Mycoplasma JCVI-Syn3A genome established that systems that repair or preempt metabolite damage are essential to life. Several genes known to have such functions were identified and experimentally validated, including 5-formyltetrahydrofolate cycloligase, coenzyme A (CoA) disulfide reductase, and certain hydrolases. Furthermore, we discovered that an enigmatic YqeK hydrolase domain fused to NadD has a novel proofreading function in NAD synthesis and could double as a MutT-like sanitizing enzyme for the nucleotide pool. Finally, we combined metabolomics and cheminformatics approaches to extend the core metabolic map of JCVI-Syn3A to include promiscuous enzymatic reactions and spontaneous side reactions. This extension revealed that several key metabolite damage control systems remain to be identified in JCVI-Syn3A, such as that for methylglyoxal.Analysis of the genes retained in the minimized  Mycoplasma mycoides subsp. capri serovar LC GM12 by DNA synthesis, recombination, and genome transplantation techniques and included only genes required for survival or to support a reasonable growth rate (428 protein-coding genes and 34 RNA genes) . These unknowns cannot all be missing parts of synthesis/breakdown pathways as the metabolic reconstruction identified only four metabolic and eight transport reactions as missing . Several were found, as follows.We first manually screened the predicted proteome of JCVI-Syn3A for homologs of known metabolite repair enzymes , 15, 16 fau/ygfA ) and are conserved in the closely related Mesoplasma florum L1 genome  were tested for activity against a panel of 94 phosphatase substrates (see Table A1 at figshare [https://doi.org/10.6084/m9.figshare.20020574]) (p-nitrophenyl phosphate (pNPP) and different physiological substrates . The JCVISYN3A_0728 enzyme hydrolyzed a wide range of nucleoside and sugar phosphates, the JCVISYN3A_0907 and JCVISYN3A_0077 enzymes hydrolyzed narrower ranges of sugar phosphates, and the JCVISYN3A_0066 enzyme hydrolyzed flavin mononucleotide (FMN) and CoA. That sugar phosphates are good substrates of JCVISYN3A_0728 is consistent with its genomically predicted role in sugar phosphate metabolism, but no specific function or substrate could be assigned. Note, however, that the 94-substrate panel did not include damaged sugar phosphates.The four HAD proteins that we were able to express in 020574]) . The fouhttps://doi.org/10.6084/m9.figshare.20020574]). Deletants were readily obtained for genes JCVISYN3A_0066, JCVISYN3A_0077, JCVISYN3A_0728, and JCVISYN3A_0907 . Attempts to delete JCVISYN3A_0710 using two different methods were unsuccessful . Deletion of this gene could have resulted in an extremely slow-growing strain that was unrecoverable under the conditions used, Alternatively, JCVISVN3A_0710 could be essential, the transposon insertions in the gene being artifacts. That the same gene is also essential in M. florum  (. florum  favors thttps://doi.org/10.6084/m9.figshare.20020574]). To conduct a metabolomics analysis, the four mutants and the JCVI-Syn3A parent were grown in SP4-KO medium  and harvested at the same point of log-phase growth. . A total of 4,152 features were detected in the samples using hydrophilic interaction liquid chromatography (HILIC) and mass spectrometry , of which 522 were annotated as known metabolites.We observed no major differences in growth rates between JCVI-Syn3A and any HAD mutant  showed little contamination from media, as determined by analysis of unused media along with mutant samples. Most of these metabolites were below the limit of detection in unused media, and most of the rest were present at a >30-fold-lower abundance in media than in samples, suggesting little or no contamination from residual media . Two metabolites (cytidine and thiamine) were found at similar abundances in media and samples, suggesting these medium contaminations.Partial least-squares discriminant analysis was used to find the variable importance in projection (VIP) scores of each annotated metabolite. The 15 metabolites with the highest VIP scores (https://doi.org/10.6084/m9.figshare.20020574]). We were not able to determine which form of glycerophosphate was increased, although the 3-phosphate is a priori more likely, being found in the metabolic model as a cardiolipin metabolism intermediate that is synthesized via phosphorylation of imported glycerol by GlpK (JCVISYN3A_0218). To further analyze the knockout metabolomics data further, all four HAD hydrolase knockout phenotypes were separately compared to wild-type JCVI-Syn3A . The conclusions are summarized below and further discussed in the Appendix at figshare (https://doi.org/10.6084/m9.figshare.20020574).Among the 15 metabolites with high VIP scores, the JCVISYN3A_0728 knockout showed significantly higher abundances of glycerophosphate, oleoyl lysophosphatidic acid, and palmitoylglycerol than other genotypes (https://doi.org/10.6084/m9.figshare.20020574), the data also suggest the residual presence of pyrimidine nucleoside phosphorylase (PyNP) activity in JCVI-Syn3A after the known MMSYN1_0734 has been removed. The lack of observed nucleotidase activity for JCVISYN3A_0066 in the in vitro substrate screen could be due to the absence of relevant effectors. In contrast, it seems that JCVISYN3A_0077 is also a dUMP-specific specific dNMPase that plays a minor role in vivo compared to JCVISYN3A_0066. The metabolomics data also suggest that JCVISYN3A_0728 is a glycerol 3-phosphate