Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

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.

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.

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.

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.

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.

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.

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.

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.

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/.

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.

Reliability and validity of Foot Posture Index (FPI-6) for evaluating foot posture in participants with low back pain.

Previous studies have demonstrated that Foot Posture Index (FPI-6) is a valid and moderately reliable tool to evaluate foot posture. However, data about reliability and validity of FPI-6 in the assessment of foot posture in people with low back pain (LBP) is lacking. To investigate reliability and validity of FPI-6 in the assessment of foot posture in people with LBP. Thirty volunteers with LBP, aged 20-64 years, were recruited for the research and assessed by two raters. The data measured by different raters on the same day were used to calculate the inter-rater reliability. The data measured by the same rater on different dates were used to calculate the test-retest reliability. The reliability of FPI-6 was tested with intraclass correlation coefficient (ICC), and absolute reliability with standard error of measurement (SEM), minimal detectable change (MDC) and Bland-Altman analysis. The validity of FPI-6 was tested with Exploratory Factor Analysis (EFA) and Spearman's correlation coefficients. The FPI-6 indicated excellent inter-rater and test-retest reliability in the evaluation of foot posture in people with LBP (ICC = 0.97 and 0.95). The agreement for inter-rater and test-retest was excellent based on the SEM (SEM = 0.12) and MDC value (MDC = 0.33). Bland-Altman plots showed that there was no significant systematic bias for the agreement on the ground of low mean difference (< 1). The EFA suggested that the fit indices were considered acceptable according to the Kaiser-Meyer-Olkin (KMO) value (KMO = 0.620) and Bartlett's sphericity test (P < 0.01). There was a statistically significant positive correlation between each item and total score of FPI-6 because the Spearman's correlation coefficient of six items were all > 0.3 (P < 0.01). The inter-rater and test-retest reliability and validity of FPI-6 on people with LBP were proved reliable. It might be considered a reliable and valid adjunctive tool to detect possible changes of foot posture after interventions in patients with LBP.

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.

Acupuncture for low back and/or pelvic pain during pregnancy: a systematic review and meta-analysis of randomised controlled trials.

OBJECTIVE: Acupuncture is emerging as a potential therapy for relieving pain, but the effectiveness of acupuncture for relieving low back and/or pelvic pain (LBPP) during the pregnancy remains controversial. This meta-analysis aims to investigate the effects of acupuncture on pain, functional status and quality of life for women with LBPP pain during the pregnancy. DESIGN: Systematic review and meta-analysis. DATA SOURCES: The PubMed, EMBASE databases, Web of Science and Cochrane Library were searched for relevant randomised controlled trials (RCTs) from inception to 15 January 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: RCTs evaluating the effects of acupuncture on LBPP during the pregnancy were included. DATA EXTRACTION AND SYNTHESIS: The data extraction and study quality assessment were independently performed by three reviewers. The mean differences (MDs) with 95% CIs for pooled data were calculated. We assessed the confidence in the evidence using the Grading of Recommendations Assessment, Development and Evaluation framework. MAIN OUTCOMES AND MEASURES: The primary outcomes were pain, functional status and quality of life. The secondary outcomes were overall effects (a questionnaire at a post-treatment visit within a week after the last treatment to determine the number of people who received good or excellent help), analgesic consumption, Apgar scores >7 at 5 min, adverse events, gestational age at birth, induction of labour and mode of birth. RESULTS: This meta-analysis included 10 studies, reporting on a total of 1040 women. Overall, acupuncture significantly relieved pain during pregnancy (MD=1.70, 95% CI: (0.95 to 2.45), p<0.00001, I2=90%) and improved functional status (MD=12.44, 95% CI: (3.32 to 21.55), p=0.007, I2=94%) and quality of life (MD=-8.89, 95% CI: (-11.90 to -5.88), p<0.00001, I2 = 57%). There was a significant difference for overall effects (OR=0.13, 95% CI: (0.07 to 0.23), p<0.00001, I2 = 7%). However, there was no significant difference for analgesic consumption during the study period (OR=2.49, 95% CI: (0.08 to 80.25), p=0.61, I2=61%) and Apgar scores of newborns (OR=1.02, 95% CI: (0.37 to 2.83), p=0.97, I2 = 0%). Preterm birth from acupuncture during he study period was reported in two studies. Although preterm contractions were reported in two studies, all infants were in good health at birth. In terms of gestational age at birth, induction of labour and mode of birth, only one study reported the gestational age at birth (mean gestation 40 weeks). Thus, prospective randomised clinical studies or clinical follow-up studies were hence desirable to further evaluate these outcomes. CONCLUSIONS: Acupuncture significantly improved pain, functional status and quality of life in women with LBPP during the pregnancy. Additionally, acupuncture had no observable severe adverse influences on the newborns. More large-scale and well-designed RCTs are still needed to further confirm these results. PROSPERO REGISTRATION NUMBER: CRD42021241771.

Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

Metabolomics Analysis Coupled with Weighted Gene Co-Expression Network Analysis Unravels the Associations of Tricarboxylic Acid Cycle-Intermediates with Edible Pigments Produced by Monascus purpureus (Hong Qu).

Monascus azaphilones pigments (MonAzPs) produced by microbial fermentation are widely used as food chemicals for coloring and supplying beneficial biological attributes. In this study, a fermentation perturbation strategy was implemented by separately adding different amino acids, and detecting the intracellular metabolome via UHPLC-Q-Orbitrap HRMS. With the aid of weighted gene co-expression network analysis, two metabolic intermediates, fumarate and malate, involved in the tricarboxylic acid cycle, were identified as the hub metabolites. Moreover, exogenous addition of fumarate or malate significantly promoted red pigment production, and reduced orange/yellow pigment production. The importance of the tricarboxylic acid cycle was further emphasized by detecting intracellular levels of ATP, NAD(P)H, and expression of oxidoreductase-coding genes located in the MonAzPs synthetic gene cluster, suggesting a considerable effect of the energy supply on MonAzPs synthesis. Collectively, metabolomics is a powerful approach to position the crucial metabolic regulatory factors, and facilitate the development of engineering strategies for targeted regulation, lower trial-and-error cost, and advance safe and controllable processes for fermented food chemistry industries.

Whole-Genome Sequencing and Analysis of the White-Rot Fungus Ceriporia lacerata Reveals Its Phylogenetic Status and the Genetic Basis of Lignocellulose Degradation and Terpenoid Synthesis.

Ceriporia lacerata is an endophytic white-rot fungus that has lignocellulolytic and terpenoid-biosynthetic abilities. However, little is known about the genomic architecture of this fungus, even at the genus level. In this study, we present the first de novo genome assembly of C. lacerata (CGMCC No. 10485), based on PacBio long-read and Illumina short-read sequencing. The size of the C. lacerata genome is approximately 36 Mb (N50, 3.4 Mb). It encodes a total of 13,243 genes, with further functional analysis revealing that these genes are primarily involved in primary metabolism and host interactions in this strain's saprophytic lifestyle. Phylogenetic analysis based on ITS demonstrated a primary evolutionary position for C. lacerata, while the phylogenetic analysis based on orthogroup inference and average nucleotide identity revealed high-resolution phylogenetic details in which Ceriporia, Phlebia, Phlebiopsis, and Phanerochaete belong to the same evolutionary clade within the order Polyporales. Annotation of carbohydrate-active enzymes across the genome yielded a total of 806 genes encoding enzymes that decompose lignocellulose, particularly ligninolytic enzymes, lytic polysaccharides monooxygenases, and enzymes involved in the biodegradation of aromatic components. These findings illustrate the strain's adaptation to woody habitats, which requires the degradation of lignin and various polycyclic aromatic hydrocarbons. The terpenoid-production potential of C. lacerata was evaluated by comparing the genes of terpenoid biosynthetic pathways across nine Polyporales species. The shared genes highlight the major part of terpenoid synthesis pathways, especially the mevalonic acid pathway, as well as the main pathways of sesquiterpenoid, monoterpenoid, diterpenoid, and triterpenoid synthesis, while the strain-specific genes illustrate the distinct genetic factors determining the synthesis of structurally diverse terpenoids. This is the first genomic analysis of a species from this genus that we are aware of, and it will help advance functional genome research and resource development of this important fungus for applications in renewable energy, pharmaceuticals, and agriculture.