To systematically compare the differences in urinary organic acid metabolic profiles between patients with urinary calculi and non-calculi individuals, to screen disease-specific characteristic metabolic biomarkers and key signaling pathways, and to elucidate the potential mechanism underlying the occurrence and progression of urinary calculi at the metabolic level, so as to provide a theoretical basis for basic research and screening of intervention targets for urinary calculi. A total of 99 patients with urinary calculi and 57 non-calculi subjects were enrolled. Twenty-four-hour urine samples were collected and subjected to untargeted urinary organic acid metabolomics detection by gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the differences in metabolic profiles between the groups and the effects of clinical covariates. Volcano plot and variable importance in the projection (VIP) analysis were applied to screen differential organic acids. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used for metabolic pathway enrichment analysis. Machine learning algorithms were adopted to screen the most representative core metabolite combinations and verify the discriminatory efficacy of organic acids for disease status. The urinary organic acid metabolic profiles showed a significant separation trend between the calculi group and the non-calculi group (OPLS-DA: R2X =0.139, R2Y =0.48, Q2 =0.457). Gender was an important covariate affecting metabolic profiles, while diabetes, hypertension and stone recurrence status exerted no significant influence. A total of 44 differential organic acids were screened (P < 0.05), including 6 upregulated and 38 downregulated metabolites. Ten core differential metabolites were identified via VIP analysis (P < 0.001, all downregulated). Differential organic acids were mainly enriched in energy metabolism pathways such as glyoxylate and dicarboxylate metabolism, as well as amino acid metabolism pathways including tryptophan metabolism. Among machine learning models, Gradient Boosting Machine and Random Forest exhibited the optimal efficacy with an average area under the curve (AUC) of 0.996. Five core metabolites including propionylglycine, 5-hydroxyindole-3-acetic acid, kynurenic acid, orotic acid and fumaric acid were identified as the optimal combination. The nomogram model constructed based on this combination yielded an AUC of 0.997; after calibration bias correction, the calibration curve was highly consistent with the ideal curve, with a mean absolute error of 0.022, which could effectively distinguish calculi patients from non-calculi individuals. Patients with urinary calculi present obvious disorders in urinary organic acid metabolism, which are mainly involved in energy and amino acid metabolic pathways and closely associated with the pathogenesis of urinary calculi. The five core metabolites (propionylglycine, 5-hydroxyindole-3-acetic acid, kynurenic acid, orotic acid and fumaric acid) can effectively distinguish calculi from non-calculi populations, and can serve as representative metabolic biomarkers to provide novel targets for subsequent mechanism exploration and targeted intervention.
The microbiome of a chicken's reproductive tract is essential for egg production and safety. It helps regulate the immune system, preventing the transmission of pathogens like Salmonella and Staphylococcus that can contaminate eggs and pose health risks. Older hens may experience reduced immune function, which can disrupt their microbiome and increase the likelihood of harmful bacterial growth. We hypothesize that age-related shifts in the oviduct microbiome influence egg production and safety. This study aims to identify microbial communities and predicted pathways in the magnum of laying hens across egg production phases and to detect any changes that may affect reproductive health and egg quality. In this study, magnum mucosa samples were aseptically collected from the hens at the peak production phase (37 weeks of age), the mid-decline phase (67 weeks of age), and the declined production phase (87 weeks of age). After DNA extraction, 16S rRNA gene sequencing was performed on an Illumina platform, and microbial diversity was analyzed using CLC bioinformatics tools. The microbial metabolic pathways were compared between groups. Raw data were analyzed using QIIME2, PICRUSt2, and STAMP v2. The level of significance was considered at P < 0.05. The magnum samples showed significant differences in alpha and beta diversity across ages. While all age groups displayed the same core phyla, there were significant changes in relative abundance in Brevibacillus, Lactobacillus, and Bacteroides. The relative abundance of the species Phocaeicola barnesiae, associated with increased egg production, significantly decreased with age. In addition, metabolic microbiome profiling showed differences in microbial biosynthesis of essential amino acids, such as l-methionine and l-lysine, between age groups. Predicted enzyme profiles revealed a functional shift in the magnum microbiota from predominantly aerobic metabolism in younger hens to enhanced anaerobic and fermentative pathways in middle and older hens, suggesting age-associated microbial remodeling. This study revealed key differences in microbial community diversity and their predicted metabolic and enzymatic pathways in the magnum across varying age and egg production levels, providing insight into age-associated functional shifts that may inform strategies to optimize reproductive health and sustained productivity in laying hens.