phosphatase. The other activities detected in vitro, if relevant in vivo, might not be apparent in the metabolomics data if these substrates do not accumulate in cells. No functional role could be proposed for JCVISYN3A_0907.The metabolomics data suggest that JCVISYN3A_0066 is the major deoxyribonucleoside monophosphatase (dNMPase) with activity against the deoxymononucleotides dAMP, dGMP, dUMP, dCMP, dTMP, and dIMP and also the ribomononucleotide IMP. Furthermore, as further discussed in the Appendix at figshare (https://doi.org/10.6084/m9.figshare.20020574]). These genomic associations led us to propose that YqeK repairs mistakes made by NadD. The canonical activity of NadD is to adenylate nicotinate-ribonucleotide (NaMN) using ATP as a donor of the AMP moiety . (The His230Ala mutation is predicted to abolish phosphatase activity that would interfere with NadD activity measurement.) Bacillus subtilis NadD was used as a benchmark. The JCVISYN3A_0380 His230Ala protein and B. subtilis NadD were tested for in vitro activity with various nucleoside triphosphates as the substrates. The adenylating activity of the JCVISYN3A_0380 His230Ala mutant was quite nonspecific and actually greater against dATP, CTP, or UTP than against the physiological substrate, ATP, whereas B. subtilis NadD strongly preferred ATP (The YqeK histidine -aspartate (HD)-domain phosphohydrolase is fused to nicotinic acid mononucleotide adenylyltransferase (NadD) in most mycoplasmas and strongly physically clustered with NadD in many other Gram-positive organisms   and uracil (NaUD) analogs of NaAD that was at least as high as that against NaAD itself , which amK is likely higher than that for the other substrates tested (E. colimutT high-mutation-rate phenotype (measured as Rifr ratios)  . The par ratios) . Taken thttps://doi.org/10.6084/m9.figshare.20020574]) suggested that the NadD domain is essential and the YqeK domain is quasiessential because a few hits in the YqeK region of the gene were detected in the first Transposon (Tn) round and disappeared after the fourth round. We could not isolate a JCVISYN3A_0380 deletant despite several attempts. We were, however, able to construct a strain carrying the His230Ala mutation that inactivates YqeK diphosphatase activity , and this strain showed no growth defect or obvious metabolite imbalance .Indeed, the available transposon insertion data  . Because this approach begins with the observed chemical results of potential metabolite damage and is not limited by our current knowledge of gene function, it will certainly find damage mechanisms that our gene-first approach will miss. Still, this approach will also miss any damage mechanisms that fail to be observed through metabolomics, either due to volatility of end products or due to extremely effective damage mitigation systems.Thus far, all of our damage and repair cases began with analysis of genes in the JCVI-Syn3A genome and uncovered clear instances of metabolite damage and repair. But are these examples isolated exceptions, or the tip of an iceberg of uncharacterized metabolic chemistry? To address this question, we adopted a systematic exploratory approach based on the metabolomics data for JCVI-Syn3A cells (see Table S3 at figshare [https://doi.org/10.6084/m9.figshare.20020574]) that satisfied two criteria: (i) the mass spectral signal was confidently identified with a defined molecular structure, and (ii) the metabolite was at least as abundant in the JCVI-Syn3A cells as in the growth medium. We compared the 480 identified peaks to the 304 metabolites in the JCVI-Syn3A model and the 33,978 compounds in the ModelSEED database (https://doi.org/10.6084/m9.figshare.20020574]).We focused specifically on a set of 480 metabolites . This solution included 145 (58%) of the 252 reactions in the JCVI-Syn3 model (purple reactions in https://doi.org/10.6084/m9.figshare.20020574]). Also, the fully expanded version of the JCVI-Syn3A model used to generate this flux solution is provided in SBML and JSON format in Supplemental data S5 at the figshare URL above.Next, we used a new flux balance analysis (FBA) formulation, metabo-FBA, to select a minimal subset of these reactions that connect the functioning JCVI-Syn3A model to as many observed metabolites as possible using mass- and energy-balanced pathways . Because our study is of a minimal genome with relatively few enzymes and specifically focuses on metabolite damage, we favored solutions that involved as many reactions generated by spontaneous reaction rules as possible. This approach produced a predicted flux profile that simultaneously pushed flux through reactions involving compounds that matched 182 observed metabolites  node of the JCVI-Syn3A model is a good example . These rAlso of note is how many of the pathways predicted in JCVI-Syn3A by our metabo-FBA method involve a mixture of database reactions, predicted spontaneous reactions, and novel enzymatic reactions . Any analysis based on just one or two of these three reaction sources would explain a far smaller number of observed metabolites due to gaps and dead ends in the predicted pathways. Thus, using all three reaction data sources provides a much fuller understanding of metabolism.Another notable point is that much of the new predicted chemistry surrounds amino acids. Many of the observed metabolomics peaks correspond to amino acid derivatives such as dipeptides and acetylated amino acids . The dipin vivo essentiality