Metabolism underpins cellular function by supplying energy, biosynthetic precursors, and redox balance and in yeast there are thousands of metabolic reactions that are tightly coordinated through multilayered regulation. The yeast Saccharomyces cerevisiae has become a central model for studying metabolism and its regulation and following publication of its genome in 1996, this yeast became pivotal in systems biology. Systems biology integrates experimental data with mathematical modeling to analyse complex cellular networks. A major advance for metabolic analysis was the development of flux balance analysis and genome sequencing enabled reconstruction of the first genome-scale metabolic model (GEM) for yeast. This initial GEM described how hundreds of genes, reactions, and metabolites interact across compartments. Subsequent models, including Yeast8 and Yeast9, expanded the coverage and predictive power, and these models enable metabolic comparison, physiological analysis, omics integration, and design of strains that can be used for production of chemicals and biopharmaceuticals. Overall, S. cerevisiae remains a cornerstone of systems biology and biotechnology, with continued advances expected in integrative modeling and engineering applications.

Metabolic burden arises from the reallocation of cellular resources, often resulting in stress-associated phenotypes and compromised cellular performance. However, the molecular mechanisms by which industrial microorganisms perceive and alleviate such burdens remain largely unexplored. Here, we present an online monitor to quantify metabolic burden imposed by genetic and environmental perturbations in the workhorse Corynebacterium glutamicum. RNA-seq analysis revealed a shared host response and enabled the identification of several early-responsive promoters through in vivo burden assays. Leveraging these elements, particularly the cg1940 promoter, we successfully engineered dynamic feedback systems to alleviate metabolic burden associated with suboptimal expression. Notably, the identified promoter retained burden responsiveness in Escherichia coli, suggesting potential cross-species applicability. As a proof of concept, this feedback controller was applied to improve cell growth, protein synthesis, and chemical bioproduction. This technology offers a strategy for bolstering the robustness of C. glutamicum and potentially other microorganisms.

Converting industrial side streams into value-added chemicals using microbial cell factories is of increasing interest, as such processes offer sustainable solutions to reduce waste and production costs. However, developing new, efficient non-model cell factories for precision fermentation remains challenging due to limited knowledge about their metabolic capabilities. Here, we investigate the lactose and galactose metabolism of the understudied yeast Sungouiella intermedia (formerly Candida intermedia), using knowledge-matching of high-quality genome-scale metabolic model (GEM) with extensive experimental analysis, and determine its potential as a future cell factory on lactose-rich industrial side-streams. We show that this yeast possesses the conserved Leloir pathway as well as an oxidoreductive route for galactose catabolism. Model simulations and experimental data from continuous and batch bioreactors, transcriptomics, and metabolite analysis indicate that while the Leloir pathway dominates galactose metabolism in S. intermedia, the oxidoreductive pathway is employed in a condition-dependent manner. The yeast produces galactitol as a carbon overflow metabolite, facilitating redox cofactor balance during both lactose and galactose growth. Furthermore, the new metabolic insights facilitated the development of an improved bioprocess design, where an engineered S. intermedia strain could achieve galactitol yields of > 90

Genome- scale metabolic models (GEMs) have become essential tools for understanding human metabolism. Here, we introduce Human2, a consensus human GEM with enhanced precision and biological relevance, which leverages large language models (LLMs) and GitHub Action checks to streamline automated, efficient, and collaborative curation. Human2 supports the reconstruction of tissue- and organ- specific models tailored to sex- and age- specific human groups. By integrating transcriptomic, proteomic, and kinetic data, we reveal distinct metabolic features across these groups, such as significant differences in arachidonic acid and leukotriene metabolism. The specific models were integrated into a dynamic whole- body framework, marking an enzyme- constrained dynamic model that simulates interorgan metabolite exchanges under varying nutritional states, from feeding to fasting. Our work highlights the transformative role of LLMs in GEM reconstruction and introduces a whole- body dynamic simulation that integrates kinetic data, offering a powerful resource for multiscale human metabolism modeling.

