Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue

Plubell, Deanna; Wilmarth, Phillip A.; Zhao, Yuqi; Fenton, Alexandra M; Minnier, Jessica; Reddy, Ashok P.; Klimek, John; Yang, Xia; David, Larry L.; Pamir, Nathalie

doi:10.1074/mcp.m116.065524

Cited by 274 publications

(254 citation statements)

References 81 publications

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“…Differential protein abundances between groups were determined by comparing the total reporter ion intensities using the Bioconductor package edgeR [10]. EdgeR was developed for serial analysis of gene expression data but its modeling is flexible enough to handle a variety of other data types such as TMT reporter ion intensities [11–13]. …”

Section: Methodsmentioning

confidence: 99%

Proteomic analysis of the glutathione-deficient LEGSKO mouse lens reveals activation of EMT signaling, loss of lens specific markers, and changes in stress response proteins

Whitson

Wilmarth

Klimek

et al. 2017

Free Radical Biology and Medicine

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Purpose To determine global protein expression changes in the lens of the GSH-deficient LEGSKO mouse model of age-related cataract for comparison with recently published gene expression data obtained by RNA-Seq transcriptome analysis. Methods Lenses were separated into epithelial and cortical fiber sections, digested with trypsin, and labeled with isobaric tags (10-plex TMT). Peptides were analyzed by LC-MS/MS (Orbitrap Fusion) and mapped to the mouse proteome for relative protein quantification. Results 1871 proteins in lens epithelia and 870 proteins in lens fiber cells were quantified. 40 proteins in LEGSKO epithelia, 14 proteins in LEGSKO fiber cells, 22 proteins in buthionine sulfoximine (BSO)-treated LEGSKO epithelia, and 55 proteins in BSO-treated LEGSKO fiber cells had significantly (p<0.05, FDR<0.1) altered protein expression compared to WT controls. HSF4 and MAF transcription factors were the most common upstream regulators of the response to GSH-deficiency. Many detoxification proteins, including aldehyde dehydrogenases, peroxiredoxins, and quinone oxidoreductase, were upregulated but several glutathione S-transferases were downregulated. Several cellular stress response proteins showed regulation changes, including an upregulation of HERPUD1, downregulation of heme oxygenase, and mixed changes in heat shock proteins. NRF2-regulated proteins showed broad upregulation in BSO-treated LEGSKO fiber cells, but not in other groups. Strong trends were seen in downregulation of lens specific proteins, including β- and γ-crystallins, lengsin, and phakinin, and in epithelial-mesenchymal transition (EMT)-related changes. Western blot analysis of LEGSKO lens epithelia confirmed expression changes in several proteins. Conclusions This dataset confirms at the proteomic level many findings from the recently determined GSH-deficient lens transcriptome and provides new insight into the roles of GSH in the lens, how the lens adapts to oxidative stress, and how GSH affects EMT in the lens.

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Section: Methodsmentioning

confidence: 99%

Proteomic analysis of the glutathione-deficient LEGSKO mouse lens reveals activation of EMT signaling, loss of lens specific markers, and changes in stress response proteins

Whitson

Wilmarth

Klimek

et al. 2017

Free Radical Biology and Medicine

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“…An alternative experimental design uses two bridge channels and takes an average between them to standardize each protein within a plex (Lapek et al, 2017;Plubell et al, 2017). These methods were unpublished until we were well in to our study, but are designed to alleviate the issues with improper bridge measurement that we correct in the CCLE through mean centering.…”

Section: Figure 5: Per-protein Batch Effect Normalization (A)mentioning

confidence: 99%

A Guide to the Quantitative Proteomic Profiles of the Cancer Cell Line Encyclopedia

Nusinow

Gygi

2020

Preprint

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We recently reported the quantitative proteomics of 375 samples as part of the Cancer Cell Line Encyclopedia (Nusinow et al., 2020). Mass spectrometry-based proteomics data is broadly unfamiliar to most biologists in our experience, resulting in questions from analysts about how to use the data. From the proteomics community there was interest about how we normalized the data, as the scope of this project was so much larger than what has been commonly available. This paper serves as a guide to the data set to answer these questions and acts as a supplement to the main manuscript. The first part addresses users of the data, describing the experimental design, interpretation of the values, and dealing with standard issues in proteomics like multiple protein isoforms per gene and missing values. The second part of the manuscript details how we arrived at the normalization procedure reported in the paper, including the diagnostics used to assess multiple normalization schemes.

