REME: an integrated platform for reaction enzyme mining and evaluation.

A key challenge in pathway design is finding proper enzymes that can be engineered to catalyze a non-natural reaction. Although existing tools can identify potential enzymes based on similar reactions, these tools encounter several issues. Firstly, the calculated similar reactions may not even have the same reaction type. Secondly, the associated enzymes are often numerous and identifying the most promising candidate enzymes is difficult due to the lack of data for evaluation. Thirdly, existing web tools do not provide interactive functions that enable users to fine-tune results based on their expertise. Here, we present REME (https://reme.biodesign.ac.cn/), the first integrated web platform for reaction enzyme mining and evaluation. Combining atom-to-atom mapping, atom type change identification, and reaction similarity calculation enables quick ranking and visualization of reactions similar to an objective non-natural reaction. Additional functionality enables users to filter similar reactions by their specified functional groups and candidate enzymes can be further filtered (e.g. by organisms) or expanded by Enzyme Commission number (EC) or sequence homology. Afterward, enzyme attributes (such as kcat, Km, optimal temperature and pH) can be assessed with deep learning-based methods, facilitating the swift identification of potential enzymes that can catalyze the non-natural reaction.

CAVE: a cloud-based platform for analysis and visualization of metabolic pathways.

Flux balance analysis (FBA) is an important method for calculating optimal pathways to produce industrially important chemicals in genome-scale metabolic models (GEMs). However, for biologists, the requirement of coding skills poses a significant obstacle to using FBA for pathway analysis and engineering target identification. Additionally, a time-consuming manual drawing process is often needed to illustrate the mass flow in an FBA-calculated pathway, making it challenging to detect errors or discover interesting metabolic features. To solve this problem, we developed CAVE, a cloud-based platform for the integrated calculation, visualization, examination and correction of metabolic pathways. CAVE can analyze and visualize pathways for over 100 published GEMs or user-uploaded GEMs, allowing for quicker examination and identification of special metabolic features in a particular GEM. Additionally, CAVE offers model modification functions, such as gene/reaction removal or addition, making it easy for users to correct errors found in pathway analysis and obtain more reliable pathways. With a focus on the design and analysis of optimal pathways for biochemicals, CAVE complements existing visualization tools based on manually drawn global maps and can be applied to a broader range of organisms for rational metabolic engineering. CAVE is available at https://cave.biodesign.ac.cn/.

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kcat data.

BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study. RESULTS: Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based kcat data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct kcat collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted kcat values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila. CONCLUSIONS: In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based kcat data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism.

ERMer: a serverless platform for navigating, analyzing, and visualizing Escherichia coli regulatory landscape through graph database.

Cellular regulation is inherently complex, and one particular cellular function is often controlled by a cascade of different types of regulatory interactions. For example, the activity of a transcription factor (TF), which regulates the expression level of downstream genes through transcriptional regulation, can be regulated by small molecules through compound-protein interactions. To identify such complex regulatory cascades, traditional relational databases require ineffective additional operations and are computationally expensive. In contrast, graph databases are purposefully developed to execute such deep searches efficiently. Here, we present ERMer (E. coli Regulation Miner), the first cloud platform for mining the regulatory landscape of Escherichia coli based on graph databases. Combining the AWS Neptune graph database, AWS lambda function, and G6 graph visualization engine enables quick search and visualization of complex regulatory cascades/patterns. Users can also interactively navigate the E. coli regulatory landscape through ERMer. Furthermore, a Q&A module is included to showcase the power of graph databases in answering complex biological questions through simple queries. The backend graph model can be easily extended as new data become available. In addition, the framework implemented in ERMer can be easily migrated to other applications or organisms. ERMer is available at https://ermer.biodesign.ac.cn/.

DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes.

The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub.

A CRISPR-based chromosomal-separation technique for Escherichia coli.

