Multiple transcription factors of Arabidopsis thaliana that are activated by LEAFY COTYLEDON 2 regulate triacylglycerol biosynthesis.

Plant triacylglycerols (TAG) are used in food and various industrial feedstocks. LEAFY COTYLEDON 2 (LEC2), a master positive regulator of TAG biosynthesis, regulates a complex network of transcription factors (TFs) during seed development. Aside from WRINKLED1 (WRI1), the TFs regulated by LEC2 related to TAG biosynthesis have not yet been identified. Previously, we identified 25 seed-expressing TFs that were upregulated in Arabidopsis leaves that overexpressed senescence-induced LEC2. In this study, each of the 25 TFs was transiently expressed in the leaves of Nicotiana benthamiana to identify unknown TFs that regulate TAG biosynthesis. The TAG content of the transformed leaves was analyzed using thin layer chromatography and gas chromatography. We observed that five TFs, ARABIDOPSIS RESPONSIVE REGULATOR 21 (ARR21), AINTEGUMENTA-LIKE 6 (AIL6), APETALA2/ETHYLENE RESPONSIVE FACTOR 55 (ERF55), WRKY DNA-BINDING PROTEIN 8 (WRKY8), and ARABIDOPSIS NAC DOMAIN CONTAINING PROTEIN 38 (ANAC038) increased TAG synthesis in the leaves. Among these, the promoters of AIL6, ERF55, WRKY8, and ANAC038 contain RY motifs, which are LEC2-binding sites activated by LEC2. AIL6 overexpression in Arabidopsis increased the total fatty acid (FA) content in seeds and altered the FA composition, with increases in 16:0, 18:1, and 18:2 and decreases in 18:0, 18:3, and 20:1 compared with those in the wild type (WT). AIL6 overexpression activates several FA and TAG biosynthesis genes. Therefore, our study successfully identified several new TFs regulated by LEC2 in TAG biosynthesis and showed that AIL6 increased the TAG content in seeds.

Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.

BACKGROUND: Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. RESULTS: Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. CONCLUSIONS: Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Intrahepatic immunoglobulin a complex induces polarization of cancer-associated fibroblasts to matrix phenotypes in the tumor microenvironment of hepatocellular carcinoma.

BACKGROUND AND AIMS: Cancer-associated fibroblasts (CAFs) play key roles in the tumor microenvironment (TME). Immunoglobulin A (IgA) contributes to inflammation and dismantling anti-tumor immunity in the human liver. In this study, we aimed to elucidate the effects of the IgA complex on CAFs in the TME of hepatocellular carcinoma (HCC). APPROACH AND RESULTS: CAF dynamics in HCC TME were analyzed via single-cell RNA sequencing of HCC samples. CAFs isolated from 50 HCC samples were treated with mock or serum-derived IgA dimers in vitro. Progression-free survival of advanced HCC patients treated with atezolizumab and bevacizumab was significantly longer in those with low serum IgA levels (p<0.05). Single-cell analysis showed that sub-cluster proportions in the CAF-fibroblast activation protein-α (FAP) matrix were significantly increased in patients with high serum IgA levels. Flow cytometry revealed a significant increase in the mean fluorescence intensity of FAP in the CD68+ cells from patients with high serum IgA levels (p<0.001). We confirmed CD71 (IgA receptor) expression in CAFs, and IgA-treated CAFs exhibited higher programmed death-ligand 1 (PD-L1) expression levels than those in mock-treated CAFs (p<0.05). Co-culture with CAFs attenuated cytotoxic function of activated CD8+ T cells. Interestingly, activated CD8+ T cells co-cultured with IgA-treated CAFs exhibited increased programmed death-1 (PD-1) expression levels than those co-cultured with mock-treated CAFs (p<0.05). CONCLUSIONS: Intrahepatic IgA induced polarization of HCC-CAFs into more malignant matrix phenotypes and attenuates cytotoxic T cell function. Our study highlighted their potential roles in tumor progression and immune suppression.

Intracellular Flux Prediction of Recombinant Escherichia coli Producing Gamma-aminobutyric Acid.

