Researchers have built the first comprehensive database dedicated to the genus Monascus, a group of fungi whose fermented products have been used for more than two thousand years and which today underpin a major industry in food coloring, brewing, and health products. The resource, called FoodFungi, brings together information on 17 Monascus species, 509 secondary metabolites, and more than 200 regulatory strategies for controlling metabolite production, all compiled from literature and public databases up to September 2025. The work, published in Fungal Biology and Biotechnology, addresses a gap the authors identify as critical: despite the genus’s economic importance, no specialized database for Monascus and its metabolites previously existed.
The industrial stakes are considerable. According to the study, China’s annual output of Monascus pigments is nearly 20,000 tons, and more than 1 billion people consume products containing these pigments in their daily lives. Monascus products are also exported to Japan, South Korea, the United States, and the European Union, accounting for roughly 10% of the total output value. The genus’s secondary metabolites include Monascus pigments, monacolins, gamma-aminobutyric acid, and citrinin. Red pigments serve as coloring agents in meat, cheese, and beer, and also find roles in the textile, cosmetics, and pharmaceutical industries, while monacolin K can lower cholesterol levels and is considered a promising candidate for hypertension supplements.
Existing databases cover natural compounds broadly but lack Monascus-specific depth. The authors point to the NPASS database, which includes fungal species, natural products, and drug targets; SuperNatural 3.0, which holds structures and physicochemical properties of natural compounds; and the Aspergillus Secondary Metabolites Database, which focuses on Aspergillus. None, they argue, offers the integrated strain, metabolite, and regulation data that Monascus research requires. FoodFungi was therefore designed to consolidate chemical composition, genetic information, and biological characteristics in a single searchable platform, freely accessible for academic and research purposes.
The technical architecture follows a front-end and back-end separated design. The backend runs on the Python web framework Django 2.2 with data stored in a MySQL relational database, while the frontend uses Vue.js combined with Bootstrap, jQuery, and JavaScript. A relational schema centers on the Monascus species table, linked one-to-many to product output and gene tables and many-to-many to compounds, which in turn connect to molecular targets. Molecular structures are parsed and drawn with OpenChemLib and RDKit, and visualization relies on the Echarts plugin for statistical charts, radar plots of scoring results, and dashboard displays of maximum yield changes.
Data collection followed a systematic mining protocol across NCBI, PubChem, ChEMBL, ADMETlab 2.0, and ClassyFire, alongside literature retrieval from PubMed, Web of Science, and CNKI. A keyword search of PubMed for articles published from 1949 to 2025 retrieved 1,267 articles, whose records were manually curated. Redundant metabolite entries were merged using standardized SMILES encoding, outliers extracted from literature figures were removed by Grubbs’ test at a significance level of 0.05, and target predictions were retained only when at least two of three tools agreed. After filtering, the dataset comprised 17 valid Monascus species, 509 secondary metabolites, and 203 regulatory strategy entries, with the results section reporting 224 regulatory methods overall.
Each metabolite entry carries nine fundamental properties, 18 ADMET attributes predicted by ADMETlab 2.0, and target information drawn from SwissTargetPrediction, the Similarity Ensemble Approach, and ChEMBL, yielding 1,760 unique targets after deduplication. Chemical classification spans 11 super-classes and 43 classes from ClassyFire. The database also archives genomic and biosynthetic gene cluster information for pigment, monacolin K, and citrinin pathways, sourced from NCBI, allowing users to browse strain-level biological information alongside chemical and pharmacological predictions.
The platform’s most distinctive feature is a scoring strategy for evaluating metabolite regulation techniques, developed because, according to the authors, no tools previously existed to predict the impact of individual or combined regulation methods on metabolite yields. The system assigns a total score out of 10, calculated as 0.4 times factor S1 plus 0.3 times S2 plus 0.3 times S3, with weights empirically defined to reflect industrial priorities of yield, specificity, and safety control. Factor S1 evaluates yield change ratios for pigments, monacolin K, and citrinin, with citrinin increases scored negatively. Factor S2 measures the precision of pigment directional regulation, and S3 assesses synchronous control of pigments and citrinin, prioritizing pigment increase alongside citrinin decrease. Each regulatory record also carries an evidence strength label of strong, moderate, or weak based on the number and consistency of supporting studies.
To validate the scoring system, the team conducted fermentation experiments with two Monascus purpureus strains, M183 and M1, testing four exogenous substances: ammonium chloride, glutamic acid, sesame oil, and cyclic adenosine monophosphate. Adding 5 grams per liter of ammonium chloride raised yellow pigment yield in strain M183 by 35.58% and total pigment yield in strain M1 by 270%. Glutamic acid increased total pigment yield by up to 64.52% in M183 and 342% in M1, with red pigment rising 258% in the latter. Sesame oil boosted total pigment yield by up to 97.58% in M183 and 408% in M1, while cAMP at 2.0 millimolar produced a 674% increase in total pigment yield in M1. Citrinin was detected in M183 but essentially absent from M1, and the experimental rankings of the four additives matched the database’s scoring order, with ammonium chloride and cAMP scoring higher than glutamic acid and sesame oil.
The authors are candid about the database’s limitations. Core data extend only to September 2025, so recent findings are not reflected. The 17 included strains are mainly common environmental types, with few from extreme environments such as high-salt or high-temperature habitats. The database lacks gene expression data, ultraviolet-visible absorption spectra, and mass spectrometry data, and it does not yet offer molecular docking or metabolic pathway simulation tools. The team also cautions that the total score simplifies complex metabolic networks into a single number and does not imply industrial readiness or safety compliance, and that in silico target predictions do not directly equate to biological effects or substitute for in vivo safety assessments.
Future plans include updating strains and regulation methods every six months, curating the missing data types from repositories such as NCBI GEO, MetaboLights, and MassBank, adding a three-dimensional molecular docking module, and eventually expanding the platform from Monascus to food fungi more broadly, including yeasts and lactic acid bacteria. A versioning and archiving policy will preserve static dataset snapshots in public repositories to guarantee long-term accessibility and citation stability. For now, the authors position FoodFungi as a bridge between fragmented literature and the researchers and food enterprises who need organized information on Monascus biology, metabolites, and the strategies used to regulate them.
Subject of Research: A new centralized database for Monascus fungi covering strains, metabolites, and metabolic regulation strategies
Article Title: A centralized database for the genus Monascus: biology, metabolites, and metabolic regulation strategies
Article References: Yang, Q., Wang, X., Dong, J., Lin, Q., Zhou, B., & Liu, J. (2026). A centralized database for the genus Monascus: biology, metabolites, and metabolic regulation strategies. Fungal Biology and Biotechnology, 13(1), Article 8. https://doi.org/10.1186/s40694-026-00209-z
Image Credits: AI Generated
DOI: 10.1186/s40694-026-00209-z
Keywords: Monascus, FoodFungi, database, secondary metabolites, Monascus pigments, monacolin K, citrinin, metabolic regulation, fermentation, ADMET, target prediction, biotechnology
News Source: Roger Howard. (October 29, 2026). Scientists Launch First Dedicated Database for Monascus Fungi and Their Metabolites. Scienmag.
