Journal of Proteome Research最新文献

Endoplasmic reticulum (ER) stress is known to impair the function of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), disrupting lipid metabolism. Despite the crucial role lipid plays in regulating adipose tissue function, the specific lipidomic alterations in VAT and SAT under ER stress remain unclear. In this study, ER stress was induced in VAT and SAT, and targeted lipidomic and transcriptomic approaches were used to analyze lipid metabolism and gene expression profiles. The results revealed that VAT exhibited a stronger ER stress response, characterized by a significant increase in binding immunoglobulin protein (BiP) expression and notable lipidomic disruptions, especially in glycerides and sterols. These disruptions were marked by a decrease in protective polyunsaturated fatty acyl species and the accumulation of lipotoxic molecules. In contrast, SAT displayed less severe lipidomic alterations. Transcriptomic analysis indicated that VAT was more susceptible to immune activation, inflammation, and metabolic dysfunction, while SAT primarily showed alterations in protein folding processes. These findings underscore the tissue-specific mechanisms of ER stress adaptation in VAT and SAT. In conclusion, VAT appears to be a critical target for addressing metabolic dysfunction in obesity and related disorders, with potential therapeutic implications for managing ER stress-induced metabolic diseases.

Uncompetitive inhibition is an effective strategy for suppressing dysregulated enzymes and their substrates, but discovery of suitable ligands depends on often-unavailable structural knowledge and serendipity. Hence, despite surging interest in mass spectrometry-based target identification, proteomic studies of substrate-dependent target engagement remain sparse. Herein, we describe a strategy for the discovery of substrate-dependent ligand binding. Using proteome integral solubility alteration (PISA) assays, we show that simple biochemical additives can enable detection of RNA-protein-small molecule complexes in native cell lysates. We apply our approach to rocaglates, molecules that specifically clamp RNA to eukaryotic translation initiation factor 4A (eIF4A), DEAD-box helicase 3X (DDX3X), and potentially other members of the DEAD-box (DDX) helicase family. To identify unexpected interactions, we used a target class-specific thermal window and compared ATP analog and RNA base dependencies for key rocaglate-DDX interactions. We report novel DDX targets of high-profile rocaglates-including the clinical candidate Zotatifin-and validate our findings using limited proteolysis-mass spectrometry and fluorescence polarization (FP) experiments. We also provide structural insight into divergent DDX3X affinities between synthetic rocaglates. Taken together, our study provides a model for screening uncompetitive inhibitors using a chemical proteomics approach and uncovers actionable DDX clamping targets, clearing a path toward characterization of novel molecular clamps and associated RNA helicases.

Metabolites are referenced in spectral, structural and pathway databases with a diverse array of schemas, including various internal database identifiers and large tables of common name synonyms. Cross-linking metabolite identifiers is a required step for meta-analysis of metabolomic results across studies but made difficult due to the lack of a consensus identifier system. We have implemented metLinkR, an R package that leverages RefMet and RaMP-DB to automate and simplify cross-linking metabolite identifiers across studies and generating common names. MetLinkR accepts as input metabolite common names and identifiers from five different databases (HMDB, KEGG, ChEBI, LIPIDMAPS and PubChem) to exhaustively search for possible overlap in supplied metabolites from input data sets. In an example of 13 metabolomic data sets totaling 10,400 metabolites, metLinkR identified and provided common names for 1377 metabolites in common between at least 2 data sets in less than 18 min and produced standardized names for 74.4% of the input metabolites. In another example comprising five data sets with 3512 metabolites, metLinkR identified 715 metabolites in common between at least two data sets in under 12 min and produced standardized names for 82.3% of the input metabolites. Outputs of MetLInR include output tables and metrics allowing users to readily double check the mappings and to get an overview of chemical classes represented. Overall, MetLinkR provides a streamlined solution for a common task in metabolomic epidemiology and other fields that meta-analyze metabolomic data. The R package, vignette and source code are freely downloadable at https://github.com/ncats/metLinkR.

Liquid chromatography-mass spectrometry (LC-MS/MS) extends the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) Zooarcheology by Mass Spectrometry (ZooMS) "mass fingerprinting" approach to species identification by providing fragmentation spectra for each peptide. However, ancient bone samples generate sparse data containing only a few collagen proteins, rendering target-decoy strategies unusable and increasing uncertainty in peptide annotation. To ameliorate this issue, we present a ZooMS/MS data pipeline that builds on a manually curated Collagen database and comprises two novel algorithms: isoBLAST and ClassiCOL. isoBLAST first extends peptide ambiguity by generating all "potential peptide candidates" isobaric to the annotated precursor. The exhaustive set of candidates created is then used to retain or reject different potential paths at each taxonomic branching point from superkingdom to species, until the greatest possible specificity is reached. Uniquely, ClassiCOL allows for the identification of taxonomic mixtures, including contaminated samples, as well as suggesting taxonomies not represented in sequence databases, including extinct taxa. All considered ambiguity is then graphically represented with clear prioritization of the potential taxa in the sample. Using public as well as in-house data acquired on different instruments, we demonstrate the performance of this universal postprocessing and explore the identification of both genetic and sample mixtures. Diet reconstruction from 40,000-year-old cave hyena coprolites illustrates the exciting potential of this approach.

