Since the pharmacological profiles of various non-steroidal anti-inflammatory drugs (NSAIDs) might depend on their differing selectivity for cyclooxygenase 1 (COX-1) and 2 (COX-2), we developed a new screening method using human peripheral monocytes. Monocytes from healthy volunteers were separated, and the cells were incubated with or without lipopolysaccharide (LPS). Monocytes without LPS stimulation exclusively expressed COX-1 on Western blotting analysis, whereas LPS stimulation induced COX-2 expression. Unstimulated monocytes (COX-1) and LPS-stimulated monocytes (COX-2) were then used to determinethe COX selectivity of various NSAIDs. The respective mean IC50 values for COX-1 and COX-2 IC50 (microM), and the COX-1/COX-2 ratio of each NSAID were as follows: celecoxib, 82, 6.8, 12; diclofenac, 0.076, 0.026, 2.9; etodolac, > 100, 53, > 1.9; ibuprofen, 12, 80, 0.15; indometacin, 0.0090, 0.31, 0.029; meloxicam, 37, 6.1, 6.1; 6-MNA (the active metabolite of nabumetone), 149, 230, 0.65; NS-398, 125, 5.6, 22; piroxicam, 47, 25, 1.9; rofecoxib, > 100, 25, > 4.0; S-2474, > 100, 8.9, > 11; SC-560, 0.0048, 1.4, 0.0034. The percentage inhibition of COX-1 activity at the IC50 of COX-2 also showed a wide variation among these NSAIDs. The bioassay system using human monocytes to assess the inhibitory effects of various NSAIDs on COX-1 and COX-2 may become a clinically useful screening method.

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.

In adaptive evolution, an increase in fitness to an environment is frequently accompanied by changes in fitness to other environmental conditions, called cross-resistance and sensitivity. Although the networks between fitness changes affect the course of evolution substantially, the mechanisms underlying such fitness changes are yet to be fully elucidated. Herein, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system, and quantified how the acquisition of resistance to one stressor alters the resistance to other stressors. We demonstrated that resistance changes could be quantitatively predicted based on changes in the transcriptome of the resistant strains. We also identified several genes and gene functions, for which mutations were commonly fixed in the strains resistant to the same stress, which could partially explain the observed cross-resistance and collateral sensitivity. The integration of transcriptome and genome data enabled us to clarify the bacterial stress resistance mechanisms.Laboratory evolution of microorganisms is a powerful approach to the elucidation of the nature of evolutionary dynamics 1,2 . Recent advances in measurement technology, including high-throughput sequencing, have enabled us to quantify phenotypic and genotypic changes during laboratory evolution, which have provided valuable information on the mechanisms and principles of adaptive evolution [3][4][5] . The impact of laboratory evolution has extended beyond the field of evolutionary biology into engineering and medicine. For example, by using laboratory evolution approaches, some candidate mutations that contribute to antibiotic resistance have been identified, and this sheds light on how to control the emergence of antibiotic-resistant strains [6][7][8][9] . Laboratory evolution has also become a widely used tool for bioengineering applications 10-13 -to generate cells with improved growth, production titer, and stress tolerance, which are essential for improving industrial microbial production.The evolutionary adaptation to a specific environment is frequently accompanied by changes in fitness in response to other environments. For example, it was demonstrated that the acquisition of resistance to one antibiotic can give rise to resistance to other drugs simultaneously, which is called cross-resistance, while it can also increase sensitivity to other drugs, which is called collateral sensitivity [14][15][16][17][18][19] . Such links between changes in fitness affect the course of evolution considerably. This can be utilized for predicting and controlling evolutionary dynamics, such as the suppression of resistance acquisition by use of multiple antibiotics in combination, with collateral sensitivity interactions. Such a "design" of evolutionary dynamics based on the links between fitness changes can contribute to the suppression of emerging multidrug-resistant pathogens [20][21][22] , and to the development of useful microorganisms for bioprodu...

Drug-resistant tuberculosis (TB) is a growing public health problem. There is an urgent need for information regarding cross-resistance and collateral sensitivity relationships among drugs and the genetic determinants of anti-TB drug resistance for developing strategies to suppress the emergence of drug-resistant pathogens. To identify mutations that confer resistance to anti-TB drugs in Mycobacterium species, we performed the laboratory evolution of nonpathogenic Mycobacterium smegmatis, which is closely related to Mycobacterium tuberculosis, against ten anti-TB drugs. Next, we performed whole-genome sequencing and quantified the resistance profiles of each drug-resistant strain against 24 drugs. We identified the genes with novel meropenem (MP) and linezolid (LZD) resistance-conferring mutation, which also have orthologs, in M. tuberculosis H37Rv. Among the 240 possible drug combinations, we identified 24 pairs that confer cross-resistance and 18 pairs that confer collateral sensitivity. The acquisition of bedaquiline or linezolid resistance resulted in collateral sensitivity to several drugs, while the acquisition of MP resistance led to multidrug resistance. The MP-evolved strains showed cross-resistance to rifampicin and clarithromycin owing to the acquisition of a mutation in the intergenic region of the Rv2864c ortholog, which encodes a penicillin-binding protein, at an early stage. These results provide a new insight to tackle drug-resistant TB.

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