Metabolic Engineering Applications Research Articles

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.

Light-inducible protein degradation in E. coli with the LOVdeg tag.

Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light-controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVdeg, a tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVdeg by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVdeg tag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVdeg system. Finally, we use the LOVdeg tag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVdeg tag system and introduce a powerful new tool for bacterial optogenetics.

Light-inducible protein degradation in E. coli with the LOVdeg tag

Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light-controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVdeg, a tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVdeg by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVdeg tag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVdeg system. Finally, we use the LOVdeg tag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVdeg tag system and introduce a powerful new tool for bacterial optogenetics.

Copper-Induced InVivo Gene Amplification in Budding Yeast.

In the biotechnological industry, multicopy gene integration represents an effective strategy to maintain a high-level production of recombinant proteins and to assemble multigene biochemical pathways. In this study, we developed copper-induced invivo gene amplification in budding yeast for multicopy gene expressions. To make copper as an effective selection pressure, we first constructed a copper-sensitive yeast strain by deleting the CUP1 gene encoding a small metallothionein-like protein for copper resistance. Subsequently, the reporter gene fused with a proline-glutamate-serine-threonine-destabilized CUP1 was integrated at the δ sites of retrotransposon (Ty) elements to counter the copper toxicity at 100 μM Cu2+. We further demonstrated the feasibility of modulating chromosomal rearrangements for increased protein expression under higher copper concentrations. In addition, we also demonstrated a simplified design of integrating the expression cassette at the CUP1 locus to achieve tandem duplication under high concentrations of copper. Taken together, we envision that this method of copper-induced invivo gene amplification would serve as a robust and useful method for protein overproduction and metabolic engineering applications in budding yeast.

A Fermentation State Marker Rule Design Task in Metabolic Engineering.

There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate μ is above 0.060 h-1, AND CO2 production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2-10, where all metabolic fluxes units are mmol ∗ gDW-1 ∗ h-1. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.

A Standardized Set of MoClo-Compatible Inducible Promoter Systems for Tunable Gene Expression in Yeast.

Small-molecule control of gene expression underlies the function of numerous engineered gene circuits that are capable of environmental sensing, computation, and memory. While many recently developed inducible promoters have been tailor-made for bacteria or mammalian cells, relatively few new systems have been built for Saccharomyces cerevisiae, limiting the scale of synthetic biology work that can be done in yeast. To address this, we created the yeast Tunable Expression Systems Toolkit (yTEST), which contains a set of five extensively characterized inducible promoter systems regulated by the small-molecules doxycycline (Dox), abscisic acid (ABA), danoprevir (DNV), 1-naphthaleneacetic acid (NAA), and 5-phenyl-indole-3-acetic acid (5-Ph-IAA). Assembly was made to be compatible with the modular cloning yeast toolkit (MoClo-YTK) to enhance the ease of use and provide a framework to benchmark and standardize each system. Using this approach, we built multiple systems with maximal expression levels greater than those of the strong constitutive TDH3 promoter. Furthermore, each of the five classes of systems could be induced at least 60-fold after a 6 h induction and the highest fold change observed was approximately 300. Thus, yTEST provides a reliable, diverse, and customizable set of inducible promoters to modulate gene expression in yeast for applications in synthetic biology, metabolic engineering, and basic research.

Establishing Tunable Genetic Logic Gates with Versatile Dynamic Performance by Varying Regulatory Parameters.

Genetic logic gates can be employed in metabolic engineering and synthetic biology to regulate gene expression based on diverse inputs. Design of tunable genetic logic gates with versatile dynamic performance is essential for expanding the usability of these toolsets. Here, using the p-coumaric acid biosensor system as a proof-of-concept, we initially investigated the parameters influencing the buffer (BUF) genetic logic gates. Subsequently, integrating binding sequences from the p-coumaric acid biosensor system and tetR or lacI regulation systems into a constitutive promoter yielded AND genetic logic gates. Additionally, characterized antisense RNAs (asRNAs) or single guide RNAs (sgRNAs) with various repression efficiencies were combined with BUF gates to construct a suite of p-coumaric acid-triggered NOT genetic logic gates. Finally, the designed BUF and NOT gates were combined to construct bifunctional genetic circuits that were subjected to orthogonality evaluation. The genetic logic gates established in this study can serve as valuable tools in future applications of metabolic engineering and synthetic biology.

POMBOX: A Fission Yeast Cloning Toolkit for Molecular and Synthetic Biology.

