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PubMed2025

Methodologies for Mitochondrial Omic Profiling During Spaceflight.

To be able to understand how spaceflight can affect human biology, there is a need for maximizing the amount of information that can be obtained from experiments flown to space. Recently there has been an influx of data obtained from astronauts through multi-omics approaches based on both governmental and commercial spaceflight missions. In addition to data from humans, mitochondrial specific data is gathered for other experiments from rodents and other organisms that are flown in space. This data has started to universally demonstrate that mitochondrial dysfunction is the key regulator associated with increasing health risks associated with spaceflight. This mitochondrial dysfunction can have influence downstream on immune suppression, inflammation, circadian rhythm issues, and more. Due to the space environment, standard methodologies have to be altered for performing mitochondrial specific analysis and in general sample collection for omics. To perform mitochondrial specific analysis and data collection from samples flown to space we will outline the current sample collection methods, processing of the samples, and specific analysis. Specifically we will highlight the different mitochondrial methodologies and challenges involved with research associated with spaceflight.

Space Flight↗
PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗
PubMed2005

Proteomics in nutrition and health.

Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, has been enabled by quantum leaps in mass spectrometric technology, which allowed identification of large, involatile biomolecules. Over the last two decades, this discipline evolved from the sole delivery of protein identities to a platform, which reveals clues to function through e.g. characterisation of protein modifications and interactions as well as through quantitative proteomics, i.e. the global comparison of protein amounts between two defined biological states. Proteomics is an integral part and key player in the family of -omic disciplines as there are genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). Considering the complexity, dynamics and protein concentration range of any given proteome, proteomics is the most challenging -omic discipline and requires the most sophisticated analysis pipeline. Proteomics represents an established technology in the pharmaceutical industry mainly for biomarker and drug target discovery. The potential of proteomics for research in the food industry is increasingly being recognised and the employment of proteomic approaches to nutrition and health issues is now emerging. This review summarizes (i) major technological achievements in mass spectrometry and proteomics, (ii) deliverables of proteomics in the context of nutrition and health, and (iii) applications of proteomics, and -- if appropriate -- transcriptomics to the research fields of digestive health, obesity and diabetes, immunity and allergy, probiotics, milk, and food preference.

Complex Mixtures↗
PubMed2026

Early oligodendrocyte dysfunction signature in Alzheimer's disease: Insights from DNA methylomics and transcriptomics.

Much research into the aetiology of Alzheimer's disease (AD) has focused on neuronal cell types, while studies on the contribution of glial cells, particularly oligodendrocytes (OLGs), are only starting to emerge. Altered brain DNA methylation, an epigenetic modification that provides the interplay between genetics and environmental cues to tightly regulate gene expression, is well documented in AD. Yet, cell-type-specific investigations remain limited. Here, we examine the role of DNA methylation and OLGs in AD, and how such changes may impact gene expression. We performed weighted-gene correlation network analysis (WGCNA) on multiple brain omics AD datasets across species: human DNA methylation data from 4 brain regions, human brain single-nuclei RNA sequencing data and mouse brain RNA sequencing data. We compared AD-associated network modules enriched for OLG genes across AD brain regions, as well as with other neurodegenerative disease DNA methylation datasets. We identified a DNA methylation signature associated with AD, enriched for OLGs, and preserved across brain regions representing early and late AD pathology stages. Genes within this signature showed altered expression in AD OLGs, confirming cell-type specificity and relevance to AD. This OLG signature was also preserved in transgenic mice with early Aβ pathology and in other neurodegenerative diseases without Aβ pathology. We reveal a consistent pattern of OLG dysfunction spanning early to late stages of AD, across DNA methylation and gene expression. Our findings highlight OLG-associated DNA methylation changes as important in AD pathogenesis, and possibly in other neurodegenerative diseases, opening new avenues for therapeutic development.

Alzheimer Disease↗
PubMed2003

Metabolomics in systems biology.