The genome-scale metabolic model (GEM) can be used to simulate cellular metabolic phenotypes under various environmental or genetic conditions. This study utilized the GEM to observe the internal metabolic fluxes of recombinant Escherichia coli producing gamma-aminobutyric acid (GABA). Recombinant E. coli was cultivated in a fermenter under three conditions: pH 7, pH 5, and additional succinic acids. External fluxes were calculated from cultivation results, and internal fluxes were calculated through flux optimization. Based on the internal flux analysis, glycolysis and pentose phosphate pathways were repressed under cultivation at pH 5, even though glutamate dehydrogenase increased GABA production. Notably, this repression was halted by adding succinic acid. Furthermore, proper sucA repression is a promising target for developing strains more capable of producing GABA.

Efficient production of an antitumor precursor actinocin and other medicinal molecules from kynurenine pathway in Escherichia coli.

Kynurenine pathway has a potential to convert L-tryptophan into multiple medicinal molecules. This study aims to explore the biosynthetic potential of kynurenine pathway for the efficient production of actinocin, an antitumor precursor selected as a proof-of-concept target molecule. Kynurenine pathway is first constructed in Escherichia coli by testing various combinations of biosynthetic genes from four different organisms. Metabolic engineering strategies are next performed to improve the production by inhibiting a competing pathway, and enhancing intracellular supply of a cofactor S-adenosyl-L-methionine, and ultimately to produce actinocin from glucose. Metabolome analysis further suggests additional gene overexpression targets, which finally leads to the actinocin titer of 719 mg/L. E. coli strain engineered to produce actinocin is further successfully utilized to produce 350 mg/L of kynurenic acid, a neuroprotectant, and 1401 mg/L of 3-hydroxyanthranilic acid, an antioxidant, also from glucose. These competitive production titers demonstrate the biosynthetic potential of kynurenine pathway as a source of multiple medicinal molecules. The approach undertaken in this study can be useful for the sustainable production of molecules derived from kynurenine pathway, which are otherwise chemically synthesized.

PDAT1 genome editing reduces hydroxy fatty acid production in transgenic Arabidopsis.

The fatty acids content of castor (Ricinus communis L.) seed oil is 80-90% ricinoleic acid, which is a hydroxy fatty acid (HFA). The structures and functional groups of HFAs are different from those of common fatty acids and are useful for various industrial applications. However, castor seeds contain the toxin ricin and an allergenic protein, which limit their cultivation. Accordingly, many researchers are conducting studies to enhance the production of HFAs in Arabidopsis thaliana, a model plant for oil crops. Oleate 12-hydroxylase from castor (RcFAH12), which synthesizes HFA (18:1-OH), was transformed into an Arabidopsis fae1 mutant, resulting in the CL37 line producing a maximum of 17% HFA content. In addition, castor phospholipid:diacylglycerol acyltransferase 1-2 (RcPDAT1-2), which catalyzes the production of triacylglycerol by transferring HFA from phosphatidylcholine to diacylglycerol, was transformed into the CL37 line to develop a P327 line that produces 25% HFA. In this study, we investigated changes in HFA content when endogenous Arabidopsis PDAT1 (AtPDAT1) of the P327 line was edited using the CRISPR/Cas9 technique. The successful mutation resulted in three independent lines with different mutation patterns, which were transmitted until the T4 generation. Fatty acid analysis of the seeds showed that HFA content decreased in all three mutant lines. These findings indicate that AtPDAT1 as well as RcPDAT1-2 in the P327 line are involved in transferring and increasing HFAs to triacylglycerol. [BMB Reports 2024; 57(2): 86-91].

Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes.

BACKGROUND: Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations. METHODS: Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples. RESULTS: In this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%). CONCLUSIONS: Our findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms.

Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes.

