实验揭示细菌如何产生耐药性

一个研究团队成功开展了关于细菌如何产生耐药性的实验。实验研究了常见细菌对多种抗生素的进化过程，确定了产生耐药性的机制和制约因素。他们的发现有助于在临床实践中制定有效药物治疗策略，以最大程度地降低细菌产生耐药性的机会。

资讯作者：RIKEN

编辑翻译：菁菁

日本RIKEN生物系统动力学研究中心（BDR）的一个研究团队成功地在多种抗生素压力下，对常见细菌大肠杆菌进行了实验进化。通过这一实验，研究团队确定了潜在的耐药性机制和制约因素。他们的发现发表在科学杂志《自然通讯》上，预期可帮助医师制定药物治疗策略，以最大程度地减少细菌产生耐药的机会。

对抗多重耐药性细菌日渐成为一项至关重要的全球性挑战。每当有新抗生素得到开发，总会在临床使用阶段出现新的耐药菌。为了赢得抗生素和细菌之间的这场猫鼠游戏，我们必须了解细菌如何进化出耐药性。当然，这一过程非常复杂，涉及基因组序列和细胞状态的众多变化。因此，在这项研究之前，研究人员从未发表过对大量抗生素耐药性动力学的全面研究。

RIKEN生物系统动力学研究中心的研究员Tomoya Maeda解释说：“将实验室进化与基因组分析相结合，预期是探索抗生素耐药性动力学的一种有效方法。然而，建设和发展实验室需要大量的工作。这些工作主要是需要长期、连续地转移培养物，并进行大量的平行实验。”此外，Maeda还认为，由于数据中包含大量遗传特征，因此鉴定细菌中允许对抗生素产生耐药性的基因并不容易。

为了克服这些局限性，该团队开发了自动化的机器人培养系统，使他们能够在95种不同抗生素的压力下，成功实现250代以上的高通量实验室大肠杆菌进化。这一新进展使得研究团队能够量化细菌转录组中所有 RNA及其转录本的变化，且记录基于基因的真实表达。结果，系统生成了192个进化菌株的抗性曲线。研究人员还开发了一种用于分析大量数据的机器学习方法，从而使他们能够识别有助于预测耐药性进化的新基因和知名基因。

Maeda说：“我们发现，大肠杆菌的进化动力学可以归因于相对较少的细胞内状态，这表明它可能只具有有限数量的抗生素耐药策略。”通过量化影响大肠杆菌中抗生素耐药性演变的限制因素， 研究团队希望他们能够预测并控制抗生素的耐药性。

例如，通过使用这一新系统，研究团队测试了2162对药物组合，并发现157对有可能抑制大肠杆菌耐药性的产生。正如Maeda所说，“我们相信，我们的研究结果可用于开发其他方法来抑制耐药菌的出现。”

英文原文

A research team at the RIKEN Center for Biosystems Dynamics Research (BDR) in Japan has succeeded in experimentally evolving the common bacteria Escherichiacoli under pressure from a large number of individual antibiotics. In doing so, they were able to identify the mechanisms and constraints underlying evolved drug resistance. Their findings, published in the scientific journal Nature Communications,can be used to help develop drug-treatment strategies that minimize the chance thatbacteria will develop resistance.

Counteracting multidrug-resistant bacteria is becoming a critical global challenge. It seems that every time wedevelop new antibiotics, novel antibiotic-resistant bacteria emerge during clinical use. To win this cat-andmouse game, we must understand how drug resistance evolves in bacteria. Naturally, this process is verycomplicated, involving numerous changes in genome sequences and cellular states. Therefore, acomprehensive study of resistance dynamics for large numbers of antibiotics has never been reported.

"Laboratory evolution combined with genomic analyses is a promising approach for understanding antibioticresistance dynamics," explains Tomoya Maeda, a researcher at RIKEN BDR who led this study. "However, laboratory evolution is highly labor-intensive, requiring serial transfer of cultures over a long period and a largenumber of parallel experiments." Additionally, Maeda says that identifying the genes that allow resistance toantibiotics is not always easy because of the large number of genetic features that are contained within the data.

To overcome these limitations, the team developed an automated robotic culture system that allowed them tosuccessfully perform high-throughput laboratory evolution of E. coli for more than 250 generations underpressure from 95 different antibiotics. With this new ability, they were able to quantify changes in the bacteria'stranscriptome -- the set of all messenger RNAs and their transcripts, which is the record of which genes areactually expressed. As a result, the system produced resistance profiles for 192 of the evolved strains. Theresearchers also developed a machine-learning method for analyzing this large amount of data, allowing themto identify both novel and well-known genes that contribute to the prediction of resistance evolution.

"We found that E. coli's evolutionary dynamics is attributable to a relatively small number of intracellular states, indicating that it is likely equipped with only a limited number of strategies for antibiotic resistance," says Maeda. By being able to quantify the constraints that affect evolution of antibiotic resistance in E. coli, the teamhopes they can predict, and thus control, antibiotic resistance.

For example, by using this new system, they were able to test 2162 pairs of drug combinations and discovered 157 pairs that have the potential to suppress antibiotic resistance acquisition in E. coli. As Maeda says, "We believe that our results can be applied to the development of alternative strategies for suppressing the emergence of drug-resistant bacteria."

参考文献

Tomoya Maeda, Junichiro Iwasawa, Hazuki Kotani, Natsue Sakata, Masako Kawada, Takaaki Horinouchi, Aki Sakai, Kumi Tanabe, Chikara Furusawa. High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli. Nature Communications, 2020; 11 (1) 

DOI:10.1038/s41467-020-19713-w

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