scholarly journals Label-Free Mass Spectrometry-Based Quantitative Proteomics Analysis of Serum Proteins During Early Pregnancy in Jennies (Equus asinus)

2020 ◽  
Vol 7 ◽  
Author(s):  
Liang Deng ◽  
Yuwei Han ◽  
Chi Tang ◽  
Qingchao Liao ◽  
Zheng Li
Keyword(s):  
Mass Spectrometry ◽  
Early Pregnancy ◽  
Quantitative Proteomics ◽  
Serum Proteins ◽  
Label Free ◽  
Proteomics Analysis ◽  
Equus Asinus

Development of Algorithms for Mass Spectrometry-based Label-free Quantitative Proteomics*

2011 ◽  
Vol 38 (6) ◽  
pp. 506-518 ◽  
Author(s):  
Wei ZHANG ◽  
Ji-Yang ZHANG ◽  
Hui LIU ◽  
Han-Chang SUN ◽  
Chang-Ming XU ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Label Free

scholarly journals Comparative Proteomics Analysis of Mouse Habu Nephritis Models with and without Unilateral Nephrectomy

2016 ◽  
Vol 39 (5) ◽  
pp. 1761-1776 ◽  
Author(s):  
Lei Chen ◽  
Yang Lu ◽  
Jun Wen ◽  
Xu Wang ◽  
Lingling Wu ◽  
...  
Keyword(s):  
Renal Injury ◽  
Quantitative Proteomics ◽  
Mesangial Cells ◽  
Unilateral Nephrectomy ◽  
Label Free ◽  
Proteomics Analysis ◽  
Single Kidney ◽  
Regulation Of Actin Cytoskeleton ◽  
Insight Into

Background/Aims: Individuals possessing a single kidney are at greater risk of renal injury upon exposure to harmful stimuli. This study aimed to explore the pathogenesis of renal injury in glomerulonephritis with versus without unilateral nephrectomy (UNX). Methods: Histological analysis and label-free quantitative proteomics were performed on two models—the Habu snake venom-induced glomerulonephritis model with versus without UNX (HabuU and Habu models, respectively). The role of villin 1, a differentially expressed protein (DEP) in mouse mesangial cells, was investigated. Results: Persistent mesangiolysis and focal hypercellularity together with reduced activation of cell proliferation in the HabuU model induced more serious renal injury compared with that in the Habu model. The DEPs between the two models were identified by label-free liquid chromatography-mass spectrometry. The KEGG pathway results indicated that regulation of actin cytoskeleton and focal adhesion were specifically enriched in the HabuU model. The cytoskeleton regulation protein villin 1 was downregulated in the HabuU model, but unchanged in the Habu model. Knockdown of villin 1 promoted apoptosis and inhibited the proliferation of mouse mesangial cells, suggesting villin 1 to be involved in qlomerular lesion self-repair insufficiency. Conclusion: By assessing the proteomic profiles of the two models, this study identified several important differences, particularly villin 1 expression, in regulatory mechanisms between the two models. Our findings provide novel insight into the mechanism of serious renal injury in glomerulonephritis with UNX.

scholarly journals NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

scholarly journals The effect of lipocalin-2 (LCN2) on apoptosis: a proteomics analysis study in an LCN2 deficient mouse model

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dongming Wu ◽  
Xiaopeng Wang ◽  
Ye Han ◽  
Yayun Wang
Keyword(s):  
Knockout Mice ◽  
Quantitative Proteomics ◽  
Energy Homeostasis ◽  
Metabolic Diseases ◽  
Differentially Expressed Proteins ◽  
Label Free ◽  
Lipocalin 2 ◽  
Wild Type ◽  
Proteomics Analysis ◽  
Associated Proteins

