Systems biology of plant metabolic interactions.

Metabolism is the key cellular process of plant physiology. Understanding metabolism and its dynamical behavior under different conditions may help plant biotechnologists to design new cultivars with desired goals. Computational systems biochemistry and incorporation of different omics data unravelled active metabolism and its variations in plants. In this review, we mainly focus on the basics of flux balance analysis (FBA), elementary flux mode analysis (EFMA), and some advanced computational tools. We describe some important results that were obtained using these tools. Limitations and challenges are also discussed.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches.

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.

Integrated Analysis of Transcriptome and Metabolome Reveals Differential Responses to Alternaria brassicicola Infection in Cabbage (Brassica oleracea var. capitata).

Black spot, caused by Alternaria brassicicola (Ab), poses a serious threat to crucifer production, and knowledge of how plants respond to Ab infection is essential for black spot management. In the current study, combined transcriptomic and metabolic analysis was employed to investigate the response to Ab infection in two cabbage (Brassica oleracea var. capitata) genotypes, Bo257 (resistant to Ab) and Bo190 (susceptible to Ab). A total of 1100 and 7490 differentially expressed genes were identified in Bo257 (R_mock vs. R_Ab) and Bo190 (S_mock vs. S_Ab), respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that "metabolic pathways", "biosynthesis of secondary metabolites", and "glucosinolate biosynthesis" were the top three enriched KEGG pathways in Bo257, while "metabolic pathways", "biosynthesis of secondary metabolites", and "carbon metabolism" were the top three enriched KEGG pathways in Bo190. Further analysis showed that genes involved in extracellular reactive oxygen species (ROS) production, jasmonic acid signaling pathway, and indolic glucosinolate biosynthesis pathway were differentially expressed in response to Ab infection. Notably, when infected with Ab, genes involved in extracellular ROS production were largely unchanged in Bo257, whereas most of these genes were upregulated in Bo190. Metabolic profiling revealed 24 and 56 differentially accumulated metabolites in Bo257 and Bo190, respectively, with the majority being primary metabolites. Further analysis revealed that dramatic accumulation of succinate was observed in Bo257 and Bo190, which may provide energy for resistance responses against Ab infection via the tricarboxylic acid cycle pathway. Collectively, this study provides comprehensive insights into the Ab-cabbage interactions and helps uncover targets for breeding Ab-resistant varieties in cabbage.

Gene identification, expression analysis, and molecular docking of SAT and OASTL in the metabolic pathway of selenium in Cardamine hupingshanensis.

KEY MESSAGE: Identification of selenium stress-responsive expression and molecular docking of serine acetyltransferase (SAT) and O-acetyl serine (thiol) lyase (OASTL) in Cardamine hupingshanensis. A complex coupled with serine acetyltransferase (SAT) and O-acetyl serine (thiol) lyase (OASTL) is the key enzyme that catalyzes selenocysteine (Sec) synthesis in plants. The functions of SAT and OASTL genes were identified in some plants, but it is still unclear whether SAT and OASTL are involved in the selenium metabolic pathway in Cardamine hupingshanensis. In this study, genome-wide identification and comparative analysis of ChSATs and ChOASTLs were performed. The eight ChSAT genes were divided into three branches, and the thirteen ChOASTL genes were divided into four branches by phylogenetic analysis and sequence alignment, indicating the evolutionary conservation of the gene structure and its association with other plant species. qRT-PCR analysis showed that the ChSAT and ChOASTL genes were differentially expressed in different tissues under various selenium levels, suggesting their important roles in Sec synthesis. The ChSAT1;2 and ChOASTLA1;2 were silenced by the VIGS system to investigate their involvement in selenium metabolites in C. hupingshanensis. The findings contribute to understanding the gene functions of ChSATs and ChOASTLs in the selenium stress and provide a reference for further exploration of the selenium metabolic pathway in plants.

Ammonium-derived nitrous oxide is a global source in streams.

Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Dynamic evolution of the mTHF gene family associated with primary metabolism across life.

