- Open Access
In silico model-guided identification of transcriptional regulator targets for efficient strain design
© The Author(s) 2018
- Received: 30 May 2018
- Accepted: 20 October 2018
- Published: 25 October 2018
Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.
We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.
In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.
- Model-guided strain design
- Genome-scale metabolic model
- Constraint-based flux analysis
- Transcriptional regulator
- Systems biology
Currently, a variety of value-added products can be newly synthesized and overproduced in microbial expression hosts at near-commercial levels through various pathway modifications such as gene up-/down-regulation and deletion in a serial and/or iterative manner [1, 2]. However, identifying such metabolic engineering targets is not trivial; more often than not, even the most intuitive enzyme manipulations may not lead to desired level of product yields due to the inherent regulation and complexity of metabolism . To circumvent this issue, manipulating the transcriptional regulators (TRs), which often globally regulate the expression levels of a group of genes within a same cellular module in the form of regulons, has been considered as a promising strategy. For example, by fine tuning the expression of FadR, the TR regulating a number of genes in fatty acid biosynthesis including fabA, fabB and iclR, in Escherichia coli, fatty acid titres could be enhanced up to 73% of the theoretical yield which was not achieved by overexpressing any of the metabolic gene combinations . Similarly, the global TR, cra, was targeted to channel more carbon flux via phosphoenolpyruvate carboxylation and the glyoxylate pathway in E. coli, thereby improving succinate yields . Another recent study showed that the combinatorial overexpression of metabolic genes, galP and glk, along with a TR, TyrR, which represses the expression of multiple l-phenylalanine pathway genes in E. coli, enhanced the yield of this amino acid significantly . However, despite such several success stories, one of the major challenges is to identify more efficient and reliable TR manipulation targets.
Constraint-based metabolic modeling (CBM) is a simple and widely used approach that requires only metabolic network stoichiometry and environmental constraints to describe the cellular phenotype from genotype, and thus can be readily exploited to characterize and predict cellular behaviours under perturbed conditions [7, 8]. In this regard, several algorithms based on CBM framework have been developed for finding relevant metabolic engineering targets towards the enhanced production [9–11]. While most of these algorithms can suggest various strain design strategies via gene knockout, upregulation and downregulation [9, 12], metabolite intensification/attenuation  and also cofactor balancing [14, 15], only a handful of them are related to TR manipulation targeting. OptORF is the first ever constraint-based method developed for TR targeting  using a previously developed combined metabolic/regulatory model  where the transcriptional-regulatory information is described via Boolean logic, i.e. ‘on’ and ‘off’ states of TR expression. A bi-level mixed-integer linear programming (MILP) based solution procedure was proposed to identify TR manipulation targets in E. coli for overproducing ethanol, isobutanol and 2-phenylethanol. Later, Vilaça et al.  used evolutionary algorithm and simulation annealing as the optimization algorithms to find TR candidates from the same combined metabolic-regulatory model. However, the use of these methods is severely limited since it assumes the transcriptional-regulatory responses to be binary which could be continuous. In order to address this critical issue, recently, Kim et al., developed Beneficial Regulator Targeting (BeReTa), on the basis of an unintegrated approach where each TR in the transcriptional regulatory network (TRN) is ranked for genetic manipulation, i.e. up-/down-regulation, based on a beneficial score . A systematic procedure was proposed to combine the regulatory strength information from the TRN and the desired flux slopes that could overproduce the desired compound.
While the unintegrated approach presented in BeReTa could effectively identify several relevant TR candidates for up-/down-regulation compared to OptORF, it still suffers from certain limitations. Firstly, BeReTa does not consider the inherent hierarchical structure of TRN; unlike metabolic genes, TRs are known to operate in a regulatory cascade when certain global TRs regulate multiple downstream TRs, all of which in turn can modulate the expression of target genes [20, 21]. Here, it should be highlighted that the regulation of TR–TR-gene in TRNs are complex which at times can be circular and negate the overall effects in a counter-intuitive manner. Therefore, it is important to incorporate the hierarchical structure of TRN while identifying the TR candidates such that the engineered TR’s effect is not masked by another higher order TR. Secondly, BeReTa only takes into account the positively correlated reactions while calculating flux slopes, ignoring the reactions that are negatively correlated to the desired product which may also serve as relevant gene manipulation, i.e. down-regulation, targets. Furthermore, it does not consider the presence of equivalent competing pathways in the product synthesis that also gives rise to the same yield of product.