The mevalonate (MVA) pathway is a central metabolic route responsible for the biosynthesis of isoprenoids with broad biological and biotechnological relevance. Due to its importance, the MVA pathway has attracted increasing interest in studies of enzymatic regulation, structural biology, metabolic engineering, and synthetic biology, particularly in fungi. This review provides a comprehensive overview of the MVA pathway, addressing its distribution across different domains of life, evolutionary aspects, and metabolic organization, with emphasis in fungi. Special attention is given to the biochemical and structural characterization of MVA-pathway enzymes, including catalytic mechanisms, structural features, and regulatory processes. The methylerythritol phosphate pathway is also presented as an alternative route for isoprenoid precursor biosynthesis and discussed in terms of its taxonomic distribution and metabolic significance. Recent advances in synthetic biology, enzyme regulation, and pathway engineering are highlighted, emphasizing their contributions to metabolic engineering and synthetic biology. Special emphasis is given to fungi, in which the MVA pathway plays a central role in ergosterol biosynthesis, protein prenylation, and secondary metabolite production. Advances in the engineering of fungal cells, including Saccharomyces cerevisiae and other emerging fungal species, are discussed in the context of sustainable isoprenoid production. Finally, strategies for optimizing microbial production are presented, highlighting the importance of fungal synthetic biology in advancing biotechnological applications.
Lignin, an abundant and renewable aromatic biopolymer, represents a largely underutilized resource for the sustainable production of high-value chemicals. Among lignin-derived intermediates, protocatechuic acid (PCA) and catechol have emerged as key platform molecules due to their versatile applications in pharmaceuticals, polymers, and fine chemicals. This review provides a critical overview of microbial lignin valorization focusing on the microbial conversion of lignin-derived aromatics into PCA and catechol. It highlights recent advances in lignin depolymerization techniques, including thermochemical and biological approaches, and examines their influence on the generation of bioavailable aromatic feedstocks. We systematically discuss microbial biofunneling pathways that converge diverse lignin-derived compounds into PCA and catechol, emphasizing the role of central metabolic nodes and enzymatic transformations such as O-demethylation, hydroxylation, and decarboxylation. We treat protocatechuate decarboxylase (PCADC) as the central enzymatic bridge linking PCA and catechol. However, it should be noted that many reported microbial production strategies have been demonstrated using purified lignin-derived aromatic model compounds (e.g., ferulate, vanillate, p-coumarate, and PCA) rather than authentic lignin streams, highlighting the need for improved integration of lignin depolymerization and downstream bioconversion processes. Furthermore, the review explores state-of-the-art metabolic engineering strategies, including gene deletions, pathway rewiring, transporter engineering, and CRISPR-based regulation, to enhance product yields and selectivity. Despite significant progress, several challenges persist, including lignin recalcitrance, heterogeneity of depolymerization products, toxicity of intermediates, and limited enzyme efficiency. This review identifies key knowledge gaps and proposes future directions for integrating synthetic biology, adaptive evolution, and systems-level optimization to develop robust microbial cell factories. Overall, this work provides a strategic framework for advancing lignin bioconversion into PCA and catechol, contributing to the development of sustainable biorefineries and a circular bioeconomy.
Combinatorial pathway optimization is a powerful approach in metabolic engineering to improve strain performance. While machine learning (ML) has shown promise in guiding the Design-Build-Test-Learn (DBTL) cycle, most applications have been limited to small design spaces, thereby restricting the potential of predictive and exploration-exploitation strategies. In this work, we applied two DBTL cycles to optimize p-coumaric acid production in Saccharomyces cerevisiae. The first cycle involved constructing a large combinatorial library of 18 genes and 20 promoters (170 million possible designs). In the second cycle, we employed a gradient bandit-based machine learning recommendation strategy, tuned to balance exploration and exploitation. Our results show that this balanced strategy outperforms greedy, feature importance-based approaches, leading to greater diversity in strain performance and improved top-producer identification. Notably, applying the same strategy to an alternative parent strain yielded the highest p-coumaric acid titer (1.23 g/L), a 2.37-fold improvement over the original. These findings highlight the value of ML-guided exploration in large design spaces and demonstrate that balancing exploration and exploitation is critical for successful strain optimization.