BACKGROUND: The folate cycle of one-carbon (C1) metabolism, which plays a central role in the biosynthesis of nucleotides and amino acids, demonstrates the significance of metabolic adaptation. We investigated the evolutionary history of the methylenetetrahydrofolate dehydrogenase (mTHF) gene family, one of the main drivers of the folate cycle, across life. RESULTS: Through comparative genomic and phylogenetic analyses, we found that several lineages of Archaea lacked domains vital for folate cycle function such as the mTHF catalytic and NAD(P)-binding domains of FolD. Within eukaryotes, the mTHF gene family diversified rapidly. For example, several duplications have been observed in lineages including the Amoebozoa, Opisthokonta, and Viridiplantae. In a common ancestor of Opisthokonta, FolD and FTHFS underwent fusion giving rise to the gene MTHFD1, possessing the domains of both genes. CONCLUSIONS: Our evolutionary reconstruction of the mTHF gene family associated with a primary metabolic pathway reveals dynamic evolution, including gene birth-and-death, gene fusion, and potential horizontal gene transfer events and/or amino acid convergence.

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts.

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.

Enhancement of acyl-CoA precursor supply for increased avermectin B1a production by engineering meilingmycin polyketide synthase and key primary metabolic pathway genes.

Avermectins (AVEs), a family of macrocyclic polyketides produced by Streptomyces avermitilis, have eight components, among which B1a is noted for its strong insecticidal activity. Biosynthesis of AVE "a" components requires 2-methylbutyryl-CoA (MBCoA) as starter unit, and malonyl-CoA (MalCoA) and methylmalonyl-CoA (MMCoA) as extender units. We describe here a novel strategy for increasing B1a production by enhancing acyl-CoA precursor supply. First, we engineered meilingmycin (MEI) polyketide synthase (PKS) for increasing MBCoA precursor supply. The loading module (using acetyl-CoA as substrate), extension module 7 (using MMCoA as substrate) and TE domain of MEI PKS were assembled to produce 2-methylbutyrate, providing the starter unit for B1a production. Heterologous expression of the newly designed PKS (termed Mei-PKS) in S. avermitilis wild-type (WT) strain increased MBCoA level, leading to B1a titer 262.2 µg/mL - 4.36-fold higher than WT value (48.9 µg/mL). Next, we separately inhibited three key nodes in essential pathways using CRISPRi to increase MalCoA and MMCoA levels in WT. The resulting strains all showed increased B1a titer. Combined inhibition of these key nodes in Mei-PKS expression strain increased B1a titer to 341.9 µg/mL. Overexpression of fatty acid ß-oxidation pathway genes in the strain further increased B1a titer to 452.8 µg/mL - 8.25-fold higher than WT value. Finally, we applied our precursor supply strategies to high-yield industrial strain A229. The strategies, in combination, led to B1a titer 8836.4 µg/mL - 37.8% higher than parental A229 value. These findings provide an effective combination strategy for increasing AVE B1a production in WT and industrial S. avermitilis strains, and our precursor supply strategies can be readily adapted for overproduction of other polyketides.

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation.

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

Self-regulated efficient production of L-threonine via an artificial quorum sensing system in engineered Escherichia coli.

Imbalance in carbon flux distribution is one of the most important factors affecting the further increase in the yield of high value-added natural products in microbial metabolic engineering. Meanwhile, the most common inducible expression systems are difficult to achieve industrial-scale production due to the addition of high-cost or toxic inducers during the fermentation process. Quorum sensing system, as a typical model for density-dependent induction of gene expression, has been widely applied in synthetic biology. However, there are currently few reports for efficient production of microbial natural products by using quorum sensing system to self-regulate carbon flux distribution. Here, we designed an artificial quorum sensing system to achieve efficient production of L-threonine in engineered Escherichia coli by altering the carbon flux distribution of the central metabolic pathways at specific periods. Under the combination of switch module and production module, the system was applied to divide the microbial fermentation process into two stages including growth and production, and improve the production of L-threonine by self-inducing the expression of pyruvate carboxylase and threonine extracellular transporter protease after a sufficient amount of cell growth. The final strain TWF106/pST1011, pST1042pr could produce 118.2 g/L L-threonine with a yield of 0.57 g/g glucose and a productivity of 2.46 g/(L· h). The establishment of this system has important guidance and application value for the production of other high value-added chemicals in microorganisms by self-regulation.