In this work, we propose “hierarchical-Beneficial Regulatory Targeting” (h-BeReTa), which extends the BeReTa by addressing the abovementioned shortcomings for identifying efficient TR targets. Specifically, h-BeReTa utilizes a TRN with hierarchies of TR clearly defined and a metabolic model to identify target candidates. Moreover, it also account for the negatively correlated reactions with the product flux, in addition to the positively correlated reactions because the flux through these reactions need to be minimized to improve product synthesis. Here, we first describe the methodology of h-BeReTa, and then demonstrate its applicability by identifying promising TR manipulation targets for overproducing various compounds in E. coli and C. glutamicum. Finally, we compare the resulting targets obtained from h-BeReTa to its preceding methods and discuss their performance.
Step 1: Identification of reactions correlated with product flux (nRAP)
Step 2: Identification of reactions correlated with product flux (nRAP)
Step 3: Calculation of normalized regulatory strength (nRS)
Step 4: TR effect on product flux (TRE)
In order to ensure that the TRE scores are not affected by false positives/or result of random chances, h-BeReTa calculations were performed using nGAP values derived from large sets (~ 1000) of nRAP values that are randomly generated within the observed ranges. TRE scores for each TR were then obtained for the 1000 randomly generated nGAP sets. Subsequently, the probability of the randomly generated TRE to fall in ± 10% range of the actual TRE scores of the corresponding TR is calculated. TRs with this probability less than 0.05 (5%) are considered true positives and therefore carry forwarded to Step 5.
Step 5: Global TR effects based on hierarchies of TRN (gTRE)
In silico models and gene expression datasets
h-BeReTa require three inputs for its implementation: a genome-scale metabolic model (GEM), TRN along with the reconstructed TR-hierarchies and gene expression datasets of two reference strains.
Genome-scale metabolic models
The iJO1366  and iAF1260  GEMs were used to evaluate the nGAP values for the E. coli case studies, and the iCW773  GEM was used for C. glutamicum case studies. All constraint-based simulations were performed using COBRA toolbox , implemented in MATLAB (http://www.mathworks.com) with Gurobi5 (http://www.gurobi.com) as the optimization solver. Note that FVA was performed by employing the FastLooplessFVA function, implemented in COBRA toolbox  which uses a fast sparsification algorithm to efficiently eliminate the thermodynamically infeasible loops .
Transcriptional regulatory networks
The TRN information of E. coli was downloaded from RegulonDB version 9.0  including a total 4787 TR-gene interactions and 200 TRs. The TRN information of C. glutamicum was obtained from the Abasy Atlas database  which accounts for 3330 TR-gene interactions excluding self-regulators and 102 TRs. Here, it should be noted that the levels of TR regulation hierarchy were manually reconstructed from the RegulonDB TRN based on the TR–TR interaction relationships.
Gene expression datasets
Apart from a metabolic model and TRN, h-BeReTa requires two specific gene-expression datasets relevant to the desired phenotype, i.e. “producer vs. non-producer”, for the identification of promising TR-manipulation targets. Such datasets can be obtained from the two different phases of a cell culture, e.g. growth vs. stationary phase, which shows differential transcriptional regulation. Alternatively, gene expression datasets obtained while comparing a wild-type to that of a transcriptional regulator engineered mutant can also be used for this purpose. Note that the gene expression datasets used are product-specific unlike BeReTa, which uses a general gene expression compendium for all products. The expression datasets for the case studies involving the production of tyrosine, acetate and fatty acids were downloaded using the GEO accessions provided in references cited for the respective case studies (see “Results”).