of phosphate acetyltransferase (JCVISYN3_0229) and acetate kinase (JCVISYN3_0230) was previously puzzling, given that the upstream genes in the pathway, the subunits of pyruvate dehydrogenase (JCVISYN3_0227/8), were found to be nonessential in vivo. It had been hypothesized that the two former enzymes were essential because buildups of acetyl-CoA or acetyl phosphate needed to be prevented, both being known protein acetylation agents . Of course, not all chemically impactful metabolites are readily observed in metabolomics data due to instability or volatility. Methylglyoxal is a good example of an important metabolite that arises from and participates in spontaneous damage reactions but could not be observed . Phylogenetic analysis places JCVISYN3A_0400 in the YajL/DJ-1 clade .Methylglyoxal is necessarily formed from the triose phosphates in JCVI-Syn3A central metabolism , but the41–E. colihchA, yajL, and yhbO deletion mutants as the sensitivity of the yajL mutant reported by the Richarme group (yajL mutant but did observe a defect both in its growth rate and in the yield of the ΔyajL/ΔhchAE. coli K-12 BW25113 strain (https://doi.org/10.6084/m9.figshare.20020574]). Expression of the E. coliyajL or JCVISYN3A_0400 gene in trans complemented this growth phenotype (https://doi.org/10.6084/m9.figshare.20020574]), suggesting that JCVISYN3A_0400 is indeed in the same DJ-1 subgroup as YajL.The members of the DJ-1 superfamily that have been functionally characterized participate in stress response and detoxification . Some arme group  was not me group . We alsokcat = 0.025 ± 0.002 s−1, mK = 1.23 ± 0.30 mM), lower than that obtained for the positive-control protein human DJ-1  but higher than that for E. coli YajL   . The lowubstrate . The ~20yoxalase . Even coantiomer , althoughttps://doi.org/10.6084/m9.figshare.20020574]). CoA was used as the thiol because the absence of glutathione biosynthetic enzymes in JCVI-Syn3A suggests that CoA is its main small-molecule thiol (see above). JCVISYN3A_0400 had no detectable deglycase activity against methylglyoxal-CoA hemithioacetal , while human DJ-1 had a low activity  against the same substrate  (http://pubseed.theseed.org//SubsysEditor.cgi?page=ShowSpreadsheet&subsystem=NadD-YqeK_fusion_display). Physical clustering was analyzed with the SEED subsystem coloring tool or the SeedViewer Compare Regions tool .The BLAST tools  and CDD ih.gov/)  were rouih.gov/)  or Multaih.gov/) . Phylogeih.gov/) . Resultsons tool , and theons tool . Phylogeons tool . Studenthttps://narrative.kbase.us/narrative/29280. This app uses the open-source RDKit package to apply sets of SMARTS-based chemical reaction rules, derived from previously published chemical damage  and a variable is added for each metabolite that has been mapped to the peak . Next, a constraint is added stating that a peak cannot be active unless one or more of its associated metabolites is active :In metabo-flux balance analysis (metabo-FBA), constraints are added to the standard FBA formulation to force flux through one or more reactions involving an observed metabolite. In this formulation, a variable is added for each observed peak :A constraint is also added stating that no metabolite can be active unless at least one reaction in which the metabolite is involved is carrying flux . Bacterial growth media were solidified with 15 g/L agar (BD Diagnostics Systems) for the preparation of plates. E. coli was routinely grown on LB medium (BD Diagnostics Systems) at 37°C unless otherwise stated. Transformations were performed following standard procedures , ampicillin , kanamycin , l-arabinose , chloramphenicol , and rifampicin  were used when appropriate. Bacterial M9 minimal medium (4Cl (20 mM) or with glycine (50 mM) as the nitrogen source. P1 transduction was performed following the classical methods -DH494/495 (int) and DH480/481 (ext)-DH482/483 (int), respectively. Plasmid constructions for expression of JCVI-Syn3A genes in E. coli are described in the Supplemental Methods at figshare (https://doi.org/10.6084/m9.figshare.20020574).All strains, plasmids, and oligonucleotides used in this study are listed in Table A4 and Table A5 at figshare . Construction of gene knockout mutants in JCVI-Syn3A was a multistep process, and two different protocols were used. These protocols are described in detail in the Supplemental data S2 file at figshare (https://doi.org/10.6084/m9.figshare.20020574).JCVI-Syn3A is a near-minimal bacterial cell first reported by Breuer et al. (P4 broth  that conOvernight cultures in LB with added antibiotics and arabinose (0.02%) were diluted 100-fold under the same conditions and grown for another 24 h before dilutions were plated on LB and LB-rifampicin (25 μg/mL) to calculate a mutation ratio (number of colonies on Rif × dilution factor)/(number of colonies on LB × dilution factor).E. coli and purified using Ni2+-NTA (nitrilotriacetic acid) columns as described in the Supplemental Methods at figshare (https://doi.org/10.6084/m9.figshare.20020574). In vitro activity assays for CoA disulfide reductase, for phosphatase with a range of substrates, NadD, glyoxalase, and deglycase are described in detail in the Supplemental Methods at the figshare URL above.All characterized JCVI-Syn3A-encoded proteins were expressed as His-tagged variants in https://doi.org/10.6084/m9.figshare.20020574.The appendix and supplemental data have been deposited in the figshare data depository with the DOI


