Proteomes最新文献

Myelodysplastic syndromes (MDS) belong to a category of malignant stem-cell and myeloid disorders that deteriorate the function of the hematopoietic system exacerbated by the omnipresent anemia that characterizes myelodysplasia. The pathogenesis of MDS is driven by cytogenetic abnormalities along with the excessive production of pro-inflammatory cytokines and disruptions in inflammatory signaling pathway, particularly through the influence of carbonylated proteins, which are linked to MDS progression. An additional and major contributor to the pathogenesis of MDS is oxidative stress marked by uncontrolled levels of reactive oxygen species (ROS), which have been suggested as potential biomarkers for assessing disease severity and stratifying MDS cases throughout a variety of methods. Excessive and non-accumulative levels of free iron can also lead to iron overload (IOL)-related promotion of a high oxidative state, whether we refer to treatment-related IOL or natural IOL mechanisms. Proteomic analysis has emerged as a powerful tool for profiling protein samples, and, consequently, understanding the molecular changes underlying MDS. In this review, we evaluated studies and their methodologies aiming in investigating distinctive proteomics signatures associated with MDS pathogenesis, focusing on the role of oxidative stress at the protein level.

Background: Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between in vitro and in vivo studies, and clinical applications. Here, we propose a 'systems modeling' workflow for KDD.

Methods: This framework includes the data collection of a composition model (various research models), processing model (proteomics) and analytical model (bioinformatics, artificial intelligence/machine leaning and pattern evaluation), knowledge presentation, and feedback loops for hypothesis generation and validation. We applied this workflow to study pancreatic ductal adenocarcinoma (PDAC).

Results: We identified the common proteins between human PDAC and various research models in vitro (cells, spheroids and organoids) and in vivo (mouse mice). Accordingly, we hypothesized potential translational targets on hub proteins and the related signaling pathways, PDAC-specific proteins and signature pathways, and high topological proteins.

Conclusions: This systems modeling workflow can be a valuable method for KDD, facilitating knowledge discovery in translational targets in general, and in particular to PADA in this case.

Background: Deinococcus radiodurans, renowned for its exceptional resistance to radiation, provides a robust model for elucidating cellular stress responses and DNA repair mechanisms. Previous studies have established PprI as a key regulator contributing to radiation resistance through its involvement in DNA damage repair pathways, oxidative stress response, and metabolic regulation.

Methods: Building upon these foundations, our study employs label-free quantitative (LFQ) proteomics coupled with high-resolution mass spectrometry to systematically map pprI deletion protein networks by comparing the global proteomic profiles of pprI knockout and wild-type D. radiodurans strains.

Results: Under stringent screening criteria, we identified 719 significantly higher and 281 significantly lower abundant proteins in the knockout strain compared to wild-type strains. Functional analysis revealed that PprI deficiency disrupts homologous recombination (HR) repair, activates nucleotide excision repair (NER) and base excision repair (BER) as a compensatory mechanism, and impairs Mn/Fe homeostasis and carotenoid biosynthesis, leading to increased oxidative stress. Furthermore, PprI deficiency induces significant metabolic reprogramming, including impaired purine synthesis, compromised cell wall integrity, etc. Conclusions: These proteomic findings delineate the extensive regulatory network influenced by PprI, revealing coordinated perturbations across multiple stress response systems when PprI is absent.

Background: We utilized data-independent acquisition (DIA) to study the poorly understood biology of Mdm2 and MdmX in a p53-null context. Mdm2 and MdmX form an E3-ligase complex that has as its most well-studied function the negative regulation of the tumor suppressor p53; however, it is also known to interact with many other proteins in a p53-independent manner.

Methods: In this work, small-molecule and siRNA-based technology were used to modify Mdm2/MdmX activity in a human non-small-cell lung carcinoma cell line lacking p53 expression. Study of the proteome of these cells helped identify biological processes where Mdm2 and MdmX may play roles in a p53-independent manner. Proteins from H1299 cells, treated with the drug MEL23 or siRNA against Mdm2 or MdmX, were analyzed.