Abstract Bisbenzylisoquinoline alkaloids (bisBIAs) are pharmacologically valuable plant metabolites with complex stereochemical architectures, yet the catalytic principles governing their assembly have remained largely unclear. Here, we elucidate the enzymatic pathway to cyclic bisBIAs and uncover a non-canonical redox-mediated mechanism for post-assembly stereochemical control. We identify cytochrome P450 enzymes that catalyze regioselective oxidative dimerization and macrocyclization of benzylisoquinoline monomers, establishing the macrocyclic scaffold. Subsequent stereochemical specification is achieved by a paired oxidase-reductase module that selectively epimerizes a single stereocenter through a transient imine formation, converting ( R , S )-configured intermediates to ( S , S )-products. Reconstitution of the pathway in yeast enabled production of both native bisBIAs and non-natural analogs, demonstrating pathway modularity and engineering potential. These results establish the biochemical principle underlying bisBIA biosynthesis and provide a framework for programmable biosynthesis of these complex natural products.

Cardiovascular diseases (CVD) remain a major global health challenge. Early markers of disease initiation and progression are urgently needed. We, and others, have previously shown changes in the gut microbiome in association with metabolic and CVD. Here, we demonstrate that gut microbiome-related changes can be detected in association with subclinical variations in heart and kidney function. Markers related to gut microbial metabolism of aromatic amino acids, phenylalanine and tyrosine, associate with circulating pro-atrial natriuretic peptide and estimated glomerular filtration rate in a metabolically healthy European population. Observational and genetic evidence further identify microbiome-related metabolites as mediators of this gut microbiome-kidney axis, with their baseline levels associating with incident CVD in an external Canadian population. Altogether, our work suggests that the gut microbiome interacts with the cardiorenal axis and participates in an interorgan crosstalk affecting host physiology and risk of CVD.

Constructing high-quality genome-scale metabolic models (GEMs) for non-model organisms remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment, protein language models (PLMSearch), and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. AlphaGEM enhances homologous relationship identification compared to traditional sequence-based methods. Crucially, it employs an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine dark metabolic functions encoded by nonhomologous proteins, thereby expanding species-specific networks. We validated AlphaGEM across prokaryotes (Klebsiella pneumoniae, Bacillus subtilis), eukaryotes (Rhodosporidium toruloides, Pichia pastoris), and complex mammals (Mus musculus, Cricetulus griseus), achieving predictions comparable to manually curated models while outperforming existing tools. Furthermore, we demonstrated its scalability by automatically reconstructing high-fidelity GEMs for 332 distinct yeast species. In summary, AlphaGEM enables precise, rapid GEM construction across diverse domains, providing a solid foundation for universal functional analysis of organisms having genome sequences available.

Establishing efficient cell factories involves a continuous process of trial and error due to metabolic complexity. This complexity makes predicting effective engineering targets a challenging task. Therefore, successful previous designs are vital for future cell factory development. In this study, we developed a method using large language models to extract metabolic engineering strategies from research articles. We created a database containing over 29 006 metabolic engineering entries, 1210 products, and 751 organisms. Using this database, we trained a deep learning model to predict engineering targets for cell factories. Our model outperformed traditional algorithms, demonstrated strong generalization to unseen products and multigene combinations, and was experimentally validated with geraniol overproduction in yeast, leading to the identification of several novel targets. Our study provides a valuable dataset, a chatbot, and an engineering target prediction model for the metabolic engineering field and exemplifies an efficient method for leveraging existing knowledge for future predictions.

