论文标题

通过水平基因转移对杂种细菌的自适应演化

Adaptive evolution of hybrid bacteria by horizontal gene transfer

论文作者

Power, Jeffrey J., Pinheiro, Fernanda, Pompei, Simone, Kovacova, Viera, Yüksel, Melih, Rathmann, Isabel, Förster, Mona, Lässig, Michael, Maier, Berenike

论文摘要

水平基因转移是细菌进化的重要因素,可以在物种边界上起作用。然而,我们对跨分节基因转移的速率和基因组靶标及其对受体生物体生理和适应性的影响了解甚少。在这里,我们在平行的进化实验中解决了这些问题,其两个枯草芽孢杆菌谱系具有7%的序列差异。我们观察到杂种生物的快速演变:基因转移掉期约12%的核心基因组的12%,而至少一个人群则取代了60%的核心基因。通过基因组学,转录组学,健身测定和统计建模,我们表明转移会在杂种中产生适应性进化和功能改变。具体而言,我们的实验揭示了固定生长阶段进化群体的强劲,可重复的适应性增加。通过对跨复制人群的转移统计数据的基因组分析,我们推断出HGT的选择具有广泛的遗传基础:40%的观察到的转移是适应性的。在功能基因网络的级别上,我们发现了负和阳性选择的特征,与混合不兼容以及网络功能的自适应演变一致。我们的结果表明,基因转移会导航一个复杂的跨训练适应性景观,并沿多个高素质路径桥接同志屏障。

Horizontal gene transfer is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer, and about its effects on the recipient organism's physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ~12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative and positive selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths.

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