Search Results (166)

Collagen type I (COL(I)) is a key component of the extracellular matrix (ECM) and is involved in cell signaling and migration through cell receptors. Collagen degradation produces bioactive peptides (matrikines), which influence cellular processes. In this study, we investigated the biological effects of nine most abundant, naturally occurring urinary COL(I)-derived peptides on human endothelial cells at physiological concentrations, using cell migration assays, mass spectrometry-based proteomics, flow cytometry, and AlphaFold 3. While none of the peptides significantly altered endothelial migration by themselves at physiological concentrations, full-length COL(I) increased cell migration, which was inhibited by Peptide 1 (²²⁹NGDDGEAGKPGRPGERGPpGp²⁴⁹). This peptide uniquely contains the DGEA and GRPGER motifs, interacting with integrin α2β1. Flow cytometry confirmed the presence of integrin α2β1 on human endothelial cells, and AlphaFold 3 modeling predicted an interaction between Peptide 1 and integrin α2. Mass spectrometry-based proteomics investigating signaling pathways revealed that COL(I) triggered phosphorylation events linked to integrin α2β1 activation and cell migration, which were absent in COL(I) plus peptide 1-treated cells. These findings identify Peptide 1 as a biologically active COL(I)-derived peptide at a physiological concentration capable of modulating collagen-induced cell migration, and provide a foundation for further investigation into its mechanisms of action and role in urine excretion. Full article

Traditional ecotoxicology primarily investigates pollutant toxicity through physiological, biochemical, and single-molecular indicators. However, the limited data obtained through this approach constrain its application in the mechanistic analysis of pollutant toxicity. Omics technologies have emerged as a major research focus in recent years, enabling the comprehensive and accurate analysis of biomolecular-level changes. The integration of multi-omics technologies can holistically reveal the molecular toxicity mechanisms of pollutants, representing a primary emphasis in current toxicological research. This paper introduces the fundamental concepts of genomics, transcriptomics, proteomics, and metabolomics, while reviewing recent advancements in integrated omics approaches within aquatic toxicology. Furthermore, it provides a reference framework for the implementation of multi-omics strategies in ecotoxicological investigations. While multi-omics integration enables the unprecedented reconstruction of pollutant-induced molecular cascades and earlier biomarker discovery (notably through microbiome–metabolome linkages), its full potential requires experimental designs, machine learning-enhanced data integration, and validation against traditional toxicological endpoints within environmentally relevant models. Full article

The potential of a protein to cause an allergic reaction is often assessed using a variety of computational techniques. Leveraging advances in high-throughput protein sequence data coupled with in silico or computational methods can be used to systematically analyze large proteomes for allergenic potential. Despite mango’s widespread consumption and growing clinical reports of hypersensitivity, the full extent of their allergenicity is yet unknown. In this study, for the first time, we conducted a genome-wide in silico analysis by analyzing a total of 54,010 protein sequences to identify the complete spectrum of potential mango allergens. These proteins were analyzed using various bioinformatics tools to predict their allergenic potential based on sequence similarity, structural features, and known allergen databases. In addition to the known mango allergens, including Man i 1, Man i 2, and Man i 3, our findings demonstrated that several isoforms of cysteine protease, non-specific lipid-transfer protein (LTP), legumin B-like, 11S globulin, vicilin, thaumatin-like protein, and ervatamin-B family proteins exhibited strong allergenic potential, with >80% 3D epitope identity, >70% linear 80 aa window identity, and matching with >80 known allergens. Thus, a genome-wide in silico study provided a comprehensive profile of the possible mango allergome, which could help identify the low-allergen-containing mango cultivars and aid in the development of accurate assays for variety-specific allergic reactions. Full article

Pear (Pyrus pyrifolia) is an important deciduous fruit tree that requires a specific period of low-temperature accumulation to trigger spring flowering. The warmer winter caused by global warming has led to insufficient winter chilling, disrupting floral initiation and significantly reducing pear yields in Southern China. In this study, we integrated targeted phytohormone metabolomics, full-length transcriptomics, and proteomics to explore the regulatory mechanisms of dormancy in ‘Mixue’, a pear cultivar with an extremely low chilling requirement. Comparative analyses across the multi-omics datasets revealed 30 differentially abundant phytohormone metabolites (DPMs), 2597 differentially expressed proteins (DEPs), and 7722 differentially expressed genes (DEGs). Integrated proteomic and transcriptomic expression clustering analysis identified five members of the dormancy-associated MADS-box (DAM) gene family among dormancy-specific differentially expressed proteins (DEPs) and differentially expressed genes (DEGs). Phytohormone correlation analysis and cis-regulatory element analysis suggest that DAM genes may mediate dormancy progression by responding to abscisic acid (ABA), gibberellin (GA), and salicylic acid (SA). A dormancy-associated transcriptional regulatory network centered on DAM genes and phytohormone signaling revealed 35 transcription factors (TFs): 19 TFs appear to directly regulate the expression of DAM genes, 18 TFs are transcriptionally regulated by DAM genes, and two TFs exhibit bidirectional regulatory interactions with DAM. Within this regulatory network, we identified a novel pathway involving REVEILLE 6 (RVE6), DAM, and CONSTANS-LIKE 8 (COL8), which might play a critical role in regulating bud dormancy in the ‘Mixue’ low-chilling pear cultivar. Furthermore, lncRNAs ONT.19912.1 and ONT.20662.7 exhibit potential cis-regulatory interactions with DAM1/2/3. This study expands the DAM-mediated transcriptional regulatory network associated with bud dormancy, providing new insights into its molecular regulatory mechanisms in pear and establishing a theoretical framework for future investigations into bud dormancy control. Full article

Background: The accumulation of agri-food waste is a major environmental and economic challenge and converting these by-products into bioactive compounds fits within the circular bioeconomy. This study aimed to evaluate the antimicrobial potential of extracts derived from Cannabis sativa L. leaves (CSE), Crocus sativus tepals (CST), and Prunus avium L. cherry waste (VCE) against four key bacterial species (Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa). Methods: Minimum inhibitory concentration (MIC) assays were performed to assess antibacterial activity, while a bioinformatic pipeline was implemented to explore possible molecular targets. Full-proteome multiple sequence alignments across the bacterial strains were used to identify conserved, strain-specific proteins, and molecular docking simulations were applied to predict binding interactions between the most abundant compounds in the extracts and their targets. Results: CSE and CST demonstrated bacteriostatic activity against S. aureus and B. subtilis (MIC = 15.6 mg/mL), while VCE showed selective activity against B. subtilis (MIC = 31.5 mg/mL). CodY was identified as a putative molecular target for CSE and CST, and ChaA for VCE. Docking results supported the possibility of spontaneous binding between abundant extract constituents and the predicted targets, with high binding affinities triggering a strong interaction network with target sensing residues. Conclusions: This study demonstrates the antimicrobial activity of these agri-food wastes and introduces a comprehensive in vitro and in silico workflow to support the bioactivity of these agri-food wastes and repurpose them for innovative, eco-sustainable applications in the biotechnology field and beyond. Full article

Traditional medicinal plants are valued for their therapeutic potential, yet the full spectrum of their bioactive compounds often remains underexplored. Recent advances in multiomics technologies, including metabolomics, proteomics, and transcriptomics, combined with in vitro culture systems and elicitor-based strategies, have revolutionized our ability to characterize and enhance the production of valuable secondary metabolites. This review synthesizes current findings on the integration of these approaches to help us understand phytochemical pathways optimising bioactive compound yields. We explore how metabolomic profiling links chemical diversity with antioxidant and antimicrobial activities, how proteomic insights reveal regulatory mechanisms activated during elicitation, and how in vitro systems enable controlled manipulation of metabolic outputs. Both biotic and abiotic elicitors, such as methyl jasmonate and salicylic acid, are discussed as key triggers of phytochemical defense pathways. Further, we examine the potential of multiomics-informed metabolic engineering and synthetic biology to scale production and discover novel compounds. By aligning traditional ethnobotanical knowledge with modern biotechnology, this integrative framework offers a powerful avenue to unlock the pharmacological potential of medicinal plants for sustainable and innovative therapeutic development. Full article

Glioblastoma multiforme (GBM) is a highly aggressive, treatment-resistant grade IV brain tumor with poor prognosis that grows rapidly and invades surrounding tissues, complicating surgery and frequently recurring. Although the crucial role of endogenous peptides has been highlighted for several tumors, the specific peptidomic profile of GBM remains unexplored to date. This study aimed to perform a preliminary characterization of the low molecular mass proteome fraction of Cavitron Ultrasonic Surgical Aspirator (CUSA) fluid collected from different tumor zones, i.e., the core and tumor periphery of newly diagnosed (ND) and recurrent (R) GBM. The samples, pooled by tumor type and collection zone, were centrifuged through molecular cut-off filter devices to collect the non-retained fraction of the proteome <10 kDa for direct full-length LC-MS analysis. A total of 40 and 24 peptides, fragments of 32 and 18 proteins, were marked as ND and R GBM COREs, respectively, while 132 peptides, fragments of 46 precursor proteins, were identified as common and included proteins which were cancer-related or involved in GBM pathophysiology. Besides providing a preliminary overview of the unexplored peptidome of GBM, this pilot study confirms peptidomics as a promising tool to discover potential GBM biomarkers in the perspective of clinical applications increasingly oriented towards a precision medicine approach. Data are available via ProteomeXchange with the identifier PXD060807. Full article

Extracellular Vesicles (EVs) from seminal plasma have achieved attention due to their potential physiopathological role in male reproductive systems. This study employed a comprehensive proteomic and transcriptomic approach to investigate the composition and molecular signatures of EVs isolated from human seminal plasma. EVs from Normozoospermic (NORMO), OligoAsthenoTeratozoospermic (OAT), and Azoospermic (AZO) subjects were isolated using a modified polymer precipitation-based protocol and characterized for size and morphology. Comprehensive proteomic analysis, using both gel-free and gel-based approaches, revealed distinct protein profiles in each group (p<0.01), highlighting potential molecules and pathways involved in sperm function and fertility. The data are available via ProteomeXchange with identifiers PXD051361 and PXD051390, respectively. Transcriptomic analysis confirmed the trend of a general downregulation of AZO and OAT compared to NORMO shedding light on regulatory mechanisms of sperm development. Bioinformatic tools were applied for functional omics analysis; the integration of proteomic and transcriptomic data provided a comprehensive understanding of the cargo content and regulatory networks present in EVs. This study contributes to elucidating the key role of EVs in the paracrine communication regulating spermatogenesis. A full understanding of these pathways not only suggests potential mechanisms regulating male fertility but also offers new insights into the development of diagnostic tools targeting male reproductive disorders. Full article

Artificial intelligence (AI) is rapidly transforming pediatric oncology by creating new means to improve the accuracy and efficacy of cancer diagnosis and treatment in children. This review critically examines current applications of AI technologies like machine learning (ML) and deep learning (DL) to the main types of pediatric cancers. However, the application of AI to pediatric oncology is prone to certain challenges, including the heterogeneity and rarity of pediatric cancer data, rapid technological development in imaging, and ethical concerns pertaining to data privacy and algorithmic transparency. Collaborative efforts and data-sharing schemes are important to surpass these challenges and facilitate effective training of AI models. This review also points to emerging trends, including AI-based radiomics and proteomics applications, and provides future directions to realize the full potential of AI in pediatric oncology. Finally, AI is a promising paradigm shift toward precision medicine in childhood cancer treatment, which can enhance the survival rates and quality of life for pediatric patients. Full article

Background: The testis, a critical reproductive organ in male animals, is responsible for sperm production and androgen secretion. Testis weight often correlates with reproductive performance, yet the genetic factors influencing testicular traits in chickens remain unclear. Methods: Previous genome-wide association studies (GWAS) have identified key genes affecting testicular traits in Kangle Yellow chickens, along with the associated regulatory pathways and Gene Ontology (GO) terms, through bioinformatic analyses. In this study, we utilized the existing literature, full-length transcriptome data, and proteome analyses to select key candidate genes. Results: We identified 13 associated markers for chicken testicular traits with 262 candidate genes. Nine candidate genes were found to regulate chicken testicular traits referred to integrated analysis, including CDH3, ZFPM1, CFAP52, ST6GAL1, IGF2BP2, SPG7, CDT1, NFAT5, and OPRK1. Physical interactions among these genes were also observed, implicating mechanisms such as cell adhesion molecules and neuroactive ligand–receptor interaction. Conclusions: These findings provide a genetic basis for improving testicular traits in Chinese native chicken breeds. Full article

