Search Results (2,482)

Botrytis cinerea is a highly versatile pathogenic fungus, causing significant damage across a wide range of plant species. A central focus of this review is the recent advances made through proteomics, an advanced molecular tool, in understanding the mechanisms of B. cinerea infection. Recent advances in mass spectrometry-based proteomics—including LC-MS/MS, iTRAQ, MALDI-TOF, and surface shaving—have enabled the in-depth characterization of B. cinerea subproteomes such as the secretome, surfactome, phosphoproteome, and extracellular vesicles, revealing condition-specific pathogenic mechanisms. Notably, in under a decade, the proportion of predicted proteins experimentally identified has increased from 10% to 52%, reflecting the rapid progress in proteomic capabilities. We explore how proteomic studies have significantly enhanced our knowledge of the fungus secretome and the role of extracellular vesicles (EVs), which play key roles in pathogenesis, by identifying secreted proteins—such as pH-responsive elements—that may serve as biomarkers and therapeutic targets. These technologies have also uncovered fine regulatory mechanisms across multiple levels of the fungal proteome, including post-translational modifications (PTMs), the phosphomembranome, and the surfactome, providing a more integrated view of its infection strategy. Moreover, proteomic approaches have contributed to a better understanding of host–pathogen interactions, including aspects of the plant’s defensive responses. Furthermore, this review discusses how proteomic data have helped to identify metabolic pathways affected by novel, more environmentally friendly antifungal compounds. A further update on the advances achieved in the field of proteomics discovery for the organism under consideration is provided in this paper, along with a perspective on emerging tools and future developments expected to accelerate research and improve targeted intervention strategies. Full article

Antibiotic resistance is considered to be one of the most complex health obstacles of our time. Methicillin-resistant Staphylococcus aureus (MRSA) represents a global health challenge due to its broad treatment resistance capacity, resulting in high mortality rates. The shikimate pathway (SP) is responsible for the biosynthesis of chorismate from glycolysis and pentose phosphate pathway intermediates. This pathway plays a crucial role in producing aromatic amino acids, folates, ubiquinone, and other secondary metabolites in bacteria. Notably, SP is absent in humans, which makes it a specific and potential therapeutic target to explore for discovering new antibiotics against MRSA. The present study characterized in vitro and in silico natural products as inhibitors of the shikimate dehydrogenase from methicillin-resistant S. aureus (SaSDH). The results showed that, from the set of compounds studied, phloridzin, rutin, and caffeic acid were the most potent inhibitors of SaSDH, with IC₅₀ values of 140, 160, and 240 µM, respectively. Furthermore, phloridzin showed a mixed-type inhibition mechanism, whilst rutin and caffeic acid showed non-competitive mechanisms. The structural characterization of the SaSDH–inhibitor complex indicated that these compounds interacted with amino acids from the catalytic site and formed stable complexes. In biological activity studies against MRSA, caffeic acid showed an MIC of 2.2 mg/mL. Taken together, these data encourage using these compounds as a starting point for developing new antibiotics based on natural products against MRSA. Full article

Background/Objectives: Antimicrobial resistance (AMR) contributes to millions of deaths globally each year, creating an urgent need for new therapeutic agents. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their potential to combat AMR pathogens. This study aimed to evaluate the antimicrobial activity of an AMP from a soil-derived bacterial isolate against Gram-negative bacteria. Method: Soil bacteria were isolated and screened for antimicrobial activity. The bioactive peptide was purified and determined its structure and antimicrobial efficacy. Genomic analysis was conducted to predict the biosynthetic gene clusters (BGCs) responsible for AMP production. Results: Genomic analysis identified the isolate as Paenibacillus sp. Na14, which exhibited low genomic similarity (61.0%) to other known Paenibacillus species, suggesting it may represent a novel species. The AMP from the Na14 strain exhibited heat stability up to 90 °C for 3 h and retained its activity across a broad pH range from 3 to 11. Structural analysis revealed that the Na14 peptide consisted of 14 amino acid residues, adopting an α-helical structure. This peptide exhibited bactericidal activity at concentrations of 2–4 µg/mL within 6–12 h, and its killing rate was concentration-dependent. The peptide was found to disrupt the bacterial membranes. The Na14 peptide shared 64.29% sequence similarity with brevibacillin 2V, an AMP from Brevibacillus sp., which also belongs to the Paenibacillaceae family. Genomic annotation identified BGCs associated with secondary metabolism, with a particular focus on non-ribosomal peptide synthetase (NRPS) gene clusters. Structural modeling of the predicted NRPS enzymes showed high similarity to known NRPS modules in Brevibacillus species. These genomic findings provide evidence supporting the similarity between the Na14 peptide and brevibacillin 2V. Conclusions: This study highlights the discovery of a novel AMP with potent activity against Gram-negative pathogens and provides new insight into conserved AMP biosynthetic enzymes within the Paenibacillaceae family. Full article

