Proteomes最新文献

Background: The aim of this study is to investigate the pathophysiology of cataract by analyzing signaling pathways in three sample types obtained from four different lens groups: age-related (ARC), diabetic (DC), post-vitrectomy cataract (PVC) and clear control lenses.

Methods: Three sample types-the aqueous humor, the anterior capsule and the phaco cassette content-were collected during cataract surgery from 39 participants (ARC = 12, DC = 11, PVC = 7 and control = 9). The samples were prepared based on Sp3 protocol. The recognition and quantification of proteins were performed with liquid chromatography online with tandem mass spectrometry using the DIA-NN software. Perseus software (v1.6.15.0) was used for statistical analysis. Data are available via ProteomeXchange with identifiers PXD045547, PXD045554, PXD045557, and PXD069667.

Results: In total, 1986 proteins were identified in the aqueous humor, 2804 in the anterior capsule, and 3337 in the phaco cassette samples. Proteins involved in actin and microtubule cytoskeleton organization, including ACTN4, were downregulated in all three cataract groups compared to controls. Proteins involved in glycolipid metabolic process, including GAL3ST1, GAL3ST4, and GLA, were upregulated in ARC compared to controls. Proteins involved in the non-canonical Wnt receptor signaling pathway, including FRZB, SFRP1, SFRP2, SFRP5, WNT5A, and WNT7A, were upregulated in ARC compared to DC, PVC, and controls.

Conclusions: Comprehensive proteomic profiles were generated using DIA proteomics by comparing ARC, DC, and PVC versus controls. This is the first study to use phaco cassette contents to investigate cataract formation in comparison to controls. Our findings significantly enhance the current understanding of human cataract pathophysiology and provide novel insights into the mechanisms underlying cataract formation.

Background: Mitoxantrone (MX) is regularly used to treat several cancers. Despite its long history in the clinic, recent studies continue to unveil novel protein targets. These targets may contribute to the cytotoxic effects of the drug, as well as potential non-canonical antitumor activity. A better understanding of MX's cellular targets is required to fully comprehend the molecular consequences of treatment and to interpret MX sensitivity in homologous recombination (HR)-deficient cancer. Methods: Here, we evaluated MX activity in HR-deficient UWB1.289 (BRCA1-) ovarian cancer cells and surveyed the binding profile of MX using TMT-labeled quantitative proteomics and chemoproteomics. Results: Mass spectrometry (MS) analysis of cellular extracts from MX-treated BRCA1-UWB1.289 cells revealed unique downregulation of pathways instrumental in maintaining genomic stability, including single-strand annealing. Moreover, the BRCA1- cells exhibited a significant upregulation of proteins involved in ribosome biogenesis and RNA processing. Additional MS analyses following affinity-purification using a biotinylated-mitoxantrone probe corroborated these findings, which showed considerable targeting of proteins involved in genome maintenance and RNA processing. Conclusions: Our results suggest that an interplay of both canonical and non-canonical MX-antitumor activity overwhelms the BRCA1- UWB1.289 cells. Furthermore, this study characterizes the target landscape of MX, providing insights into off-target effects and MX action in HR-deficient cancer.

Polyploid crops such as wheat, Brassica, and cotton are critical in the global agricultural and economic system. However, their productivity is threatened increasingly by biotic stresses such as disease, and abiotic stresses such as heat, both exacerbated by climate change. Understanding the molecular basis of stress responses in these crops is crucial but remains challenging due to their complex genetic makeup-characterized by large sizes, multiple genomes, and limited annotation resources. Proteomics is a powerful approach to elucidate molecular mechanisms, enabling the identification of stress-responsive proteins; cellular localization; physiological, biochemical, and metabolic pathways; protein-protein interaction; and post-translational modifications. It also sheds light on the evolutionary consequences of genome duplication and hybridization. Breeders can improve stress tolerance and yield traits by characterizing the proteome of polyploid crops. Functional and subcellular proteomics, and identification and introgression of stress-responsive protein biomarkers, are promising for crop improvement. Nevertheless, several challenges remain, including inefficient protein extraction methods, limited organelle-specific data, insufficient protein annotations, low proteoform coverage, reproducibility, and a lack of target-specific antibodies. This review explores the genomic complexity of three key allopolyploid crops (wheat, oilseed Brassica, and cotton), summarizes recent proteomic insights into heat stress and pathogen response, and discusses current challenges and future directions for advancing proteomics in polyploid crop improvement through proteomics.

Background: Human insulin-like growth factor 1 (hIGF-1) plays a key role in cell proliferation and tissue repair. While plant expression systems offer a cost-effective and scalable alternative for recombinant protein production, the molecular effects of plant-derived hIGF-1 on mammalian cells remain largely unexplored.

Methods: In this study, a recombinant fusion protein of hIGF-1 with human Fc (hIGF-1-Fc) was transiently expressed in Nicotiana benthamiana using the geminiviral pBYR2e system and purified by Protein A affinity chromatography. SDS-PAGE and Western blotting confirmed the predicted molecular weight, and LC-MS identified N-glycosylation at the Fc N229 site with plant-type glycans such as GnMXF, GnGnXF, and MMXF. Bioactivity was evaluated using MCF-7 cell proliferation and NIH3T3 wound healing assays. Label-free quantitative proteomics was performed on NIH3T3 fibroblasts to assess molecular changes.

Results: hIGF-1 Fc significantly promoted cancer cell migration and fibroblast proliferation. Proteomic profiling revealed an abundance of cytoskeletal proteins such as actin and tubulin and metabolic enzymes related to energy production. Gene ontology and pathway enrichment analyses indicated significant modulation of ribosome biogenesis and carbon metabolism.

Conclusions: This study presents the first proteome-level investigation of plant-produced hIGF-1-Fc in mouse fibroblasts and reveals its impact on cytoskeletal organization and metabolic pathways involved in proliferation and wound healing.

Background: The search for reliable aging biomarkers using proteomic databases and large-scale proteomic studies presents a significant challenge in biogerontology. Existing proteomic databases and studies contain valuable information; however, there is inconsistency in approaches to biomarker selection and data integration. This creates barriers to translating existing knowledge into clinical practice and use in biomedical research. This work analyzed experimental proteomic studies, the content of proteomic databases, and proposed recommendations for optimization and improvement of proteomic database formation and enrichment. Methods: The study utilized publications devoted to proteomic data acquisition methods, proteomic databases, and experimental studies. Results: Methods for obtaining proteomic data were analyzed (Protein Pathway Array (PPA), Tissue Microarray (TMA), Luminex (Bead Array), MSD (Meso Scale Discovery), Simoa (Quanterix), SOMAscan (SomaLogic), Olink (PEA), Alamar NULISA (PEA+), and Oxford Nanopore. A total of 16 proteomic databases were investigated (HAGR, KEGG, STRING, Aging Atlas, HALL, Human Protein Atlas, UniProt, AgeAnnoMO, AgeFactDB, AgingBank, iProX, jMorp, jPOSTrepo, MassIVE, MetaboAge DB, PRIDE Archive). Additionally, 22 proteomic studies devoted to aging and age-associated diseases were analyzed. Conclusions: Proteomic databases and experimental studies individually contain valuable information about aging biomarkers. Using data from different sources within biomedical research poses challenges for improving and optimizing methodological solutions for publication selection, database formation, and marker development.

Background: Poxviruses constitute a family of large dsDNA viruses that can infect a plethora of species including humans. Historically, poxviruses have caused a health burden in multiple outbreaks. The large genome of poxviruses favors reverse vaccinology approaches that can determine potential antigens and epitopes. Here, we propose the modeling of a user-friendly database containing the predicted antigens and epitopes of a large cohort of poxvirus proteomes using the existing PoxiPred method for reverse vaccinology of poxviruses.

Methods: In the present study, we obtained the whole proteomes of as many as 37 distinct poxviruses. We utilized each proteome to predict both antigenic proteins and T-cell epitopes of poxviruses with the aid of an Artificial Intelligence method, namely the PoxiPred method.

Results: In total, we predicted 3966 proteins as potential antigen targets. Of note, we considered that this protein may exist in a set of proteoforms. Subsets of these proteins constituted a comprehensive repository of 54,291 linear T-cell epitopes. We combined the outcome of the predictions in the format of a web tool that delivers a database of antigens and epitopes of poxviruses. We also developed a comprehensive repository dedicated to providing access to end-users to obtain AI-based screened antigens and T-cell epitopes of poxviruses in a user-friendly manner. These antigens and epitopes can be utilized to design experiments for the development of effective vaccines against a plethora of poxviruses.

Conclusions: The TCEPVDB repository, already deployed to the web under an open-source coding philosophy, is free to use, does not require any login, does not store any information from its users.

Background: Molecular mimicry contributes to the development of unwanted responses to self-antigens. Autoimmune phenomena have been observed in diseases caused by Aedes aegypti-transmitted arboviruses, but the occurrence of mimicry between salivary and human proteins has been unexplored.

Methods: We used bioinformatic tools to determine if peptides from Aedes aegypti salivary proteins were present in the human proteome. We further characterized the potential of shared sequences to induce immunity by analyzing their predicted binding to MHC molecules and their occurrence in peptides from the Immune Epitope Database (IEDB).

