Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Prediction of antibiotic resistance by large-scale phenotypic and genotypic data

ABSTRACT: The evolution of antibiotic resistance is a clear example of adaptation by natural selection. Although a number of mutations contributing to the resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype-genotype mapping for drug resistance, we performed parallel laboratory evolution of Escherichia coli under the selection of single antibiotics and after 90 days propagation obtained resistant strains. We find that an acquisition of resistance to one drug drastically changes the resistance and susceptibility to other drugs. Based on transcriptome data of these strains, we demonstrated that the resistances could be quantitatively predicted by the expression changes of a small number of genes. Whole-genome resequencing analysis provided several candidate mutations contributing to the resistances, while phenotype-genotype mapping was suggested to be complex and included various mutations that caused similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances. To examine the contribution of the gene expression changes to the antibiotic resistances, transcriptome of the parent strain (duplicated) and 40 resistant strains (4 parallel-evolved resistant strains for 10 antibiotics) were analyzed.

ORGANISM(S): Escherichia coli

SUBMITTER: Chikara Furusawa

PROVIDER: E-GEOD-59408 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:We compared the dynamics and mechanisms of resistance development to ceftazidime, meropenem, ciprofloxacin, and ceftolozane-tazobactam in wild-type (PAO1) and mutator (PAOMS, M-bM-^HM-^FmutS) P. aeruginosa. The strains were incubated for 24 h with 0.5 to 64M-CM-^W MICs of each antibiotic in triplicate experiments. The tubes from the highest antibiotic concentration showing growth were reinoculated in fresh medium containing concentrations up to 64M-CM-^W MIC for 7 consecutive days. The susceptibility profiles and resistance mechanisms were assessed in two isolated colonies from each step, antibiotic, and strain. Ceftolozane-tazobactam-resistant mutants were further characterized by whole-genome analysis through RNA sequencing (RNA-seq). The development of high-level resistance was fastest for ceftazidime, followed by meropenem and ciprofloxacin. None of the mutants selected with these antibiotics showed cross-resistance to ceftolozane-tazobactam. On the other hand, ceftolozane-tazobactam resistance development was much slower, and high-level resistance was observed for the mutator strain only. PAO1 derivatives that were moderately resistant (MICs, 4 to 8 ug/ml) to ceftolozane-tazobactam showed only 2 to 4 mutations, which determined global pleiotropic effects associated with a severe fitness cost. High-level-resistant (MICs, 32 to 128 ug/ml) PAOMS derivatives showed 45 to 53 mutations. Major changes in the global gene expression profiles were detected in all mutants, but only PAOMS mutants showed ampC overexpression, which was caused by dacB or ampR mutations. Moreover, all PAOMS mutants contained 1 to 4 mutations in the conserved residues of AmpC (F147L, Q157R, G183D, E247K, or V356I). Complementation studies revealed that these mutations greatly increased ceftolozane-tazobactam and ceftazidime MICs but reduced those of piperacillin-tazobactam and imipenem, compared to those in wild-type ampC. Therefore, the development of high-level resistance to ceftolozane-tazobactam appears to occur efficiently only in a P. aeruginosa mutator background, in which multiple mutations lead to overexpression and structural modifications of AmpC. Mutants of Pseudomonas aeroginosa PAO1 and PAO1 M-bM-^HM-^FmutS against Ceftolozane-tazobactam were generated and analysed using RNA-Seq

Project description:In Candida albicans, the transcription factor Upc2 is central to the regulation of ergosterol biosynthesis. UPC2-activating mutations contribute to azole resistance, whereas disruption increases azole susceptibility. In the present study, we investigated the relationship of UPC2 to fluconazole susceptibility, particularly in azole-resistant strains. In addition to the reduced fluconazole MIC previously observed with UPC2 disruption, we observed a lower minimum fungicidal concentration (MFC) for a upc2M-NM-^T/M-NM-^T mutant than for its azole-susceptible parent, SC5314. Moreover, the upc2M-NM-^T/M-NM-^T mutant was unable to grow on a solid medium containing 10 M-BM-5g/ml fluconazole and exhibited increased susceptibility and a clear zone of inhibition by Etest. Time-kill analysis showed higher fungistatic activity against the upc2M-NM-^T/M-NM-^T mutant than against SC5314. UPC2 disruption in strains carrying specific resistance mutations also resulted in reduced MICs and MFCs. UPC2 disruption in a highly azole resistant clinical isolate containing multiple resistance mechanisms likewise resulted in a reduced MIC and MFC. This mutant was unable to grow on a solid medium containing 10 M-BM-5g/ml fluconazole and exhibited increased susceptibility and a clear zone of inhibition by Etest. Time-kill analysis showed increased fungistatic activity against the upc2M-NM-^T/M-NM-^T mutant in the resistant background. Microarray analysis showed attenuated induction by fluconazole of genes involved in sterol biosynthesis, iron transport, or iron homeostasis in the absence of UPC2. Taken together, these data demonstrate that the UPC2 transcriptional network is universally essential for azole resistance in C. albicans and represents an attractive target for enhancing azole antifungal activity. We examined the genome-wide gene expression profiles of the wild-type parent strain SC5314 and its upc2M-NM-^T/M-NM-^T derivative in response to fluconazole in order to identify genes whose expression in response to fluconazole is influenced by Upc2.

Dataset Information

Prediction of antibiotic resistance by large-scale phenotypic and genotypic data

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure