Abstract ：

Micro(nano)plastics (MNPs) are emerging pollutants that can adsorb pollutants in the environment and biological molecules and ultimately affect human health. However, the aspects of adsorption of intracellular proteins onto MNPs and its biological effects in cells have not been investigated to date. The present study revealed that 100 nm polystyrene nanoplastics (NPs) could be internalized by THP-1 cells and specifically adsorbed intracellular proteins. In total, 773 proteins adsorbed onto NPs with high reliability were identified using the proteomics approach and analyzed via bioinformatics to predict the route and distribution of NPs following cellular internalization. The representative proteins identified via the Kyoto Encyclopedia of Genes and Genomes pathway analysis were further investigated to characterize protein adsorption onto NPs and its biological effects. The analysis revealed that NPs affect glycolysis through pyruvate kinase M (PKM) adsorption, trigger the unfolded protein response through the adsorption of ribophorin 1 (RPN1) and heat shock 70 protein 8 (HSPA8), and are chiefly internalized into cells through clathrin-mediated endocytosis with concomitant clathrin heavy chain (CLTC) adsorption. Therefore, this work provides new insights and research strategies for the study of the biological effects caused by NPs.

Keyword ：

proteomics nanoplastics emerging pollutants protein adsorption bioinformatics analysis

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Abstract ：

Plastic products offer remarkable convenience for modern life. However, growing concerns are emerging regarding the potential health hazards posed by nanoplastics, which formed as plastics break down. Currently, the biological effects and mechanisms induced by nanoplastics are largely underexplored. In this study, we report that polystyrene nanoplastics can enter the bloodstream and enhance thrombus formation. Our findings show that polystyrene nanoplastics adsorb plasma proteins, particularly coagulation factor XII and plasminogen activator inhibitor-1, play a key role in this process, as demonstrated by proteomics, bioinformatic analyses, and molecular dynamics simulations. The adsorption of these proteins by nanoplastics is an essential factor in thrombosis enhancement. This newly uncovered pathway of protein adsorption leading to enhanced thrombosis provides new insights into the biological effects of nanoplastics, which may inform future safety and environmental risk assessment of plastics. © 2024 Elsevier B.V.

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Nanoclay

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Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

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Long-term high sugar accelerates skin ageing via glycosylation. In this study, microalgae-derived active compounds were investigated for their potentially beneficial effects on skin ageing. Two carotenoids, namely astaxanthin (in the form of either monoester or diester) and lutein, were separated from the green alga Haematococcus pluvialis. These carotenoid fractions significantly attenuated the high glucose (HG)-induced cell injury and oxidative stress. They also inhibited the formation of N epsilon-Carboxymethyllysine (CML), a typical product of glycosylation. The degradation of collagen I and III caused by HG was substantially recovered, in which astaxanthin monoester exhibited the most prominent effect. This was attributed to the down-regulation of matrix metalloproteinase (MMP)-2 mRNA expression. Through proteomics analyses, six hub proteins were identified, which work as highly connected nodes in the protective effects of astaxanthin monoester against HG stress. These findings strongly suggest the benefits of microalgae-derived carotenoids in alleviating HG-induced skin ageing.

Keyword ：

Haematococcus pluvialis Collagen degradation Astaxanthin esters Glycosylation Lutein

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Objective and designPancreatic cancer is a highly malignant tumor that is well known for its poor prognosis. Based on glycosylation, we performed integrated quantitative N-glycoproteomics to investigate the synergistic anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells and explore the potential molecular mechanisms of chemotherapy in pancreatic cancer.Methods and resultsTwo pancreatic cancer cell lines (PANC-1 and BxPC-3) were treated with gemcitabine, aspirin, and a combination (gemcitabine + aspirin). We found that the addition of aspirin enhanced the inhibitory effect of gemcitabine on the activity of PANC-1 and BxPC-3 cells. Quantitative N-glycoproteome, proteome, phosphorylation, and transcriptome data were obtained from integrated multi-omics analysis to evaluate the anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells. Mfuzz analysis of intact N-glycopeptide profiles revealed two consistent trends associated with the addition of aspirin, which showed a strong relationship between N-glycosylation and the synergistic effect of aspirin. Further analysis demonstrated that the dynamic regulation of sialylation and high-mannose glycoforms on ECM-related proteins (LAMP1, LAMP2, ITGA3, etc.) was a significant factor for the ability of aspirin to promote the anti-tumor activity of gemcitabine and the drug resistance of pancreatic cancer cells.ConclusionsIn-depth analysis of N-glycosylation-related processes and pathways in pancreatic cancer cells can provide new insight for future studies regarding pancreatic cancer therapeutic targets and drug resistance mechanisms.

Keyword ：

Multi-omics N-glycosylation Aspirin Glycoproteomics Synergistic effect Gemcitabine Pancreatic cancer

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Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research. Retention time (RT) alignment is a crucial step in large cohort proteomics and metabolomics studies. Here, the authors introduce DeepRTAlign, a deep learning tool for RT alignment that shows high identification sensitivity and quantitative accuracy.

