A new study by Westlake University sheds light on the serum of COVID-19 patients 西湖大学研究人员发现:重症患者血清中存在独特分子变化

2020-04-09 01:16:23 source: Zhejiang News


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The proteome big data lab led by Guo Tiannan, PI of School of Life Sciences, Westlake University, made an important discovery in the research of novel coronavirus in the past few days.


Upon systematic tests of the protein and metabolite molecules from the blood of COVID-19 patients, they, along with their cooperation team, found a variety of unique molecular changes in the critically ill patients’ serum and also a series of biomarkers, which is expected to provide guidance for predicting the progression of mild cases to severe cases.


The related research results have been put online on the preprint platform medRxiv at 0:15 on April 8, Beijing time.


COVID-19 has spread rapidly worldwide, with more than one million people infected. However, we know little about the disease changes at the microscopic molecular level, seeing only some clinical symptoms and imaging features. We still know neither the impacts of the novel coronavirus infection on patients nor why some mild cases quickly evolve into severe cases in clinical treatment.


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Compared to the control (healthy people) group, the ordinary influenza group and the mild case group, 93 specific protein expressions and 204 metabolic molecules with characteristic changes have been found in the samples from critically ill patients with COVID-19. Among them, 50 proteins are related to the macrophages, the complement system, and the platelet degranulation in the patients. The research team also found significant reductions in more than 100 amino acids and more than 100 lipids in critically ill patients infected with the novel coronavirus. This may be the consumption caused by the rapid expansion of the virus and can provide a certain reference for clinicians to monitor the disease and make or adjust treatment plans.


In addition, Guo Tiannan's team further screened 22 proteins and 7 metabolites that are unique in severe patients by using the machine learning method based on the mass spectrometry analysis data. Patients with a serum sample composition that matches this combination are likely to be severely ill or have a high likelihood of developing severe cases. This finding is expected to be used in the prediction of severe patients, promoting the rational allocation of medical resources, and providing some guidance on drug selection for severe patients. Of course, the results still need to be verified in more independent clinical cohort studies.


In the next step, the laboratory will continue to conduct in-depth research on the novel coronavirus infection by using interdisciplinary and proteomic technologies, hoping to obtain more discoveries that can help to understand the disease progression and assist in existing detection and diagnosis means to achieve more accurate and efficient treatment effects.


西湖大学生命科学学院PI郭天南带领的蛋白质组大数据实验室,近日在新冠病毒研究方面又有重要发现。


他们和合作团队一起对新冠肺炎患者血液中的蛋白质和代谢物分子进行系统检测,发现重症患者的血清中存在多种独特的分子变化,并找到了一系列生物标志物,有望为预测轻症患者向重症发展提供导向。


相关研究成果已于北京时间4月8日0时15分在预印版平台medRxiv上线。


新冠肺炎疫情已在全球范围内迅速蔓延,感染人数超过百万。然而,我们只看到临床症状和影像学特征,对疾病在微观分子层面的改变知之甚少。我们至今仍不清楚新冠病毒感染对患者有什么影响,也不太清楚在临床治疗中,为什么有些轻症患者会在短时间内迅速演变为重症。


与对照(健康)组、普通流感组和轻症组相比,新冠肺炎重症患者的样本中出现了93种特有的蛋白表达和204个特征性改变的代谢分子。其中50种蛋白,与患者体内的巨噬细胞、补体系统、血小板脱颗粒有关。研究团队还发现,在新冠病毒感染的重症患者体内,有100多种氨基酸及100多种脂质均出现显著减少。这可能是病毒迅速扩增导致的消耗,为临床医生监控病情和制定调整治疗方案提供了一定的参考。


此外,郭天南团队在质谱分析数据的基础上,使用机器学习方法进一步筛选出重症患者特征性的22个蛋白质和7个代谢物。血清样本成分符合这一组合的患者,很可能是重症患者,或有很大可能性发展为重症病例。这一发现有望用于重症患者的预测,促进医疗资源的合理调配,并为重症患者的药物选择提供一定指导。当然,该结果还需要在更多的独立临床队列中验证。


下一步,该实验室将继续使用多学科交叉与蛋白质组技术对新冠病毒感染进行深入研究,以期获得更多有助于理解病情发展规律的发现,辅助已有的检测、诊断手段,实现更精准、高效的治疗。




(Executive Editor: Ye Ke)

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11861376 A new study by Westlake University sheds light on the serum of COVID-19 patients 西湖大学研究人员发现:重症患者血清中存在独特分子变化 public html

1.jpeg


The proteome big data lab led by Guo Tiannan, PI of School of Life Sciences, Westlake University, made an important discovery in the research of novel coronavirus in the past few days.