Blue light serves as a critical environmental cue regulating Glycine max (soybean) stem morphology, yet the hormonal mechanisms underlying varietal differences remain unclear. Previous studies have highlighted the role of blue light in modulating plant architecture, but the specific hormone interactions driving morphological divergence between soybean varieties remain underexplored. Two soybean varieties with contrasting stem phenotypes—Henong 60 (HN60, tall) and Heinong 48 (HN48, dwarf)—were subjected to 0% (full light) and 30% (shade) transmittance conditions, supplemented with blue light (450 nm, 45.07 ± 0.03 μmol·m⁻²·s⁻¹). Stem anatomical traits (xylem area, cell length), hormone profiles, and proteomic changes were analyzed. Grey correlation analysis quantified relationships between hormone ratios and plant height. Blue light increased soybean stem xylem area and diameter while reducing plant height and cell longitudinal length. This treatment concurrently reduced growth-promoting hormones (gibberellin A₃ (GA₃), indole-3-acetic acid (IAA), brassinolide (BR)) and increased growth-inhibiting hormones (salicylic acid (SA), jasmonic acid (JA), strigolactones (SLs)), thereby inhibiting stem elongation. Although exogenous GA₃ promoted hypocotyl elongation, it failed to counteract blue-light-induced inhibition. Proteomic analysis identified 16 differentially expressed proteins involved in hormone signal transduction pathways. Grey correlation analysis highlighted cultivar-specific hormone ratio impacts: GA₃/JA, GA₃/SA, and BR/SLs significantly influenced HN60 plant height, while GA₃/SLs, IAA/SLs, and BR/SLs were critical for HN48, demonstrating highly significant positive correlations. The differential sensitivity of growth-promoting/inhibiting hormone ratios to blue light drives varietal morphological divergence in soybean stems. This study establishes a hormonal regulatory framework for blue-light-mediated stem architecture, offering insights for crop improvement under light-limited environments. Full article

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biological mechanisms remain poorly understood. Feature selection can lead to the identification of discriminative key biomarkers by reducing dimensionality from high-dimensional medical datasets to improve machine learning model performance, explainability, and interpretability. Feature selection can uncover unique sex-specific biomarkers, determinants, and molecular profiles in patients with GBM. We analyzed high-dimensional proteomic and metabolomic profiles from serum biospecimens obtained from 109 patients with pathology-proven glioblastoma (GBM) on NIH IRB-approved protocols with full clinical annotation (local dataset). Serum proteomic analysis was performed using Somalogic aptamer-based technology (measuring 7289 proteins) and serum metabolome analysis using the University of Florida’s SECIM (Southeast Center for Integrated Metabolomics) platform (measuring 6015 metabolites). Machine learning-based feature selection was employed to identify proteins and metabolites associated with male and female labels in high-dimensional datasets. Results were compared to publicly available proteomic and metabolomic datasets (CPTAC and TCGA) using the same methodology and TCGA data previously structured for glioma grading. Employing a machine learning-based and hybrid feature selection approach, utilizing both LASSO and mRMR, in conjunction with a rank-based weighting method (i.e., GLIO-Select), we linked proteomic and metabolomic data to clinical data for the purposes of feature reduction to identify molecular biomarkers associated with biological sex in patients with GBM and used a separate TCGA set to explore possible linkages between biological sex and mutations associated with tumor grading. Serum proteomic and metabolomic data identified several hundred features that were associated with the male/female class label in the GBM datasets. Using the local serum-based dataset of 109 patients, 17 features (100% ACC) and 16 features (92% ACC) were identified for the proteomic and metabolomic datasets, respectively. Using the CPTAC tissue-based dataset (8828 proteomic and 59 metabolomic features), 5 features (99% ACC) and 13 features (80% ACC) were identified for the proteomic and metabolomic datasets, respectively. The proteomic data serum or tissue (CPTAC) achieved the highest accuracy rates (100% and 99%, respectively), followed by serum metabolome and tissue metabolome. The local serum data yielded several clinically known features (PSA, PZP, HCG, and FSH) which were distinct from CPTAC tissue data (RPS4Y1 and DDX3Y), both providing methodological validation, with PZP and defensins (DEFA3 and DEFB4A) representing shared proteomic features between serum and tissue. Metabolomic features shared between serum and tissue were homocysteine and pantothenic acid. Several signals emerged that are known to be associated with glioma or GBM but not previously known to be associated with biological sex, requiring further research, as well as several novel signals that were previously not linked to either biological sex or glioma. EGFR, FAT4, and BCOR were the three features associated with 64% ACC using the TCGA glioma grading set. GLIO-Select shows remarkable results in reducing feature dimensionality when different types of datasets (e.g., serum and tissue-based) were used for our analyses. The proposed approach successfully reduced relevant features to less than twenty biomarkers for each GBM dataset. Serum biospecimens appear to be highly effective for identifying biologically relevant sex differences in GBM. These findings suggest that serum-based noninvasive biospecimen-based analyses may provide more accurate and clinically detailed insights into sex as a biological variable (SABV) as compared to other biospecimens, with several signals linking sex differences and glioma pathology via immune response, amino acid metabolism, and cancer hallmark signals requiring further research. Our results underscore the importance of biospecimen choice and feature selection in enhancing the interpretation of omics data for understanding sex-based differences in GBM. This discovery holds significant potential for enhancing personalized treatment plans and patient outcomes. Full article

