Search Results (8,752)

During pregnancy, the regulation of autophagy and ferroptosis dynamically supports placental development and fetal health. Both processes—autophagy, clearing damaged organelles to maintain placental function, and ferroptosis, driven by iron-dependent lipid peroxidation—are involved in pathological conditions such as preeclampsia. Emerging evidence suggests that gut microbiota-derived metabolites act as key regulators of this balance, yet their specific roles across different trimesters remain unclear. This review compiles evidence on how gut microbiota metabolites, like short-chain fatty acids and trimethylamine N-oxide, serve as trimester-specific modulators of the autophagy–ferroptosis balance during pregnancy. We explain how these metabolites influence pregnancy outcomes by regulating placental autophagy and ferroptosis. Furthermore, we explore potential diagnostic and therapeutic approaches for pregnancy complications, focusing on metabolite-based biomarkers and interventions that target microbial–metabolic interactions. Full article

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research. Full article

The core perturbome is defined as a central response to multiple disturbances, functioning as a complex molecular network to overcome the disruption of homeostasis under stress conditions, thereby promoting tolerance and survival under stress conditions. Based on the biological and clinical relevance of Escherichia coli and Staphylococcus aureus, we characterized their molecular responses to multiple perturbations. Gene expression data from E. coli (8815 target genes—based on a pangenome—across 132 samples) and S. aureus (3312 target genes across 156 samples) were used. Accordingly, this study aimed to identify and describe the functionality of the core perturbome of these two prokaryotic models using a machine learning approach. For this purpose, feature selection and classification algorithms (KNN, RF and SVM) were implemented to identify a subset of genes as core molecular signatures, distinguishing control and perturbation conditions. After verifying effective dimensional reduction (with median accuracies of 82.6% and 85.1% for E. coli and S. aureus, respectively), a model of molecular interactions and functional enrichment analyses was performed to characterize the selected genes. The core perturbome was composed of 55 genes (including nine hubs) for E. coli and 46 (eight hubs) for S. aureus. Well-defined interactomes were predicted for each model, which are jointly associated with enriched pathways, including energy and macromolecule metabolism, DNA/RNA and protein synthesis and degradation, transcription regulation, virulence factors, and other signaling processes. Taken together, these results may support the identification of potential therapeutic targets and biomarkers of stress responses in future studies. Full article

The ongoing global climate change has led to an increase in the frequency and complexity of drought events. Pinus yunnanensis, a native tree species in southwest China that possesses significant ecological and economic value, exhibits a high sensitivity to drought stress, particularly in its seedlings. This study investigates the response mechanisms of non-structural carbohydrates (NSCs, defined as the sum of soluble sugars and starch) and the stoichiometric characteristics of carbon (C), nitrogen (N), and phosphorus (P) to repeated drought conditions in Pinus yunnanensis seedlings. We established three treatment groups in a potting water control experiment involving 2-year-old Pinus yunnanensis seedlings: normal water supply (CK), a single drought (D1), and three drought–rewatering cycles (D3). The findings indicated that the frequency of drought occurrences, organ responses, and their interactions significantly influenced the non-structural carbohydrate (NSC) content and its fractions, as well as the C/N/P content and its stoichiometric ratios. Under D3 treatment, stem NSC content increased by 24.97% and 29.08% compared to CK and D1 groups (p < 0.05), respectively, while root NSC content increased by 41.35% and 49.46% versus CK and D1 (p < 0.05). The pronounced accumulation of soluble sugars and starch in stems and roots under D3 suggests a potential stress memory effect. Additionally, NSC content in the stems increased significantly by 77.88%, while the roots enhanced their resource acquisition by dynamically regulating the C/P ratio, which increased by 23.26% (p < 0.05). Needle leaf C content decreased (18.77%) but P uptake increased (8%) to maintain basal metabolism (p < 0.05). Seedling growth was N-limited (needle N/P < 14) and the degree of N limitation was exacerbated by repeated droughts. Phenotypic plasticity indices and principal component analysis revealed that needle nitrogen and phosphorus, soluble sugars in needles, stem C/N ratio (0.61), root C/N ratio (0.53), and stem C/P ratio were crucial for drought adaptation. This study elucidates the physiological mechanisms underlying the resilience of Pinus yunnanensis seedlings to recurrent droughts, as evidenced by their organ-specific strategies for allocating carbon, nitrogen, and phosphorus, alongside the dynamic regulation of nitrogen storage compounds (NSCs). These findings provide a robust theoretical foundation for implementing drought-resistant afforestation and ecological restoration initiatives targeting Pinus yunnanensis in southwestern China. Full article

