Group of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain; Department of Computer Science, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain.
Group of Inverse Problems, Optimization and Machine Learning. Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007, Oviedo, Spain; DeepBioInsights, Spain.
Comput Biol Med. 2022 Oct;149:106029. doi: 10.1016/j.compbiomed.2022.106029. Epub 2022 Aug 30.
To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.
We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.
The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities.
Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.
为了设计能够预测感染严重程度的诊断工具，了解 SARS-CoV-2 感染的转录组反应至关重要。
我们对病毒转录组数据进行了深度采样分析，以进行药物重定位。使用不同的采样器，该方法的基本原则是生物学不变性，即疾病改变的途径应该独立于用于揭示它们的算法。
对改变途径的转录组分析揭示了独特的炎症反应和感染的潜在副作用。病毒复制会导致肺部急性呼吸窘迫综合征，还会影响心脏、大脑和肾脏等其他器官。因此，用于对抗 COVID-19 的重定位药物不仅应针对干扰素信号通路和炎症控制，还应针对与感染副作用相关的改变的遗传途径。我们还通过主成分分析表明，转录组特征与流感和 RSV 不同。控制胶原蛋白产生的基因 COL1A1 似乎在免疫系统调节中发挥关键/重要作用。此外，其他小规模特征基因似乎参与了 COVID-19 合并症的发展。
基于转录组的药物重定位为 COVID-19 患者提供了可能的快速抗病毒治疗方法。它呼吁使用 FDA 批准的药物对易感染和严重并发症的患者进行额外的临床研究。
