Journal of Proteome Research最新文献

Sin3 is an evolutionarily conserved repressor protein complex mainly associated with histone deacetylase (HDAC) activity. Many proteins are part of Sin3/HDAC complexes, and the function of most of these members remains poorly understood. SAP25, a previously identified Sin3A associated protein of 25 kDa, has been proposed to participate in regulating gene expression programs involved in the immune response but the exact mechanism of this regulation is unclear. SAP25 is not expressed in HEK293 cells, which hence serve as a natural knockout system to decipher the molecular functions uniquely carried out by this Sin3/HDAC subunit. Using molecular, proteomic, protein engineering, and interaction network approaches, we show that SAP25 interacts with distinct enzymatic and regulatory protein complexes in addition to Sin3/HDAC. Additional proteins uniquely recovered from the Halo-SAP25 pull-downs included the SCF E3 ubiquitin ligase complex SKP1/FBXO3/CUL1 and the ubiquitin carboxyl-terminal hydrolase 11 (USP11). Furthermore, mutational analysis demonstrates that distinct regions of SAP25 participate in its interaction with USP11, OGT/TETs, and SCF(FBXO3). These results suggest that SAP25 may function as an adaptor protein to coordinate the assembly of different enzymatic complexes to control Sin3/HDAC-mediated gene expression. The data were deposited with the MASSIVE repository with the identifiers MSV000093576 and MSV000093553.

Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior information based solely on peptide sequences remains a challenge. Here, we present LineageFilter, a new python-based AI software for refined proteotyping of complex samples using metaproteomics interpreted data and machine learning. Given a tentative list of taxa, their abundances, and the scores associated with their identified peptides, LineageFilter computes a comprehensive set of features for each identified taxon at all taxonomical ranks. Its machine-learning model then assesses the likelihood of each taxon's presence based on these features, enabling improved proteotyping and sample-specific database construction.

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

Metabolic reprogramming is a ubiquitous feature of transformed cells, comprising one of the hallmarks of cancer and enabling neoplastic cells to adapt to new environments. Accumulated evidence reports on the failure of some neoplastic cells to convert mannose-6-phosphate into fructose-6-phosphate, thereby impairing tumor growth in cells displaying low levels of mannose-6-phosphate isomerase (MPI). Thus, we performed functional analyses and profiled the proteome landscape and the repertoire of substrates of proteases (degradome) of melanoma cell lines with distinct mutational backgrounds submitted to treatment with mannose. Our results suggest a significant rearrangement in the proteome and degradome of melanoma cell lines upon mannose treatment including the activation of catabolic pathways (such as protein turnover) and differences in protein N-terminal acetylation. Even though MPI protein abundance and gene expression status are not prognostic markers, perturbation in the network caused by an exogenous monosaccharide source (i.e., mannose) significantly affected the downstream interconnected biological circuitry. Therefore, as reported in this study, the proteomic/degradomic mapping of mannose downstream effects due to the metabolic rewiring caused by the functional status of the MPI enzyme could lead to the identification of specific molecular players from affected signaling circuits in melanoma.

Distraction osteogenesis (DO) represents a highly effective method for addressing significant bone defects; however, it necessitates a long treatment period. Exosomes are key mediators of intercellular communication. To investigate their role in the angiogenesis and osteogenesis of DO, we established a canine mandibular DO model with a bone defect (BD) group as the control. Higher levels of angiogenesis were observed in the regenerating tissue from the DO group compared to those from the BD group, accompanied by earlier osteogenesis. Proteomic analysis was performed on circulating exosomes at different phases of the DO using a data-independent acquisition method. Data are available via ProteomeXchange with the identifier PXD050531. The results indicated specific alterations in circulating exosome proteins at different phases of DO, reflecting the regenerative activities in the corresponding tissues. Notably, fibronectin 1 (FN1), thrombospondin 1 (THBS1), and transferrin receptor (TFRC) emerged as potential candidate proteins related to the angiogenic response in DO. Further cellular experiments validated the potential of DO-associated circulating exosomes to promote angiogenesis in endothelial cells. Collectively, these data reveal previously unknown mechanisms that may underlie the efficacy of DO and suggest that exosome-derived proteins may be useful as therapeutic targets for strategies designed to improve DO-related angiogenesis and bone regeneration.

