Quantitative proteomic analysis of human plasma using tandem mass tags to identify novel biomarkers for herpes zoster

Wang, Tingting; Deng, Helen; Pan, Huaizhong; He, Qiuli; Ni, Huadong; Tao, Jiachun; Liu, Songlei; Xu, Longsheng; Yao, Ming

doi:10.1016/j.jprot.2020.103879

Cited by 13 publications

(15 citation statements)

References 45 publications

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“…These models exhibited high accuracy values ( Li et al, 2020 ). The development of omics technology based on large-sample sequencing has provided a potential new method for predicting PHN ( Freidin et al, 2016 ; Wang T. et al, 2020 ). Our previous results revealed that we should focus on the outcome of HZ and the prediction and intervention of PHN from the metabolism and immunity perspective because the susceptibility of PHN in the elderly is related to metabolic and immune dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling

Zhou

Li²,

Zhang³

et al. 2022

Front. Mol. Neurosci.

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Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.

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Section: Introductionmentioning

confidence: 99%

Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling

Zhou

Li²,

Zhang³

et al. 2022

Front. Mol. Neurosci.

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“…We identified 740 proteins in total using TMT-based quantitative proteomics on an Orbitrap Fusion Lumos MS instrument, which is on par with recent plasma proteomics publications [83][84][85][86][87][88][89][90]. However, with substantial increases in high pH reversed-phase fractionation in the sample processing, higher numbers of plasma protein identifications could have been identified [91].…”

Section: Discussionmentioning

confidence: 78%

Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma

Khan

Desaire

López

et al. 2021

JAD

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Background: African American/Black adults have a disproportionate incidence of Alzheimer’s disease (AD) and are underrepresented in biomarker discovery efforts. Objective: This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. Methods: We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. Results: In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. Conclusion: These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.

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“…Compared with two‐dimensional difference gel electrophoresis, TMT technology has higher sensitivity and accuracy. TMTs play an important role in the proteomic analysis of a variety of samples, such as cells, tissues and body fluids [23]. Protein analysis in CSF is critical for diagnosing GBS, a neurological disease.…”

Section: Introductionmentioning

confidence: 99%

Potential biomarkers identified by tandem mass tags based quantitative proteomics for diagnosis and classification of Guillain–Barré syndrome

Ding

Shi

Wang

et al. 2021

Euro J of Neurology

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Background and purpose: Guillain-Barré syndrome (GBS) is an acute inflammatory autoimmune and demyelinating disease of the peripheral nervous system. Currently, valid biomarkers are unavailable for the diagnosis of GBS.Methods: A comparative proteomics analysis was performed on the cerebrospinal fluid (CSF) from 10 patients with GBS and 10 patients with noninflammatory neurological disease (NND) using the tandem mass tags technique. The differentially expressed proteins were analyzed by bioinformatics, and then the candidate proteins were validated by the enzyme-linked immunosorbent assay method in another cohort containing 160 samples (paired CSF and plasma of 40 patients with GBS, CSF of 40 NND patients and plasma of 40 healthy individuals).Results: In all, 298 proteins were successfully identified in the CSF samples, of which 97 differentially expressed proteins were identified in the GBS and NND groups. Three key molecules were identified as candidate molecules for further validation. The CSF levels of TGOLN2 and NCAM1 decreased in GBS patients compared with NND patients, whereas the CSF levels of APOC3 increased. The enzyme-linked immunosorbent assay results were consistent with our proteomics analysis. Interestingly, in the validation cohort, serum APOC3 levels in the GBS group were consistent with those in the CSF samples and significantly higher than those in the healthy control group. Conclusions:Our preliminary data suggest that the CSF protein expression profile of patients with GBS is different from that of patients with NND. Moreover, alterations of TGOLN2, NCAM1and APOC3 may be used as novel biomarkers for identifying patients with GBS.

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Quantitative proteomic analysis of human plasma using tandem mass tags to identify novel biomarkers for herpes zoster

Cited by 13 publications

References 45 publications

Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling

Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling

Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma

Potential biomarkers identified by tandem mass tags based quantitative proteomics for diagnosis and classification of Guillain–Barré syndrome

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