Profiling coronaviruses Among the coronaviruses that infect humans, four cause mild common colds, whereas three others, including the currently circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), result in severe infections. Shrock et al. used a technology known as VirScan to probe the antibody repertoires of hundreds of coronavirus disease 2019 (COVID-19) patients and pre–COVID-19 era controls. They identified hundreds of antibody targets, including several antibody epitopes shared by the mild and severe coronaviruses and many specific to SARS-CoV-2. A machine-learning model accurately classified patients infected with SARS-CoV-2 and guided the design of an assay for rapid SARS-CoV-2 antibody detection. The study also looked at how the antibody response and viral exposure history differ in patients with diverging outcomes, which could inform the production of improved vaccine and antibody therapies. Science, this issue p. eabd4250 Deep serological profiling reveals discriminatory epitopes that could be used for SARS-CoV-2 antibody detection. INTRODUCTION A systematic characterization of the humoral response to severe acute respiratory system coronavirus 2 (SARS-CoV-2) epitopes has yet to be performed. This analysis is important for understanding the immunogenicity of the viral proteome and the basis for cross-reactivity with the common-cold coronaviruses. Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is notable for its variable course, with some individuals remaining asymptomatic whereas others experience fever, respiratory distress, or even death. A comprehensive investigation of the antibody response in individuals with severe versus mild COVID-19—as well as an examination of past viral exposure history—is needed. RATIONALE An understanding of humoral responses to SARS-CoV-2 is critical for improving diagnostics and vaccines and gaining insight into variable clinical outcomes. To this end, we used VirScan, a high-throughput method to analyze epitopes of antiviral antibodies in human sera. We supplemented the original VirScan library with additional libraries of peptides spanning the proteomes of SARS-CoV-2 and all other human coronaviruses. These libraries enabled us to precisely map epitope locations and investigate cross-reactivity between SARS-CoV-2 and other coronavirus strains. The original VirScan library allowed us to simultaneously investigate antibody responses to prior infections and viral exposure history. RESULTS We screened sera from 232 COVID-19 patients and 190 pre–COVID-19 era controls against the original VirScan and supplemental coronavirus libraries, assaying more than 108 antibody repertoire–peptide interactions. We identified epitopes ranging from “private” (recognized by antibodies in only a small number of individuals) to “public” (recognized by antibodies in many individuals) and detected SARS-CoV-2–specific epitopes as well as those that cross-react with common-cold coronaviruses. Several of these cross-reacting antibodies are present in pre–COVID-19 era samples. We developed a machine learning model that predicted SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity from VirScan data. We used the most discriminatory SARS-CoV-2 peptides to produce a Luminex-based serological assay, which performed similarly to gold-standard enzyme-linked immunosorbent assays. We stratified the COVID-19 patient samples by disease severity and found that patients who had required hospitalization exhibited stronger and broader antibody responses to SARS-CoV-2 but weaker overall responses to past infections compared with those who did not need hospitalization. Further, the hospitalized group had higher seroprevalence rates for cytomegalovirus and herpes simplex virus 1. These findings may be influenced by differences in demographic compositions between the two groups, but they raise hypotheses that may be tested in future studies. Using alanine scanning mutagenesis, we precisely mapped 823 distinct epitopes across the entire SARS-CoV-2 proteome, 10 of which are likely targets of neutralizing antibodies. One cross-reactive antibody epitope in S2 has been previously suggested to be neutralizing and, as it exists in pre–COVID-19 era samples, could affect the severity of COVID-19. CONCLUSION We present a highly detailed view of the epitope landscape within the SARS-CoV-2 proteome. This knowledge may be used to produce diagnostics with improved specificity and can provide a stepping stone to the isolation and functional dissection of both neutralizing antibodies and antibodies that might exacerbate patient outcomes through antibody-dependent enhancement or immune distraction. Our study reveals notable correlations between COVID-19 severity and both viral exposure history and overall strength of the antibody response to past infections. These findings are likely influenced by demographic covariates, but they generate hypotheses that may be tested with larger patient cohorts matched for age, gender, race, and other demographic variables. SARS-CoV-2 epitope mapping. VirScan detects antibodies against SARS-CoV-2 in COVID-19 patients with severe and mild disease. Heatmap color represents the strength of the antibody response in each sample (columns) to each protein (rows, left) or peptide (rows, right). VirScan reveals the precise positions of epitopes, which can be mapped onto the structure of the spike protein (S). Examination of SARS-CoV-2 and seasonal coronavirus sequence conservation explains epitope cross-reactivity. A, Ala; D, Asp; E, Glu; F, Phe; I, Ile; K, Lys; L, Leu; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; Y, Tyr. Understanding humoral responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for improving diagnostics, therapeutics, and vaccines. Deep serological profiling of 232 coronavirus disease 2019 (COVID-19) patients and 190 pre–COVID-19 era controls using VirScan revealed more than 800 epitopes in the SARS-CoV-2 proteome, including 10 epitopes likely recognized by neutralizing antibodies. Preexisting antibodies in controls recognized SARS-CoV-2 ORF1, whereas only COVID-19 patient antibodies primarily recognized spike protein and nucleoprotein. A machine learning model trained on VirScan data predicted