Lignans constitute a diverse family of plant metabolites with therapeutic potential. Among them, podophyllotoxin-type aryltetralin lignans serve as precursors for etoposide and teniposide. Etoposide is an essential anticancer medicine approved for first-line treatment of small cell lung cancer, whereas teniposide is used for treatment of leukemia and some brain tumors. Currently, these drugs depend on extraction of precursors from the endangered plant Sinopodophyllum hexandrum, followed by chemical transformations. By identifying key glycosyltransferases and executing more than 60 genetic edits involving 45 heterologous enzymes, the complex biosynthetic pathway of podophyllotoxin-type lignans was reconstructed in yeast. In this study, we established a chemoenzymatic route that streamlines the synthesis of etoposide and teniposide through a single chemical step from biosynthetic precursor 4'-demethyl-epipodophyllotoxin-4-O-glucoside, which enables a secure supply chain of these essential medicines.

Mathematical modeling is a powerful tool for gaining insights into diverse cellular processes and for guiding rational design of biological systems. A growing trend from genome-scale and multiscale to whole-cell models has emerged and these largescale models serve as valuable knowledge platforms for the reconstruction of cellular systems across diverse biological scales, ultimately enabling comprehensive understanding of cellular systems. Here, we briefly summarize the primary principles of these models and highlight how they drive biological insights. Additionally, we discuss the respective advantages of them and outline potential opportunities represented by data and algorithms for advancing this paradigm.

While single-omics analyses of Parkinson's Disease (PD) have demonstrated their ability in revealing the underlying molecular mechanisms, they often fail to provide a comprehensive view of the complete disease mechanisms. In this study, we leveraged multi-omics data from 64 heterogeneous, well-phenotyped PD patients, generated plasma metabolomics data and Olink proteomics data together with the gut and saliva metagenomics data, and investigated the altered molecular mechanisms and their interactions in association with the severity of motor function disorders in PD patients. Based on our multi-omics approach, we identified a panel of 58 biomarkers comprising one clinical variable, 10 proteins, and 17 metabolites from plasma, 26 gut species, and 4 saliva species for PD severity. These biomarkers exhibited superior predictive performance for assessing PD severity compared to those derived from single-omics datasets. The predictive power of our machine learning models based on these biomarkers was validated using additional multi-omics data from the same group of PD patients after a 3-month follow-up. The contribution of each omics dataset was evaluated by both supervised and unsupervised machine learning approaches, highlighting the importance of plasma metabolomics in disease stratification. Our study unveiled disease-related molecular alterations across multiple omics datasets, offering potential diagnostic and therapeutic insights for PD. Moreover, it underpinned the significance of employing multi-omics analyses when studying complex diseases like PD.

A systematic understanding of cellular metabolism is essential for engineering yeast and uncovering the principles of metabolic robustness and evolution, yet much of its metabolic space remains unexplored. Although yeast genome-scale metabolic models have been reconstructed and curated for over two decades, more than 90% of the yeast metabolome remains uncovered. Here, to address this gap, we have developed an integrated workflow that combines retrobiosynthesis, deep learning-based enzyme annotation and enzyme-substrate prediction to systematically explore yeast underground metabolism. Using the framework, we reconstruct a yeast metabolic twin model, Yeast-MetaTwin, comprising 16,244 metabolites, 1,976 metabolic genes and 59,865 reactions. The model reveals systematic differences in Km distributions between the known and underground networks and identifies key hub metabolites linking the underground network. Moreover, Yeast-MetaTwin predicts by-product formation in yeast cell factories, and we experimentally validate two genes converting geraniol to geranial during geraniol biosynthesis.