Results: Protein ontology and function were analyzed, revealing which pathways are affected by modulation of the proteins that form the complex. Insights into how those functions are dependent on the activity of the complex also gained via comparisons among the three groups of samples.

Conclusions: We selected a potential target from the DIA analysis and validated it by immunoblotting and qPCR, and this allows us to demonstrate a new interaction partner of the Mdm2-MdmX complex in human cells.

Understanding the heterogeneity of Rheumatoid Arthritis (RA) and identifying therapeutic targets remain challenging using traditional bulk transcriptomics alone, as it lacks the spatial and protein-level resolution needed to fully capture disease and tissue complexities. In this study, we applied Laser Capture Microdissection (LCM) coupled with mass spectrometry-based proteomics to analyze histopathological niches of the RA synovium, enabling the identification of protein expression profiles of the diseased synovial lining and sublining microenvironments compared to their healthy counterparts. In this respect, key pathogenetic RA proteins like membrane proteins (TYROBP, AOC3, SLC16A3, TCIRG1, and NCEH1), and extracellular matrix (ECM) proteins (PLOD2, OGN, and LUM) showed different expression patterns in diseased synovium compartments. To enhance our understanding of cellular dynamics within the dissected regions, we further integrated the proteomic dataset with single-cell RNA sequencing (scRNA-seq), and deduced cell type enrichment, including T cells, fibroblasts, NK cells, myeloid cells, B cells, and synovial endothelial cells. By combining high-resolution spatial proteomics and transcriptomic analyses, we provide novel insights into the molecular mechanisms driving RA, and highlight potential protein targets for therapeutic intervention. This integrative approach offers a more comprehensive view of RA synovial pathology, and mitigates the limitations of traditional bulk transcriptomics in target discovery.

Intrinsic disorder refers to protein regions that lack a fixed three-dimensional structure under physiological conditions, enabling conformational plasticity. This flexibility allows for diverse functions, including transient interactions, signaling, and phase separation via disorder-to-order transitions upon binding. Our study focused on investigating the role of intrinsic disorder and liquid-liquid phase separation (LLPS) in the human acrosome, a sperm-specific organelle essential for fertilization. Using computational prediction models, network analysis, Structural Classification of Proteins (SCOP) functional assessments, and Gene Ontology, we analyzed 250 proteins within the acrosomal proteome. Our bioinformatic analysis yielded 97 proteins with high levels (>30%) of structural disorder. Further analysis of functional enrichment identified associations between disordered regions overlapping with SCOP domains and critical acrosomal processes, including vesicle trafficking, membrane fusion, and enzymatic activation. Examples of disordered SCOP domains include the PLC-like phosphodiesterase domain, the t-SNARE domain, and the P-domain of calnexin/calreticulin. Protein-protein interaction networks revealed acrosomal proteins as hubs in tightly interconnected systems, emphasizing their functional importance. LLPS propensity modeling determined that over 30% of these proteins are high-probability LLPS drivers (>60%), underscoring their role in dynamic compartmentalization. Proteins such as myristoylated alanine-rich C-kinase substrate and nuclear transition protein 2 exhibited both high LLPS propensities and high levels of structural disorder. A significant relationship (p < 0.0001, R² = 0.649) was observed between the level of intrinsic disorder and LLPS propensity, showing the role of disorder in facilitating phase separation. Overall, these findings provide insights into how intrinsic disorder and LLPS contribute to the structural adaptability and functional precision required for fertilization, with implications for understanding disorders associated with the human acrosome reaction.

Background: Despite advances in immunotherapy, non-small-cell lung carcinoma (NSCLC)'s clinical success is limited, possibly due to substantial immunological alterations in advanced cancer patients. This study examines the immunomodulatory effects of sEVs derived from lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) on T cells.

