Journal of Proteome Research最新文献

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is difficult to distinguish from benign pulmonary nodules (BPNs), particularly at early stages. Extracellular vesicles (EVs) represent a promising source of biomarkers for the diagnosis of malignant pulmonary nodules. This study aimed to identify robust and clinically relevant EV-based protein biomarkers via isolation with EXODUS, a system that enables efficient direct capture of plasma EVs, followed by data-independent acquisition mass spectrometry (DIA–MS) for in-depth proteomic profiling. A total of 1383 proteins were identified from the plasma EVs obtained from 25 individuals (10 BPN and 15 early stage LUAD), while dysregulated protein signatures were revealed through differential expression analysis. Machine learning algorithms incorporating demographic variables, imaging features, EV protein profiles, and conventional tumor markers were applied to select diagnostic candidates. Random forest analysis revealed two upregulated proteins, NTN3 and APOA4, as promising biomarkers. Subsequently, their diagnostic performance and net clinical benefits were validated in an independent EV cohort (6 LUAD and 6 BPN) using ELISAs and decision curve analysis. In summary, we present an integrated pipeline that combines EXODUS-based isolation, DIA–MS, and machine learning to detect markers from plasma EVs for distinguishing early stage lung cancer from benign nodules.

The underlying metabolic mechanisms between healthy lifestyle behaviors and a lower risk of chronic liver diseases (CLD) remain elusive. This prospective cohort study of 168,260 UK Biobank participants without baseline liver disease identified a healthy lifestyle–associated metabolic signature using elastic net regression and examined its relationship with CLD. The resulting signature, comprising 66 metabolites, was strongly correlated with healthy lifestyle scores (r = 0.434, P < 0.001) and was inversely associated with the risks of MASLD, cirrhosis, liver cancer, and liver-related mortality, with hazard ratios ranging from 0.55 to 0.70 per standard deviation increase. Mediation analyses showed that this metabolic signature explained 20.3–49.6% of the protective effects of a healthy lifestyle on these CLDs, while Mendelian randomization suggested potential causal roles of these metabolites in CLD development. Overall, the findings underscore the importance of early lifestyle interventions and metabolic monitoring for the precise prevention of CLD.

Unstable angina (UA) represents a prevalent cardiovascular condition characterized by significant morbidity and mortality, necessitating the development of effective early diagnostic biomarkers. This study sought to identify plasma protein biomarkers associated with UA utilizing Olink proteomics. A cohort of 120 participants, comprising 60 individuals diagnosed with UA and 60 healthy controls, was recruited. A total of 92 inflammation-related plasma proteins were quantified through a proximity extension assay. In the discovery cohort, differential expression analysis revealed significant alterations in 49 proteins, which were predominantly enriched in cytokine-mediated signaling and MAPK pathways, as demonstrated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Least absolute shrinkage and selection operator regression and random forest methodologies prioritized three biomarkers─SPON2, PTX3, and GBGT1─that exhibited elevated levels in UA patients compared to controls. Receiver operating characteristic analysis demonstrated area under the curve values of 0.859, 0.937, and 0.900 for SPON2, PTX3, and GBGT1, respectively. A combined diagnostic model incorporating these three proteins achieved an AUC of 0.979 and demonstrated robust performance in an independent validation cohort. These results suggest that SPON2, PTX3, GBGT1, and their composite model hold promise as diagnostic biomarkers for UA.

Antibiotics are routinely added to mammalian cell culture media to prevent bacterial growth. However, the use of antibiotics in a cell culture can confound downstream experimental results. While genomic and transcriptomic differences between cell cultures treated with and without antibiotics are well-documented, far fewer, if any, comprehensive proteomic comparisons on the use of antibiotics in cell culture have been performed. Here, we present a study on the proteome-wide differences of culturing HepG2 cells in antibiotic (i.e., penicillin/streptomycin) and nonantibiotic-containing media. Using a longitudinal and crossover treatment study design, we analyzed 119 samples across nine passages and four conditions. On average, 9,374 proteins were detected per sample, and we identified 383 proteins that were differentially abundant between conditions. These changes included ribosomal and mitochondrial proteins, demonstrating that off-target effects of antibiotics on mammalian cells occur at the protein level. Linear mixed-effect modeling suggested that the proteomic impact of antibiotic treatment is strongest in the first passage after treatment and stabilizes after approximately three passages. Furthermore, initiating antibiotic treatment induced a greater number of differentially abundant proteins than discontinuing treatment. Lastly, we compared our results to existing literature on the use of common antibiotics in mammalian cell culture. We identified proteins and pathways conserved across studies, omics layers, and cell types. We hope that this detailed proteomic survey of the ubiquitous pencillin–streptyomcin-treated HepG2 in vitro model will aid researchers in comparing cross-study or cross-condition results from antibiotic-treated mammalian cells and inform appropriate experimental designs for the use of antibiotics in cell culture.

Mass spectrometry (MS)-based proteomics is known for its high accuracy in quantifying peptides and proteins using various calibration strategies including internal and external calibration curves. While external multipoint calibration curves are created from serial dilutions, they often fail to account for sample-specific matrix effects. In contrast, internal calibration curves account for the sample matrix but face scalability and cost challenges for whole proteome analyses. In this manuscript, we present a novel TMT-based multipoint internal calibration curve strategy, which enables the generation of internal calibration curves for all peptides identified within a proteome in a single experiment to assess their linearity prior relative quantification. We applied this strategy to human ovarian cancer cells to evaluate the linear quantitative responses of all of the identified peptides and reveal the significant proteome changes associated with cisplatin treatment.

