cE3c-Centre for Ecology, Evolution and Environmental Changes & CHANGE, Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
INIAV-National Institute for Agrarian and Veterinary Research, 2780-157 Oeiras, Portugal.
Int J Mol Sci. 2023 Jan 19;24(3):1967. doi: 10.3390/ijms24031967.
This review discusses the fate of antimicrobial resistance and virulence genes frequently present among microbiomes. A central concept in epidemiology is the mean number of hosts colonized by one infected host in a population of susceptible hosts: . It characterizes the disease's epidemic potential because the pathogen continues its propagation through susceptible hosts if it is above one. is proportional to the average duration of infections, but non-pathogenic microorganisms do not cause host death, and hosts do not need to be rid of them. Therefore, commensal bacteria may colonize hosts for prolonged periods, including those harboring drug resistance or even a few virulence genes. Thus, their is likely to be (much) greater than one, with peculiar consequences for the spread of virulence and resistance genes. For example, computer models that simulate the spread of these genes have shown that their diversities should correlate positively throughout microbiomes. Bioinformatics analysis with real data corroborates this expectation. Those simulations also anticipate that, contrary to the common wisdom, human's microbiomes with a higher diversity of both gene types are the ones that took antibiotics longer ago rather than recently. Here, we discuss the mechanisms and robustness behind these predictions and other public health consequences.
这篇综述讨论了经常存在于微生物组中的抗菌药物耐药性和毒力基因的命运。流行病学中的一个核心概念是在易感宿主群体中，一个受感染宿主定植的宿主的平均数量：. 它描述了疾病的流行潜力，因为如果它大于一，病原体就会通过易感宿主继续传播。 与感染的平均持续时间成正比，但非致病性微生物不会导致宿主死亡，宿主也不需要清除它们。因此，共生细菌可能会在宿主中定植很长时间，包括那些携带耐药性甚至少数毒力基因的宿主。因此，它们的 很可能（大）于一，这对毒力和耐药基因的传播有特殊的影响。例如，模拟这些基因传播的计算机模型表明，它们的多样性在整个微生物组中应该呈正相关。使用真实数据的生物信息学分析证实了这一预期。这些模拟还预测，与普遍观点相反，抗生素使用时间较长而不是最近的人类微生物组中这两种基因类型的多样性更高。在这里，我们讨论了这些预测背后的机制和稳健性以及其他公共卫生后果。