In metabolic engineering, predicting gene overexpression targets remains challenging because both endogenous and heterologous genes in a large metabolic space can be candidates, in contrast to gene knockout targets that are confined to endogenous genes. We report the development of iBridge that identifies positive and negative metabolites exerting positive and negative impacts on product formation, respectively, based on the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Then, "bridge" reactions converting negative metabolites to positive metabolites are identified as overexpression targets, while the opposites as downregulation targets. Using iBridge, overexpression and downregulation targets are suggested for the production of 298 chemicals and validated for 36 chemicals experimentally demonstrated in previous studies. Finally, iBridge is employed to engineer Escherichia coli strains capable of producing 10.3 g/L of D-panthenol, a compound not previously produced, as well as putrescine and 4-hydroxyphenyllactate at enhanced titers, 63.7 and 8.3 g/L, respectively.

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor.

BACKGROUND: Oviedomycin is one among several polyketides known for their potential as anticancer agents. The biosynthetic gene cluster (BGC) for oviedomycin is primarily found in Streptomyces antibioticus. However, because this BGC is usually inactive under normal laboratory conditions, it is necessary to employ systematic metabolic engineering methods, such as heterologous expression, refactoring of BGCs, and optimization of precursor biosynthesis, to allow efficient production of these compounds. RESULTS: Oviedomycin BGC was captured from the genome of Streptomyces antibioticus by a newly constructed plasmid, pCBA, and conjugated into the heterologous strain, S. coelicolor M1152. To increase the production of oviedomycin, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system was utilized in an in vitro setting to refactor the native promoters within the ovm BGC. The target promoters of refactoring were selected based on examination of factors such as transcription levels and metabolite profiling. Furthermore, genome-scale metabolic simulation was applied to find overexpression targets that could enhance the biosynthesis of precursors or cofactors related to oviedomycin production. The combined approach led to a significant increase in oviedomycin production, reaching up to 670 mg/L, which is the highest titer reported to date. This demonstrates the potential of the approach undertaken in this study. CONCLUSIONS: The metabolic engineering approach used in this study led to the successful production of a valuable polyketide, oviedomycin, via BGC cloning, promoter refactoring, and gene manipulation of host metabolism aided by genome-scale metabolic simulation. This approach can be also useful for the efficient production of other secondary molecules encoded by 'silent' BGCs.

Physaria fendleri FAD3-1 overexpression increases ɑ-linolenic acid content in Camelina sativa seeds.

Camelina (Camelina sativa) is an oil crop with a short growing period, resistance to drought and cold, low fertilizer requirements, and can be transformed using floral dipping. Seeds have a high content of polyunsaturated fatty acids, especially ɑ-linolenic acid (ALA), at 32-38%. ALA is an omega-3 fatty acid that is a substrate for eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the human body. In this study, ALA content was further enhanced by the seed-specific expression of Physaria fendleri FAD3-1 (PfFAD3-1) in camelina. The ALA content increased up to 48% in T2 seeds and 50% in T3 seeds. Additionally, size of the seeds increased. The expression of fatty acid metabolism-related genes in PfFAD3-1 OE transgenic lines was different from that in the wild type, where the expression of CsFAD2 decreased and CsFAD3 increased. In summary, we developed a high omega-3 fatty acid-containing camelina with up to 50% ALA content by introducing PfFAD3-1. This line can be used for genetic engineering to obtain EPA and DHA from seeds.

Characterization of fab2 T-DNA insertion mutants in terms of fatty acid composition and plant phenotype.

Fatty acid biosynthesis 2 (FAB2) is an essential enzyme responsible for the synthesis of unsaturated fatty acids in chloroplast membrane lipids found in leaves and triacylglycerols (TAG) in seeds. FAB2 functions at the junction of saturated to unsaturated fatty acid conversion in chloroplasts by converting 18:0-ACP to 18:1-ACP. In the present study, plant growth and seed phenotypes were examined in three Arabidopsis T-DNA mutants (fab2-1, fab2-2, and fab2-3). The three fab2 T-DNA mutants exhibited increased 18:0 fatty acid content in both the leaves and seeds. The degree of growth inhibition of the fab2 mutant was proportional to the increase in 18:0 and decrease in 18:3 fatty acids present in the leaves. The FAB2 mutation affected seed yield but not the seed phenotype. This result indicates that FAB2 affects the fatty acid composition of the leaf chloroplast membrane more than seed TAG. In summary, the characteristics of these three fab2 mutants provide information for studying leaf membrane lipid and seed oil biosynthesis.

