Journal of Proteome Research最新文献

Hepatocellular carcinoma (HCC) ranks among the most common causes of cancer-related deaths globally. The high incidence of HCC is largely linked to chronic hepatitis virus infections, liver cirrhosis, and exposure to carcinogenic substances. Egypt has one of the world’s highest burdens of HCC, with liver cirrhosis from chronic hepatitis C virus (HCV) infection as the primary risk factor. Malignant conversion of cirrhosis to HCC is often fatal in part because adequate biomarkers are not available for diagnosis of HCC in the early stage. Therefore, there is a critical need for more effective biomarkers to detect HCC at an early stage, when therapeutic intervention is more likely to be successful. Multiomics integration has emerged as a powerful strategy to uncover biomarkers and better understand the molecular underpinnings of complex diseases such as HCC. This study summarizes findings from multiple untargeted and targeted mass spectrometry-based analyses of proteins, N-linked glycans, and metabolites performed on blood samples from HCC cases and cirrhotic cohorts recruited in Egypt. Integrative analysis using machine learning methods is performed to identify a panel of multiomics features that differentiates HCC cases from the high-risk population of cirrhotic patients with liver cirrhosis.

Preterm birth (PTB), a leading cause of neonatal morbidity, remains poorly understood due to its multifactorial etiology. This study integrates quantitative proteomic profiling of paired fetal membrane and plasma exosomes from preterm (n = 5) and term (n = 5) deliveries to identify PTB-associated biomarkers. Using four-dimensional label-free quantitative liquid chromatography–tandem mass spectrometry (4D label-free LC–MS/MS) proteomic analyses, we characterized exosomal proteins and identified 435 and 330 differentially expressed proteins (DEPs) in fetal membranes and plasma, respectively, associated with PTB. Immune-related pathways dominated shared proteins between fetal membranes and plasma. Notably, REEP5 was significantly upregulated in PTB-derived exosomes across both sample types. Immunohistochemistry confirmed elevated levels of REEP5 expression and membrane localization in preterm fetal membranes, aligning with its exosomal enrichment. Additionally, inflammation- (e.g., PLA2G4C and TBXA2R) and oxidative stress-related proteins (e.g., JUN and EDNRA) were uniquely packaged in PTB exosomes. These findings highlight fetal membrane-plasma exosomal crosstalk and propose REEP5 as a potential biomarker for PTB. This study advances the understanding of exosome-mediated mechanisms in PTB and underscores the utility of proteomics in discovering clinically actionable biomarkers. However, due to the small sample size, this study is a small pilot study, and the findings require validation in larger-scale cohorts.

Chronic hepatitis B (CHB) is clinically classified into different clinical stages during which dynamic interactions occur among the virus, host immune system, and hepatic tissue. The lack of systematic proteomic analysis of liver tissue and functional change characteristics across different stages of CHB has prevented the provision of novel critical clues for precise staging interventions in HBV infection. In this study, we performed the first comprehensive proteome comparison of liver tissue across four clinical phases of CHB: the immune-tolerant (IT) phase, immune-active (IA), inactive carrier (IC), and hepatitis B e antigen (HBeAg)-negative hepatitis (ENEG) phases. The four stages of CHB liver exhibited a dynamic cascade of “metabolic stress-mitochondrial damage-immune imbalance-fibrosis”, wherein mitochondrial dysfunction represents the core pathological mechanism of CHB. Furthermore, correlations between changes in immune cell subpopulations and clinical features across the four infection stages suggest therapeutic potential in targeting alterations in immune cells, offering novel perspectives for precise stage-targeted interventions and mechanistic insights into CHB.

Background: Chronic kidney disease (CKD) is a major global health burden, and progressive renal fibrosis is a common end-stage pathway to renal failure. Lysine acetylation, as an important post-translational modification, has gradually become a reversible regulatory factor in renal injury and repair, but its systemic role in cisplatin-induced renal fibrosis remains unclear. Methods: Label-free quantitative proteomics and acetylome analyses were performed on the kidneys of C57BL/6 mice with cisplatin-induced renal fibrosis as well as the control groups (n = 4 per group). Subsequently, we conducted comprehensive bioinformatics analyses to identify key molecules that promote renal fibrosis. Results: We established proteomic and acetylomic profiles of lesions caused by cisplatin-induced renal fibrosis. Cisplatin-induced injury triggered extensive Kac remodeling, primarily involving pathways, such as the cell cycle, ATP-dependent chromatin remodeling, cell death, and extracellular matrix receptor interactions. We also identified significantly elevated lysine-96 acetylation of histone H2A (H2ac4 K96ac), whose abundance positively correlated to that of the acetyltransferase p300. This suggests that H2ac4 K96ac is a candidate epigenetic marker associated with cisplatin-induced renal fibrosis and warrants further investigation. Conclusion: This study provides comprehensive proteomic and acetylated proteomic data sets and maps for cisplatin-induced renal fibrosis. It is speculated that the H2ac4 K96ac histone acetylation site may represent a novel therapeutic target.

