Search Results (119)

Paclitaxel (Taxol), a taxane diterpenoid from Taxus species, is a clinically important microtubule-stabilizing anticancer agent widely used in chemotherapy. However, its supply remains limited by precursor scarcity and the molecule’s structural complexity. The biosynthetic pathway from geranylgeranyl diphosphate (GGPP) to paclitaxel is estimated to involve 19 to 23 enzymatic steps. Recent multi-omics approaches have substantially elucidated this pathway, yet key mechanistic questions persist, notably the formation of the oxetane ring. Complete heterologous biosynthesis is further hampered by poor cytochrome P450 (CYP) expression in non-native hosts and insufficient metabolic flux. This review synthesizes advances across four themes: (1) progressive elucidation of the biosynthetic pathway, with emphasis on the CYP-mediated oxygenation cascade and oxetane ring formation; (2) genomic and regulatory insights from Taxus genome assemblies, transcription factor networks, and spatial multi-omics; (3) metabolic engineering in microbial hosts, including Escherichia coli, Saccharomyces cerevisiae, and non-conventional chassis; and (4) plant-based heterologous production platforms. Critical bottlenecks are identified, including unresolved enzymatic steps, CYP functional expression, flux partitioning, and bioprocess scale-up. Strategies to overcome these challenges are discussed. Full article

Drought stress is the most pervasive abiotic constraint on global crop productivity, with projected intensification under climate change threatening the yields of staple crops including wheat, rice, maize, and legumes. Conventional breeding approaches have delivered limited gains against drought tolerance, constrained by the polygenic and multifactorial nature of stress adaptation, the complexity of genotype-by-environment interactions, and the inadequacy of field-based phenotyping under variable stress conditions. Omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, have substantially advanced the molecular dissection of drought tolerance by enabling high-resolution characterization of stress-responsive genes, regulatory networks, adaptive proteins, and metabolic reprogramming pathways. Specific traits targeted include root system architecture and depth, osmotic adjustment capacity through proline and glycine betaine accumulation, antioxidant defense mechanisms, ABA-mediated stomatal regulation, LEA protein accumulation, epigenetic stress memory, and yield stability under water deficit. This review systematically examines omics-based strategies for drought stress mitigation across major crops, highlighting individual omics contributions, multi-omics integration frameworks, computational tools including machine learning and AI-driven predictive modelling, and translational breeding applications. Case studies in wheat, rice, maize, and legumes illustrate how omics-driven approaches accelerate precision breeding for drought resilience through marker-assisted selection, genomic selection, and CRISPR-based gene editing. Challenges including data integration complexity, high implementation costs, limited cross-species transferability, and the need for field-scale validation of microbiome-based strategies are critically addressed. Future perspectives encompassing single-cell and spatial omics, AI-driven predictive breeding, digital agriculture integration, and international data governance frameworks are discussed. By aligning with climate-smart agriculture principles, multi-omics approaches provide a robust and transformative foundation for developing drought-resilient crop cultivars suitable for water-limited production systems worldwide. Full article

Algae represent one of the most metabolically diverse and ecologically significant groups of photosynthetic organisms, contributing fundamentally to global biogeochemical cycles while offering major potential for biotechnology applications such as biofuels, nutraceuticals, wastewater remediation, and carbon capture. However, the complexity of algal metabolism, driven by evolutionary diversity, compartmentalized cellular organization, and strong environmental coupling, makes predictive understanding of their physiology challenging. In recent years, systems biology approaches combining omics technologies, genome-scale metabolic models, and data-driven methods have begun to transform algal research from descriptive studies toward predictive frameworks. This review summarizes the current state of algal systems biology, highlighting advances in genomics, transcriptomics, proteomics, and metabolomics that enable mechanistic insights into metabolic regulation and environmental adaptation. We discuss the development, curation, and application of algal GEMs across diverse lineages, emphasizing their role in predicting metabolic flux distributions, nutrient utilization, and lipid biosynthesis. In parallel, machine learning and artificial intelligence approaches have emerged to model algal growth and cultivation performance from large physiological datasets. Finally, we discuss emerging hybrid modeling strategies that integrate mechanistic metabolic networks with data-driven predictions, outlining how these frameworks can enable next-generation predictive algal biotechnology and guide rational design of cultivation and metabolic engineering strategies. Full article

