Identification and characterization of novel plasma proteins in drug resistant HIV/AIDS patients by SWATH-MS

AbstractBackground and ObjectivesAcquired immunodeficiency syndrome is one of the most important diseases caused by human immunodeficiency virus. Understanding its molecular pathogenesis is essential to manage the disease at the population level. In this study, a quantitative analysis of plasma proteins was carried out in drug resistant and drug respondent patients using the SWATH-MS.MethodsSequential window acquisition of all theoretical mass spectra (SWATH-MS) is a prime technique to seek the key plasma proteins involved in virus replication and drug metabolism during therapy.ResultsIn total, 204 proteins were identified and quantified, 57 proteins were differentially expressed, 25 proteins were down regulated and 32 proteins were upregulated in drug resistant patients. Six proteins such as complement C4-A, immunoglobulin heavy variable 1-2, carboxylic ester hydrolase, fibulin-1, immunoglobulin lambda constant 7, secreted phosphoprotein 24 were statistically expressed in drug resistant patients compared to the drug respondent patients. Gene ontology study and protein-protein interaction networks were established in six statistically significant differentially expressed proteins of the drug resistant patients.Interpretation and ConclusionOur findings high lights the novel proteins that were differentially expressed in drug resistant patients. A label-free quantitative proteomics method for depleted human plasma samples by SWATH-MS that can be useful in plasma proteomics research in any biological system.

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Label-Free Comparative Proteomics of Differentially Expressed Mycobacterium tuberculosis Protein in Rifampicin-Related Drug-Resistant Strains

Pathogens ◽

10.3390/pathogens10050607 ◽

2021 ◽

Vol 10 (5) ◽

pp. 607

Author(s):

Nadeem Ullah ◽

Ling Hao ◽

Jo-Lewis Banga Ndzouboukou ◽

Shiyun Chen ◽

Yaqi Wu ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Targets ◽

Control Strategies ◽

Multidrug Resistant ◽

Differentially Expressed ◽

Effective Control ◽

Drug Resistant ◽

Differentially Expressed Proteins ◽

Label Free ◽

Candidate Target

Rifampicin (RIF) is one of the most important first-line anti-tuberculosis (TB) drugs, and more than 90% of RIF-resistant (RR) Mycobacterium tuberculosis clinical isolates belong to multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. In order to identify specific candidate target proteins as diagnostic markers or drug targets, differential protein expression between drug-sensitive (DS) and drug-resistant (DR) strains remains to be investigated. In the present study, a label-free, quantitative proteomics technique was performed to compare the proteome of DS, RR, MDR, and XDR clinical strains. We found iniC, Rv2141c, folB, and Rv2561 were up-regulated in both RR and MDR strains, while fadE9, espB, espL, esxK, and Rv3175 were down-regulated in the three DR strains when compared to the DS strain. In addition, lprF, mce2R, mce2B, and Rv2627c were specifically expressed in the three DR strains, and 41 proteins were not detected in the DS strain. Functional category showed that these differentially expressed proteins were mainly involved in the cell wall and cell processes. When compared to the RR strain, Rv2272, smtB, lpqB, icd1, and folK were up-regulated, while esxK, PPE19, Rv1534, rpmI, ureA, tpx, mpt64, frr, Rv3678c, esxB, esxA, and espL were down-regulated in both MDR and XDR strains. Additionally, nrp, PPE3, mntH, Rv1188, Rv1473, nadB, PPE36, and sseA were specifically expressed in both MDR and XDR strains, whereas 292 proteins were not identified when compared to the RR strain. When compared between MDR and XDR strains, 52 proteins were up-regulated, while 45 proteins were down-regulated in the XDR strain. 316 proteins were especially expressed in the XDR strain, while 92 proteins were especially detected in the MDR strain. Protein interaction networks further revealed the mechanism of their involvement in virulence and drug resistance. Therefore, these differentially expressed proteins are of great significance for exploring effective control strategies of DR-TB.