Biochar enhances dark fermentative biohydrogen production (BHP), yet conventional biochar is limited by low porosity and few active sites. While nitrogen doping and chemical activation can individually upgrade biochar, the synergistic effect of urea doping combined with sodium bicarbonate (NaHCO3) activation, and its consequence for intracellular metabolic networks, remains unclear. Herein, material characterization, 16S rRNA sequencing, and non-targeted metabolomics were integrated to elucidate how urea-doped NaHCO3-activated rice-straw biochar (UBC-A) enhances cellulolytic BHP. UBC-A achieved the highest hydrogen production of 192.52 mL·g-1, representing a 6.6-fold (561.35% relative improvement) of the control; the hydrogen production lag period was shortened to 13.93 h, and the energy conversion efficiency was 14.19%. UBC-A exhibited enhanced graphitization and hierarchical porous structure. Microbiome analysis revealed selective enrichment of hydrogen-producing taxa (Clostridia, Thermoanaerobacterium) and cellulolytic microbes, alongside suppression of competitors. Metabolomics identified 113 significantly differential metabolites (P < 0.05), revealing system-wide metabolic rewiring centered on three interconnected hubs: (i) L-glutamate-driven TCA cycle activation and GABA-mediated acid stress alleviation; (ii) 2-hydroxyglutarate as a novel indicator of enhanced NADH regeneration capacity; and (iii) glycerophospholipid-mediated membrane restructuring facilitating extracellular electron transfer. Correlation analysis established significant associations between these hydrogen producers and key upregulated metabolites, indicating that UBC-A optimizes BHP by synchronizing community assembly with metabolic pathway redirection. These findings advance a structure-microbiome-metabolism framework for agricultural-waste valorization and biohydrogen industrialization.
Chilli peppers (Capsicum species) have been widely used around the world because of their economic value and distinctive sensory characteristics. They contain abundant functional metabolites, especially a group of vanillylamide compounds belonging to the family of capsaicinoids, which have been exploited for medicinal, nutritional, agricultural, and cosmetic uses. The demand for capsaicinoid molecules is increasing day by day due to their high economic value and wide range of applications. Therefore, increasing bioactive metabolites, especially capsaicinoids in chilli peppers, is a major priority in the current scenario. Multi-omics approaches such as genomics, transcriptomics, proteomics, and metabolomics have substantially contributed to understanding the complex regulatory networks governing capsaicinoid biosynthesis. Key structural genes, transcription factors, and signaling pathways involved in the phenylpropanoid and branched-chain fatty acid pathways have been identified, providing valuable targets for metabolic engineering in chilli pepper. Despite these advances, the integration of genetic modification approaches for the targeted enhancement of capsaicinoid production remains limited in chilli pepper. Recent developments in biotechnology, particularly CRISPR/Cas-mediated genome-editing, enable the precise genetic modification of metabolic pathways and regulatory networks in plants. Therefore, it can contribute to the precise modification of key genes involved in the capsaicinoid biosynthesis pathway, offering potential strategies to enhance the capsaicinoid content in chilli pepper. However, CRISPR/Cas-mediated genome editing in chilli pepper is still in its early stages. There are currently no reports available on the successful enhancement of capsaicinoid content in chilli peppers through CRISPR/Cas-mediated genome editing. To date, no comprehensive review has evaluated the CRISPR-Cas-mediated genome-editing approaches for capsaicinoid metabolic engineering in chilli pepper. This review critically evaluates the recent advances in CRISPR/Cas-mediated metabolic engineering in chilli peppers, with particular emphasis on regulatory genes involved in capsaicinoid biosynthesis. Furthermore, multi-omics approaches are expected to complement these strategies by enabling the identification of key regulatory genes, the optimization of genome-editing targets, and the prediction of metabolic outcomes for enhanced capsaicinoid production. Overall, this review provides insights into improving capsaicinoid accumulation in chilli peppers through advanced genome-editing technologies.