BACKGROUND: Natural life systems can be significantly modified at the genomic scale by human intervention, demonstrating the great innovation capacity of genome engineering. Large epi-chromosomal DNA structures were established in Escherichia coli cells, but some of these methods were inconvenient, using heterologous systems, or relied on engineered E. coli strains. RESULTS: The wild-type model bacterium E. coli has a single circular chromosome. In this work, a novel method was developed to split the original chromosome of wild-type E. coli. With this method, novel E. coli strains containing two chromosomes of 0.10 Mb and 4.54 Mb, and 2.28 Mb and 2.36 Mb were created respectively, designated as E. coli0.10/4.54 and E. coli2.28/2.36. The new chromosomal arrangement was proved by PCR amplification of joint regions as well as a combination of Nanopore and Illumina sequencing analysis. While E. coli0.10/4.54 was quite stable, the two chromosomes of E. coli2.28/2.36 population recombined into a new chromosome (Chr.4.64MMut), via recombination. Both engineered strains grew slightly slower than the wild-type, and their cell shapes were obviously elongated. CONCLUSION: Finally, we successfully developed a simple CRISPR-based genome engineering technique for the construction of multi-chromosomal E. coli strains with no heterologous genetic parts. This technique might be applied to other prokaryotes for synthetic biology studies and applications in the future.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models.

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis.

BACKGROUND: Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks. RESULTS: Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks. CONCLUSIONS: The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Analyzing the genetic characteristics of a tryptophan-overproducing Escherichia coli.

L-tryptophan (L-trp) production in Escherichia coli has been developed by employing random mutagenesis and selection for a long time, but this approach produces an unclear genetic background. Here, we generated the L-trp overproducer TPD5 by combining an intracellular L-trp biosensor and fluorescence-activated cell sorting (FACS) in E. coli, and succeeded in elucidating the genetic basis for L-trp overproduction. The most significant identified positive mutations affected TnaA (deletion), AroG (S211F), TrpE (A63V), and RpoS (nonsense mutation Q33*). The underlying structure-function relationships of the feedback-resistant AroG (S211F) and TrpE (A63V) mutants were uncovered based on protein structure modeling and molecular dynamics simulations, respectively. According to transcriptomic analysis, the global regulator RpoS not only has a great influence on cell growth and morphology, but also on carbon utilization and the direction of carbon flow. Finally, by balancing the concentrations of the L-trp precursors' serine and glutamine based on the above analysis, we further increased the titer of L-trp to 3.18 g/L with a yield of 0.18 g/g. The analysis of the genetic characteristics of an L-trp overproducing E. coli provides valuable information on L-trp synthesis and elucidates the phenotype and complex cellular properties in a high-yielding strain, which opens the possibility to transfer beneficial mutations and reconstruct an overproducer with a clean genetic background.

iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis.

BACKGROUND: Constraint-based metabolic network models have been widely used in pheno-typic prediction and metabolic engineering design. In recent years, researchers have attempted to im-prove prediction accuracy by integrating regulatory information and multiple types of "omics" data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed. METHODS AND RESULTS: We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction bounda-ries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods. CONCLUSION: Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise pre-dictions of metabolic fluxes by using expression values normalized based on GO.

Model-based reconstruction of synthetic promoter library in Corynebacterium glutamicum.

OBJECTIVE: To develop an efficient synthetic promoter library for fine-tuned expression of target genes in Corynebacterium glutamicum. RESULTS: A synthetic promoter library for C. glutamicum was developed based on conserved sequences of the - 10 and - 35 regions. The synthetic promoter library covered a wide range of strengths, ranging from 1 to 193% of the tac promoter. 68 promoters were selected and sequenced for correlation analysis between promoter sequence and strength with a statistical model. A new promoter library was further reconstructed with improved promoter strength and coverage based on the results of correlation analysis. Tandem promoter P70 was finally constructed with increased strength by 121% over the tac promoter. The promoter library developed in this study showed a great potential for applications in metabolic engineering and synthetic biology for the optimization of metabolic networks. CONCLUSIONS: To the best of our knowledge, this is the first reconstruction of synthetic promoter library based on statistical analysis of C. glutamicum.

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

Omics data analysis reveals the system-level constraint on cellular amino acid composition.

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Independent component analysis of Corynebacterium glutamicum transcriptomes reveals its transcriptional regulatory network.