The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions. Their structure consists of benzene rings with one or more hydroxyl groups, and they can be simple phenolic molecules (i.e. phenolic acids) or be highly polymerized in complex compounds (i.e. flavonoids or tannins)4. Phenolic compounds are the most abundant natural antioxidants present in the human diet, and are found in large amounts in foods of plant origin, including fruits and plant-derived beverages6.Phenolic compounds are products of the secondary metabolism of plants7. In particular, it is well established that introducing some polyphenols with the diet or as supplements can improve the health status of people affected by cardiovascular disease, and this is confirmed by several biomarkers associated to this condition and by epidemiologic studies7. For instance, it has been indicated that a high flavonoid intake is related to a lower mortality from coronary heart disease and a lower incidence of myocardial infarction in older men8. In addition, a high dietary flavonoid intake can reduce the risk of coronary heart disease by 38% in postmenopausal women8. Similar studies about the role of phenolic compounds in other major diseases, such as cancer, diabetes and obesity, are growing and increasing the evidence for the beneficial effects of polyphenols derived from plants for human health11.There is an increasing body of evidence supporting that phenolic compounds are potent antioxidants and limit the risk of several diseases to which oxidative damage is a significant contributor12. In the intestinal lumen area, the microbiota helps to break down these complex molecules into absorbable phenolic metabolites and increases the biological availability of polyphenols through their conversion into smaller and more active compounds12. Therefore, the gut microbiota exerts a major function in the bioavailability and bioactivity of polyphenols, which has a direct influence on human health, and modifications to the composition of the former affect the availability of the latter12. This interaction is quickly becoming a major research topic in the area of personalized nutrition14.Due to their complex chemical structures, high molecular-weight polyphenols are not easily absorbed in the small intestine and reach the colon almost unchanged15 or the Model SEED database16, store reactions from species not present in the gut microbiota, and pathway extraction is not direct. In a previous work17, we addressed this issue and developed AGREDA17, an extension of AGORA18, the most comprehensive collection of metabolic reconstructions for the human gut microbiota. AGREDA17 provides a better description of the metabolic pathways of dietary compounds, including 114 phenolic compounds of Phenol-Explorer19, the largest database of phenolic compounds in the literature. However, there is still substantial room for improvement. In particular, more than 2/3 of the phenolic compounds present in Phenol-Explorer19 are not even described in universal metabolic databases, which makes the definition of their metabolic pathways more challenging, requiring the use of different approaches.The metabolism of phenolic compounds in the human gut microbiota remains largely unknown. Universal metabolic databases, such as KEGG23, typically referred to as the underground metabolism25. Several algorithms have been developed to exploit the concept of enzyme promiscuity and predict synthesis/degradation pathways for metabolites absent in universal databases27. They extract reaction rules from known enzymatic reactions, and use them to describe potential structural changes in the bonding patterns of substrates and products27. Reaction rules are defined to be as generic as possible, so that they can be applied to different substrates to establish potential unknown reactions. Possible transformations from the reaction rules define the so-called extended metabolic network, which typically suffers from combinatorial explosion27. Various algorithms address this issue by ranking tentative reactions and metabolites and adopting an appropriate search procedure to infer the most relevant pathways27. Here, we used RetroPath RL27, a recently released open-source Python package, based on Monte Carlo Tree Search strategy reinforcement learning, which significantly improves previous approaches developed by the same group29.Here, we rely on enzyme promiscuity to complete metabolic pathways of phenolic compounds in the human gut microbiota. Enzyme promiscuity assumes that enzymes could accept alternative substrates and catalyze additional reactions to the ones annotated in databases27, we analyzed tentative metabolic pathways for the phenolic compounds present in Phenol-Explorer19. We provide details as to the reactions, metabolites and species involved in the proposed pathways and evaluate their chemical and biological plausibility. Then, we integrate these predicted reactions with our previous metabolic reconstruction of the human gut microbiota, AGREDA17, and systematically analyze the metabolic capabilities acquired in the extended reconstruction. We assess the impact of our improvements in the metabolic processing of various foods detailed in the Phenol-Explorer database19, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present experimental in vitro validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.Using RetroPath RL17, a metabolic network of the human gut microbiota that more accurately