Background/Objectives: Metabolic syndrome (MetS), defined by abdominal obesity, dysglycemia, dyslipidemia, hypertension, and insulin resistance, markedly increases the risk of type 2 diabetes mellitus and cardiovascular disease. Affecting an estimated 25-30% of the global adult population, MetS represents a major and growing public health challenge. A growing body of evidence supports a significant bidirectional relationship between MetS and oral health, particularly periodontitis. The present study aimed to synthesize current evidence on the pathophysiological mechanisms, epidemiological associations, interventional outcomes, and clinical implications of the bidirectional relationship between metabolic syndrome (MetS) and periodontitis. Methods: A narrative review following the SANRA framework was performed. PubMed, Scopus, and Web of Science were searched for articles published in January 2021-March 2026 using MeSH and free-text terms including "metabolic syndrome", "periodontal disease", "insulin resistance", and "oral microbiota". Eligible studies included original research and systematic reviews in English with full-text availability; animal and in vitro studies were included if directly informative of mechanistic pathways. Results: A total of 64 references were selected for inclusion. Shared mechanisms include chronic systemic inflammation, insulin resistance, oxidative stress, adipokine imbalance, endothelial dysfunction, and oral-gut microbiome dysbiosis. Cross-sectional and longitudinal studies show that MetS components are independently associated with higher prevalence and severity of periodontitis; meta-analyses report pooled odds ratios of 1.7-1.9 compared with metabolically healthy controls. Non-surgical periodontal therapy produces modest but significant reductions in glycated hemoglobin (HbA1c) and systemic inflammatory markers. Sodium-glucose cotransporter-2 (SGLT2) inhibitors may alter oral microbiota composition and cause mucosal changes, while glucagon-like peptide-1 (GLP-1) receptor agonists may increase caries risk through gastrointestinal side effects and xerostomia; both drug classes warrant proactive dental monitoring. Conclusions: The bidirectional relationship between MetS and oral health supports integrated screening and interdisciplinary management. Routine periodontal assessment should be integrated into the metabolic risk management pathway, and dental professionals should screen patients with severe periodontitis for metabolic risk factors. The oral microbiome emerges as a promising target for future mechanistic research and therapeutic intervention. Recognition of oral health as an integral component of metabolic health may improve risk stratification, prevention, and long-term patient outcomes. Large-scale randomized controlled trials with standardized endpoints are needed to establish causal directionality and optimize combined therapeutic strategies.
Terpenoids represent the largest and most structurally diverse class of secondary metabolites, with extensive applications in the pharmaceutical, nutraceutical, cosmetic, agricultural, fragrance, and biofuel industries. The growing demand for these compounds has resulted in extensive exploitation of plant-derived terpenoids, raising concerns regarding resource availability and sustainability. Consequently, microbial production has emerged as a promising alternative because of the high genetic tractability, rapid growth, and ease of metabolic engineering offered by microbial hosts. Various metabolic engineering strategies, including heterologous gene insertion, targeted gene deletion, and redirection of carbon flux from primary metabolism toward terpenoid biosynthesis, have been employed to enhance terpenoid production. The selection of an appropriate microbial host is a critical determinant of production efficiency, as it influences metabolite yield, cultivation feasibility, genetic manipulability, scalability, environmental sustainability, and economic viability. Genetically engineered microorganisms have therefore become well-established platforms for the production of diverse classes of terpenoids. Although substantial progress has been made in reconstructing and expressing terpenoid biosynthetic pathways in microbial hosts, further strain optimization requires systematic integration of computational approaches. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for metabolic engineering by enabling pathway prediction, metabolic flux optimization, enzyme engineering, and identification of bottlenecks throughout terpenoid biosynthesis. Coupled with advances in genomics, systems biology, and synthetic biology, these technologies are accelerating the development of robust microbial cell factories for the sustainable, large-scale production of terpenoids through industrial bioprocesses.