The primary aim of "omic" technologies is the nontargeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample. By their nature, these technologies reveal unexpected properties of biological systems. A second and more challenging aspect of omic technologies is the refined analysis of quantitative dynamics in biological systems. For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with high sample numbers in reliable measurement times with respect to both technical accuracy and the identification and quantitation of small-molecular-weight metabolites. This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology. The aim of this review is to (a) provide an in-depth overview about metabolomic technology, (b) explore how metabolomic networks can be connected to the underlying reaction pathway structure, and (c) discuss the need to investigate integrative biochemical networks.

Models, Biological↗
PubMed2026

OmicsPred as a centralised resource for genetic prediction of multi-omic traits.

Genetic prediction of multi-omic data has emerged as a cost-effective alternative to direct omics profiling, particularly useful for identifying molecular features associated with disease susceptibility. However, despite its popularity, multi-omic imputation models are fragmented across studies, hindering findability, accessibility, interoperability and re-use. To address this, we developed OmicsPred (https://www.omicspred.org), a centralised platform for the deposition and dissemination of genetic prediction models of multi-omic traits. OmicsPred unifies the most commonly used molecular imputation models (e.g. from PredictDB) and other published studies totalling 3,339,469 prediction models spanning transcriptomic, proteomic, and metabolomic traits (as of May 2026). Each model is accompanied by metadata describing score development and predictive performance, and distributed in formats compatible with popular analytic tools, such as PGS Catalog Calculator and MetaXcan. To demonstrate the utility of the resource for systematic target discovery, we perform a multi-omic phenome-wide association analysis in Million Veterans Program data.

Journal Article↗
PubMed2025

Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platform's utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, .eu, .org.au, .fr).

Journal Article↗
PubMed2024

Understanding the biological processes of kidney carcinogenesis: an integrative multi-omics approach.

Biological mechanisms related to cancer development can leave distinct molecular fingerprints in tumours. By leveraging multi-omics and epidemiological information, we can unveil relationships between carcinogenesis processes that would otherwise remain hidden. Our integrative analysis of DNA methylome, transcriptome, and somatic mutation profiles of kidney tumours linked ageing, epithelial-mesenchymal transition (EMT), and xenobiotic metabolism to kidney carcinogenesis. Ageing process was represented by associations with cellular mitotic clocks such as epiTOC2, SBS1, telomere length, and PBRM1 and SETD2 mutations, which ticked faster as tumours progressed. We identified a relationship between BAP1 driver mutations and the epigenetic upregulation of EMT genes (IL20RB and WT1), correlating with increased tumour immune infiltration, advanced stage, and poorer patient survival. We also observed an interaction between epigenetic silencing of the xenobiotic metabolism gene GSTP1 and tobacco use, suggesting a link to genotoxic effects and impaired xenobiotic metabolism. Our pan-cancer analysis showed these relationships in other tumour types. Our study enhances the understanding of kidney carcinogenesis and its relation to risk factors and progression, with implications for other tumour types.

Kidney Neoplasms↗
PubMed2006

Informatics-assisted protein profiling in a transgenic mouse model of amyotrophic lateral sclerosis.

One of the causes of amyotrophic lateral sclerosis (ALS) is due to mutations in Cu,Zn-superoxide dismutase (SOD1). The mutant protein exhibits a toxic gain of function that adversely affects the function of neurons in the spinal cord, brain stem, and motor cortex. A proteomic analysis of protein expression in a widely used mouse model of ALS was undertaken to identify differences in protein expression in the spinal cords of mice expressing a mutant protein with the G93A mutation found in human ALS. Protein profiling was done on soluble and particulate fractions of spinal cord extracts using high throughput two-dimensional liquid chromatography coupled to tandem mass spectrometry. An integrated proteomics-informatics platform was used to identify relevant differences in protein expression based upon the abundance of peptides identified by database searching of mass spectrometry data. Changes in the expression of proteins associated with mitochondria were particularly prevalent in spinal cord proteins from both mutant G93A-SOD1 and wild-type SOD1 transgenic mice. G93A-SOD1 mouse spinal cord also exhibited differences in proteins associated with metabolism, protein kinase regulation, antioxidant activity, and lysosomes. Using gene ontology analysis, we found an overlap of changes in mRNA expression in presymptomatic mice (from microarray analysis) in three different gene categories. These included selected protein kinase signaling systems, ATP-driven ion transport, and neurotransmission. Therefore, alterations in selected cellular processes are detectable before symptomatic onset in ALS mouse models. However, in late stage disease, mRNA expression analysis did not reveal significant changes in mitochondrial gene expression but did reveal concordant changes in lipid metabolism, lysosomes, and the regulation of neurotransmission. Thus, concordance of proteomic and mRNA expression data within multiple categories validates the use of gene ontology analysis to compare different types of "omic" data.

