi.org/10.1128/mBio.02627-19.
    9. Katzir, I.; Cokol, M.; Aldridge, B. B.; Alon, U. Prediction of Ultra-High-Order Antibiotic Combinations Based on Pairwise Interactions. PLoS Computational Biology 2019, 15 (1). https://doi.org/10.1371/journal.pcbi.1006774.
    10. Logsdon, M. M.; Ho, P.-Y.; Papavinasasundaram, K.; Richardson, K.; Cokol, M.; Sassetti, C. M.; Amir, A.; Aldridge, B. B. A Parallel Adder Coordinates Mycobacterial Cell-Cycle Progression and Cell-Size Homeostasis in the Context of Asymmetric Growth and Organization. Current Biology 2017, 27 (21), 3367-3374.e7. https://doi.org/10.1016/j.cub.2017.09.046.
    11. Cokol, M.; Kuru, N.; Bicak, E.; Larkins-Ford, J.; Aldridge, B. B. Efficient Measurement and Factorization of High-Order Drug Interactions in Mycobacterium Tuberculosis. Science Advances 2017, 3 (10). https://doi.org/10.1126/sciadv.1701881.
    12. Richardson, K.; Bennion, O. T.; Tan, S.; Hoang, A. N.; Cokol, M.; Aldridge, B. B. Temporal and Intrinsic Factors of Rifampicin Tolerance in Mycobacteria. Proceedings of the National Academy of Sciences of the United States of America 2016, 113 (29). https://doi.org/10.1073/pnas.1600372113. (13) Aldridge, B. B.; Fernandez-Suarez, M.; Heller, D.; Ambravaneswaran, V.; Irimia, D.; Toner, M.; Fortune, S. M. Asymmetry and Aging of Mycobacterial Cells Lead to Variable Growth and Antibiotic Susceptibility. Science 2012, 335 (6064). https://doi.org/10.1126/science.1216166.
    13. Larkins-Ford, J.; Aldridge, B. B. Advances in the Design of Combination Therapies for the Treatment of Tuberculosis. Expert Opinion on Drug Discovery 2023, 18 (1), 83–97. https://doi.org/10.1080/17460441.2023.2157811.
    14. Greenstein, T.; Aldridge, B. B. Tools to Develop Antibiotic Combinations That Target Drug Tolerance in Mycobacterium Tuberculosis. Frontiers in Cellular and Infection Microbiology 2023, 12.  https://doi.org/10.3389/fcimb.2022.1085946