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and a leading cause of cancer-related mortality worldwide. Despite advances in targeted therapies and immunotherapies, clinical outcomes remain poor owing to profound molecular heterogeneity, intrinsic therapeutic resistance, and complex immune evasion mechanisms. Although genomic profiling has identified recurrent alterations in HCC, large-scale functional validation of candidate drivers and vulnerabilities remains challenging. CRISPR (clustered regularly interspaced short palindromic repeats)-based screening technologies have transformed this landscape by enabling systematic interrogation of gene function in physiologically relevant contexts. In this review, we summarize recent studies that have applied CRISPR screening approaches in HCC research. These efforts have uncovered multilayered dependency programs that govern ferroptosis resistance, metabolic reprogramming, epigenetic regulation, tumor suppressor networks, immune evasion, and resistance to targeted therapies. We also discuss the major limitations of current studies, including model bias, incomplete representation of HCC heterogeneity, and technical constraints intrinsic to pooled screening. Overall, integration of CRISPR screening with patient-derived models, single-cell readouts, and precision editing technologies is expected to accelerate mechanistic discovery and biomarker-guided therapeutic prioritization for HCC. Full article

This study conducted a genome-wide identification and functional analysis of the glutathione S-transferase (GST) gene family in the xerophytic desert shrub Reaumuria soongorica. A total of 67 GST genes were identified, classified into seven subfamilies, including Phi and Tau, with family expansion primarily attributed to small-scale duplication events. The findings revealed that ResoGST52, a member of the Tau subfamily, serves as a core gene in drought response, exhibiting significant upregulation of 2.40-fold in leaves and 9.01-fold in roots under drought stress. Mechanistic investigations indicated that the expression of ResoGST52 is likely directly regulated by the transcription factor ResoDof17, with specific hydrogen bonding interactions identified between the two. Co-expression network analysis further demonstrated that ResoGST52 cooperates with key pathways such as plant hormone signaling, MAPK cascades, and glutathione metabolism to collectively respond to drought stress. Notably, evolutionary analysis revealed that ResoGST52 has undergone positive selection, with three positively selected sites identified. Among these, the p.Ala115Ser mutation increases the volume of the protein’s active site pocket, while the remaining mutations enhance surface hydrophobicity, thereby improving protein stability and catalytic efficiency under extreme drought conditions. In summary, this study not only systematically identifies the GST gene family in R. soongorica but also elucidates the central role of ResoGST52 in drought adaptation through multiple layers—from transcriptional regulation and co-expression networks to protein structural adaptive evolution—providing valuable candidate genes and theoretical insights for genetic improvement of drought tolerance in crops. Full article

Background: It is significant to predict and fill metabolic reaction gaps (gap-fillings) for reconstructions of high-quality genome-scale metabolic models (GEMs). Currently, many existing optimization-based gap-filling methods have to rely on phenotypic data, while performances of topology-based approaches by deep learning algorithms need to be further improved. Methods: This paper proposes a novel topology-based approach (GHCN-SE) of predicting confidence scores of candidate reactions, which can be used for gap-fillings of GEMs. The topological features of GEMs are fully extracted by simultaneously using graph and hypergraph convolutional networks, such that both associations of metabolites in the same reaction and higher-order interactions of metabolites within reactions can be captured. After the feature fusion, we further employ the squeeze-and-excitation network to enhance features. Results: The reaction prediction and reaction recovery experiments through 5-fold cross validations on 108 high-quality BiGG GEMs show that the proposed GHCN-SE is superior to other related methods. The ablation study further demonstrates the contributions of the graph convolutional network, hypergraph convolutional network, and squeeze-and-excitation network in GHCN-SE. In addition, the visualization study interprets the effectiveness of GHCN-SE. Conclusions: For potential applications in metabolic engineering, biomedicine, etc., this proposed GHCN-SE can be used to further improve the phenotypic prediction accuracy of the draft GEM generated from automated reconstruction tools. Full article