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Quantitative proteomics analysis of FFPE tumor samples reveals the influences of novel targeting nanobubbles conjugated with NET-1 siRNA on tetraspanin protein involved in HCC

10.21203/rs.3.rs-20923/v1 ◽

2020 ◽

Author(s):

Bolin Wu ◽

Haitao Shang ◽

Xitian Liang ◽

Huajing Yang Huajing Yang ◽

Hui Jing ◽

...

Keyword(s):

Protein Interactions ◽

Interaction Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Differentially Expressed Proteins ◽

Label Free ◽

Protein Protein Interaction ◽

Study Results ◽

Ffpe Tumor

Abstract Background: Hepatocellular carcinoma (HCC) poses a severe threat to human health. The NET-1 protein has been proved to be strongly associated with HCC proliferation and metastasis in our previous study. Methods: Here, we developed a label-free proteome mass spectrometry workflow to analyze formalin-fixed and paraffin-embedded HCC xenograft samples collected in our previous study. Results: The result showed that 78 proteins were differentially expressed after NET-1 protein inhibited. Among them, the expression of 61 proteins up-regulated and the expression of 17 proteins were significantly down-regulated. Of the differentially expressed proteins, the vast majority of Gene Ontology enrichment terms belong to the biological process. The KEGG pathway enrichment analysis showed that the 78 differentially expressed proteins significantly enriched in 45 pathways. We concluded that the function of the NET-1 gene is not only to regulate HCC but also to participate in a variety of biochemical metabolic pathways in the human body. Furthermore, the protein-protein interaction analysis indicated that the interactions of differentially expressed proteins are incredibly sophisticated. All the protein-protein interactions happened after the NET-1 gene has been silenced. Conclusions: Finally, our study also provides a useful proposal for targeted therapy based on tetraspanin proteins to treat HCC, and further mechanism investigations are needed to reveal a more detailed mechanism of action for NET-1 protein regulation of HCC.

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Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA-mRNA pairs in cisplatin-resistant ovarian cancer

BMC Cancer ◽

10.1186/s12885-021-08166-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Bai Xue ◽

Shupeng Li ◽

Xianyu Jin ◽

Lifeng Liu

Keyword(s):

Ovarian Cancer ◽

Cell Lines ◽

Protein Interaction ◽

Molecular Mechanisms ◽

Target Genes ◽

Differentially Expressed ◽

Mirna Sequence ◽

Drug Resistant ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Background Ovarian cancer (OC) is a gynecological malignancy with the highest mortality rate. Cisplatin (DDP) based chemotherapy is a standard strategy for ovarian cancer. Despite good response rates for initial chemotherapy, almost 80% of the patients treated with DDP based chemotherapy will experience recurrence due to drug-resistant, which will ultimately result in fatality. The aim of the present study was to examine the pathogenesis and potential molecular markers of cisplatin-resistant OC by studying the differential expression of mRNAs and miRNAs between cisplatin resistant OC cell lines and normal cell lines. Methods Two mRNA datasets (GSE58470 and GSE45553) and two miRNA sequence datasets (GSE58469 and GSE148251) were downloaded from the Gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were screened by the NetworkAnalyst. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. The protein-protein interaction network was constructed using STRING and Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities. FunRich and MiRNATip databases were used to identify the target genes of the DEMs. Results A total of 380 DEGs, and 5 DEMs were identified. Protein–protein interaction (PPI) network of DEGs containing 379 nodes and 1049 edges was constructed, and 4 key modules and 24 hub genes related to cisplatin-resistant OC were screened. Two hundred ninety-nine target genes of the 5 DEMs were found out. Subsequently, one of these 299 target genes (UBB) belonging to the hub genes of GSE58470 and GSE45553 was identified by MCODE and CytoHubba,which was regulated by one miRNA (mir-454). Conclusions One miRNA–mRNA regulatory pairs (mir-454-UBB) was established. Taken together, our study provided evidence concerning the alteration genes involved in cisplatin-resistant OC, which will help to unravel the mechanisms underlying drug resistant.