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“…The reconstituted peptides were separated by two-dimensional nano reverse-phase liquid chromatography (Dionex NCS-3500 UltiMate RSLCnano UPLC) EasySpray NanoSource (Thermo Scientific), ionized using an EasySpray NanoSource (Thermo Scientific), and SPS MS3 data acquired with an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific). The liquid chromatography details and mass spectrometer settings were as previously described [13] with the modification that nonenriched peptides were eluted from the first dimension high pH column using sequential injections of 20 µL volumes of 14,17,20,21,22,23,24,25,26,27,28,29,30,35,40,50, and 90% ACN in 10 mM ammonium formate, pH 9, and enriched phosphopeptides were eluted by sequential 20 µL injections of 4,6,8,10,12,18,20,22,25,30, and 60% ACN in 10 mM ammonium formate, pH 9.…”

Section: Tmt Labeling and Mass Spectrometric Analysismentioning

confidence: 99%

“…Phosphopeptide abundance is expected to be dynamic, so TMT acquisition methods that have improved accuracy and wider dynamic ranges are necessary [12]. The larger number of replicates available with high-resolution instruments and TMT tags require improved data normalization and statistical testing methods, and we have successfully applied analysis techniques developed for large-scale protein expression studies [13] to phosphopeptide abundance data. Since PKCα-dependent changes in protein phosphorylation may be due to changes in total protein abundance, we also identified proteins that were differentially expressed between WT and PKCα-KO retinas.…”

Section: Introductionmentioning

confidence: 99%

Identification of PKCα-dependent phosphoproteins in mouse retina

Wakeham

Wilmarth

Cunliffe

et al. 2019

Preprint

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Adjusting to a wide range of light intensities is an essential feature of retinal rod bipolar cell (RBC) function. While persuasive evidence suggests this modulation involves phosphorylation by protein kinase C-alpha (PKCα), the targets of PKCα phosphorylation in the retina have not been identified. PKCα activity and phosphorylation in RBCs was examined by immunofluorescence confocal microscopy using a conformation-specific PKCα antibody and antibodies to phosphorylated PKC motifs. PKCα activity was dependent on light and expression of TRPM1, and RBC dendrites were the primary sites of light-dependent phosphorylation. PKCα-dependent retinal phosphoproteins were identified using a phosphoproteomics approach to compare total protein and phosphopeptide abundance between phorbol ester-treated wild type and PKCα knockout (PKCα-KO) mouse retinas. Phosphopeptide mass spectrometry identified over 1100 phosphopeptides in mouse retina, with 12 displaying significantly greater phosphorylation in WT compared to PKCα-KO samples. The differentially phosphorylated proteins fall into the following functional groups: cytoskeleton/trafficking (4 proteins), ECM/adhesion (2 proteins), signaling (2 proteins), transcriptional regulation (3 proteins), and homeostasis/metabolism (1 protein). Two strongly differentially expressed phosphoproteins, BORG4 and TPBG, were localized to the synaptic layers of the retina, and may play a role in PKCα-dependent modulation of RBC physiology. Data are available via ProteomeXchange with identifier PXD012906.SignificanceRetinal rod bipolar cells (RBCs), the second-order neurons of the mammalian rod visual pathway, are able to modulate their sensitivity to remain functional across a wide range of light intensities, from starlight to daylight. Evidence suggests that this modulation requires the serine/threonine kinase, PKCα, though the specific mechanism by which PKCα modulates RBC physiology is unknown. This study examined PKCα phosophorylation patterns in mouse rod bipolar cells and then used a phosphoproteomics approach to identify PKCα-dependent phosphoproteins in the mouse retina. A small number of retinal proteins showed significant PKCα-dependent phosphorylation, including BORG4 and TPBG, suggesting a potential contribution to PKCα-dependent modulation of RBC physiology.HighlightsPKCα is a major source of phosphorylation in retinal RBC dendrites and its activity in RBCs is light dependent.Proteins showing differential phosphorylation between phorbol ester-treated wild type and PKCα-KO retinas belong to the following major functional groups: cytoskeleton/trafficking (4 proteins), ECM/adhesion (2 proteins), signaling (2 proteins), transcriptional regulation (3 proteins), and homeostasis/metabolism (1 protein).The PKCα-dependent phosphoproteins, BORG4 and TPBG, are present in the synaptic layers of the retina and may be involved in PKCα-dependent modulation of RBC physiology.

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Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue

Cited by 274 publications

References 81 publications

Proteomic analysis of the glutathione-deficient LEGSKO mouse lens reveals activation of EMT signaling, loss of lens specific markers, and changes in stress response proteins

Proteomic analysis of the glutathione-deficient LEGSKO mouse lens reveals activation of EMT signaling, loss of lens specific markers, and changes in stress response proteins

A Guide to the Quantitative Proteomic Profiles of the Cancer Cell Line Encyclopedia

Identification of PKCα-dependent phosphoproteins in mouse retina

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