Type 1 diabetes (T1D) is an autoimmune condition characterized by the destruction of insulin-producing pancreatic beta cells, leading to lifelong insulin dependence and significant complications. Early detection of T1D is essential to delay disease onset and improve outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) have provided powerful tools for predicting and diagnosing T1D. This systematic review evaluates the current landscape of AI/ML-based approaches for early T1D detection. A comprehensive search across PubMed, EMBASE, Science Direct, and Scopus identified 1447 studies, of which 10 met the inclusion criteria for narrative synthesis after screening and full-text review. The studies utilized diverse ML models, including logistic regression, support vector machines, random forests, and artificial neural networks. The datasets encompassed clinical parameters, genetic risk markers, continuous glucose monitoring (CGM) data, and proteomic and metabolomic biomarkers. The included studies involved a total of 49,172 participants and employed case–control, retrospective cohort, and prospective cohort designs. Models integrating multimodal data achieved the highest predictive accuracy, with area under the curve (AUC) values reaching up to 0.993 in sex-specific models. CGM data and plasma biomarkers, such as CXCL10 and IL-1RA, also emerged as valuable tools for identifying at-risk individuals. While the results highlight the potential of AI/ML in revolutionizing T1D risk stratification and diagnosis, challenges remain. Data heterogeneity and limited model generalizability present barriers to widespread implementation. Future research should prioritize the development of universal frameworks and real-world validation to enhance the reliability and clinical integration of these tools. Ultimately, AI/ML technologies hold transformative potential for clinical practice by enabling earlier diagnosis, guiding targeted interventions, and improving long-term patient outcomes. These advancements could support clinicians in making more informed, timely decisions, thus reducing diagnostic delays and paving the way for personalized prevention strategies in both pediatric and adult populations. Full article

The hydrolysis of raw food sources by commercially available food-grade enzymes releases thousands of peptides. The full characterization of bioactive hydrolysates requires robust methods to identify and quantify key peptides in these food sources. For this purpose, the absolute quantification of specific peptides, part of a complex peptide network, is necessary. Protein quantification with synthetic tryptic peptides as internal standards is a well-known approach, yet the quantification of non-tryptic peptides contained in food hydrolysates is still largely unaddressed. Similarly, data analyses focus on proteomic applications, thus adding challenges to the study of specific peptides of interest. This paper presents an in-sample calibration curve methodology for the identification of three non-tryptic peptides present in a Vicia faba food hydrolysate (PeptiStrong™) using heavy synthetic peptides as both calibrants and internal standards. Full article

Background: To provide clues and a diagnostic basis for patients with fever of unknown origin through urinary proteomics analysis. Methods: For the first time, an attempt was made to conduct a full-library search for viruses in urine samples. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) technology was employed to analyze the urinary proteomes of patients with fever of unknown origin, and to search for and identify viral protein fragments. In this study, there is no need to pre-determine the types of substances present in the samples. As long as the relevant sequences of viruses are available in the database, virus searches can be performed on the samples. Results: In the urine samples, multiple specific peptides from various viruses, such as the monkeypox virus, salivirus A, human herpesvirus 8 type P, Middle East respiratory syndrome-related coronavirus, rotavirus A, Orf virus (strain NZ2), human herpesvirus 2 (strain HG52), human adenovirus E serotype 4, influenza A virus, human coronavirus NL63, parainfluenza virus 5 (strain W3), Nipah virus, and hepatitis C virus genotype 2k (isolate VAT96), could be observed. It was found that the detection amounts of multiple viruses in febrile patients were much higher than those in the control group. Among them, the increase multiple of salivirus A was as high as more than 4200 times, and the increase multiples of multiple viral proteins were higher than 20 times. Conclusions: Viral fragments in urinary proteins can be reliably identified using mass spectrometry, which provides clues for the investigation of unexplained fever and may also be applied to the exploration of any unknown diseases. Full article