The fission yeast Schizosaccharomyces pombe is a popular model organism in molecular biology and cell physiology. With its ease of genetic manipulation and growth, supported by in-depth functional annotations in the PomBase database and genome-wide metabolic models,S. pombe is an attractive option for synthetic biology applications. However,S. pombe currently lacks modular tools for generating genetic circuits with more than 1 transcriptional unit. We developed a toolkit to address this gap. Adapted from the MoClo-YTK plasmid kit for Saccharomyces cerevisiae and using the same modular cloning grammar, our POMBOX toolkit is designed to facilitate fast, efficient, and modular construction of genetic circuits inS. pombe. It allows for interoperability when working with DNA sequences that are functional in bothS. cerevisiae and S. pombe (e.g., protein tags, antibiotic resistance cassettes, and coding sequences). Moreover, POMBOX enables the modular assembly of multigene pathways and increases the possible pathway length from 6 to 12 transcriptional units. We also adapted the stable integration vector homology arms to Golden Gate assembly and tested the genomic integration success rates depending on different sequence sizes, from 4 to 24 kb. We included 14 S. pombe promoters that we characterized using two fluorescent proteins, in both minimally defined (EMM2─Edinburgh minimal media) and complex (YES─yeast extract with supplements) media. Then, we examined the efficacy of 6 S. cerevisiae and 6 synthetic terminators in S. pombe. Finally, we used the POMBOX kit for a synthetic biology application in metabolic engineering and expressed plant enzymes in S. pombe to produce specialized metabolite precursors, namely, methylxanthine, amorpha-4,11-diene, and cinnamic acid from the purine, mevalonate, and aromatic amino acid pathways.

Metagenomics harvested genus-specific single-stranded DNA-annealing proteins improve and expand recombineering in Pseudomonas species.

The widespread Pseudomonas genus comprises a collection of related species with remarkable abilities to degrade plastics and polluted wastes and to produce a broad setof valuable compounds, ranging from bulk chemicals to pharmaceuticals. Pseudomonas possess characteristics of tolerance and stress resistance making them valuable hosts for industrial and environmental biotechnology. However, efficient and high-throughput genetic engineering tools have limited metabolic engineering efforts and applications. To improve their genome editing capabilities, we firstemployed a computational biology workflow to generate a genus-specific library of potential single-stranded DNA-annealing proteins (SSAPs). Assessment of the library was performed in different Pseudomonas using a high-throughput pooled recombinase screen followed by Oxford Nanopore NGS analysis. Among different active variants with variable levels of allelic replacement frequency (ARF), efficient SSAPs were found and characterized for mediating recombineering in the four tested species. New variants yielded higher ARFs than existing ones in Pseudomonas putida andPseudomonas aeruginosa, andexpanded the field of recombineering in Pseudomonas taiwanensisand Pseudomonas fluorescens. These findings will enhance the mutagenesis capabilities of these members of the Pseudomonas genus, increasing the possibilities for biotransformation and enhancingtheir potential for synthetic biology applications. .

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems—biosensor-based controllers—for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

From Saccharomyces cerevisiae to Ethanol: Unlocking the Power of Evolutionary Engineering in Metabolic Engineering Applications.

Increased human population and the rapid decline of fossil fuels resulted in a global tendency to look for alternative fuel sources. Environmental concerns about fossil fuel combustion led to a sharp move towards renewable and environmentally friendly biofuels. Ethanol has been the primary fossil fuel alternative due to its low carbon emission rates, high octane content and comparatively facile microbial production processes. In parallel to the increased use of bioethanol in various fields such as transportation, heating and power generation, improvements in ethanol production processes turned out to be a global hot topic. Ethanol is by far the leading yeast output amongst a broad spectrum of bio-based industries. Thus, as a well-known platform microorganism and native ethanol producer, baker's yeast Saccharomyces cerevisiae has been the primary subject of interest for both academic and industrial perspectives in terms of enhanced ethanol production processes. Metabolic engineering strategies have been primarily adopted for direct manipulation of genes of interest responsible in mainstreams of ethanol metabolism. To overcome limitations of rational metabolic engineering, an alternative bottom-up strategy called inverse metabolic engineering has been widely used. In this context, evolutionary engineering, also known as adaptive laboratory evolution (ALE), which is based on random mutagenesis and systematic selection, is a powerful strategy to improve bioethanol production of S. cerevisiae. In this review, we focus on key examples of metabolic and evolutionary engineering for improved first- and second-generation S. cerevisiae bioethanol production processes. We delve into the current state of the field and show that metabolic and evolutionary engineering strategies are intertwined and many metabolically engineered strains for bioethanol production can be further improved by powerful evolutionary engineering strategies. We also discuss potential future directions that involve recent advancements in directed genome evolution, including CRISPR-Cas9 technology.

Regioselective O-acetylation of various glucosides catalyzed by Escherichia coli maltose O-acetyltransferase.