Abstract Background Recent studies have shown that lipocalin-2 (LCN2) has multiple functions involved in various biological and pathological processes including energy homeostasis, cancer, inflammation, and apoptosis. We aimed to investigate the effect of LCN2 on apoptosis that influences the pathogenetic process of metabolic diseases and cancer. Methods We performed a proteomics analysis of livers taken from LCN2-knockout mice and wild type mice by using label-free LC-MS/MS quantitative proteomics. Results Proteomic analysis revealed that there were 132 significantly differentially expressed proteins (49 upregulated and 83 downregulated) among 2140 proteins in the liver of LCN2-knockout mice compared with wild type mice. Of these, seven apoptosis-associated proteins were significantly upregulated and seven apoptosis-associated proteins downregulated. Conclusion Proteomics demonstrated that there were seven upregulated and seven downregulated apoptosis-associated proteins in liver of LCN2-knockout mice. It is important to clarify the effect of LCN2 on apoptosis that might contribute to the pathogenesis of insulin resistance, cancer, and various nervous system diseases.

scholarly journals Multi-sample mass spectrometry-based approach for discovering injury markers in chronic kidney disease

2020 ◽  
pp. mcp.RA120.002159
Author(s):  
Ji Eun Kim ◽  
Dohyun Han ◽  
Jin Seon Jeong ◽  
Jong Joo Moon ◽  
Hyun Kyung Moon ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Quantitative Proteomics ◽  
Kidney Tissue ◽  
Protein S ◽  
Disease Stage ◽  
Tandem Mass ◽  
Ckd Progression ◽  
Proteomics Analysis

Urinary proteomics studies have primarily focused on identifying markers of chronic kidney disease (CKD) progression. Here, we aimed to determine urinary markers of CKD renal parenchymal injury through proteomics analysis in animal kidney tissues and cells and in the urine of patients with CKD. Label-free quantitative proteomics analysis based on liquid chromatography-tandem mass spectrometry was performed on urine samples obtained from 6 normal controls and 9, 11, and 10 patients with CKD stages 1, 3, and 5, respectively, and on kidney tissue samples from a rat CKD model by 5/6 nephrectomy. Tandem mass tag-based quantitative proteomics analysis was performed for primary cultured glomerular endothelial cells (GECs) and proximal tubular epithelial cells (PTECs) before and after inducing 24-h hypoxia injury. Upon hierarchical clustering, out of 858 differentially expressed proteins (DEPs) in the urine of CKD patients, the levels of 416 decreased and 403 increased sequentially according to the disease stage, respectively. Among 2965 DEPs across 5/6 nephrectomized and sham-operated rat kidney tissues, 86 DEPs showed same expression patterns in the urine and kidney tissue. After cross-validation with two external animal proteome datasets, 38 DEPs were organized; only 10 DEPs, including serotransferrin, gelsolin, poly ADP-ribose polymerase 1, neuroblast differentiation-associated protein AHNAK, microtubule-associated protein 4, galectin-1, protein S, thymosin beta-4, myristoylated alanine-rich C-kinase substrate, and vimentin were finalized by screening human GECs and PTECs data. Among these ten potential candidates for universal CKD marker, validation analyses for protein S and galectin-1 were conducted. Galectin-1 was observed to have a significant inverse correlation with renal function as well as higher expression in glomerulus with chronic injury than protein S. This constitutes the first multi-sample proteomics study for identifying key renal-expressed proteins associated with CKD progression. The discovered proteins represent potential markers of chronic renal cell and tissue damage and candidate contributors to CKD pathophysiology.

scholarly journals proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry

2020 ◽  
Author(s):  
Constantin Ahlmann-Eltze ◽  
Simon Anders
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Statistical Power ◽  
Missing Values ◽  
Ad Hoc ◽  
Linear Models ◽  
Statistical Tests ◽  
High Sensitivity ◽  
Small Sample ◽  
Label Free

Abstract Protein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative proteomics studies. Nevertheless, well-principled statistical inference procedures are still lacking, and most practitioners adopt methods from transcriptomics. These, however, cannot properly treat the principal complication of label-free proteomics, namely many non-randomly missing values. We present proDA, a method to perform statistical tests for differential abundance of proteins. It models missing values in an intensity-dependent probabilistic manner. proDA is based on linear models and thus suitable for complex experimental designs, and boosts statistical power for small sample sizes by using variance moderation. We show that the currently widely used methods based on ad hoc imputation schemes can report excessive false positives, and that proDA not only overcomes this serious issue but also offers high sensitivity. Thus, proDA fills a crucial gap in the toolbox of quantitative proteomics.

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