Targeted and high-throughput gene knockdown in diverse bacteria using synthetic sRNAs.

Synthetic sRNAs allow knockdown of target genes at translational level, but have been restricted to a limited number of bacteria. Here, we report the development of a broad-host-range synthetic sRNA (BHR-sRNA) platform employing the RoxS scaffold and the Hfq chaperone from Bacillus subtilis. BHR-sRNA is tested in 16 bacterial species including commensal, probiotic, pathogenic, and industrial bacteria, with >50% of target gene knockdown achieved in 12 bacterial species. For medical applications, virulence factors in Staphylococcus epidermidis and Klebsiella pneumoniae are knocked down to mitigate their virulence-associated phenotypes. For metabolic engineering applications, high performance Corynebacterium glutamicum strains capable of producing valerolactam (bulk chemical) and methyl anthranilate (fine chemical) are developed by combinatorial knockdown of target genes. A genome-scale sRNA library covering 2959 C. glutamicum genes is constructed for high-throughput colorimetric screening of indigoidine (natural colorant) overproducers. The BHR-sRNA platform will expedite engineering of diverse bacteria of both industrial and medical interest.

Genome-edited HEADING DATE 3a knockout enhances leaf production in Perilla frutescens.

Environmental cues regulate the transition of many plants from vegetative to flowering development. Day length, or photoperiod, is one cue that synchronizes flowering by changing seasons. Consequently, the molecular mechanism of flowering control is prominent in Arabidopsis and rice, where essential genes like FLOWERING LOCUS T (FT) homolog, HEADING DATE 3a (Hd3a), have been connected to flowering regulation. Perilla is a nutrient-rich leaf vegetable, and the flowering mechanism remains largely elusive. We identified flowering-related genes under short-day conditions using RNA sequencing to develop an enhanced leaf production trait using the flowering mechanism in the perilla. Initially, an Hd3a-like gene was cloned from the perilla and defined as PfHd3a. Furthermore, PfHd3a is highly rhythmically expressed in mature leaves under short-day and long-day conditions. Ectopic expression of PfHd3a in Atft-1 mutant plants has been shown to complement Arabidopsis FT function, resulting in early flowering. In addition, our genetic approaches revealed that overexpression of PfHd3a in perilla caused early flowering. In contrast, the CRISPR/Cas9 generated PfHd3a-mutant perilla showed significantly late flowering, resulting in approximately 50% leaf production enhancement compared to the control. Our results suggest that PfHd3a plays a vital role in regulating flowering in the perilla and is a potential target for molecular breeding in the perilla.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis.

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Proteomic analysis for the effects of non-saponin fraction with rich polysaccharide from Korean Red Ginseng on Alzheimer's disease in a mouse model.

Background: The most common type of dementia, Alzheimer's disease (AD), is marked by the formation of extracellular amyloid beta (Aß) plaques. The impairments of axons and synapses appear in the process of Aß plaques formation, and this damage could cause neurodegeneration. We previously reported that non-saponin fraction with rich polysaccharide (NFP) from Korean Red Ginseng (KRG) showed neuroprotective effects in AD. However, precise molecular mechanism of the therapeutic effects of NFP from KRG in AD still remains elusive. Methods: To investigate the therapeutic mechanisms of NFP from KRG on AD, we conducted proteomic analysis for frontal cortex from vehicle-treated wild-type, vehicle-treated 5XFAD mice, and NFP-treated 5XFAD mice by using nano-LC-ESI-MS/MS. Metabolic network analysis was additionally performed as the effects of NFP appeared to be associated with metabolism according to the proteome analysis. Results: Starting from 5,470 proteins, 2,636 proteins were selected for hierarchical clustering analysis, and finally 111 proteins were further selected for protein-protein interaction network analysis. A series of these analyses revealed that proteins associated with synapse and mitochondria might be linked to the therapeutic mechanism of NFP. Subsequent metabolic network analysis via genome-scale metabolic models that represent the three mouse groups showed that there were significant changes in metabolic fluxes of mitochondrial carnitine shuttle pathway and mitochondrial beta-oxidation of polyunsaturated fatty acids. Conclusion: Our results suggested that the therapeutic effects of NFP on AD were associated with synaptic- and mitochondrial-related pathways, and they provided targets for further rigorous studies on precise understanding of the molecular mechanism of NFP.