Despite rapid advances in whole-genome sequencing (WGS), translating genomic findings into individualized insights remains challenging. We present GenRiskPro, a clinical decision-support and research platform, which automates WGS variant calling, annotation, prioritization, and reporting to deliver actionable findings and facilitate precision wellness. (To test the GenRiskPro platform, log on to https://www.phenomeportal.org/dashboard using the following credentials: Username: user@test.com; Password: test.) GenRiskPro integrates rare and common variant prioritization in a unified pipeline and in-house database, enabling both rare and complex disease and trait association analyses. Variant reporting is supported via LongevityCloud, which features a web portal for clinicians to review, adjust, and authorize the return of results in tabular and PDF formats, alongside a mobile app with artificial intelligence (AI) integration for sequenced individuals. Case studies using Turkish (TR, n = 275) and Swedish (SW, n = 101) WGS data assessed platform performance and variant prioritization: (a) predefined gene panels yielded a 1.82% positive rate for actionable findings per American College of Medical Genetics and Genomics (ACMG) secondary findings guidelines; (b) phenotype-driven support diagnosed cases including muscular dystrophy and microcephaly; (c) cohort-level ClinVar reassessment identified potentially misclassified pathogenic variants; (d) rare variant burden analysis revealed enrichment in ABCA4 for TR and SMPD1 in SW; and (e) population analysis highlighted carrier differences in trait-associated SNPs (rs12913832 and rs4988235) and PGx variants (CYP2B64 and CYP2B66). GenRiskPro unifies databases, literature, web development, and AI for rapid, user-friendly genomic analysis and reporting, which fosters collaboration among hospitals, researchers, clinicians, and patients.

Synthetic microbial consortia have been widely used for the production of biochemicals and biofuels. By distributing biosynthetic tasks across multiple strains, it is possible to mitigate metabolic burden, alleviate metabolic crosstalk, and expand substrate and pathway flexibility. To obtain stable microbial consortia, it is crucial to rationally design and control community composition. Here, we review recent advancements in engineering synthetic microbial consortia for biotechnological applications. We also describe the approaches to maintain the stability of synthetic microbial consortia and regulate the populations, highlighting the importance of population control. The future perspective for constructing robust microbial consortia for sustainable biomanufacturing is also discussed.

BACKGROUNDThe COVID-19 pandemic underscored the need and value of a standardised and timely surveillance system for severe acute respiratory infections (SARI) to inform epidemic preparedness and response.AIMWe aimed to develop an automated SARI surveillance system using electronic health records retrieved from pre-existing national health registers in Denmark.METHODSWe used the Danish Civil Register, the Danish National Patient Register and the Danish Microbiology Database to set up the system. First, we determined a SARI case definition for surveillance, choosing among six different potentially usable combinations of ICD-10 diagnosis codes by exploring how each combination captured patient characteristics (age, hospital admission length, mortality, laboratory tests and seasonality). Second, using this case definition, we evaluated the surveillance system's timeliness and completeness by comparing weekly data reported with a delay of 1, 8, 15, 22 and 29 days, respectively, against a complete set of data extracted after 120 days.RESULTSThe selected case definition combined ICD-10 codes for influenza (J09-J11), acute lower viral and bacterial respiratory tract infections and bronchiolitis (J12-J22) and COVID-19 (B342A and B972A). With regards to timeliness and completeness of this definition, weekly data reported with a delay of 8 days was 89-93% complete and showed very similar patterns in weekly changes in SARI cases as data reported after 120 days.CONCLUSIONOur SARI surveillance system detected fluctuations in weekly SARI cases in a consistent and timely manner. We recommend countries to explore using electronic health registers as a resource-efficient alternative to standard SARI sentinel surveillance.

Glycolysis is a fundamental metabolic pathway central to the bioenergetics and physiology of virtually all living organisms. In this comprehensive review, we explore the intricate biochemical principles and evolutionary origins of glycolytic pathways, from the classical Embden-Meyerhof-Parnas (EMP) pathway in humans to various prokaryotic and alternative glycolytic routes. By examining glycolysis across the tree of life, we explore its presence and adaptation in prokaryotes, archaea, bacteria, animals and plants, and the extension of glycolysis into sulfosugar metabolism. Further, we discuss the role of unwanted side reactions, thermodynamic principles, and metabolic control principles that underpin glycolysis and the broader metabolic network, and summarise advanced methods for quantifying glycolytic activity, including new analytical methods, alongside kinetic, constraint-based, and machine-learning based modelling. With a focus on the Pasteur, Crabtree, and Warburg effects, this review further discusses the roles of glycolysis in health and disease, highlighting its impact on global metabolic operations, inborn errors, and various pathologies as well as its role in biotechnology and metabolic engineering.