Background: Genome-scale metabolic models (GEMs) are foundational tools for systems biology, enabling quantitative interrogation of human metabolism across physiological and pathological states. However, many legacy reconstructions exhibit heterogeneous identifier usage, incomplete pathway integration, and limited thermodynamic refinement, constraining reproducibility, interoperability, and translational applicability. Methods: We present EHMN 2026, an update of the Edinburgh Human Metabolic Network. The reconstruction was refined through systematic identifier reconciliation using MetaNetX and ChEBI mappings, duplicate reaction consolidation, thermodynamic directionality assessment, and structured pathway annotation via Reactome. The final model was encoded in Systems Biology Markup Language (SBML) Level 3 Version 2 with the Flux Balance Constraints (FBC2) package, ensuring explicit gene–protein–reaction (GPR) representation and compatibility with modern constraint-based modelling toolchains. Results: EHMN 2026 comprises 11 compartments, 14,321 metabolites (species), and 22,642 reactions, supported by 3996 gene products. Of all reactions, 9638 (42.6%) contain GPR associations, linking metabolic transformations to 2887 unique Ensembl gene identifiers (ENSG). Pathway integration yielded 2194 unique Reactome identifiers, providing structured pathway-level organisation of metabolic functions. Thermodynamic refinement reduced infeasible energy-generating cycles and improved reaction directionality coherence while preserving global network connectivity. The reconstruction is fully SBML-compliant and portable across major modelling platforms. Compared with Recon3D and Human1, EHMN 2026 uniquely combines native Reactome reaction-level annotation, systematic MetaNetX identifier harmonisation, documented thermodynamic cycle elimination (37 cycles, 0 remaining), and an 11-compartment architecture supporting organelle-specific modelling—features designed for QSP and multi-layer integration applications. Conclusions: EHMN 2026 delivers a rigorously harmonised, thermodynamically refined, and pathway-annotated human metabolic reconstruction with enhanced annotation depth and standards-based interoperability. By combining genome-scale coverage with structured gene and pathway integration, the model establishes a robust computational backbone for reproducible metabolic analysis and provides a scalable foundation for future multi-layer systems pharmacology and integrative modelling frameworks. Full article

Abiotic stresses disrupt redox homeostasis and reduce crop productivity. Antioxidant networks support resilience by limiting excess reactive oxygen species (ROS) and maintaining redox signalling for stress perception, gene expression, and metabolic reprogramming. We summarize advances (2000–2025) in ROS generation, detoxification mechanisms, and signalling across organelles, including chloroplasts, mitochondria, peroxisomes, and the apoplast. This includes compartmentalized enzymes—superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), and glutathione reductase (GR)—as well as the peroxiredoxin–thioredoxin system and non-enzymatic buffers like ascorbate, glutathione, tocopherols, carotenoids, and flavonoids. We uniquely synthesize these findings in a compartment-resolved “redox rheostat” model, linking ROS concentration–time windows (signaling vs. damage) to antioxidant network design (kinetic tiers, compartmentation, and trade-offs) and identifying intervention points for breeding, genome editing, and field-scale priming. We emphasize constraints, such as NADPH supply and antioxidant recycling capacity, that lead to context-dependent outcomes. We evaluate omics, transgenic strategies, genome editing (CRISPR and Cas systems), exogenous applications, and plant–microbe associations. This synthesis clarifies how antioxidant systems protect photosynthetic and respiratory machinery while supporting signalling, thus outlining routes to climate-resilient, yield-stable crops across varied environments and stresses. Full article

The application of Genome-Scale Metabolic Network Models (GSMM) in fermentation optimization is hampered by challenges in differentiating viable from dead cells and parameter distortion induced by conventional detection methods. Using E. coli BL21(DE3) as the model organism, this study developed a flux analysis strategy that couples cell kinetics with GSMM. Key parameters were estimated using the gradient descent algorithm, thereby enabling precise prediction of viable cell concentration and glucose consumption dynamics. Integrating this with the Quadratic Programming-based parsimonious Flux Balance Analysis (QP-pFBA) algorithm, intracellular metabolic reaction fluxes were quantified. Results demonstrated that the model can effectively differentiate viable from dead cells; Batch D, adopting the gradient-increasing feeding strategy, achieved the maximum specific growth rate (μ_max) of 0.6457, the highest among the four batches. Moreover, key metabolic reaction fluxes were highly correlated with the feeding strategy. This framework forgoes specialized, high-cost equipment and offers robust cross-strain/process adaptability, thereby greatly advancing GSMM utility. It provides a powerful tool for precise fermentation control and accelerates the shift toward data-driven biomanufacturing. Full article