Schizophrenia (SCZ) is a complex psychiatric disorder with heterogeneous molecular underpinnings that remain poorly resolved by conventional single-omics approaches, limiting biomarker discovery and mechanistic insights. To address this gap, we applied an artificial intelligence (AI)-driven multi-omics framework to an open access dataset comprising plasma proteomics, post-translational modifications (PTMs), and metabolomics to systematically dissect SCZ pathophysiology. In a cohort of 104 individuals, comparative analysis of 17 machine learning models revealed that multi-omics integration significantly enhanced classification performance, reaching a maximum AUC of 0.9727 (95% CI: 0.8889–1.000) using LightGBMXT, compared to 0.9636 (95% CI: 0.8636–1.0000) with CNNBiLSTM for proteomics alone. Interpretable feature prioritization identified carbamylation at immunoglobulin-constant region sites IGKC_K20 and IGHG1_K8, alongside oxidation of coagulation factor F10 at residue M8, as key discriminative molecular events. Functional analyses identified significantly enriched pathways including complement activation, platelet signaling, and gut microbiota-associated metabolism. Protein interaction networks further implicated coagulation factors F2, F10, and PLG, as well as complement regulators CFI and C9, as central molecular hubs. The clustering of these molecules highlights a potential axis linking immune activation, blood coagulation, and tissue homeostasis, biological domains increasingly recognized in psychiatric disorders. These results implicate immune–thrombotic dysregulation as a critical component of SCZ pathology, with PTMs of immune proteins serving as quantifiable disease indicators. Our work delineates a robust computational strategy for multi-omics integration into psychiatric research, offering biomarker candidates that warrant further validation for diagnostic and therapeutic applications. Full article

One of the important traits of many plant growth-promoting rhizobacteria (PGPR) is the biocontrol of phytopathogens. Some PGPR metabolize phytohormone abscisic acid (ABA); however, the role of this trait in plant–microbe interactions is scarcely understood. Phytopathogenic fungi produce ABA and use this property as a negative regulator of plant resistance. Therefore, interactions between ABA-producing necrotrophic phytopathogen Botrytis sp. BA3 with ABA-metabolizing rhizobacterium Rhodococcus sp. P1Y were studied in a batch culture and in gnotobiotic hydroponics with sunflower seedlings. Rhizobacterium P1Y possessed no antifungal activity against BA3 and metabolized ABA, which was synthesized by BA3 in vitro and in associations with sunflower plants infected with this fungus. Inoculation with BA3 and the application of exogenous ABA increased the root ABA concentration and inhibited root and shoot growth, suggesting the involvement of this phytohormone in the pathogenesis process. Strain P1Y eliminated negative effects of BA3 and exogenous ABA on root ABA concentration and plant growth. Both microorganisms significantly modulated the hormonal status of plants, affecting indole-3-acetic, salicylic, jasmonic and gibberellic acids, as well as cytokinins concentrations in sunflower roots and/or shoots. The hormonal effects were complex and could be due to the production of phytohormones by microorganisms, changes in ABA concentrations and multiple levels of crosstalk in hormone networks regulating plant defense. The results suggest the counteraction of rhizobacteria to ABA-producing phytopathogenic fungi through the metabolism of fungal ABA. This expands our understanding of the mechanisms related to the biocontrol of phytopathogens by PGPR. Full article