describes the degradation pathways of dietary compounds, including 114 phenolic compounds from Phenol-Explorer19. Our objective here is to extend AGREDA17 and fill gaps for the remaining 258 phenolic compounds present in Phenol-Explorer19 via enzyme promiscuity. Enzyme promiscuity methods extend the metabolic space by considering that enzymes can accept substrates other than those present in annotated reactions. Here, we used RetroPath RL27, one of the most advanced retrosynthesis algorithms in the literature that is based on Monte Carlo Tree Search strategy reinforcement learning30.In a previous work, we developed AGREDA27 requires three different input data: sources, sinks and reaction rules. Sources are phenolic compounds obtained from Phenol-Explorer19, and sinks are metabolites involved in reactions existing in species present in AGORA18 (colored green). These metabolites were obtained from AGREDA17 and the Model Seed Database16 (see Methods section). Reaction rules are generic structural representations of reactions and define chemical transformations that can potentially occur. As with metabolites, we only considered rules coming from reactions annotated to the species present in AGORA18 and, thus, in the human gut microbiota. RetroPath RL27 searches for paths that link source and sink metabolites through the extended metabolic space derived from reaction rules. The steps that were followed to apply RetroPath RL27 are detailed below and summarized in Fig. RetroPath RL27 to the 372 compounds present in the Phenol-Explorer database19. We found putative degradation pathways for 303 phenolic compounds. In particular, these pathways involved 191 phenolic compounds that were not previously described in AGREDA17. 86 phenolic compounds out of these 191 were connected to the subset of sink metabolites. The remainder 105 phenolic compounds were linked to metabolites that are not included in our metabolic database and, thus, were discarded for further analysis. Full details of reactions and metabolites predicted by RetroPath RL27 can be found in Supplementary Data We applied RetroPath RL27, we assessed the predicted reactions for phenolic compounds that were already present in AGREDA17 (112 out of 303 compounds). First, we found that 52.8% of these predicted reactions were part of AGREDA17. Moreover, for 92.7% of these 112 phenolic compounds, RetroPath RL27 predicted at least one reaction that was already in AGREDA17, meaning that for each metabolite the algorithm reaches known transformations and proposes new additional reactions. These results permitted us to be confident that the RetroPath RL27 workflow is able to reach correct transformations.In order to validate the results derived from RetroPath RL27 with AGREDA17, following the gap-filling process and single-species analysis described in the Methods section, leading to a new version of the human gut microbiota metabolic network: AGREDA_1.1. To facilitate the comparison, our previous version of AGREDA is referred to as AGREDA_1.017. Overall, AGREDA_1.1 included 133 new metabolites, with 80 new input phenolic compounds, and 313 new reactions with respect to AGREDA_1.017, with 195 reactions predicted by RetroPath RL27, obtaining a final network comprising 2735 metabolites and 6257 reactions. Note here that, as in AGREDA_1.017, all reactions added in AGREDA_1.1 have taxonomic annotations to species present in AGORA18. Full details of AGREDA_1.1 can be found in Supplementary Data Then, we integrated the reactions and metabolites predicted by RetroPath RL19 compounds between AGREDA_1.0 and AGREDA_1.1 was 28% for isoflavonoids (12 vs 36 out of 86 compounds), 39% for anthocyanins (19 vs 38 out of 49 compounds) and 24% for hydroxycinnamic acids (13 vs 21 out of 33 compounds)  Fig. . On the ds) Fig. .Fig. 2Ma19, which details the nutritional composition for 458 foods. We identified 40 foods that involve at least one of the 80 phenolic compounds included in AGREDA_1.1 in their nutritional composition 32 (see Methods section). On average, AGREDA_1.1 predicted 172 output compounds that were not captured by AGREDA_1.017, with a minimum of 158 and a maximum of 199. Full details can be found in Fig. 17 and AGREDA_1.1 for the foods analyzed. All the output microbial metabolites reached using AGREDA_1.017 were present in the ones obtained with AGREDA_1.1. Moreover, the output metabolites we reached with the new reconstruction included some that were not produced with AGREDA_1.017, but were part of the original network . We contextualized each version of AGREDA with the nutritional composition of lentils and the information of the microbial community of each fecal inoculum obtained from 16 S rRNA gene sequencing data . We validated the results by means of an untargeted metabolomics approach (see Methods section).We conducted an analysis similar to the one in our previous work17 and AGREDA_1.1 in at least one of the 24 samples considered. We identified a total number of 63 metabolites that presented differences between the two models. Results obtained from the metabolomics data for the 63 metabolites accross the 24 samples can be found in Fig. p value of the association in AGREDA_1.1  identifiers for their associated metabolites, as required by RetroPath RL27. We obtained 9846 reactions and 6382 metabolites.The RetroPath RL algorithm15 and the HMDB database35, from which the InChI ID, the molecular structures in MOL files or the SMILES string were