Journal of Proteome Research最新文献

Metabolic reprogramming is important in primary biliary cholangitis (PBC) development. However, studies investigating the metabolic signature within the liver of PBC patients are limited. In this study, liver biopsies from 31 PBC patients and 15 healthy controls were collected, and comprehensive metabolomics, lipidomics, and proteomics analysis were conducted to characterize the metabolic landscape in PBC. We observed distinct lipidome remodeling in PBC with increased polyunsaturated fatty acid levels and augmented fatty acid β-oxidation (FAO), evidenced by the increased acylcarnitine levels and upregulated expression of proteins involved in FAO. Notably, PBC patients exhibited an increase in glucose-6-phosphate (G6P) and purines, alongside a reduction in pyruvate, suggesting impaired glycolysis and increased purines biosynthesis in PBC. Additionally, the accumulation of bile acids as well as a decrease in branched chain amino acids and aromatic amino acids were observed in PBC liver. We also observed an aberrant upregulation of proteins associated with ductular reaction, apoptosis, and autophagy. In conclusion, our study highlighted substantial metabolic reprogramming in glycolysis, fatty acid metabolism, and purine biosynthesis, coupled with aberrant upregulation of proteins associated with apoptosis and autophagy in PBC patients. Targeting the specific metabolic reprogramming may offer potential targets for the therapeutic intervention of PBC.

Background: Peri-implantitis is characterized as a pathological change in the tissues around dental implants. Fourier-transform infrared spectroscopy (FTIR) provides molecular information from optical phenomena observed by the vibration of molecules, which is used in biological studies to characterize changes and serves as a form of diagnosis.

Aims: this case-control study evaluated the peri-implant disease by using FTIR spectroscopy with attenuated total reflectance in the fingerprint region.

Methods: 38 saliva samples were evaluated, 17 from the control group and 21 from the peri-implantitis group. Clinical data such as plaque index (PI), gingival index, probing depth (PS), and attachment level were assessed.

Results: The results of clinical parameters showed a statistical difference between the two groups regarding an excess of the PI. In the FTIR-ATR analysis, the main components revealed vibrational modes of fatty acids, histidine, lipid esters, nucleic acids, and tryptophan, with the main molecules contributing to spectral discrimination. The five-component partial least-squares discriminant analysis classification model had an accuracy of 81%, showing differences between healthy and diseased implants.

Conclusion: the FTIR spectroscopy provides important molecular characteristics of the samples and the results in association with clinical data show the effectiveness of using this tool for diagnosing the disease.

Proteomics has become a powerful approach for the identification and characterization of type III effectors (T3Es). Members of the Ralstonia solanacearum species complex (RSSC) deploy T3Es to manipulate host cells and to promote root infection of, among others, a wide range of solanaceous plants such as tomato, potato, and tobacco. Here, we used TurboID-mediated proximity labeling (PL) in tomato hairy root cultures to explore the proxeomes of the core RSSC T3Es RipU, RipD, and RipB. The RipU proxeome was enriched for multiple protein kinases, suggesting a potential impact on the two branches of the plant immune surveillance system, being the membrane-localized PAMP-triggered immunity (PTI) and the RIN4-dependent effector-triggered immunity (ETI) complexes. In agreement, a transcriptomics analysis in tomato revealed the potential involvement of RipU in modulating reactive oxygen species (ROS) signaling. The proxeome of RipB was putatively enriched for mitochondrial and chloroplast proteins and that of RipD for proteins potentially involved in the endomembrane system. Together, our results demonstrate that TurboID-PL in tomato hairy roots represents a promising tool to study Ralstonia T3E targets and functioning and that it can unravel potential host processes that can be hijacked by the bacterial pathogen.

Non-small cell lung cancer (NSCLC) is frequently diagnosed late and has poor survival. The two predominant subtypes of NSCLC, adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), are currently differentially diagnosed using immunohistochemical markers; however, they are increasingly recognized as very different cancer types suggestive of potential for new, more targeted therapies. There are extensive efforts to find more precise and noninvasive differential diagnostic tools. Here, we examined these two NSCLC subtypes for differences that may inform treatment and identify potential novel therapeutic pathways. We presented a comparative analysis of transcriptomic and proteomic expression in tumors from a cohort of 22 NSCLC patients: 8 LUSC and 14 LUAD. Comparing NSCLC subtypes, we found differential gene expression related to cell differentiation for LUSC and cellular structure and immune response regulation for LUAD. Differential protein expression between NSCLC subtypes was related to extracellular structure for LUSC and metabolic processes, including glucose metabolism for LUAD. This direct comparison was more informative about subtype-specific pathways than between each subtype and control (nontumor) tissues. Many of our observations between NSCLC subtypes support and inform existing observations and reveal differences that may aid research seeking to identify and validate novel subtype biomarkers or druggable targets.