Application of h-BeReTa to Escherichia coli
Top-five along with additional validated (if any) transcriptional regulator targets identified by h-BeReTa for overproducing various compounds in E. coli and its comparison previously existing methods
Nature of target
nac, ihfA, tyrR, rpiR, fliZ
tdcA, tdcR, argP 
acrR, adiY, gadW, flhD, argP, tyrR 
pdhR , cra, yijC
arcA, gadX, fis, ihfA, soxS
rcsB, gadE, rcsA, ihfB, cra
arcA, nadI, trpR, cra, atoC
rob, fnr, creB, tdcR, tdcA
fur, oxyR, glpR, envY, dnaA, arcA 
Tyrosine has been used for a wide range of industrial and pharmaceutical applications as dietary supplements and precursors for the synthesis of benzylisoquinoline alkaloids and polyketides. Several metabolic engineering strategies have been carried out to increase the production of tyrosine in E. coli. Here, we apply h-BeReTa for the overproduction of tyrosine using the expression data obtained from the mutagenesis libraries of the global transcription factors rpoA and rpoD using a high tyrosine-yielding engineered parental strain . The constraint-based simulations for nGAP determination were performed on iJO1366 GEM with flux through the reaction catalysed by prephenate dehydratase (pheA) constrained to zero, in order to mimic the metabolic state of the engineered parental strain. Interestingly, many of the TR targets identified correspond to those regulating the pool of phosphoenolpyruvate (PEP), an early precursor for tyrosine biosynthesis (Table 1). This observation clearly indicates that despite undergoing sufficient modifications in the downstream module of tyrosine biosynthesis such as the deletion of repressor gene tyrR, deletion of pheA and overexpression of feedback resistant 3-deoxy-d-arabinoheptulosonate‐7‐phosphate synthase (aroGfbr) and chorismate mutase/prephenate dehydrogenase (tyrAfbr) , it still has some room for further improvement. Since the original dataset reported three different regulatory modifications (rpoA14, rpoA27, and rpoD3) , we further tested the consistency of h-BeReTa predictions across all three cases. Overall, we could predict similar TR targets across all three cases using gene expression datasets which are obtained under different regulatory conditions, thus clearly indicating the robustness of h-BeReTa.
Although bacterial hosts have been found to be a less appealing than yeasts for the industrial production of free fatty acids [34, 35], the tremendous potential of fatty acids and their derivatives for pharmaceutical and cosmetic applications and the ease to genetically manipulate have driven numerous engineering efforts in E. coli. The fatty acid metabolism in E. coli is extensively regulated at transcriptional level, and hence their overproduction would require significant interventions in the associated TRs . Here, we use the expression data generated by one such study  to rank TR-manipulation targets for fatty acid overproduction. A synthetic reaction representing fatty acid biosynthesis was added to iJO1366 GEM to perform constraint-based simulations. h-BeReTa identified relevant TR overexpression and downregulation targets for fatty acid overproduction where at least three out of the four TR targets for fatty acid overproduction that were validated using experimental evidences either activate or repress fatty acid degradation (Table 1), suggesting the dominant role of β-oxidation in controlling fatty acid accumulation in E. coli.
Lycopene is known to be an antioxidant and a potential cancer therapeutic agent, and thus, numerous attempts have been made to produce it using engineered E. coli as host . Initially, it has been shown that lycopene can be produced in E. coli via mevalonate  and non-mevalonate pathways . However, with an increased interest for lycopene, alternative strategies are being actively sought to further enhance its yields . In this regard, one of the earlier study showed that a point mutation in the global regulator, cAMP receptor protein (CRP), resulted in significant improvements to lycopene yield in E. coli, indicating the potential of transcriptional regulator engineering approach for lycopene production . Here, we used the gene expression data obtained from the study for an E. coli K12 strain capable of producing lycopene and its derivative harbouring the mutant crp gene to predict TR engineering targets. The h-BeReTa results for TRs targets potentially improving lycopene production are listed in Table 1. The identification of soxS, the TR part of the soxRS regulon involved in relieving oxidative stress , as an up-regulation target is consistent with the previous observations: measurable lycopene content decreased with increasing oxidative stress . Further, it should be noted that since a major portion of lycopene biosynthesis overlaps with the canonical isoprenoid biosynthesis, the TR targets obtained here can be generalized for the production of other carotenoid metabolites in E. coli.