extracted. Where necessary, we then used RDKit36 to turn these structures or SMILES into InChI strings. On the other hand, we used the Phenol-Explorer database19 to get the InChI strings directly for phenolic compounds.We used two approaches to obtain the InChI identifiers for metabolites. On the one hand, we used the KEGG database27 distinguishes between sink and source metabolites. In our case, source metabolites are those present in the Phenol-Explorer database19 (372 compounds) and sink metabolites are those present in the metabolic database described above (6382 compounds). We introduced the InChI identifiers of the compounds in the source and target set into RetroPath RL27.RetroPath RL27 needs reaction rules, which constitute generic representations of reactions and their underlying structural changes in bonding patterns. In particular, RetroPath RL27 requires the rules in the community-standard SMARTS (SMILES arbitrary target specification) formalism. We extracted them from the RetroRules database34, where they are defined with different levels of specificity depending on the atom distance to the reaction center (reaction diameter). In addition, we manually generated the rules for a set of 236 reactions present in AGREDA17, which were previously extracted from the literature and involve specifically other phenolic compounds. The creation of the rules was carried out using the online rule generator present in the RetroRules34 website. Once we discarded reaction rules without taxonomic evidence to species present in AGORA18, we introduced a total of 49498 reaction rules into RetroPath RL27.In addition, RetroPath RL27. First, we fixed the biosensor setting, which specifically searches for pathways that connect unknown compounds of interest (sources) to target compounds (sinks)27. In addition, following the recommendations of RetroPath RL27, we considered reaction diameters from 6 to 16 to control the level of promiscuity in the extended metabolic space. Moreover, the internal cut off scores of RetroPath RL27, biological and chemical, were fixed to 0.1 and 0.6, respectively, in order to maintain a balance that would neither be too restrictive, nor would it compare molecules that were too dissimilar  metabolite is not present in the sink set once the maximum number of iterations and/or the time limit described above is reached. To address this issue, we selected pathways that are connected to our metabolic database. This task was done in an automatic manner for each source compound under study.We applied RetroPath RL27 works with mono-substrates rules, we needed to study the predicted equations in order to have balanced molecular components and atoms. Hence, we extracted the template reactions that RetroPath RL27 used to propose the new predicted reactions and we analyzed the chemical structure of the equations, adding the missing substrates  and functional annotation (EC number).In order to integrate the phenolic compounds into the AGREDA reconstruction17. This step is necessary because predicted reactions from RetroPath RL27 may connect to metabolites present in the universal database but not in AGREDA17. The connection to AGREDA17 is done by minimizing the inclusion of reactions without taxonomic and functional annotation from the universal database mentioned above. In particular, we used the FastCoreWeighted implementation from the COBRA Toolbox39. This algorithm requires the definition of a core, which represents a set of target reactions that must be functional in the final model. We applied the algorithm sequentially for each phenolic compound, defining the core equal to the reactions present in AGREDA17 plus the reactions predicted by RetroPath RL27.Then, we applied the same gap filling strategy as the one implemented in the AGREDA reconstruction17 and the reactions FastCoreWeigthed38 added to the core at each iteration. Since the algorithm might have added some reactions without any taxonomical information, we removed them and applied fastFVA32 to eliminate blocked reactions. Additionally, we applied a single-species analysis, as done in the AGREDA reconstruction17, in order to avoid possible dead-end metabolites in the metabolic model of each organism and include transport reactions if we have sufficient evidence for them. Next, we applied fastFVA32 to the metabolic model of each organism involved in AGREDA and eliminated blocked reactions. At the end of the entire process, we were able to introduce in the reconstruction 80 out of the 86 phenolic compounds whose degradation was predicted by RetroPath RL27. In total, we added 133 metabolites and 313 reactions to AGREDA17, obtaining a final network made up of 2735 metabolites and 6257 reactions, which we call AGREDA_1.1.Finally, we integrated AGREDA17, where a mixed-bag network community model was used, we built a compartmentalized network community model with the different versions of AGREDA. In these community models, each species is considered as an independent compartment and the metabolite exchange between different species can be captured. Flux Variability Analysis (FVA) was applied to characterize the metabolic capabilities of the human gut microbiota in different contexts32. Particularly, we focus on elucidating different output microbial metabolites derived from the diet.For the various analyses conducted in the Results section, in contrast to our previous work40 and fermentation42 resembling the physiological processes along