Methods: SEVs were isolated from lung cancer cell lines and Jurkat-E6.1. SEV size and morphology were analyzed by NTA and TEM, respectively, while Western blotting confirmed sEV markers. SEV uptake was assessed, followed by resazurin assay, RNA isolation, quantification, cDNA preparation, RT-PCR, nano LC-MS, and bioinformatic analysis, before and after treating Jurkat-E6.1 cells with sEVs from A549 and SKMES1.

Results: Cancer-derived sEVs were efficiently internalized by immune cells, reducing T-cell viability. The real-time PCR analysis showed downregulation of KI67, BCL2, BAX, TNFA, IL6, TGFβ, and IL10, suggesting reduced proliferation, dysregulated apoptosis, and impaired inflammatory and immunosuppressive signaling, and the upregulation of GZMB and IL2 suggests retained cytotoxic potential but possibly dysfunctional T-cell activation. Proteomic analysis revealed 39 differentially abundant proteins (DAPs) in ADC-treated T cells and 276 in SCC-treated T cells, with 19 shared DAPs. Gene Ontology (GO) analysis of these DAPs highlighted processes such as sEV biogenesis, metabolic pathways, and regulatory functions, with ADC sEVs influencing NAD metabolism, ECM binding, and oxidoreductase activity, while SCC sEVs affected mRNA stability, amino acid metabolism, and cadherin binding. The cytoplasmic colocalization suggests the presence of these proteins in the cellular and extracellular lumen, indicating the potential of further release of these proteins in the vesicles by T cells.

Conclusion: Lung cancer-derived sEVs regulate T-cell activities through immunoregulatory signaling. The molecular interactions between sEVs and immune cells can reveal novel tumor immune regulatory mechanisms and therapeutic targets.

Background: Medulloblastoma (MB) is the most aggressive paediatric brain cancer, highlighting the urgent need for new diagnostic and prognostic biomarkers and improved treatments to enhance patient outcomes. Our previous study identified LIN28B, an RNA-binding protein, as a potential diagnostic and prognostic marker for MB and a pharmacological target to inhibit MB cell proliferation and stemness. However, the specific role of LIN28B and its mechanism of action in MB had not been studied. Methods: This study assessed LIN28B's role in Daoy MB cells using siRNA-mediated silencing. LIN28B silencing was achieved with Dharmacon ON-TARGETplus SMARTpool and confirmed by Western blotting. Proliferation and protein assays evaluated the cell metabolic activity and viability. A proteomics analysis was conducted to examine the effect of LIN28B knockdown on the MB cell protein expression profile. The intracellular lipid droplets were assessed using the Nile Red Staining Kit, and nucleolar B23 protein levels were assessed by immunofluorescence. Both were visualised with a high-content IN Cell Analyser 2200. Results: Effective LIN28B silencing (>80%) was achieved in each experiment. LIN28B knockdown reduced the MB cell viability, impaired ribosome biogenesis, and promoted cellular lipid accumulation, as supported by proteomics and cell-based assays. Conclusions: This study highlights LIN28B as a promising target for regulating MB cell growth, ribosomal biogenesis, and lipid metabolism.

Extracellular vesicles (EVs) are lipid-bound vesicles secreted by cells, including exosomes, microvesicles, and apoptotic bodies. Proteomic analyses of EVs, particularly in relation to cancer, reveal specific biomarkers crucial for diagnosis and therapy. However, isolation techniques such as ultracentrifugation, size-exclusion chromatography, and ultrafiltration face challenges regarding purity, contamination, and yield. Contamination from other proteins complicates downstream processing, leading to difficulties in identifying biomarkers and interpreting results. Future research will focus on refining EV characterization for diagnostic and therapeutic applications, improving proteomics tools for greater accuracy, and exploring the use of EVs in drug delivery and regenerative medicine. In this review, we provide a bird's eye view of various challenges, starting with EV isolation methods, yield, purity, and limitations in the proteome analysis of EVs for identifying protein targets.