Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Bioinformatics and Computational Biology Program, University of Idaho, Moscow, ID 83844, USA.
Viruses. 2023 May 31;15(6):1303. doi: 10.3390/v15061303.
Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20-30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV), followed by influenza A virus (IAV) after 3 days. The results suggest that IAV reduced the rate of RSV production, while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios that had not been examined experimentally, including a different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight-loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis shows that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to the slower clearance of IAV-infected cells by the other viruses. The improved outcome when IAV followed RV, on the other hand, could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.
呼吸道病毒感染是全球主要疾病病因之一,有 20%-30%的病例可检测到多种病毒,并且有几种病毒同时传播。一些具有独特病毒共病原体的感染导致致病性降低,而其他病毒组合则会使疾病恶化。导致这些二分结果的机制可能是不同的,并且仅在实验室和临床中才开始进行检查。为了更好地了解病毒-病毒合并感染,并预测导致不同疾病结果的潜在机制,我们首先系统地将数学模型拟合到感染呼吸道合胞病毒(RSV)的雪貂的病毒载量数据中,然后在 3 天后感染甲型流感病毒(IAV)。结果表明,IAV 降低了 RSV 的产生速度,而 RSV 降低了 IAV 感染细胞的清除速度。然后,我们探索了尚未通过实验检验的场景的可能动力学范围,包括不同的感染顺序、合并感染时间、相互作用机制和病毒组合。通过使用单感染的人类病毒载量数据以及 IAV-RV、RV-IAV 和 IAV-CoV2 合并感染的鼠体重减轻数据,来检验 IAV 与鼻病毒(RV)或 SARS-CoV-2(CoV2)的合并感染。与 RSV-IAV 合并感染的结果相似,该分析表明,在鼠 IAV-RV 或 IAV-CoV2 合并感染期间观察到的疾病严重程度增加可能是由于其他病毒导致 IAV 感染细胞的清除速度较慢所致。另一方面,当 IAV 紧随 RV 时,改善了结局,这可以通过降低 IAV 感染细胞的清除速度来复制。以这种方式模拟病毒-病毒合并感染为了解病毒-病毒相互作用如何在合并感染期间调节疾病严重程度提供了新的见解,并产生了适合实验评估的可检验假设。