Loss of function of metabolic traits in typhoidal Salmonella without apparent genome degradation.

Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars are exclusively adapted to the human host, where they can cause life-long persistent infection. A distinct feature of these serovars is the presence of a relatively high number of degraded coding sequences coding for metabolic pathways, most likely a consequence of their adaptation to a single host. As a result of convergent evolution, these serovars shared many of the degraded coding sequences although often affecting different genes in the same metabolic pathway. However, there are several coding sequences that appear intact in one serovar while clearly degraded in the other, suggesting differences in their metabolic capabilities. Here, we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. Thus, the inability of S. Typhi to metabolize Glucose-6-Phosphate or 3-phosphoglyceric acid is due to the silencing of the expression of the genes encoding the transporters for these compounds due to point mutations in the transcriptional regulatory proteins. In contrast, its inability to utilize glucarate or galactarate is due to the presence of point mutations in the transporter and enzymes necessary for the metabolism of these sugars. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars and highlight a limitation of bioinformatic approaches to predict metabolic capabilities. IMPORTANCE: Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars can only infect the human host, where they can cause life-long persistent infection. Because of their adaptation to the human host, these bacterial pathogens have changed their metabolism, leading to the loss of their ability to utilize certain nutrients. In this study we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars.

Exploring the regulatory role of non-coding RNAs in fiber development and direct regulation of GhKCR2 in the fatty acid metabolic pathway in upland cotton.

Cotton fiber holds immense importance as the primary raw material for the textile industry. Consequently, comprehending the regulatory mechanisms governing fiber development is pivotal for enhancing fiber quality. Our study aimed to construct a regulatory network of competing endogenous RNAs (ceRNAs) and assess the impact of non-coding RNAs on gene expression throughout fiber development. Through whole transcriptome data analysis, we identified differentially expressed genes (DEGs) regulated by non-coding RNA (ncRNA) that were predominantly enriched in phenylpropanoid biosynthesis and the fatty acid elongation pathway. This analysis involved two contrasting phenotypic materials (J02-508 and ZRI015) at five stages of fiber development. Additionally, we conducted a detailed analysis of genes involved in fatty acid elongation, including KCS, KCR, HACD, ECR, and ACOT, to unveil the factors contributing to the variation in fatty acid elongation between J02-508 and ZRI015. Through the integration of histochemical GUS staining, dual luciferase assay experiments, and correlation analysis of expression levels during fiber development stages for lncRNA MSTRG.44818.23 (MST23) and GhKCR2, we elucidated that MST23 positively regulates GhKCR2 expression in the fatty acid elongation pathway. This identification provides valuable insights into the molecular mechanisms underlying fiber development, emphasizing the intricate interplay between non-coding RNAs and protein-coding genes.

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae.

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

Similar metabolic pathways are affected in both Congenital Myasthenic Syndrome-22 and Prader-Willi Syndrome.