Selection and application of protein inference algorithms can have a significant impact on the data output from tandem mass spectrometry (MS/MS) experiments. However, this critical step is often taken for granted, with many studies simply utilizing the inference method embedded within the end-to-end software pipeline employed for analysis without consideration of the particular algorithm's suitability for the experiment at hand or its effects on the resulting data. Although many individual inference algorithms have been demonstrated, few unified tools are available that allow the researcher to quickly apply a variety of different inference algorithms to meet the needs of their analysis, are agnostic of other tools in the analysis pipeline, and are easy to use for the bench biologist. PyProteinInference provides a comprehensive suite of tools that enable researchers to apply different inference algorithms and compute protein-level set-based false discovery rates (FDR) from MS/MS data through a unified interface. Here, we describe the software and its application to a traditional protein inference benchmarking data set and to a K562 whole-cell lysate to demonstrate its utility in facilitating conclusions about underlying biological mechanisms in proteomic data.

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS³ approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

The scale of mass spectrometry-based proteomics data sets continues to increase, and the analysis workflows are becoming more complex as various steps are carried out using a multitude of software programs developed by both commercial providers and the research community. Manually shepherding data across multiple programs and in-house-developed scripts can be error prone and labor intensive. It is also difficult for others to follow the same steps, leading to poor repeatability. We have developed an integrated data management and analysis platform termed MSConnect that enables simple and traceable processing workflows across multiple programs, thus improving repeatability and automating common backup and analysis steps from the point of data collection through summarization and visualization. The open nature of the MSConnect platform enables the diverse omics community to seamlessly integrate third-party tools or develop and automate their own unique workflows. With an open license and design architecture, MSConnect has the potential to become a community-driven platform serving a wide range of MS-based omics researchers.

The clustering of tandem mass spectra (MS/MS) is a crucial computational step to deduplicate repeated acquisitions in data-dependent experiments. This technique is essential in untargeted metabolomics, particularly with high-throughput mass spectrometers capable of generating hundreds of MS/MS spectra per second. Despite advancements in MS/MS clustering algorithms in proteomics, their performance in metabolomics has not been extensively evaluated due to the lack of database search tools with false discovery rate control for molecule identification. To bridge this gap, this study introduces the MS1-retention time (MS-RT) method to assess MS/MS clustering performance in metabolomics data sets. Here, we validate MS-RT by comparing MS-RT to established proteomics clustering evaluation approaches that utilize database search identifications. Additionally, we evaluate the performance of several MS/MS clustering tools on metabolomics data sets, highlighting their advantages and drawbacks. This MS-RT method and the MS/MS clustering tool benchmarking will provide valuable real world practical recommendations for tools and set the stage for future advancements in metabolomics MS/MS clustering.

Regulating gene expression involves significant changes in the chromatin environment at the locus level, especially at regulatory sequences. However, their modulation following pharmacological treatments or pathological conditions remain mostly undetermined. Here, we report versatile locus-specific proteomics tools to address this knowledge gap, which combine the targeting ability of the CRISPR/Cas9 system and the protein-labeling capability of the highly reactive biotin ligases TurboID (in CasTurbo) and UltraID (in CasUltra). CasTurbo and CasUltra enabled rapid chromatin protein labeling at repetitive sequences like centromeres and telomeres, as well as nonamplified genes. We applied CasUltra to A375 melanoma cell lines to decipher the protein environment of the MYC promoter and characterize the molecular effects of the bromodomain inhibitor JQ1, which targets bromodomain and extra-terminal (BET) proteins that regulate MYC expression. We quantified the consequences of BET protein displacement from the MYC promoter and found that it was associated with a considerable reorganization of the chromatin composition. Additionally, BET protein retention at the MYC promoter was consistent with a model of increased JQ1 resistance. Thus, through the combination of proximity biotinylation and CRISPR/Cas9 genomic targeting, CasTurbo and CasUltra have successfully demonstrated their utility in profiling the proteome associated with a genomic locus in living cells.