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感染后死亡率的演变。

The evolution of post-infection mortality.

机构信息

Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA.

Department of Integrative Biology, University of California, Berkeley, CA, USA.

出版信息

Proc Biol Sci. 2024 Nov;291(2035):20241854. doi: 10.1098/rspb.2024.1854. Epub 2024 Nov 20.

DOI:10.1098/rspb.2024.1854
PMID:39561798
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11576100/
Abstract

COVID-19 infections have underlined that there can be substantial impacts on health after recovery, including elevated mortality. While such post-infection mortality (PIM) is clearly widespread, we do not yet have any understanding of its evolutionary dynamics. To address this gap, we use an eco-evolutionary model to determine conditions where PIM is evolutionarily favoured. Importantly, from a pathogen perspective, there are two potential 'resources': never-infected susceptibles and previously infected susceptibles (provided some reinfection is possible), and PIM only occurs in the latter. A key insight is that unlike classic virulence (i.e. during-infection mortality, DIM) PIM is neutral and not selected against in the absence of other trade-offs. However, PIM modulates characteristics of endemicity, and may also vary with other pathogen-specific components. If PIM is only correlated with transmission, recovery or DIM, it simply acts to modulate their impacts on the evolutionary outcome. On the other hand, if PIM trades off with the relative susceptibility to reinfection, there are important evolutionary implications that contrast with DIM. We find settings where a susceptibility-mortality trade-off (i.e. an increase in mortality leads to higher relative susceptibility to reinfection) can select against DIM but favour PIM. This provides a potential explanation for the ubiquity of PIM. Overall, our work illustrates that PIM can readily evolve in certain settings and highlights the importance of considering different sources of mortality.

摘要

COVID-19 感染表明,康复后可能会对健康造成重大影响,包括死亡率升高。虽然这种感染后死亡率(PIM)显然很普遍,但我们还没有任何关于其进化动态的了解。为了解决这一差距,我们使用生态进化模型来确定 PIM 在进化上有利的条件。重要的是,从病原体的角度来看,有两种潜在的“资源”:从未感染的易感染者和以前感染过的易感染者(前提是有可能再次感染),只有后者才会发生 PIM。一个关键的见解是,与经典的毒力(即在感染期间的死亡率,DIM)不同,PIM 是中性的,在没有其他权衡的情况下不会被选择。然而,PIM 调节了地方性的特征,并且可能因其他病原体特异性成分而异。如果 PIM 仅与传播、恢复或 DIM 相关,则它只是调节它们对进化结果的影响。另一方面,如果 PIM 与相对易感染性的再感染发生权衡,那么就会产生与 DIM 形成鲜明对比的重要进化意义。我们发现,在某些情况下,易感性-死亡率权衡(即死亡率的增加导致相对易感染性的增加)可以选择对抗 DIM,但有利于 PIM。这为 PIM 的普遍存在提供了一个潜在的解释。总的来说,我们的工作表明,PIM 在某些情况下很容易进化,并强调了考虑不同来源的死亡率的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/f04b482e9c2c/rspb.2024.1854.f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/f9d01512b057/rspb.2024.1854.f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/ceac559f0a76/rspb.2024.1854.f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/cf694bcf3c0f/rspb.2024.1854.f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/f04b482e9c2c/rspb.2024.1854.f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/f9d01512b057/rspb.2024.1854.f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/ceac559f0a76/rspb.2024.1854.f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/cf694bcf3c0f/rspb.2024.1854.f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b60/11576100/f04b482e9c2c/rspb.2024.1854.f004.jpg

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