Amyotrophic Lateral Sclerosis↗
PubMed2026

Epigenetic alterations of AKT1 orchestrate a metabolic reprogramming in advanced lipedema: translational insights from an integrated multi-omics study.

BACKGROUND: lipedema is a chronic, progressive adipose disorder predominantly affecting women, characterized by painful, symmetrical subcutaneous fat accumulation, and typically resistant to lifestyle interventions. The pathophysiology of advanced-stage lipedema remains poorly defined, and no validated biomarkers or targeted therapies are currently available. METHODS: in this observational study, we applied a comprehensive multi-omics approach to dissect the molecular and metabolic alterations underlying late-stage lipedema. RESULTS: Genome-wide DNA methylation profiling identified over 5,000 differentially methylated CpG sites affecting genes involved in receptor tyrosine kinase signaling, phospho-metabolism, and immune pathways. Transcriptomic analysis revealed profound downregulation of mitochondrial functions, including oxidative phosphorylation, the TCA cycle, and fatty acid β-oxidation, alongside disruption of the sirtuin pathway and extracellular matrix remodeling. Integrative analysis pinpointed AKT1 as a central regulatory node: its promoter region was hypomethylated, correlating with increased gene expression and protein phosphorylation. Metabolomic profiling confirmed AKT1-linked metabolic dysregulation, including altered levels of L-arginine, NADP+, ATP, guanosine, glycerol, and glutamate, indicating impaired redox balance and energy metabolism. Trans-omic network analysis positioned AKT1 at the intersection of multiple dysregulated pathways, suggesting its key role in advanced-stage lipedema. CONCLUSIONS: the consistent enhancing of AKT pathway signaling across omic layers highlights its potential not only as a biomarker for disease stratification but also as a putative druggable target for therapeutic intervention. These findings offer new mechanistic insights into lipedema pathophysiology and provide a rationale for future personalized treatment strategies guided by AKT1-centric molecular profiling.

Proto-Oncogene Proteins c-akt↗
PubMed2026

Deciphering the Genetic Underpinnings of Liver Cirrhosis-Heart Failure Comorbidity Through Multi-Omics: CRIM1 as a Key Endothelial Mediator.

The co-occurrence of liver cirrhosis (LC) and heart failure (HF) poses considerable clinical challenges, yet the cellular and molecular determinants of this comorbidity remain poorly characterized. To address this, we developed an integrative multi-omics pipeline encompassing GWAS meta-analysis, gsMap-based spatial transcriptomic projection, GeneEnrich functional annotation, single-cell atlas construction, seismicGWAS and ECLIPSER cell-type scoring, eCAVIAR and fastenloc colocalization, hdWGCNA network inference, scTenifoldKnk in silico gene perturbation, and GCTA-COJO fine-mapping. Quality-controlled meta-analysis yielded 12,347,758 and 9,256,862 variant-level associations for LC and HF, respectively. Spatial projection confirmed preferential enrichment of disease signals within embryonic hepatic and cardiac compartments. Pathway analyses disclosed that LC-linked loci were concentrated in lipid metabolic programs, whereas HF-linked loci implicated mitochondrial bioenergetics and lysosomal degradation. At the cellular level, endothelial cells emerged as the dominant HF-associated population. Convergent evidence from five orthogonal algorithms pinpointed CRIM1 as the sole robustly supported shared gene, selectively enriched in HF endothelial cells; virtual perturbation further identified LCP1 and PTPRC as downstream regulatory nodes. Fine-mapping of the chromosome 2 locus harboring rs12476437 revealed multiple statistically independent signals in the vicinity of CRIM1. Collectively, these findings computationally prioritize the endothelial-CRIM1 axis as a previously unappreciated candidate mechanistic bridge between LC and HF requiring experimental validation.