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BackgroundMicrovascular invasion (MVI) is the main factor affecting the prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to identify accurate diagnostic biomarkers from urinary protein signatures for preoperative prediction.MethodsWe conducted label-free quantitative proteomic studies on urine samples of 91 HCC patients and 22 healthy controls. We identified candidate biomarkers capable of predicting MVI status and combined them with patient clinical information to perform a preoperative nomogram for predicting MVI status in the training cohort. Then, the nomogram was validated in the testing cohort (n = 23). Expression levels of biomarkers were further confirmed by enzyme-linked immunosorbent assay (ELISA) in an independent validation HCC cohort (n = 57).ResultsUrinary proteomic features of healthy controls are mainly characterized by active metabolic processes. Cell adhesion and cell proliferation-related pathways were highly defined in the HCC group, such as extracellular matrix organization, cell-cell adhesion, and cell-cell junction organization, which confirms the malignant phenotype of HCC patients. Based on the expression levels of four proteins: CETP, HGFL, L1CAM, and LAIR2, combined with tumor diameter, serum AFP, and GGT concentrations to establish a preoperative MVI status prediction model for HCC patients. The nomogram achieved good concordance indexes of 0.809 and 0.783 in predicting MVI in the training and testing cohorts.ConclusionsThe four-protein-related nomogram in urine samples is a promising preoperative prediction model for the MVI status of HCC patients. Using the model, the risk for an individual patient to harbor MVI can be determined.

Keyword ：

Biomarker diagnosis Microvascular invasion (MVI) Hepatocellular carcinoma (HCC) Urine proteomics

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Chromatography and mass spectrometry (MS) techniques have greatly improved the power of proteomic analyses. However, sample processing methods used prior to MS, including protein extraction and digestion, remain bottlenecks in the large-scale clinical application of proteomics. Ionic liquids, composed entirely of ions, have high solubility in various solvents. In this study, the effects of the cationic surfactant 1-dodecyl-3-methylimidazolium chloride (C12Im-Cl) on protein digestion were evaluated for clinical proteomic applications. C12Im-Cl was compatible with trypsin and reduced the protein digestion time from 16 h to 1 h. Residual C12Im-Cl was easily removed with a strong anion exchange membrane before MS. We evaluated the performance of C12Im-Cl extraction and rapid protein digestion using formalin-fixed paraffin-embedded liver cancer tissues. The number of proteins and peptides identified was nearly equal to that identified by the traditional filter-aided sample preparation method (2705 vs. 2739 and 16 682 vs. 17 214). In general, the C12Im-Cl-aided rapid sample preparation method is promising for proteomic applications.

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Ophiopogonin D (OPD) and Ophiopogonin D ' (OPD ') are two bioactive ingredients in Ophiopogon japonicus. Previously published studies have often focused on the therapeutic effects related to OPD's antioxidant capacity but underestimated the cytotoxicity-related side effects of OPD ', which may result in unpredictable risks. In this study, we reported another side effect of OPD ', hemolysis, and what was unexpected was that this side effect also appeared with OPD. Although hemolysis effects for saponins are familiar to researchers, the hemolytic behavior of OPD or OPD ' and the interactions between these two isomers are unique. Therefore, we investigated the effects of OPD and OPD ' alone or in combination on the hemolytic behavior in vitro and in vivo and adopted chemical compatibility and proteomics methods to explain the potential mechanism. Meanwhile, to explain the drug-drug interactions (DDIs), molecular modeling was applied to explore the possible common targets. In this study, we reported that OPD ' caused hemolysis both in vitro and in vivo, while OPD only caused hemolysis in vivo. We clarified the differences and DDIs in the hemolytic behavior of the two isomers. An analysis of the underlying mechanism governing this phenomenon showed that hemolysis caused by OPD or OPD ' was related to the destruction of the redox balance of erythrocytes. In vivo, in addition to the redox imbalance, the proteomics data demonstrated that lipid metabolic disorders and mitochondrial energy metabolism are extensively involved by hemolysis. We provided a comprehensive description of the hemolysis of two isomers in Ophiopogon japonicus, and risk warnings related to hemolysis were presented. Our research also provided a positive reference for the development and further research of such bioactive components.

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Functional module detection in protein-protein interaction (PPI) network is one important content of the proteomics research in the post-genomic era. Nowadays the swarm intelligence and evolutionary based approaches have become effective ways for detecting functional modules. This paper proposes a novel hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in PPI networks (called HFADE-FMD). HFADE-FMD first initializes each firework individual into a candidate functional module partition based on label propagation according to the topological and functional information between protein nodes. Then HFADE-FMD uses the explosion operator of firework algorithm, and mutation, crossover and selection strategies of differential evolution algorithm to iteratively search for better functional module partitions. To verify the performance of HFADE-FMD, this paper compared it with ten competitive methods on four public PPI datasets. The experimental results show that HFADE-FMD achieves prominent performance with respective to Recall, Sn, PPV, and ACC metrics while performing well in terms of Precision and F-measure metrics. Thus, it is able to more accurately detect functional modules and help biologists to find some novel biological insights.

Keyword ：

Protein-protein interaction network Functional module detection Explosion operation Differential evolution strategies Fireworks algorithm

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