Project description:Bacteria often evolve antibiotic resistance through mutagenesis. However, the processes causing the mutagenesis have not been fully resolved. Here we found that a broad range of ribosome-targeting antibiotics caused mutations through an underexplored pathway. Focusing on the clinically important aminoglycoside gentamicin, we found that the translation inhibitor caused genome-wide premature stalling of RNA polymerase (RNAP) in a loci-dependent manner. Further analysis showed that the stalling was caused by disruption of transcription-translation coupling. Anti-intuitively, the stalled RNAPs subsequently induced lesions to the DNA via transcription-coupled repair. While most of the bacteria were killed by genotoxicity, a small subpopulation acquired mutations via SOS-induced mutagenesis. Given that these processes were triggered shortly after antibiotic addition, resistance rapidly emerged in the population. Our work revealed a new mechanism of action of ribosomal antibiotics, illustrates the importance of dissecting the complex interplay between multiple molecular processes in understanding antibiotic efficacy, and suggests new strategies for countering the development of resistance.
Project description:Safety assessment in retroviral vector-mediated gene therapy remains challenging. In clinical trials for different blood and immune disorders, insertional mutagenesis led to myeloid and lymphoid leukemia. We previously developed the In Vitro Immortalization Assay (IVIM) and Surrogate Assay for Genotoxicity Assessment (SAGA) for pre-clinical genotoxicity prediction of integrating vectors. Murine hematopoietic stem and progenitor cells (mHSPC) transduced with mutagenic vectors acquire a proliferation advantage under limiting dilution (IVIM) and activate stem cell- and cancer-related transcriptional programs (SAGA). However, both assays present an intrinsic myeloid bias due to culture conditions. To detect lymphoid mutants, we differentiated mHSPC to mature T cells and analyzed their phenotype, insertion site pattern, and gene expression changes after transduction with retroviral vectors. Mutagenic vectors induced a block in differentiation at an early progenitor stage (double-negative 2) compared to fully differentiated untransduced mock cultures. Arrested samples harbored high-risk insertions close to Lmo2, frequently observed in clinical trials with severe adverse events. Lymphoid insertional mutants displayed a unique gene expression signature identified by the machine learning algorithm of SAGA. The gene expression-based highly sensitive molecular readout will broaden our understanding of vector-induced oncogenicity and help in pre-clinical prediction of retroviral genotoxicity.
Project description:Safety assessment in retroviral vector-mediated gene therapy remains challenging. In clinical trials for different blood and immune disorders, insertional mutagenesis led to myeloid and lymphoid leukemia. We previously developed the In Vitro Immortalization Assay (IVIM) and Surrogate Assay for Genotoxicity Assessment (SAGA) for pre-clinical genotoxicity prediction of integrating vectors. Murine hematopoietic stem and progenitor cells (mHSPC) transduced with mutagenic vectors acquire a proliferation advantage under limiting dilution (IVIM) and activate stem cell- and cancer-related transcriptional programs (SAGA). However, both assays present an intrinsic myeloid bias due to culture conditions. To detect lymphoid mutants, we differentiated mHSPC to mature T cells and analyzed their phenotype, insertion site pattern, and gene expression changes after transduction with retroviral vectors. Mutagenic vectors induced a block in differentiation at an early progenitor stage (double-negative 2) compared to fully differentiated untransduced mock cultures. Arrested samples harbored high-risk insertions close to Lmo2, frequently observed in clinical trials with severe adverse events. Lymphoid insertional mutants displayed a unique gene expression signature identified by the machine learning algorithm of SAGA. The gene expression-based highly sensitive molecular readout will broaden our understanding of vector-induced oncogenicity and help in pre-clinical prediction of retroviral genotoxicity.
