Modelling the dynamics of viral load to reveal mechanisms of protection in COVID-19

对病毒载量动态进行建模以揭示 COVID-19 的保护机制

• 批准号: MR/V027409/1
• 负责人: Aubrey Cunnington
• 金额: $ 24.99万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV027409%2F1
• 关键词: Modelling; dynamics; viral; load; reveal

Variation in the severity of COVID-19 may be due to differences in how well the immune system fights the virus. The most severe cases have the highest levels of virus, which may be driving such a strong immune response that it damages the body. The virus may have reached high levels because it grew very fast or because the immune system did not respond fast enough or in the right way. We can only find out which parts of the immune response are protective if we know how the levels of virus and immune response influence each other over time. We have previously shown that we can use data from lots of patients to develop a model of how the levels of virus and the immune response interact, and then use this as a framework to estimate how effectively the immune response of each individual is controlling the virus. This then allows us to identify which of the thousands of molecules involved of the immune response are actually responsible for protection. Using data which has already been collected in other studies to develop a similar model for COVID-19, we will identify the protective immune response, leading better treatments and vaccines.

COVID-19严重程度的差异可能是由于免疫系统对抗病毒的能力不同。最严重的病例中病毒水平最高，这可能会导致如此强烈的免疫反应，从而损害身体。这种病毒可能已经达到了很高的水平，因为它生长得非常快，或者因为免疫系统反应不够快或反应方式不正确。我们只有知道病毒水平和免疫反应如何随着时间的推移相互影响，才能找出免疫反应的哪些部分是保护性的。我们之前已经证明，我们可以使用来自大量患者的数据来开发病毒水平和免疫反应如何相互作用的模型，然后将其作为一个框架来估计每个人的免疫反应如何有效地控制病毒。然后，这使我们能够识别参与免疫反应的数千个分子中哪些实际上负责保护。利用其他研究中已经收集的数据来开发COVID-19的类似模型，我们将确定保护性免疫反应，从而获得更好的治疗和疫苗。

项目成果

Analysis of blood and nasal epithelial transcriptomes to identify mechanisms associated with control of SARS-CoV-2 viral load in the upper respiratory tract

• DOI: 10.1016/j.jinf.2023.10.009
• 发表时间: 2023-11-24
• 期刊: JOURNAL OF INFECTION
• 影响因子: 28.2
• 作者: Marjaneh，Mahdi Moradi;Challenger，Joseph D.;Cunnington，Aubrey J.
• 通讯作者: Cunnington，Aubrey J.

Additional file 1 of Modelling upper respiratory viral load dynamics of SARS-CoV-2

SARS-CoV-2 上呼吸道病毒载量动态建模的附加文件 1

• DOI: 10.6084/m9.figshare.18318241
• 发表时间: 2022
• 作者: Challenger J

Modelling upper respiratory viral load dynamics of SARS-CoV-2.

• DOI: 10.1186/s12916-021-02220-0
• 发表时间: 2022-01-13
• 期刊: BMC medicine
• 影响因子: 9.3
• 作者: Challenger JD;Foo CY;Wu Y;Yan AWC;Marjaneh MM;Liew F;Thwaites RS;Okell LC;Cunnington AJ
• 通讯作者: Cunnington AJ