Upon systematic tests of the protein and metabolite molecules from the blood of COVID-19 patients, they, along with their cooperation team, found a variety of unique molecular changes in the critically ill patients’ serum and also a series of biomarkers, which is expected to provide guidance for predicting the progression of mild cases to severe cases.


The related research results have been put online on the preprint platform medRxiv at 0:15 on April 8, Beijing time.


COVID-19 has spread rapidly worldwide, with more than one million people infected. However, we know little about the disease changes at the microscopic molecular level, seeing only some clinical symptoms and imaging features. We still know neither the impacts of the novel coronavirus infection on patients nor why some mild cases quickly evolve into severe cases in clinical treatment.


2.png



Compared to the control (healthy people) group, the ordinary influenza group and the mild case group, 93 specific protein expressions and 204 metabolic molecules with characteristic changes have been found in the samples from critically ill patients with COVID-19. Among them, 50 proteins are related to the macrophages, the complement system, and the platelet degranulation in the patients. The research team also found significant reductions in more than 100 amino acids and more than 100 lipids in critically ill patients infected with the novel coronavirus. This may be the consumption caused by the rapid expansion of the virus and can provide a certain reference for clinicians to monitor the disease and make or adjust treatment plans.


In addition, Guo Tiannan's team further screened 22 proteins and 7 metabolites that are unique in severe patients by using the machine learning method based on the mass spectrometry analysis data. Patients with a serum sample composition that matches this combination are likely to be severely ill or have a high likelihood of developing severe cases. This finding is expected to be used in the prediction of severe patients, promoting the rational allocation of medical resources, and providing some guidance on drug selection for severe patients. Of course, the results still need to be verified in more independent clinical cohort studies.


In the next step, the laboratory will continue to conduct in-depth research on the novel coronavirus infection by using interdisciplinary and proteomic technologies, hoping to obtain more discoveries that can help to understand the disease progression and assist in existing detection and diagnosis means to achieve more accurate and efficient treatment effects.


西湖大学生命科学学院PI郭天南带领的蛋白质组大数据实验室,近日在新冠病毒研究方面又有重要发现。


他们和合作团队一起对新冠肺炎患者血液中的蛋白质和代谢物分子进行系统检测,发现重症患者的血清中存在多种独特的分子变化,并找到了一系列生物标志物,有望为预测轻症患者向重症发展提供导向。


相关研究成果已于北京时间4月8日0时15分在预印版平台medRxiv上线。


新冠肺炎疫情已在全球范围内迅速蔓延,感染人数超过百万。然而,我们只看到临床症状和影像学特征,对疾病在微观分子层面的改变知之甚少。我们至今仍不清楚新冠病毒感染对患者有什么影响,也不太清楚在临床治疗中,为什么有些轻症患者会在短时间内迅速演变为重症。


与对照(健康)组、普通流感组和轻症组相比,新冠肺炎重症患者的样本中出现了93种特有的蛋白表达和204个特征性改变的代谢分子。其中50种蛋白,与患者体内的巨噬细胞、补体系统、血小板脱颗粒有关。研究团队还发现,在新冠病毒感染的重症患者体内,有100多种氨基酸及100多种脂质均出现显著减少。这可能是病毒迅速扩增导致的消耗,为临床医生监控病情和制定调整治疗方案提供了一定的参考。


此外,郭天南团队在质谱分析数据的基础上,使用机器学习方法进一步筛选出重症患者特征性的22个蛋白质和7个代谢物。血清样本成分符合这一组合的患者,很可能是重症患者,或有很大可能性发展为重症病例。这一发现有望用于重症患者的预测,促进医疗资源的合理调配,并为重症患者的药物选择提供一定指导。当然,该结果还需要在更多的独立临床队列中验证。


下一步,该实验室将继续使用多学科交叉与蛋白质组技术对新冠病毒感染进行深入研究,以期获得更多有助于理解病情发展规律的发现,辅助已有的检测、诊断手段,实现更精准、高效的治疗。




(Executive Editor: Ye Ke)

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patients;重症患者;data;discovery;cases;protein;critically;important;mild;Sciences