Evolving antibiotic spectrum.
Evolving antibiotic spectrum.
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不断发展的抗生素光谱。
DOI：
10.1073/pnas.2214267119
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发表时间：
2022-10-11
影响因子：
11.1
通讯作者：
Brown, Sam P.
中科院分区：
文献类型：
--
作者：
Waldetoft, Kristofer Wollein;Brown, Sam P.
微生物能够进行复杂的社会组织，集体工作来操纵它们的局部环境。当他们的当地环境包含竞争的物种或菌株时，场景被设置为微生物战争，由化学和生物大规模杀伤性武器介导（1，2）。生物医学科学长期以来一直对微生物之间的化学战感兴趣，因为这些化学物质可以被选为预防和治疗细菌感染的基本药物，即我们所知的抗生素（3）。帕尔默和福斯特（4）从进化的角度看待微生物战争，提出了一个核心但令人震惊地被忽视的问题，即为什么化学武器在物种破坏或“光谱”范围上各不相同。作者将联合收割机数学模型和生物信息学分析相结合，以确定有利于窄谱和广谱抗生素的条件，为进化和生物医学研究开辟了道路。生物医学科学家早就认识到，抗生素的活性从窄谱到广谱不等，并且历史上一直珍视“广谱”药物，因为它们增加了药物消灭不明病原体的机会。Palmer和Foster（4）使用数学模型来探索这种临床逻辑是否也适用于制造药物的微生物。在进行微生物战争时，更广泛的光谱更好吗？通过使用一系列数学模型，作者确定了广谱方法的一个关键逻辑限制;每当化学武器杀死一种细菌时，它就被浪费了，而这种细菌不是产生杀虫剂的生物体的竞争对手（图1A）。相比之下，专门调整为仅结合和杀死直接竞争物种或菌株的窄谱化合物将通过有针对性地去除关键竞争对手而产生更高的投资回报（图1B）。如果这种精准战的逻辑是正确的，为什么我们会看到那么多广谱抗生素的例子呢？Palmer和Foster（4）通过将生态异质性引入他们的数学模型，为这个问题提供了一个潜在的解决方案。具体来说，他们模拟了一种情况，即一种主要的产草物种有时很罕见，有时在一个群落中占主导地位。在这种情况下，生态优势时期驱动了广谱抗生素生产的选择，因为在杀死罕见的非竞争对手时抗生素的损失并不限制抗生素杀死竞争对手的可用性（图1C）。在阐述了他们对有利于窄谱抗生素与广谱抗生素的条件的数学基础预测之后，作者转向比较生物信息学方法来测试他们的想法。他们的模型预测，如果生产商能够至少周期性地实现当地的生态优势，广谱抗生素将受到青睐。由于几十年来对抗生素活性范围的生物医学驱动研究（以及已知的相关化学武器），寻找抗生素谱的数据相对简单
Microbes are capable of complex feats of social organization, working collectively to manipulate their local environment. When their local environment contains competing species or strains, the scene is set for microbial war, mediated by chemical and biological weapons of mass destruction (1, 2). Biomedical science has long been interested in chemical warfare among microbes, as these chemicals can be co-opted as essential medicines in the prevention and treatment of bacterial infections—what we know as antibiotics (3). Palmer and Foster (4) take an evolutionary perspective on microbial warfare to ask a central yet startlingly overlooked question of why chemical weapons vary in their range of species destruction or “spectrum.” The authors combine math models and bioinformatic analyses to identify conditions favoring narrow-and broad-spectrum antibiotics, raising avenues for both evolutionary and biomedical research. Biomedical scientists have long recognized that antibiotics vary from narrow-to broad-spectrum activity and have historically prized “broad-spectrum” drugs as they increase the chances that an unidentified pathogen will be taken down by the drug. Palmer and Foster (4) use mathematical models to explore whether this clinical logic also works for the microbes that make the drug. Is broader spectrum better when engaged in microbial war? Using a series of mathematical models, the authors identify a key logical limitation of a broad-spectrum approach; the chemical weapon will be wasted whenever it kills a bacterium that is not a competitor to the antibiotic-producing organism (Fig. 1A). In contrast, narrow-spectrum compounds that are specifically tuned to only bind and kill directly competing species or strains will produce a higher return on investment via their targeted removal of key competitors (Fig. 1B). If this logic of precision warfare is correct, why do we see so many examples of broad-spectrum antibiotics? Palmer and Foster (4) provide a potential solution to this question by introducing ecological heterogeneities into their math models. Specifically, they model a scenario where a focal antibiotic-producing species is sometimes rare and sometimes dominant within a community. Under this scenario, periods of ecological dominance drive selection for broad-spectrum antibiotic production, as the loss of the antibiotic when killing rare noncompetitors does not limit the availability of the antibiotic to kill competitors (Fig. 1C).After setting out their math-grounded predictions for conditions favoring narrow-vs. broad-spectrum antibiotics, the authors turn to a comparative bioinformatic approach to test their ideas. Their model predicts that broad-spectrum antibiotics will be favored if the producer is capable of at least periodically achieving local ecological dominance. Finding data on the antibiotic spectrum is relatively straightforward, thanks to decades of biomedically driven research on the range of activity of antibiotics (and related chemical weapons known
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影响因子：
6.4
作者：
Waldetoft, Kristofer Wollein;Gurney, James;Brown, Sam P.
通讯作者：
Brown, Sam P.
影响因子：
4.1
作者：
Brown SP;Fredrik Inglis R;Taddei F
通讯作者：
Taddei F
影响因子：
64.8
作者：
Ling LL;Schneider T;Peoples AJ;Spoering AL;Engels I;Conlon BP;Mueller A;Schäberle TF;Hughes DE;Epstein S;Jones M;Lazarides L;Steadman VA;Cohen DR;Felix CR;Fetterman KA;Millett WP;Nitti AG;Zullo AM;Chen C;Lewis K
通讯作者：
Lewis K
DOI：
10.1073/pnas.2205407119
发表时间：
2022-09-20
影响因子：
11.1
作者：
通讯作者：
--
影响因子：
4.4
作者：
Nichols, D.;Cahoon, N.;Epstein, S. S.
通讯作者：
Epstein, S. S.