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在SARS-CoV-2基因组中的序列签名可以用来预测宿主源

Josip Rudar^1,2, Peter Kruczkiewicz¹, Oksana Vernygora¹

¹National Centre for Foreign Animal Disease, Canadian Food Inspection Agency, Winnipeg, Manitoba, Canada.

Microbiology spectrum

|March 4, 2024

概括

机器学习和基因组分析确定了SARS-CoV-2 (严重急性呼吸系统综合征冠状病毒2) 中的关键突变,这些突变驱动了鹿,人类和的宿主适应. 这些发现揭示了对病毒免疫逃避和跨物种传播的洞察力.

科学领域:

病毒学病毒学
基因组学就是基因组学.
机器学习机器学习

背景情况:

严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 已在多种宿主物种中显示持续传播,包括人类,子和白尾鹿.
了解驱动SARS-CoV-2宿主适应的遗传因素对于控制病毒传播和防止新变种出现至关重要.
野生动物水库构成重新引入人类群体中新型病毒系的风险.

研究的目的:

用计算方法研究导致SARS-CoV-2宿主适应的遗传因素,用于白尾鹿,人类和子.
为了确定在不同宿主物种中区分SARS-CoV-2菌株的特定突变和基因组特征.
探索病毒进化在免疫逃避和跨物种传播中的作用.

主要方法:

在使用机器学习分类模型 (极端随机树) 的in silico分析中,对单核酸和氨基酸差异进行了训练.
植物遗传学方法和选择分析,以确定处于正负选择压力下的地点.
分析超越尖端蛋白的基因内突变,包括预测的B细胞和T细胞表位.

主要成果:

机器学习模型准确地按宿主物种 (白尾鹿,人,) 分类了SARS-CoV-2样本,准确度高 (0.984 ± 0.006).
在受到显著选择性压力的地方发现了88种预测性突变,其中大多数位于非尖端基因中.
发现非同义和无声突变 (例如C7303T,C9430T) 在区分宿主衍生序列方面都很重要,特别是在鹿样本中.
有证据表明,宿主适应包括免疫逃避,MHC I类的调节,以及减少CD8+ T细胞表皮图识别.
在白尾鹿中出现的B.1.641血统和密歇根州的相关人类序列之间发现了潜在的联系.

结论:

机器学习和进化基因组学是识别病毒跨物种传播和宿主适应相关因素的强大工具.
SARS-CoV-2 主体适应受到编码和非编码区域的突变的影响,影响免疫系统相互作用.
这些发现凸显了动物水库有可能促进新型病毒系的出现的潜力,并强调了需要持续监测的必要性.

关键词:

在 COVID-19 疫情中,生物标志物发现发现机器学习是机器学习.计量学学习学习的方法选择压力选择压力病毒宿主适应性病毒宿主适应性

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