Early and accurate diagnosis of gastric cancer (GC) is essential for reducing mortality and improving patient well-being. However, methods for the early diagnosis of GC are still lacking. In this study, by isobaric tagging for relative and absolute quantitation (iTRAQ), we identified 336 proteins that overlapped among the upregulated differentially expressed proteins (DEPs) in early gastric cancer (EGC) versus progressive gastric cancer (PGC), upregulated DEPs in EGC versus nongastric cancer (NGC), and nonsignificant proteins in EGC versus NGC. These DEPs were involved primarily in the neutrophil-related immune response. Network analysis of proteins and pathways revealed that fibrinogen α (FGA), β (FGB), and γ (FGG) are candidates for distinguishing EGC. Furthermore, parallel reaction monitoring (PRM), immunohistochemistry (IHC), and Western blot (WB) assays of clinical samples confirmed that, compared with that in PGC and NGC, only FGG was uniquely and significantly upregulated in the gastric mucosa of EGC. Our results demonstrated that FGG in the gastric mucosa could be a novel biomarker to diagnose EGC patients via endoscopy.

Metabolic dysfunction in the liver represents a predominant feature in the early stages of alcohol-associated liver disease (ALD). However, the mechanisms underlying this are only partially understood. To investigate the metabolic characteristics of the liver in ALD, we did a relative quantification of polar metabolites and lipids in the liver of mice with experimental ALD using untargeted metabolomics and untargeted lipidomics. A total of 99 polar metabolites had significant abundance alterations in the livers of alcohol-fed mice. Pathway analysis revealed that amino acid metabolism was the most affected by alcohol in the mouse liver. Metabolites involved in glycolysis and the TCA cycle were decreased, while glycerol 3-phosphate (G3P) and long-chain fatty acids were increased. Relative quantification of lipids unveiled an upregulation of multiple lipid classes, suggesting that alcohol consumption drives metabolism toward lipid synthesis. Results from enzyme expression and activity detection indicated that the decreased activity of mitochondrial glycerol 3-phosphate dehydrogenase contributed to the disordered metabolism.

Glioblastoma multiforme (GBM) is the most prevalent and aggressive brain tumor found in adult humans with a poor prognosis and average survival of 14-15 months. In order to have a comprehensive understanding of proteome and identify novel therapeutic targets, this study focused mainly on the differentially abundant proteins (DAPs) of Ras^V12-induced GBM. Ras^V12 is a constitutively active Ras mutant form essential for tumor progression by continuously activating signaling pathways leading to uncontrolled tumor growth. This study used a transgenic Drosophila model with Ras^V12 overexpression using the repo-GAL4 driver line, specifically in glial cells, to study GBM. The high-resolution mass spectrometry (HRMS)-based proteomic analysis of the GBM larval central nervous system identified three novel DAPs specific to mitochondria. These DAPs, probable maleylacetoacetate isomerase 2 (Q9VHD2), bifunctional methylene tetrahydrofolate dehydrogenase (Q04448), and glutamine synthetase1 (P20477), identified through HRMS were further validated by qRT-PCR. The protein-protein interaction analysis revealed interactions between Ras^V12 and DAPs, with functional links to mitochondrial dynamics regulators such as Drp1, Marf, Parkin, and HtrA2. Notably, altered expressions of Q9VHD2, P20477, and Q04448 were observed during GBM progression, which offers new insights into the involvement of mitochondrial dynamic regulators in Ras^V12-induced GBM pathophysiology.

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.