A Polyvinyl Alcohol (PVA) degrading bacterial strain designated as DW01, was isolated from textile wastewater. Based on 16S rRNA gene sequence analysis (carried out at the EzTaxon server and Ribosomal Database project site), DW01 was identified as Burkholderia contaminans, and its biological characteristics, biodegradation performance, metabolic strategy, and molecular mechanisms were investigated. Haldane kinetic analysis revealed a substrate inhibition pattern, with optimal PVA concentrations of 2.31 g·L-1 for growth and 2.83 g·L-1 for biodegradation. Under sole PVA conditions, DW01 adopts a "growth-first, biodegradation-secondary" strategy. Multi-spectroscopic analyses elucidated the PVA biodegradation pathway and its molecular constraints. DW01 acts as a pioneer by cleaving the PVA main chain and generating carbonyl and carboxylic acid intermediates. XRD and 1H NMR revealed the constraints on efficiency: DW01 exhibits stereochemical preference for syndiotactic PVA segments, while isotactic and heterotactic segments are recalcitrant, constituting the molecular bottleneck limiting efficiency; thus, a new mechanism was proposed. The study first reports PVA-degrading capability within Burkholderia, elucidates its "growth-first" metabolic strategy, biodegradation mechanism, and tacticity-dependent bottleneck, providing microbial resources and theoretical guidance for PVA control. Construction of "pioneer-terminator" consortia via metabolic complementarity and stereochemistry-targeted enzyme engineering are two priority directions to achieve complete PVA mineralization in future research. First report of Burkholderia contaminans DW01 degrading polyvinyl alcohol (PVA), defining optimal metabolic window (2.31–2.83 g·L−1). Under PVA-only conditions, strain DW01 prioritizes growth first, degrading PVA second.DW01 is a pioneer bacteria: cleaves PVA main chain into intermediates but requires microbial synergy for full mineralization.Multi-spectroscopy elucidates the PVA degradation pathway, identifying isotactic segments as the molecular bottleneck.
Metabolically deranged tumor microenvironment (TME) with compromised innate immune signaling induces severe immunosuppression and markedly blunts the efficacy of cancer immunotherapy. Activation of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway can boost antitumor immunotherapy, and a manganese complex, called TPE-Mn, was developed in this work. Nevertheless, the excess lactate accumulation and poor tumor-targeted delivery jointly restrict the clinical translation of Mn2+-based immune stimulation. Herein, we designed glutathione (GSH)-sensitive nanoparticles (NPMn/Syro) co-loaded with TPE-Mn and the monocarboxylate transporter 1/4 (MCT1/4) inhibitor syrosingopine (Syro) at an optimized ratio to simultaneously remodel tumor metabolism and activate innate immunity. Upon effective intratumoral accumulation, NPMn/Syro concurrently release two payloads: Syro inhibit lactate efflux and elevates intracellular lactate, while TPE-Mn disrupts mitochondrial dynamics. Collectively, these synergistic effects shift the mitochondrial fusion-fission balance toward excessive fission, leading to mitochondrial fragmentation and cytosolic mitochondrial DNA (mtDNA) leakage. The leaked mtDNA activates cGAS, while Mn2+ further amplifies the activation of the STING pathway to boost innate immune responses. Moreover, metabolic disruption and mitochondrial injury cooperatively trigger immunogenic cell death (ICD) and potentiate systemic antitumor immunity. Furthermore, combined with anti-PD-1 antibody (α-PD-1), NPMn/Syro exerts synergistic antitumor efficacy, providing a promising therapeutic strategy for clinical management of tumor. This work presents a metabolic-metal synergistic strategy to augment the cGAS-STING pathway activation and significantly reverse metabolism-mediated immunosuppressive TME.
The ever-increasing disparity between lifespan and healthspan represents a challenging global issue, with metabolic dysregulation playing a central role in the initiation and progression of chronic non-communicable diseases (NCDs). This review highlights the importance of maintaining optimal redox homeostasis, with particular emphasis on reduced glutathione (GSH), for preserving metabolic health during aging. GSH participates in several physiological processes, including antioxidant defense, xenobiotic detoxification, redox signaling, and metabolic regulation. Diminished GSH levels are consistently reported in obesity, insulin resistance, type 2 diabetes mellitus, non-alcoholic fatty liver, and cardiovascular diseases. Current evidence from human clinical studies indicates that foods rich in bioactive constituents can enhance GSH levels and stimulate GSH-dependent enzyme activity, with the Nrf2/Are signaling pathway being a central mechanistic link. Fasting may promote adaptive redox responses by inducing mild oxidative stress and activating the same molecular mechanism, although the effects on GSH-related antioxidant mechanisms remain heterogeneous across fasting protocols and study populations. Altogether, the available clinical evidence suggests that these nutritional and lifestyle interventions exhibit more consistent beneficial effects in individuals characterized by increased oxidative burden and underlying metabolic dysfunction. Interindividual differences in GSH responses further underscore the need for targeted, tailor-made approaches that account for genetic, epigenetic, and lifestyle factors. Collectively, targeting GSH homeostasis through nutritional and lifestyle interventions represents a promising strategy for improving metabolic health and may further contribute to healthy aging, positioning redox biology at the forefront of aging research and NCD prevention.
