Jeonghun Yeom

The structural basis for the negative regulation of thioredoxin by thioredoxin-interacting protein

The redox-dependent inhibition of thioredoxin (TRX) by thioredoxin-interacting protein (TXNIP) plays a pivotal role in various cancers and metabolic syndromes. However, the molecular mechanism of this regulation is largely unknown. Here, we present the crystal structure of the TRX–TXNIP complex and demonstrate that the inhibition of TRX by TXNIP is mediated by an intermolecular disulphide interaction resulting from a novel disulphide bond-switching mechanism. Upon binding to TRX, TXNIP undergoes a structural rearrangement that involves switching of a head-to-tail interprotomer Cys63-Cys247 disulphide between TXNIP molecules to an interdomain Cys63-Cys190 disulphide, and the formation of a de novo intermolecular TXNIP Cys247-TRX Cys32 disulphide. This disulphide-switching event unexpectedly results in a domain arrangement of TXNIP that is entirely different from those of other arrestin family proteins. We further show that the intermolecular disulphide bond between TRX and TXNIP dissociates in the presence of high concentrations of reactive oxygen species. This study provides insight into TRX and TXNIP-dependent cellular regulation.

Quantitative analysis of mTRAQ-labeled proteome using full MS scans.

Proteomic techniques are mostly used these days to identify proteins in a biological sample. Quantification of the differences between two or more physiological conditions, such as disease or no disease, has become an increasingly challenging task in proteomics. Mass tags introducing stable isotopes into peptides or proteins provide means for quantification in mass spectrometry. The mass tags are recognized by mass spectrometry and at the same time provide quantitative information. In the current study, we introduce mTRAQ for the purpose of quantification by full MS scans. Although mTRAQ reagent was initially designed for multiple reaction monitoring, we verified the utility of mTRAQ for MS1-based relative quantification using standard protein mixtures and blood plasma samples. mTRAQ-labeled peptides showed better quality MS2 spectra with increased XCorr values in a SEQUEST search output than corresponding unlabeled peptides. The improved spectral quality was due mostly to the enhanced matching of b-type ions. By combining mTRAQ with ICAT and applying them to colon cancer tissues, we identified and quantified a total of 3,320 proteins. mTRAQ covered a wider range of the proteome than did ICAT, and only 1053 proteins were shared by the two independent methods. Our results suggest the usefulness of mTRAQ, alone or in combination with ICAT, as a comparative profiling method in quantitative proteomics.

WIP1, a Homeostatic Regulator of the DNA Damage Response, Is Targeted by HIPK2 for Phosphorylation and Degradation

WIP1 (wild-type p53-induced phosphatase 1) functions as a homeostatic regulator of the ataxia telangiectasia mutated (ATM)-mediated signaling pathway in response to ionizing radiation (IR). Here we identify homeodomain-interacting protein kinase 2 (HIPK2) as a protein kinase that targets WIP1 for phosphorylation and proteasomal degradation. In unstressed cells, WIP1 is constitutively phosphorylated by HIPK2 and maintained at a low level by proteasomal degradation. In response to IR, ATM-dependent AMPKα2-mediated HIPK2 phosphorylation promotes inhibition of WIP1 phosphorylation through dissociation of WIP1 from HIPK2, followed by stabilization of WIP1 for termination of the ATM-mediated double-strand break (DSB) signaling cascade. Notably, HIPK2 depletion impairs IR-induced γ-H2AX foci formation, cell-cycle checkpoint activation, and DNA repair signaling, and the survival rate of hipk2+/- mice upon γ-irradiation is markedly reduced compared to wild-type mice. Taken together, HIPK2 plays a critical role in the initiation of DSB repair signaling by controlling WIP1 levels in response to IR.

Analysis of Nuclear High Mobility Group Box 1 (HMGB1)-Binding Proteins in Colon Cancer Cells: Clustering with Proteins Involved in Secretion and Extranuclear Function

HMGB1 is a nuclear protein that is overexpressed and secreted in cancer cells. However, little is known about the roles of HMGB1 in the cytoplasm and secretory pathway in cancer cells. To clarify this aspect of HMGB1 function, we fractionated the cytoplasm of HCT116 colon cancer cells and used a proteomic approach to analyze cytoplasmic HMGB1-binding proteins. Pull-down experiments using recombinant HMGB1 protein as bait, followed by mass spectrometry analysis identified 162 interacting proteins. Among them were 74 proteins known to be localized exclusively to the extra-nuclear region, and 60 proteins known to be localized to both nuclear and extranuclear regions. The functions of these binding proteins include involvement in cell-cycle progression, cell proliferation, anti-apoptosis, and angiogenesis. In addition, nine of the identified proteins are related to protein translocation and secretion. These include annexin A2, myosin IC isoform a, myosin-9, and Ras-related protein Rab10, which are involved in unconventional protein secretion. Cytoplasmic HMGB1 was primarily associated with the lysosomal cytosol fraction and was colocalized with the lysosomal marker LAMP1. Our findings suggest that cytoplasmic HMGB1 binds to a number of molecules related to cancer progression and the unconventional secretory pathway.

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry ☆

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.

