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人类病毒组的血清学反应定义了意大利感染 SARS-CoV-2 患者的临床结局。

Serological responses to human virome define clinical outcomes of Italian patients infected with SARS-CoV-2.

机构信息

Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892.

These authors contributed equally.

出版信息

Int J Biol Sci. 2022 Sep 1;18(15):5591-5606. doi: 10.7150/ijbs.78002. eCollection 2022.

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic respiratory infectious disease COVID-19. However, clinical manifestations and outcomes differ significantly among COVID-19 patients, ranging from asymptomatic to extremely severe, and it remains unclear what drives these disparities. Here, we studied 159 sequentially enrolled hospitalized patients with COVID-19-associated pneumonia from Brescia, Italy using the VirScan phage-display method to characterize circulating antibodies binding to 96,179 viral peptides encoded by 1,276 strains of human viruses. SARS-CoV-2 infection was associated with a marked increase in immune antibody repertoires against many known pathogenic and non-pathogenic human viruses. This antiviral antibody response was linked to longitudinal trajectories of disease severity and was further confirmed in additional 125 COVID-19 patients from the same geographical region in Northern Italy. By applying a machine-learning-based strategy, a viral exposure signature predictive of COVID-19-related disease severity linked to patient survival was developed and validated. These results provide a basis for understanding the role of memory B-cell repertoire to viral epitopes in COVID-19-related symptoms and suggest that a unique anti-viral antibody repertoire signature may be useful to define COVID-19 clinical severity.

摘要

严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)是导致大流行呼吸道传染病 COVID-19 的病原体。然而,COVID-19 患者的临床表现和结局差异很大,从无症状到极重度,目前尚不清楚是什么导致了这些差异。在这里,我们使用 VirScan 噬菌体展示方法研究了来自意大利布雷西亚的 159 例连续住院的 COVID-19 相关肺炎患者,该方法用于表征针对 1276 株人类病毒编码的 96179 个病毒肽的循环抗体结合情况。SARS-CoV-2 感染与针对许多已知致病性和非致病性人类病毒的免疫抗体库的显著增加有关。这种抗病毒抗体反应与疾病严重程度的纵向轨迹相关,并在意大利北部同一地理区域的另外 125 例 COVID-19 患者中得到了进一步证实。通过应用基于机器学习的策略,开发并验证了一种预测 COVID-19 相关疾病严重程度与患者生存相关的病毒暴露特征。这些结果为理解记忆 B 细胞对病毒表位的反应在 COVID-19 相关症状中的作用提供了基础,并表明独特的抗病毒抗体反应特征可能有助于确定 COVID-19 的临床严重程度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d55/9576512/5a0c7b070c3a/ijbsv18p5591g001.jpg

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