Background and Objectives: Pediatric-onset metabolic bone diseases, including osteogenesis imperfecta (OI), hypophosphatemic rickets (XLH), hypoparathyroidism, and McCune-Albright syndrome (MAS), require lifelong follow-up because of persistent skeletal fragility, biochemical abnormalities, and functional morbidity extending into adulthood. However, evidence regarding structured transition from pediatric to adult care in these rare disorders remains limited. This study evaluated one-year outcomes of a multidisciplinary transition program for adolescents and young adults with rare metabolic bone diseases. Materials and Methods: This retrospective cohort study included 20 patients aged ≥17 years who underwent evaluation through a structured transition pathway consisting of multidisciplinary team meetings, a joint pediatric-adult transition clinic, and subsequent follow-up in adult endocrinology. Demographic, clinical, treatment, and transition-related data were extracted from medical records. The primary outcome was successful transition, defined as at least one adult endocrinology visit within 12 months. Secondary outcomes included attendance at the transition clinic, follow-up continuity, and treatment modifications. Results: All patients underwent multidisciplinary evaluation, and 85% attended the joint transition clinic. Successful transfer to adult endocrinology was achieved in 90% (18/20), while regular follow-up during the first year was maintained in 75%. Retention was highest in patients with OI, MAS, XLH, vitamin D-dependent rickets, and DiGeorge syndrome (100%). Greater variability was observed in postoperative and primary hypoparathyroidism. Treatment adjustments were required in 40% of patients, including optimization of phosphate/calcitriol replacement and reassessment of bisphosphonate or burosumab therapy. Three patients were lost to follow-up. No acute transition-related complications were observed. Conclusions: In this small exploratory cohort, implementation of a structured multidisciplinary transition pathway was feasible and was accompanied by high transfer and one-year retention rates. Observed differences across diagnostic subgroups should be interpreted cautiously, and larger multicenter comparative studies are needed to evaluate the effectiveness of structured transition frameworks.
Polycystic ovary syndrome (PCOS) is the most common heterogeneous disorder among women of reproductive age, characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphological changes as its core clinical features. This disease severely affects women's reproductive health and significantly increases the risk of metabolic complications. The pathogenesis of PCOS is complex, involving various pathophysiological mechanisms such as oxidative stress, chronic low-grade inflammation, and insulin resistance. Currently, lifestyle interventions and first-line pharmacological treatments are the primary clinical strategies for managing PCOS. However, existing approaches face numerous limitations in terms of efficacy, safety, and patient compliance, including incomplete therapeutic effects, drug-related side effects, poor adherence, and a lack of long-term safety data, which necessitate further optimization and breakthroughs. Natural compounds have been widely utilized as therapeutic agents worldwide, with some showing potential advantages in preclinical and pharmacological studies, positioning them as potential alternatives to modern drugs. In recent years, astaxanthin, a natural compound, has garnered attention for its auxiliary effects in the treatment of PCOS due to its potent antioxidant and anti-inflammatory properties. Astaxanthin, a strong natural antioxidant derived from Haematococcus pluvialis, exhibits significant antioxidant, anti-inflammatory, anti-proliferative, and anti-apoptotic activities. To systematically evaluate the therapeutic potential of astaxanthin for PCOS, this study conducted a systematic review in strict accordance with the PICOS principles. A comprehensive literature search was performed across PubMed, Web of Science, and Scopus for relevant studies published between January 2020 and March 2026. The retrieved records were screened based on predefined inclusion and exclusion criteria, and the finally included studies were subjected to mechanistic analysis. This review provides a comprehensive analysis of the potential mechanisms by which astaxanthin, as a dietary supplement, improves PCOS through various pathways, including enhancing insulin sensitivity, activating the Nrf2 antioxidant pathway, inhibiting the NF-κB inflammatory signaling pathway, and modulating cellular apoptosis. Furthermore, it delineates the limitations and therapeutic prospects of astaxanthin supplementation, clarifying its significant value in the adjuvant treatment of PCOS and highlighting the key issues that warrant further investigation.