Skeletal muscle atrophy is a critical health issue affecting the quality of life of elderly individuals and patients with chronic diseases. These conditions induce dysregulation of glucocorticoid (GC) secretion. GCs play a critical role in maintaining homeostasis in the stress response and glucose metabolism. However, prolonged exposure to GC is directly linked to muscle atrophy, which is characterized by a reduction in muscle size and weight, particularly affecting fast-twitch muscle fibers. The GC-activated glucocorticoid receptor (GR) decreases protein synthesis and facilitates protein breakdown. Numerous antagonists have been developed to mitigate GC-induced muscle atrophy, including 11β-HSD1 inhibitors and myostatin and activin receptor blockers. However, the clinical trial results have fallen short of the expected efficacy. Recently, several emerging pathways and targets have been identified. For instance, GC-induced sirtuin 6 isoform (SIRT6) expression suppresses AKT/mTORC1 signaling. Lysine-specific demethylase 1 (LSD1) cooperates with the GR for the transcription of atrogenes. The kynurenine pathway and indoleamine 2,3-dioxygenase 1 (IDO-1) also play crucial roles in protein synthesis and energy production in skeletal muscle. Therefore, a deeper understanding of the complexities of GR transactivation and transrepression will provide new strategies for the discovery of novel drugs to overcome the detrimental effects of GCs on muscle tissues. Full article

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

Background/Objectives: The diminished efficacy of azoles in treating fungal infections is attributed to the emergence of resistance among pathogenic fungi. Employing a synergistic approach with other compounds to enhance the antifungal activity of azoles has shown promise, yet the availability of clinically valuable adjuvants for azoles and allylamines remains limited. Studies have demonstrated that the human host environment provides multiple carbon sources, which can influence the susceptibility of C. albicans to antifungal agents. Therefore, a comprehensive investigation into the mechanisms by which carbon sources modulate the susceptibility of C. albicans to azoles may uncover a novel pathway for enhancing the antifungal efficacy of azoles. Methods: This study explored the impact of various carbon sources on the antifungal efficacy of azoles through methodologies including minimum inhibitory concentration (MIC) assessments, super-MIC growth (SMG) assays, disk diffusion tests, and spot assays. Additionally, the mechanism by which galactose augments the antifungal activity of azoles was investigated using a range of experimental approaches, such as gene knockout and overexpression techniques, quantitative real-time PCR (qRT-PCR), Western blot analysis, and cycloheximide (CHX) chase experiments. Results: This study observed that galactose enhances the efficacy of azoles against C. albicans by inhibiting the translation of Erg1. This results in the suppression of Erg1 protein levels and subsequent inhibition of ergosterol biosynthesis in C. albicans. Conclusions: In C. albicans, the translation of Erg1 is inhibited when galactose is utilized as a carbon source instead of glucose. This novel discovery of galactose’s inhibitory effect on Erg1 translation is expected to enhance the antifungal efficacy of azoles. Full article

Background: Nasopharyngeal carcinoma (NPC) is an epithelial malignancy arising from the nasopharyngeal mucosa. Despite treatment advances such as the use of intensity-modulated radiotherapy and immune checkpoint inhibitors, resistance remains a significant clinical challenge. Many tumours are also diagnosed at an advanced stage associated with poor prognosis. Objective: This review aims to explore the biological roles of autophagy in NPC, primarily highlighting its involvement in disease pathogenesis and treatment resistance. Methods: We performed a review of the recent literature examining the role of autophagy-related pathways in NPC pathogenesis, biomarker discovery, and therapeutic targeting. Results: Autophagy plays a dual role in NPC as it contributes to both tumour suppression and progression. It is involved in tumour initiation, metastasis, immune modulation, and treatment resistance. Autophagy-related genes such as SQSTM1, Beclin-1, and AURKA may serve as prognostic and therapeutic biomarkers. Various strategies are being investigated for their role to modulate autophagy using pharmacologic inhibitors, RNA interventions, and natural compounds. Conclusions: Further research into autophagy’s context-dependent roles in NPC may inform the development of personalised therapies and allow progress in translational and precision oncology. Full article

