Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype–genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

了解影响抗生素耐药性演变的限制因素对于预测和控制耐药性至关重要。尽管具有重要意义，但仍缺乏对进化限制的系统研究。在这里，我们进行了大肠杆菌的高通量实验室进化在添加95种抗菌化学物质的条件下，并量化了进化菌株的转录组，抗性和基因组图谱。利用机器学习技术，我们分析了表型-基因型数据，并确定了进化菌株之间的低维表型状态。进一步的分析揭示了导致这些不同状态的潜在生物学过程，从而确定了与耐药相关的权衡关系。我们还报告了β-内酰胺抗性的下降，这是某些菌株在各种应力​​下所经历的现象，与由β-内酰胺直接选择的菌株相比，导致对β-内酰胺的更高的获得性抗性。这些发现弥合了基因型，基因表达和耐药性缺口，