the gastrointestinal tract. Four groups of children  were used as fecal donors to check the effect of different kinds of gut microbiotas.Lentils were submitted to in vitro digestionRegarding in vitro digestion, 5 g of sample were weighed into a screw-cap 50 mL tube. In vitro digestion consisted of three steps: oral, gastric and intestinal. Five milliliters of simulated salivary fluid with 150 U/mL of alpha-amylase were added and mixed into the 50 mL tube carrying the sample and kept at 37 °C for 2 min. Secondly, 10 mL of simulated gastric fluid with 4000 U/mL of gastric pepsin were added to the mix, the pH lowered to 3 and kept at 37 °C for 2 h. Enzyme activity was halted by immersion in ice for 15 min. Tubes were centrifuged, the supernatant  collected and the pellet (fraction not digested that would reach the colon) used for in vitro fermentation. Salt composition of simulated fluids can be found in Supplementary Table Fecal samples from three donors of each children population  were used for the in vitro fermentation. Common exclusion criteria were diagnosis of chronic gastrointestinal disorders or any other chronic disease or special diet other than those specific for celiac or allergic children, as well as having taken antibiotics or probiotics three months before the start of the study. Recruitment of the study participants was done via the pediatric unit at the hospital in Athens (Greece). Parents were given an informed consent as well as information and questionnaires for inclusion/exclusion criteria. The study was approved by ethics committee at the University General Hospital in Athens.Fecal material was pooled by donor group  to account for inter-individual variability. In vitro fermentation was carried out at 37 °C for 20 h, in oscillation. For this purpose, 0.5 g of the pellet obtained after in vitro gastrointestinal digestion were used, as well as 10% of the supernatant. Fermentation medium composed of peptone  was added to the fermentation tube at a volume of 7.5 mL. A fecal inoculum was made from fecal material by mixing it with PBS at a concentration of 33%. Two milliliters of inoculum were added to the fermentation tube. Afterwards, nitrogen was bubbled into the tube until reaching anaerobic conditions (transparent solution as opposed to pink when oxygen is dissolved). After 20 h at 37 °C, microbial activity was halted by immersion in ice for 15 minutes and tubes were centrifuged to collect the supernatant (fraction available for absorption at the large intestine), which was stored at −80 °C until further analysis. Blanks carrying water instead of sample were included in the in vitro digestion as well as in the in vitro fermentation.Fermented extracts were filtered prior to UPLC injection (2.5 μL). A quality control sample was randomly prepared and injected during analysis. This control was performed to attenuate the resulting analytical variation and to monitor the stability of the system.MassLynx v4.1. software was used to control the complete system. The system included a time of flight-mass spectrometer detector  coupled to LC equipment ACQUITY UPLC M-Class System . The UPLC column used was a Poroshell 120, SB-C18 . The mobile phases used were A acidified water and mobile phase B acetonitrile. A linear gradient was applied maintaining a fixed flow rate of 0.6 mL/min and 25 °C throughout the gradient. Mass spectrometry (MS) analyses were carried out in full-scan mode using an electrospray interface. All MS data were acquired using LockSpray to ensure mass accuracy and reproducibility. The molecular masses of the product ions and precursor ion were accurately determined with leucine encephalin.http://cosmos-fp7.eu/msi). Finally, potential metabolites that exceeded the mass accuracy detection threshold, showed significantly different trends from the control  and had plausible peak characteristics in the chromatogram were considered as possible fermentation markers for the different conditions.Raw data were processed with MassLynx v4.1 software  according to the “find-by-formula” algorithm. To achieve a higher confidence in metabolite identification, the spectral isotope pattern was used together with accurate mass information. The data were analyzed based on their coefficient of variation with the quality-control sample. Phenol-Explorer 3.6 and Human Metabolome Database were used as references for compound identification. The identification was carried out as established by the COSMOS Metabolomics Standards Initiative (Supplementary MaterialSupplementary TablesSupplementary Data 1Supplementary Data 2


Scopoletin is a typical example of coumarins, which can be produced in plants. Scopoletin acts as a precursor for pharmaceutical and health care products, and also possesses promising biological properties, including antibacterial, anti-tubercular, anti-hypertensive, anti-inflammatory, anti-diabetic, and anti-hyperuricemic activity. Despite the potential benefits, the production of scopoletin using traditional extraction processes from plants is unsatisfactory. In recent years, synthetic biology has developed rapidly and enabled the effective construction of microbial cell factories for production of high value-added chemicals. Herein, this review summarizes the progress of scopoletin biosynthesis in artificial microbial cell factories. The two main pathways of scopoletin biosynthesis are summarized firstly. Then, synthetic microbial cell factories are reviewed as an attractive