The accumulation of malignant ascites in the peritoneal cavity is a hallmark of high-grade serous ovarian cancer (HGSC). This fluid contains three-dimensional multicellular aggregates known as spheroids, which contribute to chemoresistance and are an accessible source of tumor material for proteomic-based biomarker discovery studies. Although heterogeneous ascitic spheroids can be generated from primary cell suspensions for ex vivo applications, they suffer from long generation times and reduced biological relevance. Here, we compare their ex vivo chemotherapy responses and proteomes to native spheroids that are collected directly from HGSC ascites, with the aim of assessing their suitability for proteomic-based chemoresponse prediction strategies that yield results within a clinically relevant time frame. We demonstrate that the chemoresponses of native spheroids better correlate with patients’ clinical treatment responses in 4 of 5 cases and that their proteomes uniquely segregate according to ex vivo carboplatin response along the first component. This pilot study suggests key proteins and biological pathways that may facilitate a global proteomic-based screening strategy for personalized HGSC treatment, with particular emphasis on extracellular matrix proteins. As such, native spheroids have the potential to progress the personalized treatment of HGSC patients with malignant ascites.

Mitochondrial dysfunction induces numerous diseases, yet current proximity labeling methods require gene transfection and membrane potential-sensitive probes, limiting their use in hard-to-transfect cells and disease models. We developed TAG-PL (Tailored Antibody-Guided Proximity Labeling), a transfection-free approach for in-depth mapping of the mitochondrial proteome, achieving >90% specificity and identifying >450 mitochondrial proteins─more than the coverage of existing nontransfection methods. Applied to heat-stressed macrophages, TAG-PL revealed dynamic mitochondrial proteome remodeling, including antioxidant responses and metabolic shifts during heat stress. Notably, we discovered physical interactions between stress granules and mitochondria, identifying 10 interaction mediators (including MSRA and UBA1). These findings establish stress granules as regulatory hubs for organelle dynamics and immune responses. TAG-PL’s high performance and broad applicability across diverse sample types, particularly immune cells and tissues, make it a powerful tool for dissecting mitochondrial function in disease models without genetic manipulation.

Peroxynitrite is capable of inducing protein nitration, which alters protein structure and interferes with signaling pathways dependent on phosphorylation. This study aims to investigate the effects of peroxynitrite-driven nitration and phosphorylation on proteins in HEK293T cells. Through label-free quantitative mass spectrometry, we successfully identified over 5,000 proteins from 0.5 μg of cell extracts collected at five different time intervals (0, 2, 15, 30, and 60 min) after exposure to peroxynitrite. We found that protein expression profiles from 2 to 60 min were distinct from those at 0 min. LC-MS/MS was also applied to quantify and map the nitration and phosphorylation sites. Analysis of protein nitration and phosphorylation revealed distinct temporal profiles, suggesting that nitration may contribute to the altered phosphorylation of the same protein or its interacting proteins. This study provides valuable insights for further mechanistic studies in peroxynitrite-related pathways.

This study presents a comparative analysis of three LysC endopeptidase homologues from Achromobacter lyticus (A. lyticus),Pseudomonas aeruginosa and Lysobacter enzymogenes for mass spectrometry-based proteomics. Utilizing a protein aggregation capture workflow with HeLa cell lysates, we assessed the enzymes’ cleavage specificity, digestion efficiency, and performance across various experimental conditions. Results showed that while all three LysC homologues exhibited high cleavage specificity at lysine residues, A. lyticus LysC outperformed the two others with superior peptide identification, digestion efficiency, and protein coverage, especially at shorter digestion times. Our experiments using a combination ofA. lyticusLysC and trypsin demonstrated the importance of employing LysC for significantly minimizing missed cleavage rates in tryptic digests, especially with regard to lysine-containing peptides. This study underscores A. lyticus LysC’s potential as an optimal choice for enhancing mass spectrometry-based proteomics.

Bacillus subtilis is a Gram-positive bacterium widely used in biotechnology due to its efficient secretion systems. Among these, the twin-arginine (Tat) pathway facilitates the export of fully folded cofactor-containing proteins across the cytoplasmic membrane. The TatAyCy translocase, which is expressed constitutively, is key to this process. Previous studies showed that this translocase not only consists of the TatAy and TatCy subunits, but that it also recruits the LiaH protein upon overexpression. Presumably, the recruitment of LiaH represents an intrinsic protective mechanism against the potentially detrimental effects of facilitating the membrane passage of large, fully folded proteins. However, to date the full spectrum of physiological consequences of protein translocation via TatAyCy has remained elusive. In this study, we employed a ¹⁴N/¹⁵N metabolic labeling approach combined with subcellular fractionation to quantitatively analyze proteomic changes in the cytoplasm, membrane, and extracellular milieu upon TatAyCy overexpression. Our findings show that high-level TatAyCy expression leads to a prolonged vegetative state and disrupts key cellular processes, including genetic competence, motility, chemotaxis, and biofilm formation. Notably, arginine metabolism emerges as a central factor in the cellular adaptation to TatAyCy-induced stress.