Following Zhou Qiang's lab, the proteomic big data lab led by Tennan Guo, a PI in the School of Life Sciences at Xihu University, recently made another important discovery in the study of new coronaviruses. Together with a collaborative team, they systematically examined protein and metabolite molecules in the blood of patients with neocoronavirus pneumonia and found a variety of unique molecular changes in the serum of severe patients, and a series of biomarkers that are expected to provide guidance for predicting the progression of mild to severe disease in patients.
The relevant research results, which were made available online at 0:15 a.m. Beijing time on April 8 on the preprint platform medRxiv.
The new Crown pneumonia epidemic has spread rapidly around the world, infecting more than a million people. However, we see only clinical symptoms and imaging features and know little about how the disease is altered at the microscopic molecular level. We still don't know how neocoronavirus infection affects patients, and we're not quite sure why, in clinical care, some patients with mild disease rapidly evolve into severe disease in a short period of time.
Tiannan Guo's team, in collaboration with clinical and metabolomic research teams, safely processed and analyzed by mass spectrometry 99 serum samples treated for viral inactivation. Based on current clinical diagnostic criteria, these blood samples were categorized into a control group, a suspected but actual common influenza group, a mild new crown infection group, and a severe new crown infection group. Team members used high-resolution mass spectrometry equipment to obtain proteomic and metabolomic profiles of the samples, providing a panoramic view of the relative concentrations of proteins and metabolites in the serum samples, thus revealing a variety of unique molecular regulations in the critically ill patients.
Experimental design and procedure
Ninety-three unique protein expressions and 204 characteristically altered metabolic molecules were seen in samples from critically ill patients with neocoronavirus pneumonia, compared with controls, common influenza, and mild disease groups. Fifty of these proteins, were associated with macrophages, the complement system, and platelet degranulation in the patients. The team also found that more than 100 amino acids and more than 100 lipids were significantly reduced in critically ill patients infected with the new coronavirus. This may be a depletion due to rapid viral amplification, and provides some reference for clinicians to monitor the condition and develop adjustments to treatment regimens.
Pathway maps of the macrophage, platelet, and complement systems in critically ill patients after COVID-19 infection as hypothesized based on histologic data. These proteins and metabolites are expected to be biomarkers for early diagnosis of critically ill patients and targets for therapy.
In addition, based on the mass spectrometry data, Guo's team used machine learning methods to further "pick out the gold in the sand" and screened out 22 proteins and 7 metabolites that are characteristic of critically ill patients. Patients whose serum samples match this combination are likely to be critically ill, or have a high likelihood of developing a severe case. This finding is expected to be used for the prediction of critically ill patients, to facilitate the rational allocation of medical resources, and to provide some guidance for drug selection in critically ill patients. Of course, the results need to be validated in more independent clinical cohorts.
Protein expression is an important basis for clinical diagnosis, and the effectiveness of disease treatment also depends on the regulation of the protein machinery. Tennan Guo's team, together with a collaborative team, initiated the study from the end of February this year. Using new mass spectrometry detection technology and machine learning methods, they integrated multidisciplinary data from proteomics, clinical, biological, metabolomic and computational disciplines within a short period of time, and repeatedly screened, analyzed, compared and validated them, and took the lead in completing the analysis of serum proteomes and metabolomes of patients with mild and severe diseases of COVID-19, which provided a clear picture of what was occurring in sera of patients with neocoronazole severe disease, unique and currently unknown. A panoramic description of the unique and currently unknown molecular pathological alterations occurring in the serum of patients with neocoronary severe disease is provided.
Proteomic Big Data Laboratory
In the next step, the laboratory will continue to use multidisciplinary intersection with proteomic technology to conduct in-depth research on neocoronavirus infection, with a view to obtaining more discoveries that will help to understand the pattern of the disease's development, and to supplement the existing testing and diagnostic tools to achieve more precise and efficient treatment.
This study was supported by Zhejiang Taizhou Hospital of Wenzhou Medical University and Dean Diagnostics' Kelaplan Metabolomics Laboratory. The Tencent Foundation also funded this project.
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