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Identification of the Differentially Expressed Genes Involved in the Synergistic Neurotoxicity of an HIV Protease Inhibitor and Methamphetamine

Current HIV Research ◽

10.2174/1570162x17666190924200354 ◽

2019 ◽

Vol 17 (4) ◽

pp. 290-303

Author(s):

Sangsang Li ◽

Yanfei Li ◽

Bingpeng Deng ◽

Jie Yan ◽

Yong Wang

Keyword(s):

Protease Inhibitor ◽

Growth Inhibition ◽

Quantitative Polymerase Chain Reaction ◽

Differentially Expressed Gene ◽

Antiretroviral Drugs ◽

Differentially Expressed ◽

Hiv Protease ◽

Hiv Protease Inhibitor ◽

Dopaminergic Cells ◽

Immunodeficiency Syndrome

Background: The abuse of psychostimulants such as methamphetamine (METH) is common in human immunodeficiency virus (HIV)-infected individuals. Acquired immunodeficiency syndrome (AIDS) patients taking METH and antiretroviral drugs could suffer severe neurologic damage and cognitive impairment. Objective: To reveal the underlying neuropathologic mechanisms of an HIV protease inhibitor (PI) combined with METH, growth-inhibition tests of dopaminergic cells and RNA sequencing were performed. Methods: A combination of METH and PI caused more growth inhibition of dopaminergic cells than METH alone or a PI alone. Furthermore, we identified differentially expressed gene (DEG) patterns in the METH vs. untreated cells (1161 genes), PI vs. untreated cells (16 genes), METH-PI vs. PI (3959 genes), and METH-PI vs. METH groups (14 genes). Results: The DEGs in the METH-PI co-treatment group were verified in the brains of a mouse model using quantitative polymerase chain reaction and were involved mostly in the regulatory functions of cell proliferation and inflammation. Conclusion: Such identification of key regulatory genes could facilitate the study of their neuroprotective potential in the users of METH and PIs.

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Label-Free Mass Spectrometry-Based Plasma Proteomics Identified LY6D, DSC3, CDSN, SERPINB12, and SLURP1,as Novel Protein Biomarkers For Pulmonary Tuberculosis

Current Proteomics ◽

10.2174/1570164617666191210105122 ◽

2019 ◽

Vol 17 ◽

Author(s):

Xiaoli Yu ◽

Lu Zhang ◽

Na Li ◽

Peng Hu ◽

Zhaoqin Zhu ◽

...

Keyword(s):

Pulmonary Tuberculosis ◽

Search Algorithm ◽

Differentially Expressed ◽

P Value ◽

Plasma Samples ◽

Label Free ◽

Protein Biomarkers ◽

Protein Database ◽

Leave One Out ◽

Potential Biomarkers

Aim: We aimed to identify new plasma biomarkers for the diagnosis of Pulmonary tuberculosis. Background: Tuberculosis is an ancient infectious disease that remains one of the major global health problems. Until now, effective, convenient, and affordable methods for diagnosis of Pulmonary tuberculosis were still lacked. Objective: This study focused on construct a label-free LC-MS/MS based comparative proteomics between six tuberculosis patients and six healthy controls to identify differentially expressed proteins (DEPs) in plasma. Method: To reduce the influences of high-abundant proteins, albumin and globulin were removed from plasma samples using affinity gels. Then DEPs from the plasma samples were identified using a label-free Quadrupole-Orbitrap LC-MS/MS system. The results were analyzed by the protein database search algorithm SEQUEST-HT to identify mass spectra to peptides. The predictive abilities of combinations of host markers were investigated by general discriminant analysis (GDA), with leave-one-out cross-validation. Results: A total of 572 proteins were identified and 549 proteins were quantified. The threshold for differentially expressed protein was set as adjusted p-value < 0.05 and fold change ≥1.5 or ≤0.6667, 32 DEPs were found. ClusterVis, TBtools, and STRING were used to find new potential biomarkers of PTB. Six proteins, LY6D, DSC3, CDSN, FABP5, SERPINB12, and SLURP1, which performed well in the LOOCV method validation, were termed as potential biomarkers. The percentage of cross-validated grouped cases correctly classified and original grouped cases correctly classified is greater than or equal to 91.7%. Conclusion: We successfully identified five candidate biomarkers for immunodiagnosis of PTB in plasma, LY6D, DSC3, CDSN, SERPINB12, and SLURP1. Our work supported this group of proteins as potential biomarkers for pulmonary tuberculosis, and be worthy of further validation.