Nuclear magnetic resonance (NMR) spectroscopy is increasingly employed in research to quantify lipoprotein subfractions, offering potential utility in clinical diagnostics, particularly for cardiovascular risk assessment. However, the independent validation of proprietary NMR-based lipoprotein profiling methods is crucial for verifying clinical accuracy and reliability. This study presents a posthoc evaluation of concordance between the NMR-based B.I.LISA method and standard enzymatic assays for total cholesterol (TC), triglycerides (TGs), and high-density lipoprotein cholesterol (HDL-C), measured in 620 plasma samples from the OMNI-Heart study, focusing on their performance in evaluating the dietary intervention outcomes. Despite involving independently acquired data not designed for an intermethod validation, the comparison showed a high correlation between methods (R = 0.85–0.92), with median deviations of −4, −5, and −15% for HDL-C, TC, and TGs, respectively. The larger TG deviations are attributed to known issues arising from heterogeneity in high-TG samples, although intervention outcomes remained unaffected. Albumin was identified as a potential interfering factor affecting the TC and HDL-C measurements. HDL-C could also be affected by lipoprotein degradation, contributing to divergence in comparisons of marginal intervention outcomes. Extreme discrepancies were observed in atypical hypercholesterolemia samples. These findings highlight the reliability of the NMR approach despite revealing minor but significant deviations that warrant further research.

Diagnosing metabolic dysfunction-associated steatohepatitis (MASH) within the spectrum of metabolic dysfunction-associated steatotic liver disease (MASLD) remains challenging. This study evaluates the relationship between plasma proteins and their liver tissue counterparts to assess their potential as diagnostic biomarkers for MASH. An untargeted proteomic analysis was performed on paired liver tissue and plasma samples obtained during biopsies from 7 controls and 64 MASLD patients. Plasma proteins that showed significant correlations with their liver counterparts and exhibited consistent gradients across progressive MASLD stages were evaluated as biomarkers. The study found that liver tissue proteomics showed a strong correlation with MASLD histological severity. Of 356 plasma proteins, 30 showed significant positive correlations (r > 0.5, p < 0.01) with their liver tissue counterparts. Eight proteins exhibited consistent changes across disease stages and distinguished MASH from non-MASH with an area under the receiver operating curve (AUROC) of 0.786 and advanced fibrosis from nonfibrosis cases with an AUROC of 0.874. In conclusion, a limited subset of plasma proteins reflects liver proteomic changes and may serve as biomarkers for distinguishing MASH and fibrosis stages within MASLD.

The recent COVID-19 pandemic has heightened public interest in noninvasive methods for early diagnosis of infectious diseases. In addition, various government agencies have implemented “infectious disease preparedness” to mitigate future outbreaks. This review highlights conventional and advanced methods for infectious disease diagnosis with an emphasis on emerging mass spectrometry methods. Conventional methods for pathogen identification, such as culture-based techniques and molecular methods, have limitations with respect to sensitivity, specificity, and turnaround time. Recent advances in high-resolution mass spectrometry have revolutionized the field of infectious disease biomarker discovery. These techniques enable the comprehensive profiling of metabolites in various biological samples, identification of disease-specific biomarkers, and elucidation of complex host–pathogen interactions. While liquid chromatography–mass spectrometry has been extensively used to identify metabolic alterations in diseases, such as COVID-19, tuberculosis, pneumonia, and influenza, this often requires the use of body fluids. On the other hand, advances in gas chromatography-high resolution mass spectrometry are enabling noninvasive detection of infectious diseases by means of breath-based volatile organic compounds. These methods offer high sensitivity and specificity, enabling the detection of low-abundance biomolecules and the elucidation of complex biological pathways. This review further examines the limitations of each approach while emphasizing the essential applications of metabolomics in infectious disease diagnosis.

Large language models (LLMs) originally developed for human text have been adapted to proteomics as protein language models (pLMs). These models treat amino acid sequences like sentences, and they learn patterns from millions of sequences. pLMs are used for several key tasks, including the prediction of protein structures, annotating protein functions, designing novel protein sequences with specific characteristics, and mapping the interactions between proteins and other molecules. Compared with traditional approaches, pLMs deliver insights more quickly but demand large computing resources and careful data management. Developers are focused on decreasing prediction inaccuracies and biases by exploring more efficient training techniques and smaller models to decrease the resources required. As sequence databases continue to grow, pLMs will improve to uncover links between proteins and disease pathways, speeding drug development and basic research while offering new proteome-scale insights that support experimental design and validation.

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.