The microbial community surrounding plant roots plays a vital role in plant growth, nutrient uptake, stress resilience and other potential functions. This review synthesizes available evidence that plant growth-promoting microorganisms (PGPMs) not only directly benefit the plant but also modulate the rhizospheric microbiome by mediating metabolic reprogramming of the host plant. PGPMs modify the composition of root exudates through the regulation of phytohormone signaling and transcriptional networks, thereby promoting beneficial microbes and suppressing disease. Key mechanisms involve the jasmonate, ethylene, and strigolactones signaling pathways. Transcription factors MYB72, ERF1 regulate biosynthesis and secretion of metabolites like organic acids and coumarins. The exudates serve as specific signals for microbial community assembly and as enhancers of feedback loops that reinforce plant-microbe mutualism. We examine the ecological and agricultural significance of PGPM-induced metabolic reprogramming of the host due to PGPMs that enhances disease suppression, abiotic stress tolerance, and nutrient use efficiency. Lastly, we address advanced methods and strategies for transferring these biological pathways to the agricultural realm and on to a more sustainable agricultural practice with emphasis on the need to integrate multi-omics (whole genomics, transcriptomics, and metabolomics), synthetic microbial communities and plant genetic engineering for microbiome-assisted agriculture. This synthesis reveals that PGPM-induced metabolic reprogramming operates through an integrated cross-scale framework linking microbial perception, phytohormone signaling, transcriptional regulators, and transporter-mediated exudate efflux, with root exudates functioning as plant-controlled ecological filters that selectively shape the rhizosphere microbiome. We further identify key translational challenges, including context-dependent efficacy and the lab-to-field gap, and propose a roadmap combining multi-omics, synthetic communities, and genome editing to realize the potential of microbiome-assisted sustainable agriculture. Full article

Background: Colorectal cancer (CRC) is a malignancy that ranks among the top three in terms of both global mortality and incidence. Although numerous studies have demonstrated that gut microbes are implicated in CRC pathogenesis, the precise mechanisms underlying host–microbiota metabolic crosstalk remain poorly understood. Objective: This study aims to identify and delineate key co-metabolites and their associated metabolic pathways that modulate the biomass of CRC-related gut bacteria within healthy individuals, through the construction of host–gut microbiota co-metabolic network models. We seek to elucidate the underlying mechanisms of metabolic interplay between the host and CRC-related gut microbiota, thereby offering novel perspectives on the microbial involvement in the initiation and progression of CRC. Methods: We coupled a colon tissue-specific host Genome-Scale Metabolic Model (GEM), which utilized transcriptomic data from healthy human colon tissues, with 12 CRC-associated pro-/anti-carcinogenic gut bacterial GEMs to construct a co-metabolic network. Through a comparative analysis of the network structure and systemic methods (including Flux Sampling and metabolic difference analysis), we simulated scenarios of constrained host co-metabolite supply. Finally, metabolic subsystem enrichment analysis was employed to elucidate the specific molecular mechanisms by which key co-metabolites affect microbial function. Results: The 17 key co-metabolites identified include chloride ions, zinc ions, and acetate. Among these, thirteen metabolites (e.g., ferric iron, succinate, and acetate) were confirmed by literature to be associated with CRC. All 17 key co-metabolites were found to significantly modulate the biomass of CRC-associated gut bacteria. These regulatory effects primarily influence microbial function through core pathways such as glycerophospholipid metabolism and folate metabolism. Conclusion: This research provides a systemic perspective for elucidating the mechanisms of host–gut microbiota metabolic interplay in CRC, thereby complementing the existing theoretical framework concerning microbial regulation by the host genetic background. Full article

Background: Periodontitis is a chronic inflammatory disease mostly associated with Porphyromonas gingivalis infection and characterized by progressive destruction of the supporting structures of the tooth, including the gingiva, periodontal ligament and alveolar bone. However, the impact of other members of the periodontal microbiome on stage of the severity of the periodontitis remains largely uncharacterized. Methods: This exploratory study employs whole-genome shotgun (WGS) metagenomics to characterize the periodontal microbiome in patients suffering from mild and severe periodontitis, aiming to identify microbial signatures linked to disease severity via analysis of taxonomic composition, predicted metabolic pathways and metagenome-assembled genomes (MAGs). After initial selection, 28 adult patients with a computer tomography (CT)-confirmed diagnosis of mild and severe stage of periodontitis from 2 clinics were included in the research project. Results: Taxonomic analysis confirms the presence of various commensal and pathogenic bacteria detectable at the species level, especially belonging to so-called “red, orange and green periodontal complexes”—P. gingivalis, T. forsythia, C. rectus, and Capnocytophaga spp. that may contribute to disease heterogeneity. The conducted investigation suggests that non-microbial factors such as cardiovascular diseases and antibiotic usage in the last 6 months prior to the hospital admission could explain variance of disease progression and impact on severity. Analysis of microbial functional composition revealed metabolic traits showing positive correlations with severe stage of periodontitis. Robust network analysis suggested interactions between pathogenic bacteria of the red complex and other members of the periodontal microbiome. Conclusions: These findings underscore the multifactorial nature of periodontitis pathogenesis, highlighting the need for integrated approaches combining microbial, host, and environmental data to unravel drivers of disease progression. The study provides a foundation for future large-scale investigations into personalized diagnostic or therapeutic strategies. Full article

