scholarly journals Analysing Complex Oral Protein Samples: Complete Workflow and Case Analysis of Salivary Pellicles

2021 ◽  
Vol 10 (13) ◽  
pp. 2801
Author(s):  
Chen-Xuan Wei ◽  
Michael Francis Burrow ◽  
Michael George Botelho ◽  
W. Keung Leung
Keyword(s):  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Identification ◽  
Protein Quantification ◽  
Structural Genomic ◽  
Protein Protein Interaction ◽  
Functional Connections ◽  
Salivary Pellicle ◽  
Complex Protein ◽  
Biological Context

Studies on small quantity, highly complex protein samples, such as salivary pellicle, have been enabled by recent major technological and analytical breakthroughs. Advances in mass spectrometry-based computational proteomics such as Multidimensional Protein Identification Technology have allowed precise identification and quantification of complex protein samples on a proteome-wide scale, which has enabled the determination of corresponding genes and cellular functions at the protein level. The latter was achieved via protein-protein interaction mapping with Gene Ontology annotation. In recent years, the application of these technologies has broken various barriers in small-quantity-complex-protein research such as salivary pellicle. This review provides a concise summary of contemporary proteomic techniques contributing to (1) increased complex protein (up to hundreds) identification using minute sample sizes (µg level), (2) precise protein quantification by advanced stable isotope labelling or label-free approaches and (3) the emerging concepts and techniques regarding computational integration, such as the Gene Ontology Consortium and protein-protein interaction mapping. The latter integrates the structural, genomic, and biological context of proteins and genes to predict protein interactions and functional connections in a given biological context. The same technological breakthroughs and computational integration concepts can also be applied to other low-volume oral protein complexes such as gingival crevicular or peri-implant sulcular fluids.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

scholarly journals An Effective Statistical Integrative Algorithm (Aeiapp) for Protein Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 132-137 ◽  
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Protein Identification ◽  
Research Work ◽  
Interaction Network ◽  
Integrative Approach ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

The task of predicting target proteins for new drug discovery is typically difficult. Target proteins are biologically most important to control a keen functional process. The recent research of experimental and computational -based approaches has been widely used to predict target proteins using biological networks analysis techniques. Perhaps with available methods and statistical algorithm needs to be modified and should be clearer to tag the main target. Meanwhile identifying wrong protein leads to unwanted molecular interaction and pharmacological activity. In this research work, a novel method to identify essential target proteins using integrative graph coloring algorithm has been proposed. The proposed integrative approach helps to extract essential proteins in protein-protein interaction network (PPI) by analyzing neighborhood of the active target protein. Experimental results reviewed based on protein-protein interaction network for homosapiens showed that AEIAPP based approach shows an improvement in the essential protein identification by assuming the source protein as biologically proven protein. The AEIAPP statistical model has been compared with other state of art approaches on human PPI for various diseases to produce good accurate outcome in faster manner with little memory consumption.

ARID1A interacts with nonmuscle myosin IIA to regulate cancer cell motility.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17036-e17036
Author(s):  
Oloruntoba Ismail Osagie ◽  
Zhigui Li ◽  
Shijun Mi ◽  
Jennifer T Aguilan ◽  
Gloria S. Huang
Keyword(s):  
Cell Migration ◽  
Cell Motility ◽  
Cell Lines ◽  
Cancer Cell ◽  
Protein Interaction ◽  
Protein Complex ◽  
Protein Identification ◽  
Cancer Cell Motility ◽  
Protein Protein Interaction ◽  
Myosin Iia