Humans↗
PubMed2006

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data.

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.

Computational Biology↗
PubMed2025

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited. MATERIALS AND METHODS: We analyzed CpG sites from two publicly available datasets, the TCGA-PAAD used as discovery set and the CPTAC-PDA as external test set. Single mutations and co-mutation of KRAS and TP53 genes were identified as targets, and differentially methylated CpG sites (DMC) were detected accordingly. We trained and validated Random Forest (RF) models to predict each target. Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision-Recall curve (AUPRC) were used as performance metrics. Then, we performed consensus clustering from the DMCs to identify novel patients' profiles. Finally, we trained and validated a combination of eXtreme Gradient Boosting (XGB) and tree models to select an epigenomic prognostic determinant. RESULTS: From 598 DMCs extracted, an RF model predicted KRAS and TP53 co-mutation on the external test set with AUROC of 0.77 and AUPRC of 0.87. The consensus clustering allowed us to identify 4 clusters (C1, C2, C3, and C4) of patients. The C4 cluster captured a subgroup of patients with favorable Overall Survival (OS) with respect to others. The XGB model perfectly predicted C4 vs other clusters on the discovery set. In both cohorts, patients were stratified into two risk groups according to methylation levels of cg16854533, individuated as the most important CpG site. CONCLUSION: We analyzed methylation data to develop a classifier for the TP53 and KRAS mutational status. Four prognostic clusters were pointed out and a prognostic model using a CpG site was validated in an independent cohort. Our results evidence that the proposed use of methylation data facilitates risk stratification for PDAC.

Humans↗
PubMed2025

Dual genetic loci and flavonoid metabolism orchestrate fruiting body coloration in Flammulina filiformis: a multi-omic roadmap for fungal pigmentation.

BACKGROUND: The fruiting bodies of macrofungi exhibit diverse coloration, traditionally attributed to melanin and carotenoid biosynthesis. This study is the first to reveal that flavonoids, rather than these classical pigments, are the predominant contributors to yellow pigmentation in the Flammulina filiformis. OBJECTIVE: To uncover the genetic basis and key regulatory genes involved in pigment formation in F. filiformis fruiting bodies, and to establish a model framework for studying color genetics in macrofungi. METHODS: Metabolomic profiling was conducted on yellow and white F. filiformis fruiting bodies to identify key pigment components. A segregating population was constructed, followed by integrated multi-omics analyses-including bulk segregant analysis (BSA), genome-wide association study (GWAS), and transcriptomics-to map regulatory loci and candidate genes. Functional roles were validated via genetic transformation and protein structural modeling. RESULTS: Flavonoid accumulation was identified as the biochemical hallmark of pigmented fruiting bodies. Genetic analysis revealed a dual regulatory mechanism: a qualitative locus governing pigmentation presence and a quantitative trait determining color intensity. Combined BSA and GWAS pinpointed a major locus, Ffcrs, within a recombination-suppressed region. Transcriptomic analysis identified two key regulators, Ffakr (a transcriptional activator) and Ffpal (encoding phenylalanine ammonia-lyase). Functional verification via transformation, structural modeling, and metabolite profiling in transgenic lines confirmed their essential roles in flavonoid biosynthesis and pigmentation. CONCLUSION: This study uncovers a flavonoid-based pigmentation mechanism in F. filiformis and elucidates a complex genetic architecture shaped by both qualitative and quantitative loci, providing a new paradigm for understanding pigment formation in macrofungi. The identified regulatory factors establish a molecular foundation for the precise manipulation of economically important pigmentation traits in edible mushroom.

