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

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.

scholarly journals Proteomics Investigations of Potential Protein Biomarkers in Sera of Rabbits Infected With Schistosoma japonicum

2021 ◽  
Vol 11 ◽  
Author(s):  
Nian-Nian Bi ◽  
Song Zhao ◽  
Jian-Feng Zhang ◽  
Ying Cheng ◽  
Chen-Yang Zuo ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Schistosoma Japonicum ◽  
Serum Proteins ◽  
Public Health Problem ◽  
Temporal Analysis ◽  
Host Protein ◽  
Label Free ◽  
Protein Biomarkers ◽  
Depth Analysis ◽  
Potential Biomarkers

Schistosomiasis is a chronic parasitic disease that continues to be a pressing public health problem in many developing countries. The primary pathological damage from the disease is granuloma and fibrosis caused by egg aggregation, and early treatment can effectively prevent the occurrence of liver fibrosis. Therefore, it is very important to identify biomarkers that can be used for early diagnosis of Schistosoma japonicum infection. In this study, a label-free proteomics method was performed to observe the alteration of proteins before infection, 1 and 6 weeks after infection, and 5 and 7 weeks after treatment. A total of 10 proteins derived from S. japonicum and 242 host-derived proteins were identified and quantified as significantly changed. Temporal analysis was carried out to further analyze potential biomarkers with coherent changes during infection and treatment. The results revealed biological process changes in serum proteins compared to infection and treatment groups, which implicated receptor-mediated endocytosis, inflammatory response, and acute-phase response such as mannan-binding lectin serine peptidase 1, immunoglobulin, and collagen. These findings offer guidance for the in-depth analysis of potential biomarkers of schistosomiasis, host protein, and early diagnosis of S. japonicum and its pathogenesis. Data are available via ProteomeXchange with identifier PXD029635.

scholarly journals Circulating miRNA Profiling in Plasma Samples of Ovarian Cancer Patients

2019 ◽  
Vol 20 (18) ◽  
pp. 4533 ◽  
Author(s):  
András Penyige ◽  
Éva Márton ◽  
Beáta Soltész ◽  
Melinda Szilágyi-Bónizs ◽  
Róbert Póka ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Cancer Development ◽  
Plasma Samples ◽  
Circulating Mirna ◽  
Differentially Expressed Mirnas ◽  
Potential Biomarkers

Ovarian cancer is one of the most common cancer types in women characterized by a high mortality rate due to lack of early diagnosis. Circulating miRNAs besides being important regulators of cancer development could be potential biomarkers to aid diagnosis. We performed the circulating miRNA expression analysis in plasma samples obtained from ovarian cancer patients stratified into FIGO I, FIGO III, and FIGO IV stages and from healthy females using the NanoString quantitative assay. Forty-five miRNAs were differentially expressed, out of these 17 miRNAs showed significantly different expression between controls and patients, 28 were expressed only in patients, among them 19 were expressed only in FIGO I patients. Differentially expressed miRNAs were ranked by the network-based analysis to assess their importance. Target genes of the differentially expressed miRNAs were identified then functional annotation of the target genes by the GO and KEGG-based enrichment analysis was carried out. A general and an ovary-specific protein–protein interaction network was constructed from target genes. Results of our network and the functional enrichment analysis suggest that besides HSP90AA1, MYC, SP1, BRCA1, RB1, CFTR, STAT3, E2F1, ERBB2, EZH2, and MET genes, additional genes which are enriched in cell cycle regulation, FOXO, TP53, PI-3AKT, AMPK, TGFβ, ERBB signaling pathways and in the regulation of gene expression, proliferation, cellular response to hypoxia, and negative regulation of the apoptotic process, the GO terms have central importance in ovarian cancer development. The aberrantly expressed miRNAs might be considered as potential biomarkers for the diagnosis of ovarian cancer after validation of these results in a larger cohort of ovarian cancer patients.

A BASELINE GENE EXPRESSION-BASED PROGNOSTIC FOR ANTI-TNFα THERAPY RESPONSE IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

2021 ◽  
Vol 27 (Supplement_1) ◽  
pp. S8-S8
Author(s):  
Suraj Sakaram ◽  
Yudong He ◽  
Timothy Sweeney
Keyword(s):  
Gene Expression ◽  
Effect Size ◽  
Search Algorithm ◽  
Geometric Mean ◽  
Differentially Expressed ◽  
Expression Level ◽  
P Value ◽  
Predictive Test ◽  
Forward Search ◽  
Discriminatory Performance