Gene expression in bacteria is regulated by multiple transcription factors. Clarifying the regulation mechanism of gene expression is necessary to understand bacterial physiological activities. To further understand the structure of the transcriptional regulatory network of Corynebacterium glutamicum, we applied independent component analysis, an unsupervised machine learning algorithm, to the high-quality C. glutamicum gene expression profile which includes 263 samples from 29 independent projects. We obtained 87 robust independent regulatory modules (iModulons). These iModulons explain 76.7% of the variance in the expression profile and constitute the quantitative transcriptional regulatory network of C. glutamicum. By analyzing the constituent genes in iModulons, we identified potential targets for 20 transcription factors. We also captured the changes in iModulon activities under different growth rates and dissolved oxygen concentrations, demonstrating the ability of iModulons to comprehensively interpret transcriptional responses to environmental changes. In summary, this study provides a genome-scale quantitative transcriptional regulatory network for C. glutamicum and informs future research on complex changes in the transcriptome.

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework.

Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved. Here, we report HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques. HDMLF is composed of an embedding core and a learning core; the embedding core adopts the latest protein language model for protein sequence embedding, and the learning core conducts the EC number prediction. Specifically, HDMLF is designed on the basis of a gated recurrent unit framework to perform EC number prediction in the multi-objective hierarchy, multitasking manner. Additionally, we introduced an attention layer to optimize the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against 4 representative methods demonstrate that HDMLF stably delivers the highest performance, which improves accuracy and F1 score by 60% and 40% over the state of the art, respectively. An additional case study of tyrB predicted to compensate for the loss of aspartate aminotransferase aspC, as reported in a previous experimental study, shows that our model can also be used to uncover the enzyme promiscuity. Finally, we established a web platform, namely, ECRECer (https://ecrecer.biodesign.ac.cn), using an entirely could-based serverless architecture and provided an offline bundle to improve usability.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501.

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments.

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

Artificial Diploid Escherichia coli by a CRISPR Chromosome-Doubling Technique.

Synthetic biology has been represented by the creation of artificial life forms at the genomic scale. In this work, a CRISPR-based chromosome-doubling technique is designed to first construct an artificial diploid Escherichia coli cell. The stable single-cell diploid E. coli is isolated by both maximal dilution plating and flow cytometry, and confirmed with quantitative PCR, fluorescent in situ hybridization, and third-generation genome sequencing. The diploid E. coli has a greatly reduced growth rate and elongated cells at 4-5 µm. It is robust against radiation, and the survival rate after exposure to UV increased 40-fold relative to WT. As a novel life form, the artificial diploid E. coli is an ideal substrate for research fundamental questions in life science concerning polyploidy. And this technique may be applied to other bacteria.

Insight into the phylogeny and metabolic divergence of Monascus species (M. pilosus, M. ruber, and M. purpureus) at the genome level.

Background: Species of the genus Monascus are economically important and widely used in the production of food colorants and monacolin K. However, they have also been known to produce the mycotoxin citrinin. Currently, taxonomic knowledge of this species at the genome level is insufficient. Methods: This study presents genomic similarity analyses through the analysis of the average nucleic acid identity of the genomic sequence and the whole genome alignment. Subsequently, the study constructed a pangenome of Monascus by reannotating all the genomes and identifying a total of 9,539 orthologous gene families. Two phylogenetic trees were constructed based on 4,589 single copy orthologous protein sequences and all the 5,565 orthologous proteins, respectively. In addition, carbohydrate active enzymes, secretome, allergic proteins, as well as secondary metabolite gene clusters were compared among the included 15 Monascus strains. Results: The results clearly revealed a high homology between M. pilosus and M. ruber, and their distant relationship with M. purpureus. Accordingly, all the included 15 Monascus strains should be classified into two distinctly evolutionary clades, namely the M. purpureus clade and the M. pilosus-M. ruber clade. Moreover, gene ontology enrichment showed that the M. pilosus-M. ruber clade had more orthologous genes involved with environmental adaptation than the M. purpureus clade. Compared to Aspergillus oryzae, all the Monascus species had a substantial gene loss of carbohydrate active enzymes. Potential allergenic and fungal virulence factor proteins were also found in the secretome of Monascus. Furthermore, this study identified the pigment synthesis gene clusters present in all included genomes, but with multiple nonessential genes inserted in the gene cluster of M. pilosus and M. ruber compared to M. purpureus. The citrinin gene cluster was found to be intact and highly conserved only among M. purpureus genomes. The monacolin K gene cluster was found only in the genomes of M. pilosus and M. ruber, but the sequence was more conserved in M. ruber. Conclusion: This study provides a paradigm for phylogenetic analysis of the genus Monascus, and it is believed that this report will lead to a better understanding of these food microorganisms in terms of classification, metabolic differentiation, and safety.