Project description:Cationic antimicrobial peptides (CAPs) are promising novel alternatives to conventional antibacterial agents, but the overlap in resistance mechanisms between small-molecule antibiotics and CAPs is unknown. Does evolution of antibiotic resistance decrease (cross-resistance) or increase (collateral sensitivity) susceptibility to CAPs? We systematically addressed this issue by studying the susceptibilities of a comprehensive set of antibiotic resistant Escherichia coli strains towards 24 antimicrobial peptides. Strikingly, antibiotic resistant bacteria frequently showed collateral sensitivity to CAPs, while cross-resistance was relatively rare. We identified clinically relevant multidrug resistance mutations that simultaneously elevate susceptibility to certain CAPs. Transcriptome and chemogenomic analysis revealed that such mutations frequently alter the lipopolysaccharide composition of the outer cell membrane and thereby increase the killing efficiency of membrane-interacting antimicrobial peptides. Furthermore, we identified CAP-antibiotic combinations that rescue the activity of existing antibiotics and slow down the evolution of resistance to antibiotics. Our work provides a proof of principle for the development of peptide based antibiotic adjuvants that enhance antibiotic action and block evolution of resistance.
Project description:Ceftazidime-avibactam use selects multidrug-resistance and prevents designing collateral sensitivity-based therapies against Pseudomonas aeruginosa
Project description:Broad-spectrum multi-target tyrosine kinase inhibitors (mTKIs) are clinically approved for the treatment of soft tissue sarcomas (STS). However, acquired resistance inevitably arises in the majority of STS patients. There is therefore an urgent need to identify new strategies to overcome resistance and achieve durable treatment responses. Here we show that STS cells that acquire resistance to clinically relevant mTKIs are cross-resistant to one another and sequential treatment does not delay the acquisition of drug resistance. Instead, we find that en route to acquiring drug resistance, STS cells develop collateral sensitivities to alternative drugs. We demonstrate that the mTKI sitravatinib rapidly induces collateral sensitivity to the FGFR inhibitor infigratinib which can be exploited for adaptive therapy to suppress STS cell growth. This study provides proof-of-principle that collateral sensitivity may be an effective strategy for overcoming resistance to mTKIs and this novel approach should be explored in the design of future trials.
Project description:In this work we describe a robust fosfomycin collateral sensitivity phenotype of Pseudomonas aeruginosa resistant mutants selected by antibiotics from different structural families. The underlying mechanism was the reduced expression of the genes encoding the peptidoglycan-recycling pathway, which preserves the peptidoglycan synthesis in situations where the de novo synthesis is blocked, and of fosA, encoding a fosfomycin-inactivating enzyme.
Project description:Therapeutic options for patients with relapsed or refractory Ewings sarcoma (EWS) remain limited. Collateral sensitivity, where resistance to one drug confers sensitivity to another, could be leveraged to optimize chemotherapy for EWS. Gene expression signatures that predict collateral sensitivity states can be used to guide treatment selection in an evolution-informed manner. To generate such biomarkers, we experimentally evolved resistance to first-line EWS chemotherapy in independent replicates of an EWS cell line, all originating from the same ancestor population. Throughout, we measured collateral responses across a panel of anticancer drugs and quantified transcriptomic changes. Collateral drug responses varied across replicate evolutionary trajectories, but convergent states of collateral sensitivity emerged across different replicates at different times. By associating these convergent phenotypes with gene expression patterns, we derived a library of predictive signatures for numerous drugs. These signatures accurately distinguished states of collaterally sensitivity from states of collateral resistance within our dataset, irrespective of a replicate's evolutionary history. Our findings demonstrate that gene expression signatures can predict collateral sensitivity in EWS, providing a foundation for personalized therapeutic strategies. This approach also establishes a generalizable workflow for developing predictive biomarkers to guide chemotherapy selection in patients with rare diseases that lack reliable second-line chemotherapy regimens.