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Bioinformatics Analysis Reveals Functions of MicroRNAs in Rice Under the Drought Stress

Current Bioinformatics ◽

10.2174/1574893615666200207092410 ◽

2021 ◽

Vol 15 (8) ◽

pp. 927-936 ◽

Cited By ~ 3

Author(s):

Yan Peng ◽

Yuewu Liu ◽

Xinbo Chen

Keyword(s):

Drought Stress ◽

Target Genes ◽

Hot Spot ◽

Interaction Network ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Protein Protein Interaction ◽

Promising Tool ◽

Differentially Expressed Mirnas ◽

Aba Biosynthesis

Background: Drought is one of the most damaging and widespread abiotic stresses that can severely limit the rice production. MicroRNAs (miRNAs) act as a promising tool for improving the drought tolerance of rice and have become a hot spot in recent years. Objective: In order to further extend the understanding of miRNAs, the functions of miRNAs in rice under drought stress are analyzed by bioinformatics. Method: In this study, we integrated miRNAs and genes transcriptome data of rice under the drought stress. Some bioinformatics methods were used to reveal the functions of miRNAs in rice under drought stress. These methods included target genes identification, differentially expressed miRNAs screening, enrichment analysis of DEGs, network constructions for miRNA-target and target-target proteins interaction. Results: (1) A total of 229 miRNAs with differential expression in rice under the drought stress, corresponding to 73 rice miRNAs families, were identiﬁed. (2) 1035 differentially expressed genes (DEGs) were identified, which included 357 up-regulated genes, 542 down-regulated genes and 136 up/down-regulated genes. (3) The network of regulatory relationships between 73 rice miRNAs families and 1035 DEGs was constructed. (4) 25 UP_KEYWORDS terms of DEGs, 125 GO terms and 7 pathways were obtained. (5) The protein-protein interaction network of 1035 DEGs was constructed. Conclusion: (1) MiRNA-regulated targets in rice might mainly involve in a series of basic biological processes and pathways under drought conditions. (2) MiRNAs in rice might play critical roles in Lignin degradation and ABA biosynthesis. (3) MiRNAs in rice might play an important role in drought signal perceiving and transduction.

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Label-free quantitative proteomic analysis of the inhibition effect of Lactobacillus rhamnosus GG on Escherichia coli biofilm formation in co-culture

Proteome Science ◽

10.1186/s12953-021-00172-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Huiyi Song ◽

Ni Lou ◽

Jianjun Liu ◽

Hong Xiang ◽

Dong Shang

Keyword(s):