Efficient microbial conversion of heterogeneous carbohydrate mixtures remains a central bottleneck in industrial biotechnology, yet scalable chassis architectures capable of coordinated multi-sugar utilization are limited. Recently, substrate-informed metabolic alignment enabled balanced glucose-xylose co-utilization in Corynebacterium glutamicum. Here, we examine whether this alignment principle is scalable and transferable to complex industrial feedstocks. We progressively expanded the substrate spectrum of C. glutamicum through genome-encoded integration of mannose, xylose, arabinose, galactose, and rhamnose utilization modules, establishing a stable seven-sugar chassis. Rather than relying on adaptive evolution or high-copy expression, pathway capacities were quantitatively aligned with native uptake and central metabolism. Progressive expansion increased total volumetric carbon uptake and attenuated substrate hierarchy without compromising transcriptional stability. Complementary 13C tracer experiments showed that carbon from all seven sugars was incorporated into biomass in proportion to substrate utilization while revealing characteristic precursor-level incorporation patterns consistent with their distinct metabolic entry routes. Using authentic spent sulfite liquor (SSL) as a chemically heterogeneous validation substrate, the engineered strain achieved near-complete depletion of all fermentable sugars and substantially accelerated conversion kinetics compared to the wild type. Transfer of the aligned substrate module into a glucose-optimized glutarate production strain enabled sustained fed-batch conversion of SSL to 30 g L-1 glutarate without pathway erosion or performance decline. Together, these results demonstrate that substrate-informed metabolic engineering can provide a scalable and industrially transferable strategy for chassis development and position C. glutamicum as a robust multi-substrate platform for heterogeneous renewable feedstocks.
l-arginine is widely used in food, feed, pharmaceutical, and cosmetic industries. However, its industrial-scale biosynthesis is limited by insufficient coordination between metabolic regulation, pathway engineering, and fermentation optimization. In this study, an enzyme-constrained model (ec_iML1515) was used to identify 11 gene targets affecting l-arginine production. Based on these targets, metabolic reprogramming was performed in strain Arg4 to rebalance precursor pools (oxaloacetate, aspartate, and citrulline), generating strain Arg10 with an l-arginine titer of 87.24 g/L. Subsequently, the rate-limiting enzyme argininosuccinate synthetase (ArgG) was engineered to the optimal mutant ArgGY131F/K132R and genomically integrated to construct the strain Arg11, increasing the l-arginine titer to 94.80 g/L while reducing aspartate accumulation 7.6-fold to 1.1 g/L. Finally, after the optimization of fermentation temperature and pH, the l-arginine titer, yield, and productivity of strain Arg11 were 114.18 g/L, 0.57 g/g, and 2.27 g/L/h, respectively, in a 3-m3 fermenter, achieving the best performance reported to date.
α-Bisabolol, a natural sesquiterpene alcohol with notable physiological activities, exhibits broad application prospects in the pharmaceutical, cosmetic, and flavor industries. Traditional plant extraction suffers from low efficiency and resource scarcity, while chemical synthesis faces stereoisomerism and environmental issues. In contrast, microbial synthesis overcomes these limitations by utilizing renewable agricultural feedstocks to address plant resource scarcity and enabling highly stereoselective biosynthesis to produce the more bioactive (-)-α-bisabolol. This review summarizes the physiological activities of α-bisabolol, describes its microbial biosynthetic pathway, and focuses on the key enzyme (-)-α-bisabolol synthase. Unlike previous reviews focused on pharmacological effects or chemical synthesis, this review uniquely integrates recent metabolic engineering strategies for enhancing (-)-α-bisabolol production, including chassis cell development, metabolic flux regulation, competitive pathway knockout, cofactors and global regulatory factors optimization, and fermentation scale-up. Finally, current challenges and future directions are discussed, with an emphasis on green biomanufacturing through transporter engineering, enzyme evolution, and subcellular compartmentalization.