The tightly regulated central carbon metabolism in Saccharomyces cerevisiae, intricately linked to carbon sources utilized, poses a significant challenge to engineering efforts aimed at increasing the flux through its different pathways. Here, we present a modular deregulation strategy that enables high conversion rates of xylose through the central carbon metabolism. Specifically, employing a multifaceted approach encompassing five different engineering strategies-promoter engineering, transcription factor manipulation, biosensor construction, introduction of heterologous enzymes, and expression of mutant enzymes we engineer different modules of the central carbon metabolism at both the genetic and enzymatic levels. This leads to an enhanced conversion rate of xylose into acetyl-CoA-derived products, with 3-hydroxypropionic acid (3-HP) serving as a representative case in this study. By implementing a combination of these approaches, the developed yeast strain demonstrates a remarkable enhancement in 3-HP productivity, achieving a 4.7-fold increase when compared to our initially optimized 3-HP producing strain grown on xylose as carbon source. These results illustrate that the rational engineering of yeast central metabolism is a viable approach for boosting the metabolic flux towards acetyl-CoA-derived products on a non-glucose carbon source.

Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model.

Phosphofructokinase (Pfk), a key regulatory enzyme in glycolysis, is composed of Pfk1 and Pfk2 subunits in Saccharomyces cerevisiae. However, the distinct roles of these subunits in central carbon metabolism remain unclear. Here, we examined the metabolic consequences of deleting PFK1 or PFK2. The pfk2Δ strain exhibited more severe defects than pfk1Δ. Its maximum specific growth rate was reduced by approximately 54 % in pfk2Δ and by about 15 % in pfk1Δ, both relative to the reference strain. Ethanol production decreased by 36 % and 82 % in pfk1Δ strain and pfk2Δ strain, respectively, relative to the reference strain. Both deletion strains accumulated higher acetate levels compared to the reference strain, increasing by 25.4 % in the pfk1Δ strain and 82 % in the pfk2Δ strain. Flux balance analysis (FBA) revealed a markedly increased carbon flux to the tricarboxylic acid cycle (TCA) in the pfk2Δ strain, with respiration-associated carbon flux elevated 1.5-fold compared to the pfk1Δ strain. Consistently, transcriptomic profiling showed significant upregulation of respiration-related genes in the pfk2Δ strain compared to the reference strain. Notably, deletion of PFK2 enhanced acetyl-CoA-derived product formation, with free fatty acid (FFA) titers increasing from 412 mg L-1 to 517 mg L-1 (a 33.3 % increase). These findings establish PFK2 as a key regulatory node redirecting carbon flux from fermentation toward respiration and biosynthesis, offering new opportunities for metabolic engineering of acetyl-CoA-derived products.

In recent years, the overuse of antibiotics has led to the emergence of antimicrobial-resistant (AMR) bacteria. To evaluate the spread of AMR bacteria, the reservoir of AMR genes (resistome) has been identified in environmental samples, hospital environments, and human populations, but the functional role of AMR bacteria and their persistence within individuals has not been fully investigated. Here, we performed a strain-resolved in-depth analysis of the resistome changes by reconstructing a large number of metagenome-assembled genomes from the gut microbiome of an antibiotic-treated individual. Interestingly, we identified two bacterial populations with different resistome profiles: extensively acquired antimicrobial-resistant bacteria (EARB) and sporadically acquired antimicrobial-resistant bacteria, and found that EARB showed broader drug resistance and a significant functional role in shaping individual microbiome composition after antibiotic treatment. Our findings of AMR bacteria would provide a new avenue for controlling the spread of AMR bacteria in the human community.