Menaquinone (vitamin K2)
Vitamin K2 or menaquinones is a group of molecules is essential for healthy arteries and bones whose deficiency in humans could result in osteoporosis, impairment in blood coagulation and cardiovascular disease . The average intake of vitamin K among the adults in the United States has been estimated to be only about 70–90% of the recommended intake value , emphasizing the relevance of its large-scale production to pharmaceutical and food industries. The pathway of menaquinone biosynthesis, which partially overlaps with that of aromatic amino acid and isoprenoid biosynthesis is subjected to a high level of transcriptional regulation. In this regard, we use the gene expression data obtained for the wild type E. coli and a mutant strain accumulating higher menaquinone pool to predict potential TR targets for vitamin K2 overproduction (Table 1). The prediction of trpR as an upregulation target is interesting as it represses the aromatic amino acid biosynthesis which also competes for chorismate, a common precursor for both compounds.
Application of h-BeReTa to Corynebacterium glutamicum
In this work, we also applied h-BeReTa to C. glutamicum, an industrially important gram-positive bacterium and a representative host lesser studied compared to E. coli, in order to test its wider applicability. The most comprehensive TRN of C. glutamicum available to date  was used to retrieve six levels of top–down TR hierarchy (Additional file 1: Table S3). We specifically applied h-BeReTa in C. glutamicum to identify the TR manipulation targets for glutamate, an amino acid which it naturally produces under several conditions, and lycopene.
Top-five along with additional validated (if any) transcriptional regulator targets identified by h-BeReTa for overproducing various compounds in C. glutamicum
Recently, it was reported that overexpression of the housekeeping sigma factor, sigA, resulted in more reddish coloured cells compared to the control strain of C. glutamicum, indicating the overproduction of lycopene . Hence, we used this gene expression data obtained to characterize the transcriptional regulation of sigA and to suggest other TR targets to improve lycopene production even further. Interestingly, two of the TR targets identified by h-BeReTa have been validated by the same study to either increase or decrease the lycopene yields when overexpressed in C. glutamicum (Table 2). Here, it should be highlighted that among all targets identified, relA is a promising target for lycopene production, as it induces stringent response which is shown to be counteracted by one of the enzymes (4-hydroxy-3-methylbut-2-enyl diphosphate reductase or lytB or ispH) involved in the flux limiting branch point of lycopene (isoprenoid) biosynthesis [50, 51].
Comparison of h-BeReTa with other TR-based approaches
Comparison of h-BeReTa and BeReTa through statistical binary classification tests
True positives (TP)
False positives (FP)
False negatives (FN)
Sensitivity or true positive rate (TPR), TP/(TP + FN)
Precision or positive predictive value (PPV), TP/(TP + FP)
False negative rate (FNR), 1-TPR
False discovery rate, 1-PPV
F1 score (0 = worst, 1 = best)
In this study, we introduced a new method, h-BeReTa, for identifying TRs which need to be up-/down-regulated for the overproduction of desired compounds. Unlike earlier methods, it accounts for the hierarchies of TRs in the regulatory cascade and also considers the reaction fluxes which compete with the product flux while identifying relevant TR candidates. h-BeReTa is able to identify efficient TR manipulation strategies as it is successfully demonstrated via several case studies of E. coli and C. glutamicum for overproducing various products including acetate, tyrosine, fatty acids, menaquinone, and lycopene. Here, it is important to note that the validation of the TR target predictions was only based on those examples that are available from published literature and hence, many targets remain to be validated.
