A High-Resolution Proteomic Landscaping of Primary Human Dental Stem Cells: Identification of SHED- and PDLSC-Specific Biomarkers

Taraslia, Vasiliki; Lymperi, Stefania; Pantazopoulou, Vasiliki I.; Anagnostopoulos, Athanasios K.; Papassideri, Issidora S.; Basdra, Efthimia K.; Bei, Marianna; Kontakiotis, Evangelos G.; Tsangaris, George Th.; Stravopodis, Dimitrios J.; Anastasiadou, Ema

doi:10.3390/ijms19010158

Cited by 17 publications

(18 citation statements)

References 82 publications

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“…The analysis of stem cell contents and the composition of membrane molecules is one way to solve this problem. Proteomics has been applied to the characterization of stem cells, including twodimensional electrophoresis, iTRAQ or TMT-labeled quantitative proteomics, and nanomass spectrometry [129][130][131]. However, studies have rarely analyzed the essential differences in one individual by omics analyses.…”

Section: Discussionmentioning

confidence: 99%

Characteristics, Classification, and Application of Stem Cells Derived from Human Teeth

Tong

Zhang

2021

Stem Cells International

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Since mesenchymal stem cells derived from human teeth are characterized as having the properties of excellent proliferation, multilineage differentiation, and immune regulation. Dental stem cells exhibit fibroblast-like microscopic appearance and express mesenchymal markers, embryonic markers, and vascular markers but do not express hematopoietic markers. Dental stem cells are a mixed population with different sensitive markers, characteristics, and therapeutic effects. Single or combined surface markers are not only helpful for understanding the subpopulation of mixed stem cell populations according to cell function but also for improving the stable treatment effect of dental stem cells. Focusing on the discovery and characterization of stem cells isolated from human teeth over the past 20 years, this review outlines the effect of marker sorting on cell proliferation and differentiation ability and the assessment of the clinical application potential. Classified dental stem cells from markers and functional molecules can solve the problem of heterogeneity and ensure the efficacy of cell therapy strategies including dentistry, neurologic diseases, bone repair, and tissue engineering.

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Section: Discussionmentioning

confidence: 99%

Characteristics, Classification, and Application of Stem Cells Derived from Human Teeth

Tong

Zhang

2021

Stem Cells International

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“…Studying the protein-protein interaction networks and the predicted protein interactors provides a signi cance data to explore the functions of proteins [105], [106]. In PPI networks, proteins that have direct interaction with several other proteins are called hub proteins (hub nodes).…”

Section: Discussionmentioning

confidence: 99%

Elevated Expression Levels of Lung Complement Anaphylatoxin, Neutrophil Chemoattractant Chemokine IL-8, and RANTES in MERS-CoV-Infected Patients: Predictive Biomarkers for Disease Severity and Mortality

Awadalla

Naeem

Alkadi

et al. 2021

Preprint

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The complement system represents an innate immune response consisting of a protein network. Over-activation of the complement system plays an important role in inflammation, tissue damage, and infectious disease severity. The prevalence of MERS-CoV in Saudi Arabia remains significant and cases are still being reported. The role of complement in Middle East Respiratory Syndrome coronavirus (MERS-CoV) pathogenesis and complement‐modulating treatment strategies has received limited attention, and studies involving MERS-CoV-infected patients have not been reported. This study offers the first insight into the pulmonary expression profile including Seven complement proteins including complement regulatory factors during MERS-CoV infection. We also measured the expression of lung neutrophil chemoattractant chemokine IL-8 (CXCL8) and RANTES (CCL5). Our results significantly indicate high expression levels of complement anaphylatoxins (C3a and C5a), IL-8, and RANTES in the lungs of MERS-CoV-infected patients. The upregulation of lung complement anaphylatoxins, C5a and C3a, was positively correlated with IL-8, RANTES and the fatality rate. Our results also showed upregulation of the positive regulatory complement factor P (properdin), suggesting positive regulation of the complement during MERS-CoV infection. In addition, we also demonstrated that a high viral load in all patients with MERS-CoV correlated with C5a and C3a levels. Pulmonary complement mediators, disease severity, and an increased fatality rate may be linked to the degree of complement activation against MERS-CoV. High levels of lung C5a, C3a, factor P, IL-8 and RANTES may contribute to the immunopathology, disease severity, ARDS development, and a higher fatality rate in MERS-CoV-infected patients. These findings highlight the potential prognostic utility of C5a, C3a, IL-8 and RANTES as biomarkers for MERS-CoV disease severity and mortality. To further explore the functional partners (protiens) prediction of highly expressed proteins (C5a, C3a, factor P, IL-8 and RANTES), the computational protein–protein interaction (PPI) network was constructed, and six proteins (hub nodes) were identified.

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“…Studying the interaction between proteins and the network formed by their interaction is of great significance to reveal the functions of proteins [ 39 , 40 ]. In PPI networks, proteins that interact directly with many other proteins are called hubs.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Odontogenic Differentiation from Human Dental Pulp Stem Cells Using TMT-Based Proteomic Analysis

Xiao

Xin

Zhang

et al. 2020

BioMed Research International

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Background. The repair of dental pulp injury relies on the odontogenic differentiation of dental pulp stem cells (DPSCs). To better understand the odontogenic differentiation of DPSCs and identify proteins involved in this process, tandem mass tags (TMTs) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied to compare the proteomic profiles of induced and control DPSCs. Methods. The proteins expressed during osteogenic differentiation of human DPSCs were profiled using the TMT method combined with LC-MS/MS analysis. The identified proteins were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Then, a protein-protein interaction (PPI) network was constructed. Two selected proteins were confirmed by western blotting (WB) analysis. Results. A total of 223 proteins that were differentially expressed were identified. Among them, 152 proteins were significantly upregulated and 71 were downregulated in the odontogenic differentiation group compared with the control group. On the basis of biological processes in GO, the identified proteins were mainly involved in cellular processes, metabolic processes, and biological regulation, which are connected with the signaling pathways highlighted by KEGG pathway analysis. PPI networks showed that most of the differentially expressed proteins were implicated in physical or functional interaction. The protein expression levels of FBN1 and TGF-β2 validated by WB were consistent with the proteomic analysis. Conclusions. This is the first proteomic analysis of human DPSC odontogenesis using a TMT method. We identified many new differentially expressed proteins that are potential targets for pulp-dentin complex regeneration and repair.

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A High-Resolution Proteomic Landscaping of Primary Human Dental Stem Cells: Identification of SHED- and PDLSC-Specific Biomarkers

Cited by 17 publications

References 82 publications

Characteristics, Classification, and Application of Stem Cells Derived from Human Teeth

Characteristics, Classification, and Application of Stem Cells Derived from Human Teeth

Elevated Expression Levels of Lung Complement Anaphylatoxin, Neutrophil Chemoattractant Chemokine IL-8, and RANTES in MERS-CoV-Infected Patients: Predictive Biomarkers for Disease Severity and Mortality

Characterization of Odontogenic Differentiation from Human Dental Pulp Stem Cells Using TMT-Based Proteomic Analysis

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