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Zhaodi Li

Bio: Zhaodi Li is an academic researcher from Protein Sciences. The author has contributed to research in topics: Lipoprotein metabolic process & Platelet degranulation. The author has an hindex of 2, co-authored 2 publications receiving 22 citations.

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Posted ContentDOI
06 May 2020-medRxiv
TL;DR: This study demonstrates the COVID-19 pathophysiology related molecular alterations could be detected in the urine and the potential application of urinary proteome in auxiliary diagnosis, severity determination and therapy development of CO VID-19.
Abstract: SUMMARY The atypical pneumonia (COVID-19) caused by SARS-CoV-2 is an ongoing pandemic and a serious threat to global public health. The COVID-19 patients with severe symptoms account for a majority of mortality of this disease. However, early detection and effective prediction of patients with mild to severe symptoms remains challenging. In this study, we performed proteomic profiling of urine samples from 32 healthy control individuals and 6 COVID-19 positive patients (3 mild and 3 severe). We found that urine proteome samples from the mild and severe COVID-19 patients with comorbidities can be clearly differentiated from healthy proteome samples based on the clustering analysis. Multiple pathways have been compromised after the COVID-19 infection, including the dysregulation of immune response, complement activation, platelet degranulation, lipoprotein metabolic process and response to hypoxia. We further validated our finding by directly comparing the same patients’ urine proteome after recovery. This study demonstrates the COVID-19 pathophysiology related molecular alterations could be detected in the urine and the potential application of urinary proteome in auxiliary diagnosis, severity determination and therapy development of COVID-19.

32 citations

Journal ArticleDOI
01 Jan 2020
TL;DR: In this paper, the authors demonstrated the COVID-19 pathophysiology related molecular alterations could be detected in the urine and the potential application in auxiliary diagnosis of COVID19.
Abstract: The atypical pneumonia (COVID-19) caused by SARS-CoV-2 is a serious threat to global public health. However, early detection and effective prediction of patients with mild to severe symptoms remain challenging. The proteomic profiling of urine samples from healthy individuals, mild and severe COVID-19 positive patients with comorbidities can be clearly differentiated. Multiple pathways have been compromised after the COVID-19 infection, including the dysregulation of complement activation, platelet degranulation, lipoprotein metabolic process and response to hypoxia. This study demonstrates the COVID-19 pathophysiology related molecular alterations could be detected in the urine and the potential application in auxiliary diagnosis of COVID-19.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: It is proposed that the combined effects of complement activation, dysregulated neutrophilia, endothelial injury, and hypercoagulability appear to be intertwined to drive the severe features of COVID-19 and create a basis for clinical trials of complement inhibitors in life-threatening illness.
Abstract: Coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in a global pandemic and a disruptive health crisis. COVID-19-related morbidity and mortality have been attributed to an exaggerated immune response. The role of complement activation and its contribution to illness severity is being increasingly recognized. Here, we summarize current knowledge about the interaction of coronaviruses with the complement system. We posit that (a) coronaviruses activate multiple complement pathways; (b) severe COVID-19 clinical features often resemble complementopathies; (c) the combined effects of complement activation, dysregulated neutrophilia, endothelial injury, and hypercoagulability appear to be intertwined to drive the severe features of COVID-19; (d) a subset of patients with COVID-19 may have a genetic predisposition associated with complement dysregulation; and (e) these observations create a basis for clinical trials of complement inhibitors in life-threatening illness.

280 citations

Journal ArticleDOI
TL;DR: Applications of proteomics to COVID-19 and SARS are reviewed and how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases are outlined.
Abstract: The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.

62 citations

Journal ArticleDOI
TL;DR: A brief of the existing knowledge, current challenges, and opportunities for MS-based techniques as a promising avenue in studying emerging pathogen outbreaks such as COVID-19 is given.

54 citations

Journal ArticleDOI
TL;DR: In this article, a mass spectrometry-based approach that employed an enrichment step to capture and detect SARS-CoV-2 nucleocapsid protein directly from urine of COVID-19 patients without any culture was employed.
Abstract: SARS-CoV-2 infection has become a major public health burden and affects many organs including lungs, kidneys, the liver, and the brain. Although the virus is readily detected and diagnosed using nasopharyngeal swabs by reverse transcriptase polymerase chain reaction (RT-PCR), detection of its presence in body fluids is fraught with difficulties. A number of published studies have failed to detect viral RNA by RT-PCR methods in urine. Although microbial identification in clinical microbiology using mass spectrometry is undertaken after culture, here we undertook a mass spectrometry-based approach that employed an enrichment step to capture and detect SARS-CoV-2 nucleocapsid protein directly from urine of COVID-19 patients without any culture. We detected SARS-CoV-2 nucleocapsid protein-derived peptides from 13 out of 39 urine samples. Further, a subset of COVID-19 positive and COVID-19 negative urine samples validated by mass spectrometry were used for the quantitative proteomics analysis. Proteins with increased abundance in urine of SARS-CoV-2 positive individuals were enriched in the acute phase response, regulation of complement system, and immune response. Notably, a number of renal proteins such as podocin (NPHS2), an amino acid transporter (SLC36A2), and sodium/glucose cotransporter 5 (SLC5A10), which are intimately involved in normal kidney function, were decreased in the urine of COVID-19 patients. Overall, the detection of viral antigens in urine using mass spectrometry and alterations of the urinary proteome could provide insights into understanding the pathogenesis of COVID-19.

26 citations

Journal ArticleDOI
TL;DR: Based on the unique affinity between dermatan sulfate and autoantigens, the authors identified 348 proteins from human lung A549 cells, of which 198 are known targets of autoantibodies.

22 citations