Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli.
Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli.
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DOI：
10.1038/s41467-022-30889-1
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发表时间：
2022-06-09
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
由于抗生素开发滞后，我们通过定向进化实验寻找潜在的药物靶点。一个挑战是，许多抗性基因隐藏在一个嘈杂的突变背景中，因为突变克隆出现在适应性群体中。在这里，为了克服这种噪音，我们通过进化行动（EA）量化突变的影响。在对不同突变方案下生长的环丙沙星或粘菌素耐药菌株进行测序后，我们发现基因中突变的进化作用的总和升高，从而识别出已知的耐药驱动因素。这种EA整合方法也表明了新的抗生素抗性基因，然后显示出在竞争实验中提供了适应性优势。此外，还对临床分离株和环境分离株的耐药株进行了EA整合分析。大肠杆菌鉴定了标准方法失败的耐药性基因驱动因素。总之，这些结果通知从头粘菌素耐药性的遗传基础，并支持强大的发现表型驱动基因通过遗传扰动的进化作用，在健身景观。抗生素耐药性的出现，即使是对最后一线抗生素如粘菌素的耐药性，也是一个严重的公共卫生威胁。为了指导治疗和药物开发策略，Marciano等人在实验进化实验中应用进化作用（EA）分析来识别噪声突变背景中的驱动突变，并告知新生粘菌素耐药驱动因素。
Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes. The emergence of antibiotic resistance, even against last-line antibiotics such as colistin, is a serious public health threat. To guide treatment and drug development strategies, Marciano et al. apply evolutionary action (EA) analysis to identify driver mutations in a noisy mutational background in experimental evolution experiments and inform about de novo colistin resistance drivers.
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DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
影响因子：
9
作者：
Chun YS;Passot G;Yamashita S;Nusrat M;Katsonis P;Loree JM;Conrad C;Tzeng CD;Xiao L;Aloia TA;Eng C;Kopetz SE;Lichtarge O;Vauthey JN
通讯作者：
Vauthey JN
DOI：
10.1126/science.aad3292
发表时间：
2016-01-01
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Baym M;Stone LK;Kishony R
通讯作者：
Kishony R
影响因子：
64.5
作者：
Cancer Genome Atlas Research Network. Electronic address: wheeler@bcm.edu;Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
影响因子：
6.7
作者：
Conley ZC;Bodine TJ;Chou A;Zechiedrich L
通讯作者：
Zechiedrich L