Flavonoids↗
PubMed2026

ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data.

MOTIVATION: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualization and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data. RESULTS: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualization and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualize phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customizable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. AVAILABILITY AND IMPLEMENTATION: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

Software↗
PubMed2026

X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.

MOTIVATION: The rapid accumulation of multi-omics data presents a valuable opportunity to advance our understanding of complex diseases and biological systems, driving the development of integrative computational methods. However, the complexity of biological processes, spanning multiple molecular layers and involving intricate regulatory interactions, requires models that can capture both intra- and cross-omics relationships. Most existing integration methods primarily focus on sample-level similarities or intra-omics feature interactions, often neglecting the interactions across different omics layers. This limitation can result in the loss of critical biological information and suboptimal performance. To address this gap, we propose X-intNMF, a network-regularized non-negative matrix factorization (NMF) framework that simultaneously integrates intra- and cross-omics feature interactions into a shared low-dimensional representation (see Fig. 1). By modeling these multi-layered relationships, X-intNMF enhances the representation of biological interactions and improves integration quality and prediction accuracy. RESULTS: For evaluation, we applied X-intNMF to predict breast cancer phenotypes and classify clinical outcomes in lung and ovarian cancers using mRNA expression, microRNA expression, and DNA methylation data from TCGA. The results show that X-intNMF consistently outperforms state-of-the-art methods. Ablation studies confirm that incorporating both cross-omics and intra-omics interactions contributes significantly to the model's improved performance. Additionally, survival analysis on 25 TCGA cancer datasets demonstrates that the integrated multi-omics representation offers strong prognostic value for both overall survival and disease-free status. These findings highlight X-intNMF's ability to effectively model multi-layered molecular interactions while maintaining interpretability, robustness, and scalability within the NMF framework. AVAILABILITY AND IMPLEMENTATION: The source code and datasets supporting this study are publicly available at GitHub (https://github.com/compbiolabucf/X-intNMF) and archived on Zenodo (https://doi.org/10.5281/zenodo.18238385).

Multiomics↗
PubMed2025

Advances in tumor subclone formation and mechanisms of growth and invasion.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Humans↗
PubMed2026

Engineering Bacillus Subtilis for Efficient Biosynthesis of Riboflavin: Current Knowledge and Future Perspectives.

Riboflavin is an essential water-soluble vitamin that serves as a precursor for the biosynthesis of the flavin cofactors FMN and FAD, which play pivotal roles in numerous redox and energy metabolism reactions. With the growing global demand for sustainable vitamin production, microbial fermentation has become an attractive alternative to chemical synthesis due to its environmental and economic advantages. Among microbial hosts, Bacillus subtilis has emerged as a leading cell factory for riboflavin production owing to its GRAS status, well-characterized genetics, and efficient protein secretion system. This review provides a comprehensive overview of recent advances in metabolic engineering strategies to enhance riboflavin biosynthesis in B. subtilis. Key topics include strengthening biosynthetic and precursor pathways, relieving feedback inhibition, balancing metabolic flux and cell growth, employing adaptive laboratory evolution, and utilizing omics-guided optimization and 13C metabolic flux analysis. Moreover, the integration of synthetic biology tools such as riboswitch engineering, regulatory element design, and high-throughput screening has significantly accelerated strain improvement. Despite remarkable progress, challenges remain in achieving precise regulatory control, optimizing multi-gene expression, and enhancing genome integration efficiency. Future research combining multi-omics data, synthetic regulatory design, and machine learning-driven predictive modeling is expected to further advance the development of intelligent B. subtilis cell factories. However, the practical implementation of these systems remains constrained by the metabolic burden of overproduction and the lack of universal regulatory models that can predict strain performance across varying industrial scales.

Bacillus subtilis↗