Abstract Background Although anti-TNFα therapies have revolutionized the management and care of IBD, their administration and usage remain suboptimal because 1) over 50% of patients do not have a lasting therapeutic response, 2) they increase risk of infections, liver problems, arthritis, and lymphoma, and 3) they are expensive. With approximately 1.6 million people suffering from IBD in the US and global prevalence of IBD on the rise, a predictive test for anti-TNFα response would greatly improve the efficacy and cost-to-benefit ratio of these biologics. Methods We hypothesized that a multicohort analysis of the publicly available IBD gene expression datasets would yield a robust set of mRNAs for distinguishing anti-TNFα responders vs non-responders in the IBD patient population prior to treatment. We identified 5 datasets (n = 160) where whole-genome transcriptomic data was derived from colonic mucosal biopsies of IBD patients who were then subjected to anti-TNFα therapy and subsequently adjudicated for response. We used the MetaIntegrator framework which leverages a leave-one-study-out cross-validation technique in conjunction with effect size and FDR adjusted p-value to identify significant differentially expressed (DE) genes associated with a patient’s predisposition to a response outcome. DE genes were subjected to a greedy forward search to derive a parsimonious gene signature for a response score (geometric mean of the expression level for all positive mRNAs minus the geometric mean of the expression level of all negative mRNAs, multiplied by the ratio of counts of positive to negative genes). Area under the receiver operating characteristic curve (AUC) was subsequently calculated in a leave-one-study-out manner to assess discriminatory performance. Results We first identified 170 genes that were present in at least 40% of cohorts and significantly differentially expressed between responders and non-responders with effect size &gt; 0.8 and q value &lt; 0.1. A score based on these genes predicts responder vs non-responder across the 5 discovery cohorts with AUC of 0.82. Optimizing the variables with a greedy forward search algorithm allowed us to downselect to 7 genes from the set, and a score based on this parsimonious set of 7 genes improved the discriminatory performance to an AUC of 0.87. Choosing a high sensitivity (90%) for a rule-in scenario, the score had moderate specificity (60%); alternatively choosing a high specificity (90%) for a rule-out scenario, the score still had a good sensitivity (80%). Conclusions These initial findings suggest that there is a strong signal for predicting anti-TNFα response in colonic biopsies. In particular, we showed using the leave-one-study-out approach that a predictive signature using mRNA can be generalizable (works in independent cohorts). These initial results warrant further investigation.

scholarly journals iTRAQ-Based Proteomics Identification of Serum Biomarkers of Two Chronic Hepatitis B Subtypes Diagnosed by Traditional Chinese Medicine

2016 ◽  
Vol 2016 ◽  
pp. 1-29 ◽  
Author(s):  
Jiankun Yang ◽  
Lichao Yang ◽  
Baixue Li ◽  
Weilong Zhou ◽  
Sen Zhong ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Hepatitis B ◽  
Serum Proteins ◽  
Differentially Expressed ◽  
Protein Biomarkers ◽  
Complement Components ◽  
Spleen Deficiency ◽  
Shock Proteins ◽  
Potential Biomarkers

Background.Chronic infection with hepatitis B virus (HBV) is a leading cause of cirrhosis and hepatocellular carcinoma. By traditional Chinese medicine (TCM) pattern classification, damp heat stasis in the middle-jiao (DHSM) and liver Qi stagnation and spleen deficiency (LSSD) are two most common subtypes of CHB.Results.In this study, we employed iTRAQ proteomics technology to identify potential serum protein biomarkers in 30 LSSD-CHB and 30 DHSM-CHB patients. Of the total 842 detected proteins, 273 and 345 were differentially expressed in LSSD-CHB and DHSM-CHB patients compared to healthy controls, respectively. LSSD-CHB and DHSM-CHB shared 142 upregulated and 84 downregulated proteins, of which several proteins have been reported to be candidate biomarkers, including immunoglobulin (Ig) related proteins, complement components, apolipoproteins, heat shock proteins, insulin-like growth factor binding protein, and alpha-2-macroglobulin. In addition, we identified that proteins might be potential biomarkers to distinguish LSSD-CHB from DHSM-CHB, such as A0A0A0MS51_HUMAN (gelsolin), PON3_HUMAN, Q96K68_HUMAN, and TRPM8_HUMAN that were differentially expressed exclusively in LSSD-CHB patients and A0A087WT59_HUMAN (transthyretin), ITIH1_HUMAN, TSP1_HUMAN, CO5_HUMAN, and ALBU_HUMAN that were differentially expressed specifically in DHSM-CHB patients.Conclusion.This is the first time to report serum proteins in CHB subtype patients. Our findings provide potential biomarkers can be used for LSSD-CHB and DHSM-CHB.