Computational prediction of interactions between Paxlovid and prescription drugs.

Pfizer's Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug-drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.

Regulation of Reactive Oxygen Species during Salt Stress in Plants and Their Crosstalk with Other Signaling Molecules-Current Perspectives and Future Directions.

Salt stress is a severe type of environmental stress. It adversely affects agricultural production worldwide. The overproduction of reactive oxygen species (ROS) is the most frequent phenomenon during salt stress. ROS are extremely reactive and, in high amounts, noxious, leading to destructive processes and causing cellular damage. However, at lower concentrations, ROS function as secondary messengers, playing a critical role as signaling molecules, ensuring regulation of growth and adjustment to multifactorial stresses. Plants contain several enzymatic and non-enzymatic antioxidants that can detoxify ROS. The production of ROS and their scavenging are important aspects of the plant's normal response to adverse conditions. Recently, this field has attracted immense attention from plant scientists; however, ROS-induced signaling pathways during salt stress remain largely unknown. In this review, we will discuss the critical role of different antioxidants in salt stress tolerance. We also summarize the recent advances on the detrimental effects of ROS, on the antioxidant machinery scavenging ROS under salt stress, and on the crosstalk between ROS and other various signaling molecules, including nitric oxide, hydrogen sulfide, calcium, and phytohormones. Moreover, the utilization of "-omic" approaches to improve the ROS-regulating antioxidant system during the adaptation process to salt stress is also described.

Recent development of machine learning models for the prediction of drug-drug interactions.

Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Factors affecting the competitiveness of bacterial fermentation.

Sustainable production of chemicals and materials from renewable non-food biomass using biorefineries has become increasingly important in an effort toward the vision of 'net zero carbon' that has recently been pledged by countries around the world. Systems metabolic engineering has allowed the efficient development of microbial strains overproducing an increasing number of chemicals and materials, some of which have been translated to industrial-scale production. Fermentation is one of the key processes determining the overall economics of bioprocesses, but has recently been attracting less research attention. In this Review, we revisit and discuss factors affecting the competitiveness of bacterial fermentation in connection to strain development by systems metabolic engineering. Future perspectives for developing efficient fermentation processes are also discussed.

Predicting the effects of cultivation condition on gene regulation in Escherichia coli by using deep learning.

Cell's physiology is affected by cultivation conditions at varying degrees, including carbon sources and inorganic nutrients in growth medium, and the presence or absence of aeration. When examining the effects of cultivation conditions on the cell, the cell's transcriptional response is often examined first among other phenotypes (e.g., proteome and metabolome). In this regard, we developed DeepMGR, a deep learning model that predicts the effects of culture media on gene regulation in Escherichia coli. DeepMGR specifically classifies the direction of gene regulation (i.e., upregulation, no regulation, or downregulation) for an input gene in comparison with M9 minimal medium with glucose as a control condition. For this classification task, DeepMGR uses a feedforward neural network to process: i) DNA sequence of a target gene, ii) presence or absence of aeration and trace elements, and iii) concentration and structural information (SMILES) of up to ten nutrients. The complete DeepMGR showed accuracy of 0.867 and F1 score of 0.703 for a test set from the gold standard dataset. DeepMGR was further subjected to simulation studies for validation where regulation directions for groups of homologous genes were predicted, and the DeepMGR results were compared with the literature with focus on carbon sources that upregulate specific genes. DeepMGR will be useful for designing experiments to understand gene regulations, especially in the context of metabolic engineering.