Background/Objective: Pharmacological interventions often exert systemic effects beyond their primary targets, underscoring the need for a comprehensive evaluation of their metabolic impact. Etodolac is a nonsteroidal anti-inflammatory drug (NSAID) that alleviates pain, fever, and inflammation by inhibiting cyclooxygenase-2 (COX-2), thereby reducing prostaglandin synthesis. While its pharmacological effects are well known, the broader metabolic impact and potential mechanisms underlying improved clinical outcomes remain underexplored. Untargeted metabolomics, which profiles the metabolome without prior selection, is an emerging tool in clinical pharmacology for elucidating drug-induced metabolic changes. In this study, untargeted metabolomics was applied to investigate metabolic changes following a single oral dose of etodolac in healthy male volunteers. By analyzing serial blood samples over time, we identified endogenous metabolites whose concentrations were positively or inversely associated with the drug’s plasma levels. This approach provides a window into both therapeutic pathways and potential off-target effects, offering a promising strategy for early-stage drug evaluation and multi-target discovery using minimal human exposure. Methods: Thirty healthy participants received a 400 mg dose of Etodolac. Plasma samples were collected at five time points: pre-dose, before Cmax, at Cmax, after Cmax, and 36 h post-dose (n = 150). Samples underwent LC/MS-based untargeted metabolomics profiling and pharmacokinetic analysis. A total of 997 metabolites were significantly dysregulated between the pre-dose and Cmax time points, with 875 upregulated and 122 downregulated. Among these, 80 human endogenous metabolites were identified as being influenced by Etodolac. Results: A total of 17 metabolites exhibited time-dependent changes closely aligned with Etodolac’s pharmacokinetic profile, while 27 displayed inverse trends. Conclusions: Etodolac influences various metabolic pathways, including arachidonic acid metabolism, sphingolipid metabolism, and the biosynthesis of unsaturated fatty acids. These selective metabolic alterations complement its COX-2 inhibition and may contribute to its anti-inflammatory effects. This study provides new insights into Etodolac’s metabolic impact under healthy conditions and may inform future therapeutic strategies targeting inflammation. Full article

Chemoresistance is a major challenge in the treatment of triple-negative breast cancer (TNBC). Multicellular spheroids are an attractive platform for investigating chemoresistance in TNBC, as they replicate the cues of the tumour microenvironment in vivo. We conducted a comprehensive literature search to summarise the multifactorial and interlinked mechanisms driving chemoresistance in TNBC spheroids. These mechanisms include spatial heterogeneity, hypoxia, extracellular matrix remodelling, tumour–stroma crosstalk, drug efflux, apoptotic resistance, and cancer stem cell signalling. Strategies for overcoming chemoresistance in TNBC spheroids include nanocarrier systems to overcome spatial diffusion limitations, pathway inhibition, and targeting tumour–microenvironment interactions. Despite their advantages, some spheroid models face challenges such as low reproducibility, a lack of heterogeneity, variability in size and shape, limited vascularisation, and constraints in long-term culture. Advanced culturing platforms such as clinostat bioreactors allow for extended culture periods, enabling mature spheroid drug testing. Furthermore, advanced analytical techniques provide spatially resolved spheroid data. These multifactorial and interlinked mechanisms reflect the tumour microenvironment in vivo that spheroids recapitulate, rendering them valuable models for studying chemoresistance. The incorporation of stromal components and advanced analytical workflows will enhance the utility and translational relevance of spheroids as reliable preclinical models for drug discovery in TNBC. Full article