High-throughput peptide quantification using mTRAQ reagent triplex

BackgroundProtein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.Methods and resultsHere we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.ConclusionsWe showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

Regulation of IκB kinase by GβL through recruitment of the protein phosphatases

G protein β-like (GβL) is a member of WD repeat-containing family which are involved in various intracellular signaling events. In our previous report, we demonstrated that GβL regulates TNFα-stimulated NF-κB signaling by interacting with and inhibiting phosphorylation of IκB kinase. However, GβL itself does not seem to regulate IKK directly, because it contains no functional domains except WD domains. Here, using immunoprecipitation and proteomic analyses, we identified protein phosphatase 4 as a new binding partner of GβL. We also found that GβL interacts with PP2A and PP6, other members of the same phosphatase family. By interacting with protein phosphatases, which do not directly bind to IKKβ, GβL mediates the association of phosphatases with IKKβ. Overexpression of protein phosphatases inhibited TNFκ-induced activation of NF-κB signaling, which is an effect similar to that of GβL overexpression. Down-regulation of GβL by small interfering RNA diminished the inhibitory effect of phosphatases, resulting in restoration of NF-κB signaling. Thus, we propose that GβL functions as a negative regulator of NF-κB signaling by recruiting protein phosphatases to the IKK complex.

Improved Quantitative Analysis of Mass Spectrometry using Quadratic Equations

Protein quantification is one of the principal computational problems in mass spectrometry (MS) based proteomics. For robust and trustworthy protein quantification, accurate peptide quantification must be preceded. In recent years, stable isotope labeling has become the most popular method for relative quantification of peptides. However, some stable isotope labeling methods may carry a critical problem, which is an overlap of isotopic clusters. If the mass difference between the light- and heavy-labeled peptides is very small, the overlap of their isotopic clusters becomes larger as the mass of original peptide increases. Here we propose a new algorithm for peptide quantification that separates overlapping isotopic clusters using quadratic equations. It can be easily applied in Trans-Proteomic Pipeline (TPP) instead of XPRESS. For the mTRAQ-labeled peptides obtained by an Orbitrap mass spectrometer, it showed more accurate ratios and better standard deviations than XPRESS. Especially, for the peptides that do not contain lysine, the ratio difference between XPRESS and our algorithm became larger as the peptide masses increased. We expect that this algorithm can also be applied to other labeling methods such as (18)O labeling and acrylamide labeling.

SERBP1 affects homologous recombination-mediated DNA repair by regulation of CtIP translation during S phase

DNA double-strand breaks (DSBs) are the most severe type of DNA damage and are primarily repaired by non-homologous end joining (NHEJ) and homologous recombination (HR) in the G1 and S/G2 phase, respectively. Although CtBP-interacting protein (CtIP) is crucial in DNA end resection during HR following DSBs, little is known about how CtIP levels increase in an S phase-specific manner. Here, we show that Serpine mRNA binding protein 1 (SERBP1) regulates CtIP expression at the translational level in S phase. In response to camptothecin-mediated DNA DSBs, CHK1 and RPA2 phosphorylation, which are hallmarks of HR activation, was abrogated in SERBP1-depleted cells. We identified CtIP mRNA as a binding target of SERBP1 using RNA immunoprecipitation-coupled RNA sequencing, and confirmed SERBP1 binding to CtIP mRNA in S phase. SERBP1 depletion resulted in reduction of polysome-associated CtIP mRNA and concomitant loss of CtIP expression in S phase. These effects were reversed by reconstituting cells with wild-type SERBP1, but not by SERBP1 ΔRGG, an RNA binding defective mutant, suggesting regulation of CtIP translation by SERBP1 association with CtIP mRNA. These results indicate that SERBP1 affects HR-mediated DNA repair in response to DNA DSBs by regulation of CtIP translation in S phase.

Impact of data‐dependent exclusion list based mass spectrometry on label‐free proteomic quantification

RATIONALE Spectral count analysis via data-dependent acquisition (DDA) mode mass spectrometry is used as label-free protein quantification. However, combination of the DDA mode with exclusion list based DDA (DDA-EL) for the similar purpose has not yet been tested. Therefore, we have taken the initiative to check the protein abundance using DDA-EL and measured their suitability. METHODS To check the protein abundance correlation between different samples, multiple replicates of mass spectrometric analysis of peptides were conducted primarily in DDA mode. Subsequently, peptides were analyzed in multiple replicates in DDA-EL mode with an exclusion mass list prepared from the previous DDA analyses. The normalized spectral abundance factor (NSAF) for each identified protein was compared among replicated datasets of single DDA, DDA-EL, merged two DDAs, and merged DDA + DDA-EL or between different types of datasets. RESULTS A strong and linear NSAF correlation with an average correlation coefficient of 0.939 was observed in the comparison between each pair of DDA data. Similar connotation was also monitored in the comparison among DDA-EL data (r =0.928) or among merged DDA + DDA-EL data (r =0.960) while a reduced correlation coefficient (r =0.892) with increased deviation was marked between DDA and DDA-EL data. CONCLUSIONS Evaluation of protein abundance patterns from different cellular states can successfully be conducted by DDA-EL-based mass spectrometric analysis. Therefore, the new workflow, DDA-EL merged to DDA mode, is a potential alternative to protein identification and quantification method.