Journal of Proteome Research最新文献

An interconnected loop of messages and counter-messages determine the outcome of host–pathogen interactions. Multihost pathogenicity across plants and animals, particularly nematode, is a major source of new infectious diseases. Fusarium oxysporum, a multihost pathogen, causes vascular wilt in chickpea and fusariosis in worm and humans. To comprehend Fusarium-responsive multihost pathogenicity, we temporally profiled cross-kingdom species, chickpea and worm using SWATH-mass spectrometry. Morphological analyses revealed that increased wilting and intestinal disintegration elicits a disease response in chickpea and worm. Peptide-spectrum library consisted of 5629 and 3138 proteins from Fusarium infected chickpea and worm, respectively. SWATH analysis identified 1573 and 2249 disease-responsive chickpea (CaDRPs) and worm proteins (CeDRPs) linked to diverse organs, organelles, and functionality. Pairwise comparisons; over-representation analysis between time, treatment, and organism; wilt, and fusariosis diseasome revealed common and unique modules. CaDRPs involved in preformed defense, biomolecule synthesis, phytohormone regulation, ser/thr kinase, and ATP signaling have perturbed interactions and functions, majorly in chloroplast. CeDRPs linked to the cuticular support, muscle organization, neuronal information, intestinal metabolism, G-protein, and notch signaling showed a deregulated function, especially in the cytoplasm. Common biological processes, included primary metabolism, ribosome biogenesis, calcium signaling, and proteostasis. Our data provide first evidence of translational plasticity in the Fusarium diseasome providing novel insights into multihost pathogenesis.

Previous studies have shown the link between sedentary behavior and health outcomes. However, self-reported sedentary behavior is less accurate than accelerometer-measured sedentary behavior. Additionally, the proteomic signatures associated with sedentary behavior and their potential impact on biological pathways remain unknown. In this study, we identified proteomic signatures of sedentary behavior using whole-proteome association analysis and LASSO regression based on data from 2921 plasma proteins in 10,848 participants with complete accelerometer measurements from the UK Biobank. Our results indicate that both sedentary behavior and its related proteomic signatures are significantly linked to the risk of developing diabetes, hypertension, and chronic kidney disease. Furthermore, mediation analysis showed that proteomic signatures of sedentary behavior mediate the relationships between sedentary activity and the risk of diabetes and chronic kidney disease. These findings suggest that sedentary behavior and its associated proteomic signatures contribute to increased risks of these diseases. Investigating how sedentary behavior affects diseases through peripheral physiology is essential for public health.

Lipoic acid (LA), an endogenous small molecule, is widely utilized in metabolic regulation and disease intervention. While its mechanism of action has been largely attributed to protein lipoylation, the dynamic chemical properties of its five-membered cyclic disulfide moiety remain underexplored. Here, we designed and synthesized alkyne-functionalized lipoic acid-derived probes, LAN-yne and LAO-yne. Employing a chemoproteomic strategy, we systematically mapped the covalent targets of the LAN-yne across the cellular proteome and assessed their potential biological functions. Quantitative proteomic analysis identified 852 probe-modified proteins and 1,157 corresponding cysteine modification sites. This study demonstrates that LA’s endocyclic disulfide bond can be mimicked to mediate specific protein modification via thiol–disulfide exchange, providing novel molecular insights and a technical platform for investigating LA’s potential pharmacological mechanisms and structural derivatization efforts beyond lipoylation.

Isobaric labeling is widely used in quantitative proteomics for its multiplexing capabilities, but scaling beyond current limits remains a challenge. Here, we introduce UltraPlex-TMT, a streamlined and scalable workflow that integrates orthogonal protease digestion with hyperplex TMT/TMTpro labeling to effectively double sample throughput. UltraPlex-TMT can be readily implemented without custom chemistry or instrumentation. We benchmarked UltraPlex-TMT using lysine- and arginine-specific protease digests of a two-species proteome labeled with TMT11plex and TMT18plex across four subplexes in a proof-of-concept pseudo-58-plex design. MS2 acquisition quantified ∼6,000–7,000 proteins per subplex and ∼9,000 in total, with ∼50% overlap across all conditions, generating a robust core proteome set with high quantitative reproducibility. RTS-MS3 acquisition showed similar coverage trends, albeit with fewer quantified proteins. Despite reduced depth, MS3 data provided higher quantification accuracy, illustrating a trade-off between proteome depth and precision. Gene set enrichment analysis revealed strong biological concordance between MS2 and MS3 data, and in all conditions tested, the use of orthogonal protease digestion did not introduce systematic quantification bias. UltraPlex-TMT offers a flexible foundation for isobaric labeling-based high-throughput proteomics and is poised to benefit from faster acquisition platforms and extended multiplexing chemistries, supporting future studies exceeding 200-plex scale, potentially equivalent to subminute analysis.

Phosphorylation is a key post-translational modification that can impact the function of a protein and the outcome of cell signaling pathways. Using phosphoproteomics to characterize the phosphorylation changes downstream of trophic factor signaling is important for better understanding the pleiotropic actions of this class of molecules. Insulin-like growth factor-1 (IGF-1) is a trophic factor that can influence cellular growth and differentiation, and IGF-1 signaling in the brain has been linked to cognitive processes. While its main signaling molecules are well characterized, we sought to perform a more in-depth and unbiased analysis of the IGF-1 phosphoproteome in neuronal cells. To obtain insight into the IGF-1 signaling pathway in neuronal cells, we performed a quantitative mass spectrometric phosphoproteomics analysis in rat pheochromocytoma (PC-12) cells. Our results illustrate a diverse phosphoproteome downstream of IGF-1, with insight into novel phosphoprotein sites, such as Plcβ3 (Ser¹¹⁰⁵), that likely influence IGF-1 signaling in neuronal cells. We also identify a robust upregulation of the Rho GTPase cycle and an activation of synaptogenesis signaling downstream of IGF-1. These results pave the way for more targeted studies on specific phosphoprotein sites, which will facilitate a better understanding of how these phosphorylation events impact IGF-1-related signaling outcomes in neuronal cells.

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.