As mentioned earlier, h-BeReTa utilizes an unintegrated approach which treats the cellular metabolism and regulation as two modules in the framework and then combines them systematically as previously proposed in BeReTa algorithm. However, h-BeReTa still encompasses several differences at various levels of the formulation, thereby resulting in improved performance. First, the TR hierarchy information is newly incorporated into the framework, thus identifying TR targets with higher regulatory impact on the product formation. The importance of such considerations can be perceived from h-BeReTa results which exclusively include global TRs such as phoB, ihfA, ihfB, cra and fis as top candidates where several of them are experimentally validated in the literature (Table 1). In addition, the TR hierarchy can provide some clues regarding the potential outcomes of global TR targeting. For example, Fig. 2 shows that fnr is regulated by ihfB (positive), ihfA (positive) and fur (negative), which occupy the upper levels of the TR hierarchy. It is furthermore clear from Table 1 that the prediction of fnr as an experimentally validated downregulation target for ethanol production has been consistently translated to ihfB and fur as downregulation and overexpression targets, respectively. Another important difference between the two approaches is that h-BeReTa uses a different constraint-based flux analysis formulation in which it also takes into account reactions with negative nRAP scores, i.e., those reactions whose fluxes compete with product formation. The inclusion of reactions with negative nRAP scores is important because high-value products are often secondary metabolites which the cells does not produce naturally and experiences direct competition from a large part of the fluxes in the metabolic network which are associated with biomass precursor biosynthesis. Furthermore, the accounting of negatively correlated fluxes in h-BeReTa allows it to rank the global TRs accordingly, considering that it could regulate multiple genes in other parts of the metabolic network in addition to the product flux. In contrast, since BeReTa does not consider the negatively correlated reaction fluxes there could be a bias for TRs to be identified just by considering the positive beneficial scores calculated.
Although h-BeReTa is able to identify efficient TR targets consistently, one major limitation is the inability to predict the extent of changes to product yields as a function of TR manipulation which is mainly due to the unintegrated nature of the methodology. However, the actual increase in product yield might mainly depend on several contributing factors, including the degree of correlation between the mRNA and protein levels of the TR, nature of interaction, saturation kinetics between the TR and its regulatory targets, and the intracellular metabolite concentrations, which are generally ignored in CBM approaches. Therefore, further improvements in h-BeReTa predictions could be made possible by incorporating concepts such as the metabolite dilution  or molecular crowding constraints . Incorporation of such additional constraints into constraint-based flux analysis could potentially improve the flux predictions and therefore yield more promising TR targets. Furthermore, the use of ± 10% cut-off for assessing false positive TRE scores was arbitrary and can be subjected to scrutiny. However, with this cut-off range we observed a minimal rejection of true positive (literature validated) TR targets. Additionally, using more than one set of transcriptomic data representing the desired phenotype, i.e. producer and non-producer, to calculate the regulatory strength (nRS) values may increase the accuracy of TR candidate predictions. Alternatively, if no relevant datasets could be found for the desired phenotype, a general gene expression compendium can be used as it is in BeReTa.
Despite its limitations and scope for further improvements, the agreement of h-BeReTa predictions with experimental evidences from literature was substantial. Although the gene expression datasets used in this study for various case studies correspond to exponentially growing cells cultures, the method can also be readily extended to those of stationary phase cultures, provided an appropriate objective function is employed during the computation of nRAP scores. We believe that the less-stringent resource requirements and the computationally less-intensive methodology make h-BeReTa to be more readily employed in comparison to the existing methods for identifying non-intuitive TR targets, thereby advancing metabolic engineering applications.
LK, ML and D-YL conceived the project. LK implemented the algorithm and analysed the data. ML analysed the data. LK, ML and D-YL drafted, edited and revised the manuscript. D-YL supervised the work. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
All data generated or analyzed during this study are included and/or cited appropriately in this published article and its additional file.
Consent for publication
All authors agreed to publish this article.
Ethics approval and consent to participate
This work was supported by the Biomedical Research Council of A*STAR (Agency for Science, Technology and Research), Singapore, and the Next-Generation BioGreen 21 Program of the Rural Development Administration, Republic of Korea (Systems and Synthetic Agrobiotech Center; Grant No. PJ01334605).
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