SARS-CoV-2 exposure history with 99% sensitivity and 98% specificity; a rapid Luminex-based diagnostic was developed from the most discriminatory SARS-CoV-2 peptides. Individuals with more severe COVID-19 exhibited stronger and broader SARS-CoV-2 responses, weaker antibody responses to prior infections, and higher incidence of cytomegalovirus and herpes simplex virus 1, possibly influenced by demographic covariates. Among hospitalized patients, males produce stronger SARS-CoV-2 antibody responses than females.

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COVID-19 患者的病毒表位分析揭示了交叉反应性和严重程度的相关性

冠状病毒概况 在感染人类的​​冠状病毒中，有四种会引起轻度普通感冒，而其他三种，包括目前流行的严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2)，会导致严重感染。史洛克等。使用一种称为 VirScan 的技术来探测数百名 2019 年冠状病毒病 (COVID-19) 患者和 COVID-19 时代之前的对照者的抗体库。他们确定了数百个抗体靶标，包括轻度和重度冠状病毒共有的几个抗体表位，以及许多特定于 SARS-CoV-2 的抗体表位。机器学习模型对感染 SARS-CoV-2 的患者进行了准确分类，并指导设计了一种快速检测 SARS-CoV-2 抗体的方法。该研究还研究了结果不同的患者的抗体反应和病毒接触史有何不同，这可以为改进疫苗和抗体疗法的生产提供信息。科学，这个问题 p。eabd4250 深度血清学分析揭示了可用于 SARS-CoV-2 抗体检测的歧视性表位。引言 尚未对严重急性呼吸系统冠状病毒 2 (SARS-CoV-2) 表位的体液反应进行系统表征。该分析对于了解病毒蛋白质组的免疫原性以及与普通感冒冠状病毒交叉反应的基础非常重要。由 SARS-CoV-2 引起的 2019 年冠状病毒病 (COVID-19) 以其多变的病程而著称，一些人没有症状，而另一些人则出现发烧、呼吸窘迫甚至死亡。需要对重度和轻度 COVID-19 患者的抗体反应进行全面调查，并检查过去的病毒接触史。基本原理 了解对 SARS-CoV-2 的体液反应对于改进诊断和疫苗以及深入了解不同的临床结果至关重要。为此，我们使用了 VirScan，这是一种分析人血清中抗病毒抗体表位的高通量方法。我们用跨越 SARS-CoV-2 和所有其他人类冠状病毒蛋白质组的附加肽库补充了原始 VirScan 库。这些文库使我们能够精确定位表位位置并研究 SARS-CoV-2 与其他冠状病毒株之间的交叉反应。最初的 VirScan 库使我们能够同时研究抗体对先前感染和病毒接触史的反应。结果 我们针对原始 VirScan 和补充冠状病毒文库筛选了 232 名 COVID-19 患者的血清和 190 名 COVID-19 时代之前的对照，分析了超过 108 种抗体库-肽相互作用。我们确定了从“私有”（仅少数个体的抗体识别）到“公共”（许多个体的抗体识别）的表位，并检测了 SARS-CoV-2 特异性表位以及那些发生交叉反应的表位与普通感冒冠状病毒。COVID-19 时代之前的样品中存在其中几种交叉反应抗体。我们开发了一种机器学习模型，可以根据 VirScan 数据以 99% 的灵敏度和 98% 的特异性预测 SARS-CoV-2 接触史。我们使用最具鉴别力的 SARS-CoV-2 肽来产生基于 Luminex 的血清学测定，其性能与黄金标准酶联免疫吸附测定相似。我们按疾病严重程度对 COVID-19 患者样本进行分层，发现需要住院治疗的患者对 SARS-CoV-2 表现出更强和更广泛的抗体反应，但与不需要住院治疗的患者相比，对既往感染的总体反应较弱。此外，住院组的巨细胞病毒和单纯疱疹病毒 1 的血清阳性率更高。这些发现可能受到两组人口组成差异的影响，但它们提出了可能在未来研究中检验的假设。使用丙氨酸扫描诱变，我们在整个 SARS-CoV-2 蛋白质组中精确定位了 823 个不同的表位，其中 10 个可能是中和抗体的目标。S2 中的一个交叉反应抗体表位先前被认为具有中和作用，并且由于它存在于 COVID-19 时代之前的样本中，可能会影响 COVID-19 的严重程度。结论 我们展示了 SARS-CoV-2 蛋白质组内表位景观的高度详细视图。这些知识可用于产生具有更高特异性的诊断，并且可以为中和抗体和可能通过抗体依赖性增强或免疫干扰而加剧患者预后的抗体的分离和功能解剖提供垫脚石。我们的研究揭示了 COVID-19 的严重程度与病毒接触史和抗体对过去感染的总体反应强度之间存在显着相关性。这些发现可能受到人口统计协变量的影响，但它们产生的假设可以用与年龄、性别、种族和其他人口统计变量匹配的更大的患者队列进行检验。SARS-CoV-2 表位作图。VirScan 检测 COVID-19 重症和轻症患者的 SARS-CoV-2 抗体。热图颜色表示每个样本（列）对每种蛋白质（左行）或肽（右行）的抗体反应强度。VirScan 揭示了表位的精确位置，可以将其映射到刺突蛋白 (S) 的结构上。对 SARS-CoV-2 和季节性冠状病毒序列保守性的检查解释了表位交叉反应性。A，丙氨酸；D、天冬氨酸；E，谷氨酸；F，苯丙氨酸；我，艾尔；K，赖氨酸；L，亮氨酸；N, 天冬氨酸；P, 临; Q, 谷氨酰胺; R，精氨酸；S, 塞尔; 苏氨酸；V，瓦尔；W, 色氨酸; Y，蒂尔。了解对严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 的体液反应对于改进诊断、治疗和疫苗至关重要。使用 VirScan 对 232 名 2019 年冠状病毒病 (COVID-19) 患者和 190 名 COVID-19 时代之前的对照进行深度血清学分析，揭示了 SARS-CoV-2 蛋白质组中的 800 多个表位，包括 10 个可能被中和抗体识别的表位。对照中预先存在的抗体识别 SARS-CoV-2 ORF1，而只有 COVID-19 患者抗体主要识别刺突蛋白和核蛋白。基于 VirScan 数据训练的机器学习模型以 99% 的灵敏度和 98% 的特异性预测 SARS-CoV-2 接触史；一种基于 Luminex 的快速诊断是从最具鉴别力的 SARS-CoV-2 肽中开发出来的。患有更严重的 COVID-19 的个体表现出更强和更广泛的 SARS-CoV-2 反应，对先前感染的抗体反应更弱，以及巨细胞病毒和单纯疱疹病毒 1 的发生率更高，这可能受人口统计学协变量的影响。在住院患者中，男性比女性产生更强的 SARS-CoV-2 抗体反应。