Journal of Proteome Research最新文献

The primary computational challenge in mass spectrometry-based proteomics is determining the peptide sequence responsible for generating each measured tandem mass spectrum. This task is traditionally addressed through sequence database searching as well as alternative approaches such as spectral library searching. ANN-SoLo is a powerful spectral library search engine optimized for open modification searching, enabling the detection of peptides carrying any post-translational modification. Here, we present an enhanced version of ANN-SoLo that combines the strengths of both spectral library searching and sequence database searching by integrating with Prosit to generate predicted spectral libraries from protein sequence databases. Additionally, it provides functionality to generate decoys at both the spectrum and the peptide levels, introduces an optimized internal file structure for large-scale analytics, and improves search accuracy by incorporating complementary ion information into spectrum vector representations. These advancements collectively address challenges associated with missing spectral libraries and enhance peptide identification in large-scale and complex proteomics workflows.

The increasing exposure risks of tetrabromobisphenol A (TBBPA) have drawn much attention, owing to its widely produced and ubiquitous occurrence. Despite the potential toxicities are largely explored in vitro experiments, limited evidence exists about the fact that TBBPA induced the targeting organ effects on the metabolism. Herein, we found that TBBPA exposure for 2 weeks induced oxidative stress and lipometabolism disturbance in the liver of male ICR mice. Using the promising approach combining UHPLC–MS with MALDI imaging, 78 discrepant lipid molecules between the control and TBBPA exposure groups were simultaneously screened and identified, with spatial visualization. After the enrichment analysis, the biosynthesis of glycerophospholipid metabolism was the most distinct in the eight metabolic pathways that were involved with TBBPA in the process of liver injury, which was closely associated with hepatocyte membrane stability and lipid transport functions. Furthermore, the Q-PCR analysis and molecular docking results demonstrated that TBBPA promoted the accumulation of lipids like triglyceride (TG), DG, LPE, and LPC, as well as the deficiency of phosphatidylcholine and PE by inhibiting the key genes LPCAT3 and Pcyt2 involved in glycerophospholipid synthesis, simultaneously enhancing the expression of phosphatidylethanolamine N-methyltransferase and Diacylglyceryl acyltransferase 1, the latter being the crucial gene for TG synthesis. This study affords novel insight into further understanding the potential effect of liver organ by TBBPA exposure.

Mass spectrometers and their attached liquid chromatography (LC) systems, often referred to as LC-MS/MS instrumentation, have become an indispensable tool in biomedical research to identify and quantify proteins, metabolites, and other molecules of interest. However, these sophisticated instruments are very susceptible to malfunction or suboptimal performance, and as a result, quality control (QC) samples are typically acquired at regular intervals to assess their performance. Not surprisingly, several QC software packages have been developed in recent years to analyze and interrogate a variety of QC samples. However, existing QC software predominantly supports proteomic QC samples, with limited options for metabolomic and lipidomic QC samples. In addition, pipelines and workflows that can accommodate both types of QC samples are largely missing. To address this unmet demand, we have developed MaSpeQC, which is a free, easy-to-install, interactive and fully customizable web application to track LC-MS/MS performance across proteomic, metabolomic, and/or lipidomic workflows. MaSpeQC is vendor-agnostic and can handle any commercially available or in-house-generated QC sample from which it extracts relevant metrics. Furthermore, MaSpeQC provides an intuitive web interface for performance monitoring and early detection of issues through customizable email alerts.

Diabetic cardiomyopathy (DCM), a severe complication of type 2 diabetes mellitus (T2DM), lacks specific and effective biomarkers for early diagnosis. This study constructed a plasma-specific spectral library by integrating proteomic and nonenzymatic glycation data from eight pretreatment workflows via data-dependent acquisition. Data-independent acquisition was then applied to profile plasma proteomes and glycation modifications in controls, DM patients, and DCM patients, revealing clear disparities in protein abundance and glycation modification patterns among the three groups. Functional enrichment analysis indicated that these differentially expressed proteins and modified peptides were involved primarily in immune responses, inflammatory processes, and metabolic pathways. Subsequently, parallel reaction monitoring was used to validate the proteins and glycation sites with significant changes. Specific peptides of complement 5 and specific glycation modifications on human serum albumin demonstrated a strong capacity to discriminate DCM from DM, achieving the highest area under the curve values of 0.97 in receiver operating characteristic analyses, underscoring their promising potential as DCM biomarkers. In conclusion, integrated proteomic and glycation modification analysis revealed candidate biomarkers for DCM diagnosis and offered novel insights into DCM pathogenesis.

Vitronectin (VTN) is a multifunctional glycoprotein that promotes cell adhesion and survival signaling through interactions with integrins. Elevated serum VTN levels have recently emerged as diagnostic and prognostic markers for hepatocellular carcinoma (HCC), yet its mechanistic role in HCC progression remains unclear. Here, we show that VTN knockdown in HCC cells has minimal effects on cell migration and viability, raising the possibility that VTN may promote tumor progression by shaping the tumor microenvironment rather than via cell-intrinsic mechanisms. To investigate this, we conducted secretome profiling after VTN knockdown in HCC cells, identifying 756 secreted proteins. Functional enrichment analysis revealed critical biological pathways and protein–protein interaction modules potentially regulated by VTN. Notably, a subset of proteins downregulated upon VTN silencing was associated with poor HCC prognosis. Using Parallel Reaction Monitoring (PRM) proteomics, we validated that the pro-tumorigenic cytokines CXCL5 and CXCL8 were significantly decreased following VTN knockdown. These findings indicate that VTN promotes expression of cytokines involved in HCC progression, implicating autocrine and paracrine mechanisms in its tumor-promoting effects.