Comparative serum proteome analysis reveals potential early pregnancy-specific protein biomarkers in pigs

2019 ◽  
Vol 31 (3) ◽  
pp. 613 ◽  
Author(s):  
Ankan De ◽  
Mohammad Ayub Ali ◽  
Tukheswar Chutia ◽  
Suneel Kumar Onteru ◽  
Parthasarathi Behera ◽  
...  
Keyword(s):  
Early Pregnancy ◽  
Inflammatory Responses ◽  
Proteome Analysis ◽  
Specific Protein ◽  
Protein Profiling ◽  
Differentially Expressed ◽  
Label Free ◽  
Protein Biomarkers ◽  
Serum Proteome ◽  
Pregnancy Zone Protein

In this study, the comparative serum proteome profile of Day 5, 12 and 16 of gestation, representing three early embryonic events, namely formation, elongation and implantation of blastocysts, and non-pregnant control were explored by a label-free quantitation-based mass spectrometric approach to identify early pregnancy biomarkers in pigs. A total of 131 proteins were identified with respect to different groups, out of which 105 were found to be differentially expressed proteins (DEPs). Among the DEPs, 54 and 66 proteins were found to be up and downregulated respectively in early pregnancy groups (fold change &gt;2) and the maximum number of upregulated proteins was observed in the Day 12 pregnancy stage. Functional classification and pathway analysis of the DEPs revealed involvement of most of the proteins in complement and coagulation cascades, metabolic processes and immune and inflammatory responses. Proteins such as glutathione peroxidise (GPX), pregnancy zone protein (PZP), thrombospondin-1 (THBS1), α-1-antitrypsin (AAT) and mannose-binding lectin C (MBLC) were differentially expressed during early pregnancy and actively involved in different pregnancy-related activities. To the best of our knowledge, this is the first report on comparative serum protein profiling of different early pregnancy stages in pigs and our results provide a set of proteins that can be used as potential biomarkers for early pregnancy diagnosis in pigs.

scholarly journals OpenPepXL: An Open-Source Tool for Sensitive Identification of Cross-Linked Peptides in XL-MS

2020 ◽  
Vol 19 (12) ◽  
pp. 2157-2167
Author(s):  
Eugen Netz ◽  
Tjeerd M. H. Dijkstra ◽  
Timo Sachsenberg ◽  
Lukas Zimmermann ◽  
Mathias Walzer ◽  
...  
Keyword(s):  
Open Source ◽  
Structural Information ◽  
Search Algorithm ◽  
Protein Structures ◽  
Peptide Identification ◽  
Search Space ◽  
Cloud Services ◽  
Label Free ◽  
Protein Database ◽  
Data Set

Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.

scholarly journals Proteomic identification of novel plasma biomarkers associated with spontaneous preterm birth in women with preterm labor without infection/inflammation

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259265
Author(s):  
Ji Eun Lee ◽  
Kyo Hoon Park ◽  
Hyeon Ji Kim ◽  
Yu Mi Kim ◽  
Ji-Woong Choi ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Calcium Binding ◽  
Preterm Labor ◽  
Coagulation Factor ◽  
Farnesoid X Receptor ◽  
Differentially Expressed ◽  
Plasma Samples ◽  
Label Free ◽  
Spontaneous Preterm Birth ◽  
Plasma Biomarkers

Objective We sought to identify plasma biomarkers associated with spontaneous preterm birth (SPTB, delivery within 21 days of sampling) in women with preterm labor (PTL) without intra-amniotic infection/inflammation (IAI) using label-free quantitative proteomic analysis, as well as to elucidate specific protein pathways involved in these cases. Methods This was a retrospective cohort study comprising 104 singleton pregnant women with PTL (24–32 weeks) who underwent amniocentesis and demonstrated no evidence of IAI. Analysis of pooled plasma samples collected from SPTB cases and term birth (TB) controls (n = 10 for each group) was performed using label-free quantitative mass spectrometry for proteome profiling in a nested case-control study design. Eight candidate proteins of interest were validated by ELISA-based assay and a clot-based assay in the total cohort. Results Ninety-one proteins were differentially expressed (P < 0.05) in plasma samples obtained from SPTB cases, of which 53 (58.2%) were upregulated and 38 (41.8%) were downregulated when compared to TD controls. A validation study confirmed that plasma from women who delivered spontaneously within 21 days of sampling contained significantly higher levels of coagulation factor Ⅴ and lower levels of S100 calcium binding protein A9 (S100A9), especially the former which was independent of baseline variables. The top-ranked pathways related to the 91 differentially expressed proteins were liver-X-receptor/retinoid X receptor (RXR) activation, acute phase response signaling, farnesoid X receptor/RXR activation, coagulation system, and complement system. Conclusions Proteomic analyses in this study identified potential novel biomarkers (i.e., coagulation factor V and S100A9) and potential protein pathways in plasma associated with SPTB in the absence of IAI in women with PTL. The present findings provide novel insights into the molecular pathogenesis and therapeutic targets specific for idiopathic SPTB.

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

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.

scholarly journals Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Leticia Díaz-Beltrán ◽  
Carmen González-Olmedo ◽  
Natalia Luque-Caro ◽  
Caridad Díaz ◽  
Ariadna Martín-Blázquez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Biomarker Discovery ◽  
Molecular Subtypes ◽  
Breast Cancer Subtype ◽  
P Value ◽  
Plasma Samples ◽  
Breast Cancer Patients ◽  
Clinical Value ◽  
Molecular Phenotypes

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

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