Escherichia coli, a well-known prokaryotic organism, has been widely employed as a versatile host for heterologous overexpression of proteins/biocatalysts and the production of pharmaceutically important intermediates/small molecules. However, some E. coli endogenous enzymes showing substrate promiscuity may disturb the heterologous metabolic flux, which will result in the reduction of substrates, intermediates, and target products. Here we reported an unexpected E. coli-catalyzed regioselective O-acetylation of various glucosides. The regioselectively O-acetylated products, 6'-O-acetyl-loganin and 6'-O-acetyl-loganic acid, were obtained and characterized from the enzymatic reaction in which the supernatants of E. coli expressing either CaCYP72A565 and CaCPR, the key enzymes involved in camptothecin biosynthesis, or empty vector were used as catalyst and loganin and loganic acid as independent substrate. An alkaloidal glucoside strictosamide was converted into the regioselectively O-acetylated product 6'-O-acetyl-strictosamide, implying substrate promiscuity of the E. coli-catalyzed O-acetylation reaction. Furthermore, 8 glucosides, including 5 iridoid glucosides and 3 flavonoid glucosides, were successfully converted into the regioselectively O-acetylated products by E. coli, indicating the wide substrate range for the unexpected E. coli-catalyzed O-acetylation. E. coli maltose O-acetyltransferase was demonstrated to be responsible for the mentioned regioselective O-acetylation at the 6-OH of the glucopyranosyl group of multiple classes of natural product glucosides through candidate acetyltransferase-encoding gene analysis, gene knock-out, gene complementation, and the relevant enzymatic reaction activity assays. The present study not only provides an efficient biocatalyst for regioselective O-acetylation but also notifies cautions for metabolic engineering and synthetic biology applications in E. coli. KEY POINTS: • 6-OH of glucosyl of multiple glucosides was regioselectively O-acetylated by E. coli. • Endogenous EcMAT is responsible for the regioselective O-acetylation reaction.

Engineering the oleaginous yeast Yarrowia lipolytica to produce nutraceuticals: From metabolic design to industrial applications

AbstractThe oleaginous yeast, Yarrowia lipolytica, has garnered much attention and is gaining recognition as a potential industrial platform for nutraceutical production. The superior properties of Y. lipolytica include its transparent inherited background, broad substrate spectrum, and superior environmental tolerance. Specifically, advances in genetic tools and engineering strategies have further widened its applications in metabolic engineering and biomanufacturing. In this review, we summarized developments in synthetic biology and engineering strategies for constructing Y. lipolytica to produce nutraceuticals. We also proposed perspectives on the limitations and challenges in developing microbial Y. lipolytica factories.

Study on Metabolic Effects of protein for Microbial Metabolic Engineering

Microbial metabolic engineering is a new bioengineering technology, which can improve the yield of compounds, optimize energy utilization and solve various problems faced by human beings by changing the genetic information, metabolic pathway and metabolic flux of microorganisms. Protein is the main undertaker of life activities, and its metabolic process involves complex biochemical reactions and cell regulation mechanisms. Protein metabolism plays an important role and application in microbial metabolic engineering. The purpose of this paper is to explore the metabolic effects of protein for microbial metabolic engineering, so as to deeply understand the metabolic characteristics and regulation mechanism of microorganisms. Microbial metabolic engineering has important applications in improving the yield of compounds, optimizing energy utilization, environmental treatment and medicine. The research and application of protein metabolism can provide more in-depth theoretical basis and technical support for microbial metabolism engineering, thus promoting the development and utilization of microbial resources.

CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction.

Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects. We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exploited via Contrastive Multiview Coding to learn embeddings that maximize the mutual information across congruent data views. CSI assigns a key and multiple views to each data point, where data partitions under a particular key form congruent views of the data. We showcase the effectiveness of CSI by applying it to the compound-protein sequence interaction prediction problem, a pressing problem whose solution promises to expedite drug delivery (drug-protein interaction prediction), metabolic engineering, and synthetic biology (compound-enzyme interaction prediction) applications. Comparing CSI with a baseline model that does not utilize data stratification and contrastive learning, and show gains in average precision ranging from 13.7% to 39% using compounds and sequences as keys across multiple drug-target and enzymatic datasets, and gains ranging from 16.9% to 63% using reaction features as keys across enzymatic datasets. Code and dataset available at https://github.com/HassounLab/CSI.

Retron-mediated multiplex genome editing and continuous evolution in Escherichia coli.

While there are several genome editing techniques available, few are suitable for dynamic and simultaneous mutagenesis of arbitrary targeted sequences in prokaryotes. Here, to address these limitations, we present a versatile and multiplex retron-mediated genome editing system (REGES). First, through systematic optimization of REGES, we achieve efficiency of ∼100%, 85±3%, 69±14%and 25±14% for single-, double-, triple- and quadruple-locus genome editing, respectively. In addition, we employ REGES to generate pooled and barcoded variant libraries with degenerate RBS sequences to fine-tune the expression level of endogenous and exogenous genes, such as transcriptional factors to improve ethanol tolerance and biotin biosynthesis. Finally, we demonstrate REGES-mediated continuous in vivo protein evolution, by combining retron, polymerase-mediated base editing and error-prone transcription. By these case studies, we demonstrate REGES as a powerful multiplex genome editing and continuous evolution tool with broad applications in synthetic biology and metabolic engineering.

EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework.

In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters. In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter. In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R2). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R2) compared with non-EVMP SOTA record. In further study, extended vision (or k-mer) is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP.

Designing Microbial Cell Factories Through the Application of Metabolic Engineering

Concepts and Applications of Metabolic Engineering for Metabolites Production Engineering: Open Access New avenues for a deeper knowledge of the organism's physiology and metabolism have been made possible with the advancement in molecular biology tools and access to the whole genome sequencing data that open the doors for metabolic engineers and sped up the metabolic engineering application. It is pertinent to say that without molecular biology which has numerous applications in modern biotechnology, metabolic engineering couldn’t be in its present bloomy position. Metabolic engineering permits the introduction of novel, beneficial features such as drought and salt tolerance in plant biotechnology, assisting the discovery of disease-causing genes and their therapy in medical biotechnology. Nikel & de Lorenzo, (2021) accentuated the use of metabolic engineering for the degradation of recalcitrant compounds in environmental biotechnology. Metabolic engineering can fabricate the coveted metabolite using renewable resources by altering the endogenous pathways or employing the heterologous biosynthetic pathways in microbes. Thus, microbes perform as a cell factory for producing different metabolites using several native and non-native enzymes. Due to this, metabolic engineers and synthetic biologists can produce a plethora of metabolites in cell factories in a jiff.

The construction of a PAM-less base editing toolbox in Bacillus subtilis and its application in metabolic engineering

The necessity of a specific protospacer adjacent motif (PAM) greatly limited the editing scope and the application range of Cas9-dependent base editors. Here, we showed that SpRY breaks the PAM recognition barrier in Bacillus subtilis with a preference toward NRN than NYN while the PAM specificity is slightly distinct from other organisms. We developed a highly efficient PAM-less base editing toolbox for B. subtilis based on a variant of Cas9 nickase, nSpRY, yielding PAM-less adenine and/or cytosine base editors (PLABE/PLCBE/PLACBE) for A-to-G and/or C-to-T conversions. The conversion efficiency was optimized to 100% at high-activity sites and the editing window was refined to 1–2 nucleotides at pre-selected sites. Multiplexed editing for double, triple, and quadruple genes was validated simply by one cycle of editing. As a proof of concept, PLCBE was utilized to introduce premature stop codons for yielding gene knockouts and PLABE was applied to change start codons from ATG to GTG for realizing translation control. A website was designed to accelerate the inactivation of any genes of interest in B. subtilis by incorporating PAM specificity information for identifying the high-efficiency sites. Facilitated by multiplexed PAM-less editing, we successfully obtained a diversified library of strain variants for chemical production with multi-level regulations in central and competing metabolism pathways. We recruited this library to modulate metabolic fluxes in B. subtilis, achieving up to a 26.3% increase in acetoin production with a final titer of 20.8 g/L in shake-flask fermentation. By enabling in situ base editing of nearly all As and Cs in the Bacillus genome, this toolbox shows great potential in applications of metabolic engineering and codon expansion.

Plant Secondary Metabolites: The Weapons for Biotic Stress Management.

The rise in global temperature also favors the multiplication of pests and pathogens, which calls into question global food security. Plants have developed special coping mechanisms since they are sessile and lack an immune system. These mechanisms use a variety of secondary metabolites as weapons to avoid obstacles, adapt to their changing environment, and survive in less-than-ideal circumstances. Plant secondary metabolites include phenolic compounds, alkaloids, glycosides, and terpenoids, which are stored in specialized structures such as latex, trichomes, resin ducts, etc. Secondary metabolites help the plants to be safe from biotic stressors, either by repelling them or attracting their enemies, or exerting toxic effects on them. Modern omics technologies enable the elucidation of the structural and functional properties of these metabolites along with their biosynthesis. A better understanding of the enzymatic regulations and molecular mechanisms aids in the exploitation of secondary metabolites in modern pest management approaches such as biopesticides and integrated pest management. The current review provides an overview of the major plant secondary metabolites that play significant roles in enhancing biotic stress tolerance. It examines their involvement in both indirect and direct defense mechanisms, as well as their storage within plant tissues. Additionally, this review explores the importance of metabolomics approaches in elucidating the significance of secondary metabolites in biotic stress tolerance. The application of metabolic engineering in breeding for biotic stress resistance is discussed, along with the exploitation of secondary metabolites for sustainable pest management.