The survival rates of high-grade serous ovarian cancer have not improved in the last three decades, despite extensive research into the molecular determinants of chemoresistance that could inform personalized therapies. This systematic review synthesizes proteomic studies that have used varied sample types, including cell lines, serum, plasma, and ascites, to propose molecular markers of response to treatment regimens consisting of platinum-based chemotherapeutics, taxanes, doxorubicin, and combinations thereof. Gene ontology analyses of differentially expressed proteins across all studies highlight key biological functions, such as heat shock response, cell adhesion, and cell migration. Frequently implicated protein families include keratins, annexins, thioredoxin-related proteins, and SERPINs. We evaluate methodological rigor, orthogonal validation attempts, and adherence to MIAPE data reporting standards to contextualize current knowledge and promote reproducibility in future studies. Collectively, this review underscores proteomics as a promising tool for the prediction of chemotherapy response in high-grade serous ovarian cancer, while emphasizing the need for prospective, standardized approaches that align with data reporting guidelines.

The air–liquid interface (ALI) model using Calu-3 cells has been used to model lung diseases. In ALI, Calu-3 polarizes and changes to a mucus-producing cell. Polarized Calu-3 similarity with primary cells has been proven; however, no studies have been focusing on the pathways differentially expressed in ALI. Here, we profiled the proteome and transcriptome of Calu-3 from submerged (nonpolarized) to ALI (polarized) conditions, and in the omics data, we observed an increase in cell replication in the nonpolarized condition while polarized cells presented higher activation of cellular energy production, protein maturation and recycle, and expression of immune molecules. Moreover, the omics findings showed upregulation of different biological processes related to the protein quality control system and antigen processing presentation in polarized cells. Immunoblot and fluorescence microscopy confirmed increased expression of bronchial epithelium integrity components such as mucus and tight junctions in polarized cells and revealed a characteristic protein expression and cellular organization found in normal lung epithelium. Furthermore, SARS-CoV-2 infection in polarized cells revealed increased cell death associated with the higher expression of ACE2. The differences observed in this study give us a better understanding of how ALI can mimic human bronchial-epithelial cells and its applications in different contexts of lung diseases.

Numerous genetic variants have been identified by genome-wide association studies as being associated with colorectal cancer (CRC) risk. Metabolome-wide association analysis was performed for 187 CRC-associated genetic variants using genomic data and untargeted ¹H nuclear magnetic resonance urine metabolomics from 1951 Airwave Health Monitoring Study participants. We identified statistically significant associations between seven CRC single-nucleotide polymorphisms (SNPs) and urinary metabolites. This included SNPs within or close to RHPN2 with sucrose (P = 1.2 × 10^–7), SLC6A18 with amino acids (P = 6.9 × 10^–5 with tyrosine, P = 9.9 × 10^–5 with leucine), and MAP2K5 and BMP2 with gut microbial metabolites (P = 1.6 × 10^–4 and P = 4.4 × 10^–4). The most significant correlation was followed by functional experiments in Caco-2 colon cancer cells. CRISPR-mediated knockout of a 48-nt RHPN2 intronic region containing rs10411210 in colon cancer cells compromised cell growth. RNA sequencing was performed in the two sets of clones (3 edited and 3 unedited) followed by pathway enrichment, and gene ontology analysis depicted extensive deregulation of genes (448 up- and 195 downregulated) involved in cell division and several metabolic processes. Overall, these findings demonstrate that integrating genetic and metabolomic data highlights the importance of the RHPN2 intronic locus in CRC potentially through metabolic processes affecting excretion of dietary and other metabolites.

Recent advances in mass spectrometry permit unbiased proteome profiling of thousands of proteins from single cells using both label-free and labeling approaches. However, a major limitation of unbiased approaches is missing data, which worsens as the sample size increases. In addition, the reproducible measurement of post-translational modifications (PTMs) at the single cell level, particularly those present at a lower stoichiometry than their unmodified counterparts, poses an even greater challenge. To overcome this limitation, we developed a targeted strategy that combines tandem mass tag (TMT) multiplexing with SureQuant-based triggered MS/MS using super heavy TMT-labeled peptides that are 9 Da heavier than the TMTpro tags as triggers. To demonstrate the feasibility of our approach, we established a method quantifying four PTMs on the histone H3 protein (i.e., K14ac, K23ac, K27me, K27me3, and K79me) at single-cell resolution. We demonstrated robustness in quantitation compared to conventional approaches of data-dependent acquisition and standard parallel reaction monitoring. Further, we applied this strategy to single cells and revealed cellular heterogeneity in histone PTMs. Overall, we developed a targeted strategy with improved sensitivity and throughput for analyzing PTMs in single cells, which we expect will be broadly applicable to multiple types of PTMs while enabling focused analysis.