The diagnosis of Wilson disease (WD) is complicated by heterogeneous clinical phenotypes and inadequate performance of routine biomarkers. This study sought to screen age-specific urinary metabolic biomarkers to assist WD diagnosis via ensemble tree-based machine learning algorithms. Sixty participants were enrolled, comprising 30 WD patients (10 pediatric, 20 adult) and 30 healthy controls. Morning urine samples were analyzed using UPLC-Q-TOF-MS in both ionization modes. Orthogonal partial least squares discriminant analysis identified differential metabolites. Four ensemble algorithms (Random Forest, GBDT, XGBoost, LightGBM) were compared using nested cross-validation to minimize overfitting risk. LASSO regression selected optimal metabolite panels. Model performance was evaluated using 5-fold cross-validation with AUC as the primary metric. XGBoost achieved AUC values of 0.87±0.03 (pediatric) and 0.96±0.02 (adult) in nested validation, with permutation testing confirming performance above chance levels (p>0.001). Sixty-eight differential metabolites were identified in pediatric patients versus 109 in adults, with 15 metabolites consistently altered across both age groups. Pathway analysis revealed age-specific disruptions: nucleotide metabolism in pediatric patients and oxidative stress pathways in adults. LASSO feature selection identified 7 metabolites for pediatric and 3 for adult classification while maintaining high diagnostic accuracy. This study successfully developed the first age-specific metabolomics-based diagnostic approach for WD using ensemble machine learning. The distinct metabolic signatures between age groups provide mechanistic insights into WD pathophysiology and support personalized diagnostic strategies for improved patient outcomes. These exploratory findings require independent validation before clinical implementation.
Leptobotia elongata, an endangered freshwater fish endemic to the upper Yangtze River, increasingly depends on artificial reproduction for conservation. However, a captive juvenile population fed a conventional diet commonly exhibits ovarian development arrested at stages I-II, representing a critical bottleneck for captive reproduction. To address this, juvenile L. elongata were reared for 6 months on either a basal diet (control group, CG) or a lipid-enriched diet in which soybean oil was replaced by fish oil and soybean lecithin, with vitamin E added (treatment group, TG). Histological examination revealed that TG ovaries advanced from stage I-II to stage III, with markedly larger oocytes and centralized nucleoli. LC-MS/MS-based untargeted metabolomics profiling identified 1773 metabolites, of which 566 differed significantly between groups (374 up- and 192 down-regulated in TG). KEGG enrichment revealed 15 significantly perturbed pathways, with nucleotide metabolism showing the highest enrichment (Rich factor = 0.22), accompanied by changes in fatty acid biosynthesis, biosynthesis of unsaturated fatty acids, glycerophospholipid metabolism, aminoacyl-tRNA biosynthesis, pantothenate and CoA biosynthesis, and TCA-related modules. Coordinated up-regulation of long-chain polyunsaturated fatty acids (DHA, EPA, ARA, α-linolenic acid), DHA-phosphatidylethanolamine, phosphatidylcholine and CDP-choline, together with depletion of their precursors (sn-glycerol-3-phosphate, LPI(20:4), pyrophosphate), was consistent with enhanced phospholipid remodeling and Lands-cycle activity. A multi-tier regulatory network was constructed, anchored on three pathway hubs (nucleotide metabolism, pyrimidine metabolism and fatty acid elongation) and five functional modules. This network indicated that dietary lipid supplementation was associated with coordinated changes across four interconnected metabolic axes: lipid supply and membrane remodeling, nucleotide synthesis and turnover, amino acid-protein translation, and energy-coenzyme metabolism. Collectively, these findings propose a metabolic framework and identify candidate biomarkers (e.g., DHA-PE, PC) for optimizing feed formulations of L. elongata.
Betalains are water-soluble pigments containing nitrogen, and they exist naturally in the plants of the order Caryophyllales. They have gained increasing attention in recent years because of their intense colours, antioxidant activity, and safety, thus making them suitable replacements for artificial dyes. The increasing interest in natural pigments has led to intensified research on betalain biosynthesis and optimization of pigment production. Nonetheless, their application in industry faces limitations, such as their low natural occurrence, sensitivity to environmental conditions, and instability during manufacturing and storage. Unlike previous reviews that primarily focused on betalain chemistry, biosynthesis pathways, or biological activity, the present review highlights recent developments in the engineering of the biosynthesis pathways, synthetic biology, elicitation approaches, omics-based pathway identification, and nanobiotechnology for betalain pigments. Special attention is paid to the comparison of plant, plant cell, yeast, and bacterial production systems, as well as recent advancements towards industrial production of betalain pigments and bottlenecks in the commercialization of sustainable betalain bio-factories.