Background/Objectives: Genome-scale metabolic models (GSM) are key tools for predicting microbial physiology, yet species within the genus Lentilactobacillus remain largely unexplored. Lentilactobacillus kefiri DH5 is an obligately heterofermentative lactic acid bacterium with unique redox metabolism, but no curated GSM model exists for this species. This study aimed to generate the first GSM model for L. kefiri DH5, evaluate multiple reconstruction tools, and characterize metabolic features underlying its heterofermentative metabolism. Methods: Draft GSM models were generated from the L. kefiri DH5 genome annotation using five reconstruction tools. For each tool, gap-filling was performed on a CDM, followed by quality assessment using the MEMOTE. Manual curation was performed using the COBRApy library. Results: Among the five reconstructions, the KBase-derived draft demonstrated the highest quality and production potential for metabolites characteristic of heterofermentative fermentation. During manual curation of this model, reaction directions in central carbon metabolism and amino acid pathways were corrected. Analysis further identified an alternative NADH-regenerating glucose shunt via D-gluconate, supported by omics data and enzyme promiscuity considerations. Incorporation of this pathway resolved the redox imbalance and allowed the model to reproduce metabolic exchange profiles characteristic of obligate heterofermenters. Conclusions: We developed the first manually curated genome-scale model of L. kefiri DH5 and showed that the choice of reconstruction tool substantially affects model quality and predictive power. We also proposed an alternative glucose assimilation shunt via gluconolactone, which resolved the redox imbalance in the model and enabled representation of the heterofermentative metabolism. Full article

Human induced pluripotent cells (hiPSCs), generated in vitro, represent a groundbreaking tool for tissue regeneration and repair. Understanding the metabolic intricacies governing hiPSCs is crucial for optimizing their performance across diverse environmental conditions and improving production strategies. To this end, in this work, we introduce hiPSCGEM01, the first genome-scale, context-specific metabolic model (GEM) uniquely tailored to fibroblast-derived hiPSCs, marking a clear distinction from existing models of embryonic and cancer stem cells. hiPSCGEM01 was developed using relevant genome expression data carefully selected from the Gene Expression Omnibus (GEO), and integrated with the RECON 3D framework, a comprehensive genome-scale metabolic model of human metabolism. Redundant and unused reactions and genes were identified and removed from the model. Key reactions, including those facilitating the exchange and transport of metabolites between extracellular and intracellular environments, along with all metabolites required to simulate the growth medium, were integrated into hiPSCGEM01. Finally, blocked reactions and dead-end metabolites were identified and adequately solved. Knockout simulations combined with flux balance analysis (FBA) were employed to identify essential genes and metabolites within the metabolic network, providing a comprehensive systems-level view of fibroblast-derived hiPSC metabolism. Notably, the model uncovered the unexpected involvement of nitrate and xenobiotic metabolism—pathways not previously associated with hiPSCs—highlighting potential novel mechanisms of cellular adaptation that merit further investigation. hiPSCGEM01 establishes a robust platform for in silico analysis and the rational optimization of in vitro experiments. Future applications include the evaluation and refinement of culture media, the design of new formulations, and the prediction of hiPSC responses under varying growth conditions, ultimately advancing both experimental and clinical outcomes. Full article

Terpenoids, which are essential pharmaceutical compounds, encounter significant production challenges due to their low yields in native plants and associated ecological concerns. This review summarizes recent advances in metabolic engineering strategies applied across three complementary platforms: native medicinal plants, microbial systems, and heterologous plant hosts. We present how the “Genomic Insights to Biotechnological Applications” paradigm, supported by multi-omics technologies such as genomics, transcriptomics, metabolomics, and related disciplines, contributes to advancing research in this field. These technologies enable the systematic identification of key biosynthetic genes and regulatory networks. CRISPR-based tools, enzyme engineering, and subcellular targeting are presented as pivotal transformative strategies in advancing metabolic engineering approaches. Strategic co-expression and optimization approaches have achieved substantial improvements in product yields, as demonstrated by a 25-fold increase in paclitaxel production and a 38% enhancement in artemisinin yield. Persistent challenges, such as metabolic flux balancing, cytotoxicity, and scale-up economics, are discussed in conjunction with emerging solutions, including machine learning and photoautotrophic chassis systems. We conclude by proposing a strategic roadmap for industrial translation that highlights the essential integration of systems biology and synthetic biology approaches to accelerate the transition of terpenoid biomanufacturing from discovery to commercial-scale application. Full article