Stomata, highly specialized structures that evolved on the aerial surfaces of plants, play a crucial role in regulating hydration, mitigating the effects of abiotic stress. Stomatal lineage development involves a series of coordinated events, such as initiation, stem cell proliferation, and cell fate determination, ultimately leading to the differentiation of guard cells. While core transcriptional regulators and signaling pathways controlling stomatal cell division and fate determination have been characterized over the past twenty years, the molecular mechanisms linking stomatal development to dynamic environmental cues remain poorly understood. Therefore, stomatal development is considered an active and compelling frontier in plant biology research. On the one hand, this review aims to provide an understanding of the molecular networks governing stomatal ontogenesis, which relies on the activation and function of the transcription factors SPEECHLESS (SPCH), MUTE, and FAMA; the EPF–TMM and ERECTA receptor systems; and downstream MAPK signaling. On the other hand, it synthesizes current discoveries of how hormonal signaling pathways regulate stomatal development in response to environmental changes. As the climate crisis intensifies, the understanding of the complex interplay between stress stimuli and key factors regulating stomatal development may reveal key mechanisms that enhance plant resilience under adverse environmental conditions. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by amyloid-β (Aβ) plaques, neurofibrillary tangles, blood–brain barrier dysfunction, oxidative stress (OS), and neuroinflammation. Current treatments provide symptomatic relief, but do not halt the disease’s progression. OS plays a crucial role in AD pathogenesis by promoting Aβ accumulation. Nuclear factor erythroid 2-related factor 2 (NRF2) is a key regulator of the antioxidant response, influencing genes involved in OS mitigation, mitochondrial function, and inflammation. Dysregulation of NRF2 is implicated in AD, making it a promising therapeutic target. Emerging evidence suggests that adherence to a Mediterranean diet (MD), which is particularly rich in polyphenols from extra virgin olive oil (EVOO), is associated with improved cognitive function and a reduced risk of mild cognitive impairment. Polyphenols can activate NRF2, enhancing endogenous antioxidant defenses. This study employs a computational approach to explore the potential of bioactive compounds in EVOO to modulate NRF2-related pathways in AD. We analyzed transcriptomic data from AD and EVOO-treated samples to identify NRF2-associated genes, and used chemical structure-based analysis to compare EVOO’s bioactive compounds with known NRF2 activators. Enrichment analysis was performed to identify common biological functions between NRF2-, EVOO-, and AD-related pathways. Our findings highlight important factors and biological functions that provide new insight into the molecular mechanisms through which EVOO consumption might influence cellular pathways associated with AD via modulation of the NRF2 pathway. The presented approach provides a different perspective in the discovery of compounds that may contribute to neuroprotective mechanisms in the context of AD. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Mitochondria, pivotal organelles in cellular metabolism and energy production, have emerged as critical players in the pathogenesis of cancer. This review outlines the progress in mitochondrial profiling through mass spectrometry-based metabolomics and its applications in cancer research. We provide unprecedented insights into the mitochondrial metabolic rewiring that fuels tumorigenesis, metastasis, and therapeutic resistance. The purpose of this review is to provide a comprehensive guide for the implementation of mitochondrial metabolomics, integrating advanced methodologies—including isolation, detection, and data integration—with insights into cancer-specific metabolic rewiring. We first summarize current methodologies for mitochondrial sample collection and pretreatment. Furthermore, we then discuss the recent advancements in mass spectrometry-based methodologies that facilitate the detailed profiling of mitochondrial metabolites, unveiling significant metabolic reprogramming associated with tumorigenesis. We emphasize how recent technological advancements have addressed longstanding challenges in the field and explore the role of mitochondrial metabolism-driven cancer development and progression for novel drug discovery and translational research applications in cancer. Collectively, this review delineates emerging opportunities for therapeutic discovery and aims to establish a foundation for future investigations into the therapeutic modulation of mitochondrial pathways in cancer, thereby paving the way for innovative diagnostic and therapeutic approaches targeting mitochondrial pathways. Full article

Psoriasis is a chronic inflammatory skin disease characterized by abnormal proliferation of keratinocytes and infiltration of inflammatory cells. Significant challenges remain in developing effective and safe targeted therapies for psoriasis. Here, we reported the discovery of novel cystamine derivatives for the treatment of psoriasis. These compounds effectively attenuated LPS-induced inflammation in vitro, and the optimal candidate CS1 ameliorated imiquimod-induced psoriasis-like inflammation in mice. Mechanistically, CS1 bound and inhibited the deacetylase HDAC6, subsequently inhibited the AKT, MAPK, and STAT3 pathways, attenuated the hyperproliferation and altered differentiation of keratinocytes and reduced the infiltration of immune cells. These findings suggest that HDAC6 may serve as a potential target for drug development in the treatment of psoriasis. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major global health challenge characterized by complex adipose–liver interactions mediated by adipokines and hepatokines. Despite rapid field evolution, a comprehensive understanding of research trends and translational advances remains fragmented. This study systematically maps the scientific landscape through bibliometric analysis, identifying emerging domains and future clinical translation directions. Methods: A comprehensive bibliometric analysis of 1002 publications from 2004 to 2025 was performed using thematic mapping, temporal trend evaluation, and network analysis. Analysis included geographical and institutional distributions, thematic cluster identification, and research paradigm evolution assessment, focusing specifically on adipokine–hepatokine signaling mechanisms and clinical implications. Results: The United States and China are at the forefront of research output, whereas European institutions significantly contribute to mechanistic discoveries. The thematic map analysis reveals the motor/basic themes residing at the heart of the field, such as insulin resistance, fatty liver, metabolic syndrome, steatosis, fetuin-A, and other related factors that drive innovation. Basic clusters include metabolic foundations (obesity, adipose tissue, FGF21) and adipokine-centered subjects (adiponectin, leptin, NASH). New themes focus on inflammation, oxidative stress, gut microbiota, lipid metabolism, and hepatic stellate cells. Niche areas show targeted fronts such as exercise therapies, pediatric/novel adipokines (chemerin, vaspin, omentin-1), and advanced molecular processes that focus on AMPK and endoplasmic-reticulum stress. Temporal analysis shows a shift from single liver studies to whole models that include the gut microbiota, mitochondrial dysfunction, and interactions between other metabolic systems. The network analysis identifies nine major clusters: cardiovascular–metabolic links, adipokine–inflammatory pathways, hepatokine control, and new therapeutic domains such as microbiome interventions and cellular stress responses. Conclusions: In summary, this study delineates current trends and emerging areas within the field and elucidates connections between mechanistic research and clinical translation to provide guidance for future research and development in this rapidly evolving area. Full article