Wenbo Zhi

Personal Information
Title Assistant Professor
Expertise Nephropathy
Institution Augusta University
Data Summary
TypeCount
Grants/SubContracts 1
Progress Reports 1
Publications 1
Protocols 0
Committees 2
Experiments 0
Strains 0
Models 0

SubContract(s)


Developing a metabolite biomarker model for nephropathy in T1D patients
Despite appropriate treatment, diabetic nephropathy (DN) remains a major complication in diabetic patients and is the leading cause of end- stage renal disease. This unfavorable result is most likely due to the lack of efficient early detection method and the poor understanding of the pathogenic mechanisms of DN. Solving these two problems rely heavily on the extensive and systematic study of biomarkers for DN. Metabolome, the collection of all metabolites, is the closest to phenotype and metabolomics is a more direct way to study the effect of disease on human body since a subtle change at mRNA or protein level may lead to drastic alternations of the metabolites. This technique is becoming increasingly popular to study complex disease mechanisms with the aim to identify metabolites that are novel outcome or mechanistic biomarkers. In our proof-of-concept study, I have profiled urine samples from 40 T1D nephropathy patients and 40 matched T1D patients without any complication using liquid chromatography-mass spectrometry (LC-MS). The multivariate analysis results showed very good separation of the two groups, indicating the efficacy of the metabolite panel in disease classification. Based on these exciting results, we propose to further profile additional 160 urine samples using comphrehensive LC-MS technique and generate corrsponding metabolites candidates panel. These metabolite candidates will be further identified by MS and their multiple reaction monitoring (MRM) transitions developed and optimized. This metabolite panel will then be analyzed in another 440 urine samples using quantitative MRM-MS assay to confirm its performance. Extensive multivariate analysis and network-based function analysis will then be used to establish the metabolite panel/mode, which will be further evaluated using known samples and then used to predict the risk of DN in T1D patients. Our main goal is to establish an efficient metabolite biomarker panel that will provide sensitive and accurate prediction and new insights in understanding the mechanism of DN. These results will provide critical information for the next stage of clinical application of this biomarker panel and hence greatly hasten the biomarker development for DN.


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