Escherichia Coli ◽

Proteomic Analysis ◽

Biofilm Formation ◽

Lactobacillus Rhamnosus ◽

Differentially Expressed ◽

Differentially Expressed Proteins ◽

Label Free ◽

Quantitative Proteomic Analysis ◽

E Coli ◽

Protein Ubiquitination

Abstract Background Escherichia coli (E. coli) is the principal pathogen that causes biofilm formation. Biofilms are associated with infectious diseases and antibiotic resistance. This study employed proteomic analysis to identify differentially expressed proteins after coculture of E. coli with Lactobacillus rhamnosus GG (LGG) microcapsules. Methods To explore the relevant protein abundance changes after E. coli and LGG coculture, label-free quantitative proteomic analysis and qRT-PCR were applied to E. coli and LGG microcapsule groups before and after coculture, respectively. Results The proteomic analysis characterised a total of 1655 proteins in E. coli K12MG1655 and 1431 proteins in the LGG. After coculture treatment, there were 262 differentially expressed proteins in E. coli and 291 in LGG. Gene ontology analysis showed that the differentially expressed proteins were mainly related to cellular metabolism, the stress response, transcription and the cell membrane. A protein interaction network and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis indicated that the differentiated proteins were mainly involved in the protein ubiquitination pathway and mitochondrial dysfunction. Conclusions These findings indicated that LGG microcapsules may inhibit E. coli biofilm formation by disrupting metabolic processes, particularly in relation to energy metabolism and stimulus responses, both of which are critical for the growth of LGG. Together, these findings increase our understanding of the interactions between bacteria under coculture conditions.

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Alteration of protein expression and spliceosome pathway activity during Barrett’s carcinogenesis

Journal of Gastroenterology ◽

10.1007/s00535-021-01802-2 ◽

2021 ◽

Author(s):

Christoph Stingl ◽

Angela Bureo Gonzalez ◽

Coşkun Güzel ◽

Kai Yi Nadine Phoa ◽

Michail Doukas ◽

...

Keyword(s):

Micro Rna ◽

Differentially Expressed ◽

Epithelial Tissue ◽

Label Free ◽

Tissue Samples ◽

Damage Repair ◽

Inter Observer Variability ◽

Stromal Tissue ◽

The Face ◽

Abundance And Diversity

Abstract Background Barrett’s esophagus (BE) is a known precursor lesion and the strongest risk factor for esophageal adenocarcinoma (EAC), a common and lethal type of cancer. Prediction of risk, the basis for efficient intervention, is commonly solely based on histologic examination. This approach is challenged by problems such as inter-observer variability in the face of the high heterogeneity of dysplastic tissue. Molecular markers might offer an additional way to understand the carcinogenesis and improve the diagnosis—and eventually treatment. In this study, we probed significant proteomic changes during dysplastic progression from BE into EAC. Methods During endoscopic mucosa resection, epithelial and stromal tissue samples were collected by laser capture microdissection from 10 patients with normal BE and 13 patients with high-grade dysplastic/EAC. Samples were analyzed by mass spectrometry-based proteomic analysis. Expressed proteins were determined by label-free quantitation, and gene set enrichment was used to find differentially expressed pathways. The results were validated by immunohistochemistry for two selected key proteins (MSH6 and XPO5). Results Comparing dysplastic/EAC to non-dysplastic BE, we found in equal volumes of epithelial tissue an overall up-regulation in terms of protein abundance and diversity, and determined a set of 226 differentially expressed proteins. Significantly higher expressions of MSH6 and XPO5 were validated orthogonally and confirmed by immunohistochemistry. Conclusions Our results demonstrate that disease-related proteomic alterations can be determined by analyzing minute amounts of cell-type-specific collected tissue. Further analysis indicated that alterations of certain pathways associated with carcinogenesis, such as micro-RNA trafficking, DNA damage repair, and spliceosome activity, exist in dysplastic/EAC.

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Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820983298 ◽

2021 ◽

Vol 20 ◽

pp. 153303382098329

Author(s):

Yujie Weng ◽

Wei Liang ◽

Yucheng Ji ◽

Zhongxian Li ◽

Rong Jia ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Protein Interaction ◽

Differentially Expressed ◽

Protein Kinase Activity ◽

Protein Protein Interaction ◽

Risk Of Death ◽

Her2 Breast Cancer ◽

Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

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