Results: We analyzed 9513 octapeptides from 29 Aedes aegypti salivary proteins against the human proteome and found 47 peptides identical to sequences from 52 human proteins, ranging in length from 8 to 18 amino acids. We found 302 matches of peptides predicted to bind with high affinity to MHC-I and MHC-II alleles associated with autoimmune diseases, and 14 human peptides containing shared sequences with Aedes aegypti salivary proteins validated as immunogenic in the IEDB.

Conclusions: These results support the existence of molecular mimicry between Aedes aegypti salivary proteins and human antigens and provide a framework for studies to determine its contribution to responses directed to self-antigens in the context of arboviral infections.

Background: Chronic ischemic heart disease (CIHD) is characterized by persistent myocardial ischemic due to long-term reduced coronary blood flow. In the past, we mainly relied on surgical intervention or drug therapy to alleviate symptoms, but effective targeted treatments were scarce. Proteomics serves as a key tool to identify novel therapeutic targets.

Methods: This study performed a bidirectional Mendelian randomization (MR) analysis by integrating genome-wide association study (GWAS) data on CIHD (10,894,596 single-nucleotide polymorphisms) with plasma proteomic data encompassing 4907 proteins. We conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify pathways linked to candidate protein biomarkers, searched the National Genomics Data Center (NGDC) database for existing evidence of their association with CIHD, and evaluated druggability through multi-dimensional analysis integrating the DSIGDB, ChEMBL, and clinical trial databases.

Results: After eliminating the reverse effect, ultimately identifying 28 protein markers, including 16 risk-associated and 12 protective proteins. We also investigated their roles in the pathways related to CIHD. Meanwhile, the search confirmed that five of them were newly discovered protein markers. Ultimately, through evaluation, three priority therapeutic targets (CXCL12, PLAU, CD14) were identified for development.

Conclusions: This study identified some biomarkers related to CIHD and analyzed the possible pathways involved. It also provided some new insights into the identification of the target and druggability.

Protein-protein interactions (PPIs) are significant in understanding the complex molecular processes of plant growth, disease resistance, and stress responses. Machine learning (ML) has recently emerged as a powerful tool that can predict and analyze PPIs, offering complementary insights into traditional experimental approaches. It also accounts for proteoforms, distinct molecular variants of proteins arising from alternative splicing, or genetic variations and modifications, which can significantly influence PPI dynamics and specificity in rice. This review presents a comprehensive summary of ML-based methods for PPI predictions in rice (Oryza sativa) based on recent developments in algorithmic innovation, feature extraction processes, and computational resources. We present applications of these models in the discovery of candidate genes, unknown protein annotations, identification of plant-pathogen interactions, and precision breeding. Case studies demonstrate the utility of ML-based methods in improving rice resistance to abiotic and biotic stresses. Additionally, this review highlights key challenges like data limits, model generalizability, and future directions like multi-omics, deep learning and artificial intelligence (AI). This review provides a roadmap for researchers aiming to use ML to generate predictive and mechanistic insights on rice PPI networks, hence helping to achieve enhanced crop improvement programs.

Marine-derived bioactive peptides (MBPs) are emerging as promising natural agents for regulating inflammatory responses. MBPs, typically obtained through enzymatic hydrolysis of proteins from various marine organisms such as fish, mollusks, and algae, exhibit diverse biological activities, including antioxidant, immunomodulatory, and anti-inflammatory effects. The ability of MBPs to modulate key inflammatory mediators such as TNF-α, IL-6, and COX-2, primarily through pathways like NF-κB and MAPK, highlights the therapeutic potential of MBPs in managing chronic inflammatory diseases. However, most existing studies are confined to in vitro assays or animal models, with limited translation to human clinical applications. This review explores the stability, bioavailability, and metabolic rate of MBPs under physiological conditions, which remain poorly understood. In addition, a lack of standardized protocols for peptide extraction, purification, and efficacy evaluation hinders comparative analysis across studies and also different proteomics approaches for separation, purification, identification, and quantification of marine-derived peptides with therapeutic properties. The structure-function relationship of MBPs is also underexplored, limiting rational design and targeted applications in functional foods or therapeutic products. These limitations are largely due to a lack of consolidated information and integrated research efforts. To address these challenges, this review summarizes recent progress in identifying MBPs with anti-inflammatory potentials, outlines key mechanisms, and highlights current limitations. Additionally, this review also emphasizes the need to enhance mechanistic understanding, optimize delivery strategies, and advance clinical validation to fully realize the therapeutic potential of MBPs.