Nitrogen (N) and potassium (K) are essential macronutrients for plants whose functions and interactions profoundly influence plant physiological metabolism, environmental adaptation, and agricultural production efficiency. This review summarizes research advances in plant N and K nutrition and their interaction mechanisms, elucidating the key physiological functions of N and K individually and their respective absorption and transport mechanisms involving transporters such as NRTs and HAKs/KUPs. The review discusses the types of nutrient interactions (synergism and antagonism), with a primary focus on the physiological basis of N–K interactions and their interplay in root absorption and transport (e.g., K⁺-NO₃⁻ co-transport; NH₄⁺ inhibition of K⁺ uptake), photosynthesis (jointly optimizing CO₂ conductance, mesophyll conductance, and N allocation within photosynthetic machinery to enhance photosynthetic N use efficiency, PNUE), as well as sensing, signaling, co-regulation, and metabolism. This review emphasizes that N–K balance is crucial for improving crop yield and quality, enhancing fertilizer use efficiency (NUE/KUE), and reducing environmental pollution. Consequently, developing effective N–K management strategies based on these interaction mechanisms and implementing Balanced Fertilization Techniques (BFT) to optimize N–K ratios and application strategies in agricultural production represent vital pathways for ensuring food security, addressing resource constraints, and advancing green, low-carbon agriculture, including through coordinated management of greenhouse gas emissions. Full article

Background/Objectives: The breast cancer susceptibility gene 1 (BRCA1) is a tumor suppressor gene whose mutations are associated with increased susceptibility to develop breast or ovarian cancer. BRCA1 mainly exerts its protective effects through DNA double-strand break repair. Although not itself a transcriptional factor, BRCA1, through its multiple protein interaction domains, exerts transcriptional coregulation. In addition, BRCA1 expression alters cellular metabolism including inhibition of de novo fatty acid synthesis, changes in cellular bioenergetics, and activation of antioxidant defenses. Some of these actions may contribute to its global oncosuppressive effects. However, the breadth of metabolic pathways reprogrammed by BRCA1 is not fully elucidated. Methods: Breast cancer cells expressing BRCA1 were investigated by multiplatform metabolomics, metabolism-related transcriptomics, and joint metabolomics/transcriptomics data processing techniques, namely two-way orthogonal partial least squares and pathway analysis. Results: Joint analyses revealed the most important metabolites, genes, and pathways of metabolic reprogramming in BRCA1-expressing breast cancer cells. The breadth of metabolic reprogramming included fatty acid synthesis, bioenergetics, HIF-1 signaling pathway, antioxidation, nucleic acid synthesis, and other pathways. Among them, rewiring of glycerophospholipid (including phosphatidylcholine, -serine and -inositol) metabolism and increased arginine metabolism have not been reported yet. Conclusions: Rewired glycerophospholipid and arginine metabolism were identified as components of BRCA1-induced metabolic reprogramming in breast cancer cells. The study helps to identify metabolites that are candidate biomarkers of the BRCA1 genotype and metabolic pathways that can be exploited in targeted therapies. Full article

With the rising concerns about climate change and continuous increase in the salinity of soil, it is essential to understand the C-cycling functioning of saline soil to better predict the ecological functions and health of soil. Microbes play critical roles in C-cycling. However, limited research has been conducted to understand the impact of soil salinity on the microbial functional genes involved in C-cycling. In this study, effects of varying soil salinity levels in wetlands on the C-cycling functions and diversity of soil microbes were investigated by metagenomic sequencing. The results showed a higher relative abundance of genes related to decomposition of easily degradable organic C at low salinity. On the other hand, higher abundance of genes participating in the decomposition of recalcitrant organic C were observed at high salinity. These findings indicate distinct metabolic bias of soil microbes based on the salinity levels. Proteobacteria and Actinobacteria were dominant in soils with low to medium salinity levels, while Bacteroidetes phyla was prominent in highly saline soils. Furthermore, partial least squares path modeling (PLS-PM) identified electrical conductivity, total nitrogen, and total phosphorus as key regulators of C-cycling gene expression. Overall, the present study highlights the intricate connections between salinity, microbial attributes, and carbon metabolism in soil, suggesting that the soil microbes adapt to saline stress through divergent eco-adaptations. The findings of this study highlight the significance of exploring these microbial interactions for effective management and conservation of saline wetlands. Full article