e17036 Background: ARID1A (BAF250A), a member of the SWI/SNF chromatin remodeling complex, is one of the most frequently mutated genes in human cancer. Here we report the discovery of a novel protein-protein interaction between ARID1A and the actin-binding motor protein, non-muscle myosin IIA (NM IIA) encoded by the myosin heavy chain 9 ( MYH9). Methods: The ARID1A immunoprecipitated protein complex was separated by gel electrophoresis followed by analysis of the peptide digested gel bands by C18-Reversed Phase chromatography using an Ultimate 3000 RSLCnano System (Thermo Scientific) equipped with an Acclaim PepMap C18 column (Thermo Scientific) and connected to a TriVersa NanoMate nanoelectrospray source (Advion) and a linear ion trap LTQ-XL mass spectrometer (Thermo Scientific). Protein identification was performed by Mascot search engine v. 2.5.1 (Matrix Science) against NCBI Homo sapiens database. Scaffold software v. 4.5.1 (Proteome Software Inc.) was used to validate the MS/MS peptide and protein identification based on 99% protein and 95% peptide probabilities. Immunoprecipitation and immunoblotting were done to evaluate the protein-protein interaction in ARID1A-wild type cell lines. Isogenic engineered cell lines, ES2 shRNA-control or shRNA- ARID1A stable transfection , and HCT116 control or ARID1A knockout by CRISPR-Cas9 (Horizon Discovery) were used to evaluate the effect of ARID1A loss on NM IIA expression and phosphorylation, and on cell migration by in vitro scratch assay with time lapse imaging. Results: Scaffold analysis of peptide spectra identified NM IIA with > 99% probability in the ARID1A immunopurified protein complex. In the ARID1A wildtype cell lines ES2 and KLE, endogenous NM IIA co-immunoprecipitated with ARID1A and vice versa. ES2 sh ARID1A cells had decreased total and phosphorylated NM IIA expression, and impaired cell migration compared to control cells. Similarly, HCT116 ARID1A homozygous knockout cells had impaired cell migration compared with HCT116 control cells. Conclusions: We report for the first time that ARID1A interacts with NM IIA to regulate cancer cell motility. Further investigation is ongoing to elucidate the significance of this newly identified function of ARID1A.

Dynamic sandwich-type electrochemical assay for protein quantification and protein–protein interaction

The Analyst ◽  
2017 ◽  
Vol 142 (23) ◽  
pp. 4399-4404 ◽  
Author(s):  
Chao Li ◽  
Yaqin Tao ◽  
Yi Yang ◽  
Chang Feng ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Electrochemical Method ◽  
Protein Quantification ◽  
Interaction Study ◽  
Electrochemical Assay ◽  
Protein Protein Interaction ◽  
Sandwich Type

A versatile and sensitive electrochemical method for protein–protein interaction study based on DNAzyme has been proposed.

scholarly journals Similarity of regulatory network between leukemia stem cells and normal hemopoietic stem cells

2018 ◽  
Vol 6 (4) ◽  
pp. 129-140
Author(s):  
Zhi-Jian Li ◽  
Xing-Ling Sui ◽  
Xue-Bo Yang ◽  
Wen Sun
Keyword(s):  
Stem Cells ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Impact Factors ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractTo reveal the biology of AML, we compared gene-expression profiles between normal hematopoietic cells from 38 healthy donors and leukemic blasts (LBs) from 26 AML patients. We defined the comparison of LB and unselected BM as experiment 1, LB and CD34+ isolated from BM as experiment 2, LB and unselected PB as experiment 3, and LB and CD34+ isolated from PB as experiment 4. Then, protein–protein interaction network of DEGs was constructed to identify critical genes. Regulatory impact factors were used to identify critical transcription factors from the differential co-expression network constructed via reanalyzing the microarray profile from the perspective of differential co-expression. Gene ontology enrichment was performed to extract biological meaning. The comparison among the number of DEGs obtained in four experiments showed that cells did not tend to differentiation and CD34+ was more similar to cancer stem cells. Based on the results of protein–protein interaction network,CREBBP,F2RL1,MCM2, andTP53were respectively the key genes in experiments 1, 2, 3, and 4. From gene ontology analysis, we found that immune response was the most common one in four stages. Our results might provide a platform for determining the pathology and therapy of AML.

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