Loss of prolyl endopeptidase-like (PREPL) encoding a serine hydrolase with (thio)esterase activity leads to the recessive metabolic disorder Congenital Myasthenic Syndrome-22 (CMS22). It is characterized by severe neonatal hypotonia, feeding problems, growth retardation, and hyperphagia leading to rapid weight gain later in childhood. The phenotypic similarities with Prader-Willi syndrome (PWS) are striking, suggesting that similar pathways are affected. The aim of this study was to identify changes in the hypothalamic-pituitary axis in mouse models for both disorders and to examine mitochondrial function in skin fibroblasts of patients and knockout cell lines. We have demonstrated that Prepl is downregulated in the brains of neonatal PWS-IC-p/+m mice. In addition, the hypothalamic-pituitary axis is similarly affected in both Prepl-/- and PWS-IC-p/+m mice resulting in defective orexigenic signaling and growth retardation. Furthermore, we demonstrated that mitochondrial function is altered in PREPL knockout HEK293T cells and can be rescued with the supplementation of coenzyme Q10. Finally, PREPL-deficient and PWS patient skin fibroblasts display defective mitochondrial bioenergetics. The mitochondrial dysfunction in PWS fibroblasts can be rescued by overexpression of PREPL. In conclusion, we provide the first molecular parallels between CMS22 and PWS, raising the possibility that PREPL substrates might become therapeutic targets for treating both disorders.

Understanding and computational design of genetic circuits of metabolic networks.

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli's strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli's circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.

Machine learning enables identification of an alternative yeast galactose utilization pathway.

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype-phenotype map, and their application will uncover novel biology, even in well-studied traits.

Multi-modular metabolic engineering and efflux engineering for enhanced lycopene production in recombinant Saccharomyces cerevisiae.

Lycopene has been widely used in the food industry and medical field due to its antioxidant, anti-cancer, and anti-inflammatory properties. However, achieving efficient manufacture of lycopene using chassis cells on an industrial scale remains a major challenge. Herein, we attempted to integrate multiple metabolic engineering strategies to establish an efficient and balanced lycopene biosynthetic system in Saccharomyces cerevisiae. First, the lycopene synthesis pathway was modularized to sequentially enhance the metabolic flux of the mevalonate pathway, the acetyl-CoA supply module, and lycopene exogenous enzymatic module. The modular operation enabled the efficient conversion of acetyl-CoA to downstream pathway of lycopene synthesis, resulting in a 3.1-fold increase of lycopene yield. Second, we introduced acetate as an exogenous carbon source and utilized an acetate-repressible promoter to replace the natural ERG9 promoter. This approach not only enhanced the supply of acetyl-CoA but also concurrently diminished the flux toward the competitive ergosterol pathway. As a result, a further 42.3% increase in lycopene production was observed. Third, we optimized NADPH supply and mitigated cytotoxicity by overexpressing ABC transporters to promote lycopene efflux. The obtained strain YLY-PDR11 showed a 12.7-fold increase in extracellular lycopene level compared to the control strain. Finally, the total lycopene yield reached 343.7 mg/L, which was 4.3 times higher than that of the initial strain YLY-04. Our results demonstrate that combining multi-modular metabolic engineering with efflux engineering is an effective approach to improve the production of lycopene. This strategy can also be applied to the overproduction of other desirable isoprenoid compounds with similar synthesis and storage patterns in S. cerevisiae. ONE-SENTENCE SUMMARY: In this research, lycopene production in yeast was markedly enhanced by integrating a multi-modular approach, acetate signaling-based down-regulation of competitive pathways, and an efflux optimization strategy.

Porphyrin overdrive rewires cancer cell metabolism.

All cancer cells reprogram metabolism to support aberrant growth. Here, we report that cancer cells employ and depend on imbalanced and dynamic heme metabolic pathways, to accumulate heme intermediates, that is, porphyrins. We coined this essential metabolic rewiring "porphyrin overdrive" and determined that it is cancer-essential and cancer-specific. Among the major drivers are genes encoding mid-step enzymes governing the production of heme intermediates. CRISPR/Cas9 editing to engineer leukemia cell lines with impaired heme biosynthetic steps confirmed our whole-genome data analyses that porphyrin overdrive is linked to oncogenic states and cellular differentiation. Although porphyrin overdrive is absent in differentiated cells or somatic stem cells, it is present in patient-derived tumor progenitor cells, demonstrated by single-cell RNAseq, and in early embryogenesis. In conclusion, we identified a dependence of cancer cells on non-homeostatic heme metabolism, and we targeted this cancer metabolic vulnerability with a novel "bait-and-kill" strategy to eradicate malignant cells.