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy characterized by complex interactions within the tumor microenvironment (TME) that facilitate immune evasion and tumor progression. The TME consists of diverse cellular components, including cancer-associated fibroblasts, immune and endothelial cells, and extracellular matrix elements, that collectively modulate tumor growth, metastasis, and resistance to therapy. Immune evasion in HNSCC is orchestrated through multiple mechanisms, including the suppression of cytotoxic T lymphocytes, recruitment of immunosuppressive cells, such as regulatory T and myeloid-derived suppressor cells, and upregulation of immune checkpoint molecules (e.g., PD-1/PD-L1 and CTLA-4). Natural killer (NK) cells, which play a crucial role in anti-tumor immunity, are often dysfunctional within the HNSCC TME due to inhibitory signaling and metabolic constraints. Additionally, endothelial cells contribute to tumor angiogenesis and immune suppression, further exacerbating disease progression. Recent advancements in immunotherapy, particularly immune checkpoint inhibitors and NK cell-based strategies, have shown promise in restoring anti-tumor immunity. Moreover, TP53 mutations, frequently observed in HNSCC, influence tumor behavior and therapeutic responses, highlighting the need for personalized treatment approaches. This review provides a comprehensive analysis of the molecular and cellular mechanisms governing immune evasion in HNSCC with a focus on novel therapeutic strategies aimed at improving patient outcomes. Full article

Pregnane X receptor (PXR) is an important nuclear receptor that regulates diverse physiological functions, including drug metabolism. Although PXR activation is potentially involved in adverse events, the full scope of its impact has yet to be elucidated. In this study, we developed a machine learning model to predict the activity of PXR agonists and applied the model to drugs listed in the US Food and Drug Administration Adverse Event Reporting System database. Analysis of the predicted agonist–active drug interactions and adverse event reports revealed statistically significant risks (lnROR > 1 and −logp > 1.3) for multiple cardiac disorders. These findings suggest that PXR activity is involved in cardiovascular adverse effects and may contribute to drug safety through the early identification of risks. Full article

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

Objectives: Diabetes Mellitus involves demanding challenges that interfere with family functioning and routines. In turn, family and social context impacts individual glycemic control. This study aims to identify this recursive interplay, the mutual influences of family systems and diabetes management. Design: Data was collected through a cross-sectional design comparing patients, aged 22–55, with and without metabolic control. Methods: Participants filled out a set of self-report measures of sociodemographic, clinical and family systems assessment. Patients (91) were also invited to describe their perception about disease management interference regarding family functioning. We first examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Results: Cluster analysis results identify a two-cluster solution validating initial classification of two groups of patients: 49 with metabolic control (MC) and 42 without metabolic control (NoMC). Independent sample tests suggested statistically significant differences between groups in family subscales- family difficulties and family communication (p < 0.05). Binary logistic regression shed light on predictors of explained variance to no metabolic control, in four models: Sociodemographic, Clinical data, SCORE-15/Congruence Scale and Eating Behavior. Furthermore, groups differ on family support, level and sources of family conflict caused by diabetes management issues. Considering only patients who co-habit with a partner for more than one year (N = 44), NoMC patients score lower on marital functioning in all categories (p < 0.05). Discussion: Family-Chronic illness interaction plays a significant role in a patient’s adherence to treatment. This study highlights the Standards of Medical Care for Diabetes, considering caregivers and family members on diabetes care. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

The relationship between metabolic dysfunction and mental health disorders is complex and has received increasing attention. This review integrates current research to explore how stress-related growth differentiation factor 15 (GDF15) signaling, ceramides derived from gut microbiota, and mitochondrial dysfunction in the brain interact to influence both metabolic and psychiatric conditions. Evidence suggests that these pathways converge to regulate brain energy homeostasis through feedback mechanisms involving the autonomic nervous system and the hypothalamic–pituitary–adrenal axis. GDF15 emerges as a key stress-responsive biomarker that links peripheral metabolism with brainstem GDNF family receptor alpha-like (GFRAL)-mediated anxiety circuits. Meanwhile, ceramides impair hippocampal mitochondrial function via membrane incorporation and disruption of the respiratory chain. These disruptions may contribute to sustained pathological states such as depression, anxiety, and cognitive dysfunction. Although direct mechanistic data are limited, integrating these pathways provides a conceptual framework for understanding metabolic–psychiatric comorbidities. Furthermore, differences in age, sex, and genetics may influence these systems, highlighting the need for personalized interventions. Targeting mitochondrial function, GDF15-GFRAL signaling, and gut microbiota composition may offer new therapeutic strategies. This integrative perspective helps conceptualize how metabolic and psychiatric mechanisms interact for understanding the pathophysiology of metabolic and psychiatric comorbidities and highlights therapeutic targets for precision medicine. Full article