Growing evidence shows that lysine methylation is a widespread protein post-translational modification (PTM) that regulates protein function on histone and nonhistone proteins. Numerous studies have demonstrated that the dysregulation of lysine methylation mediators contributes to cancer growth and chemotherapeutic resistance. While changes in histone methylation are well-documented with extensive analytical techniques available, there is a lack of high-throughput methods to reproducibly quantify changes in the abundances of the mediators of lysine methylation and nonhistone lysine methylation (Kme) simultaneously across multiple samples. Recent studies by our group and others have demonstrated that antibody enrichment is not required to detect lysine methylation, prompting us to investigate the use of tandem mass tag (TMT) labeling for global Kme quantification without antibody enrichment in four different breast cancer cell lines (MCF-7, MDA-MB-231, HCC1806, and MCF10A). To improve the quantification of KDMs, we incorporated a lysine demethylase (KDM) isobaric trigger channel, which enabled 96% of all KDMs to be quantified while simultaneously quantifying 326 Kme sites. Overall, 142 differentially abundant Kme sites and eight differentially abundant KDMs were identified among the four cell lines, revealing cell line-specific patterning.

Recent improvements in methods and instruments used in mass spectrometry have greatly enhanced the detection of protein post-translational modifications (PTMs). On the computational side, the adoption of open modification search strategies now allows for the identification of a wide variety of PTMs, potentially revealing hundreds to thousands of distinct modifications in biological samples. While the observable part of the proteome is continuously growing, the visualization and interpretation of this vast amount of data in a comprehensive fashion is not yet possible. There is a clear need for methods to easily investigate the PTM landscape and to thoroughly examine modifications on proteins of interest from acquired mass spectrometry data. We present PTMVision, a web server providing an intuitive and simple way to interactively explore PTMs identified in mass spectrometry-based proteomics experiments and to analyze the modification sites of proteins within relevant context. It offers a variety of tools to visualize the PTM landscape from different angles and at different levels, such as 3D structures and contact maps, UniMod classification summaries, and site specific overviews. The web server's user-friendly interface ensures accessibility across diverse scientific backgrounds. PTMVision is available at https://ptmvision-tuevis.cs.uni-tuebingen.de/.

Skeletal muscle aging poses a major threat to the health and quality of life of elderly individuals. Fisetin, a natural polyphenolic compound, exhibits various biological activities; however, its role in preventing skeletal muscle cell aging is still unclear. This study aimed to elucidate the effects of fisetin on skeletal muscle aging using a d-galactose-induced C2C12 myoblast senescence model. Fisetin treatment effectively ameliorated d-galactose-induced aging damage and restored cellular functionality by improving cell viability, reducing the accumulation of the senescence marker enzyme SA-β-gal, and decreasing the expression of key aging marker proteins, p16 and p53. NMR-based metabolomics and RNA-seq transcriptomics analyses revealed that fisetin regulates several critical metabolic pathways, including glutathione metabolism, glycine, serine and threonine metabolism, as well as taurine and hypotaurine metabolism. This regulation led to the restoration of amino acid metabolism, stabilization of cellular energy homeostasis, and the preservation of membrane integrity. In addition, fisetin inhibited calcium signaling and JAK-STAT pathways, reduced cellular stress responses and reversed senescence-induced cell cycle arrest. Together, these findings highlight the potential of fisetin as a therapeutic agent to combat skeletal muscle aging and restore cellular homeostasis, offering a promising avenue for the development of antiaging treatments for skeletal muscle degeneration.

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.

In the past decade, a major goal in biomedical research has been to understand why individuals differ in disease susceptibility, disease dynamics, and progression. In many pathologies, this variability stems from evolved immune mechanisms that resist inflammatory stress from various diseases that have been encountered throughout life. These may provide advantages against other diseases, reduce comorbidities, and enhance longevity. This study evaluates prior immunity as a prognostic factor in COVID-19 patients, crucial for understanding plasmatic signaling cascades in different disease stages and their impact on disease progression. COVID-19, caused by SARS-CoV-2, primarily affects the respiratory system and presents a wide range of symptoms, posing significant challenges to medicine. This study systematically analyzed prior immunity and inflammation in two independent cohorts of infected patients. A serological profile is determined by protein microarrays, which identify IgM and IgG responses against 37 prevalent microbial pathogens and provide a comprehensive plasma analysis of 21 acute-phase proteins. Our results reveal distinct serological profiles correlating with disease severity, indicating that immune system dysregulation in COVID-19 patients is linked to existing immunity. These findings highlight the relevance of prior immunity for monitoring disease progression, particularly in infections and vaccine failure, and underscore the importance of functional proteomics in determining prognostic biomarkers.