improvement strategy for biosynthesis. Emerging techniques in synthetic biology and metabolic engineering are introduced as innovative tools for the efficient synthesis of scopoletin. This review showcases the potential of biosynthesis of scopoletin in artificial microbial cell factories. Coumarins are a family of secondary metabolites that are widely found in various plants, fungi, and microorganisms. According to their biosynthetic pathways, coumarins are divided into three main groups, i.e., the shikimate acid pathway, cinnamic acid pathway, and phenylalanine metabolic pathway . CoumariArabidopsis thaliana [3+, and rescue the chlorotic phenotypes of wild-type plants under conditions of deficient iron availability [Scopoletin (7-hydroxy-6-methoxy coumarin), is a typical representative of the coumarin family. The substance is derived from the carbon skeleton C6-C3 and contains a flavonoid skeleton core in a 1,2-benzopyrone structure , with methaliana and otherthaliana  with thethaliana . Scopolethaliana , 15, antthaliana  propertithaliana –21, neurthaliana , 22–24, thaliana , 25 and thaliana  propertithaliana , 28. Frathaliana , anti-hythaliana , antitumthaliana , 32, etclability .Fig. 1ThCurrently, coumarins are mainly produced by chemical synthesis, and extraction from plants. With the increasing demand for coumarins, the traditional method of extraction from plants or chemical synthesis is considered unsustainable, due to the high production costs involved. Recently, artificial microbial cell factories seems to be a promising strategy for the large-scale and economically viable production of coumarins. With this aim in mind, this review focuses on the production of scopoletin in microbial cell factories (MCFs) using synthetic biology methods.O-methyltransferase (CCoAOMT), and the cytochrome P450 family B2 subfamily C polypeptide 4 enzyme (CYP82C4).The metabolic pathway for scopoletin biosynthesis involves multiple types of chemical reactions catalyzed by the following catalytic enzymes: tyrosine ammonia lyase (TAL), phenylalanine ammonia lyase (PAL), 4-coumarate CoA ligase (4CL), feruloyl-CoA synthase (FCS), cinnamate-4-hydroxylase (C4H), 4-hydroxyphenylacetic acid 3-hydroxylase A (HHA), coumarate-3-hydroxylase (C3H), feruloyl-CoA 6'-hydroxylase (F6’H), coumaroyl CoA 2'-hydroxylase (C2'H), coumarin synthase (COSY), scopoletin 8-hydroxylase (S8H), caffeoyl-CoA p-coumaric acid via C4H catalysis. Tyrosine is catalyzed by TAL to generate p-coumaric acid directly. With the o-hydroxylation of p-coumaric acid by HHA or C3H, caffeic acid is produced and can be further methylated via CCoAOMT to generate ferulic acid. The formation of feruloyl CoA is catalyzed by 4CL or FCS. F6'H or C2'H is capable of catalyzing o-hydroxylation of feruloyl CoA and subsequent spontaneous reactions (isomerization and lactonization), leading to the formation of scopoletin. Recently, coumarin synthase (COSY) was reported to further enhance the formation of scopoletin. COSY is an acyltransferase catalyzing the trans–cis isomerization and lactonization in coumarin biosynthesis in plants [o-hydroxylation at the C8 position. Overexpression of S8H improved fraxetin biosynthesis in Escherichia coli [Scopoletin and other coumarin derivatives (fraxetin and sideretin) can be produced in microbial cell factories from both glucose and lignin-derived aromatics Fig. . When gln plants . COSY wan plants . It was n plants . Scopolen plants , 28, andhia coli . In the hia coli .Fig. 2Bip-coumaric acid and ferulic acid, can be generated in alkaline hydrolysate from pretreatment and used for the biosynthesis of scopoletin. By means of the metabolic funnel principle, the same steps described above catalyze these compounds to produce scopoletin [The synthesis of coumarins can also be produced from aromatic compounds derived from lignin by pretreatment , 38. Foropoletin . As one In recent years, the biosynthesis of scopoletin has been developed in microbial cell factories, and a variety of regulation methods have been applied to improve scopoletin biosynthesis including promoter engineering, enzyme engineering, heterologous expression, and carbon metabolic flux Fig. .Fig. 3SyE. coli were screened and identified based on RNA-seq data for accurate analysis of the transcriptional network in E. coli cells [Bacillus subtilis for the production of inosine and acetaldehyde, and their yields increased sevenfold and 1.4-fold, respectively [Saccharomyces cerevisiae genome to construct a promoter library with gradient intensities, and high-throughput screening techniques were used to screen the strains with an enhanced capacity for (2S)-naringenin production. Finally, 1.21 g/L (2S)-naringenin was achieved at the 5-L fermenter, which represented the highest naringin yield. Based on promoter optimization and directed evolutionary strategies, (2S)-eriodictyol from (2S)-naringenin was efficiently synthesized in S. cerevisiae [S. cerevisiae promoter was achieved through a combination of computer simulation and wet experimental verification, showing the potential to improve the biosynthesis of natural products such as coumarins [4CL gene was changed to galactose-inducible promoter Gal1 via CRISPR-Cas9, an increased yield of the target product was achieved [Promoter engineering is a significant basis for regulating gene expression and optimizing metabolite biosynthesis in metabolic engineering and synthetic biology. Gene expression levels must be coordinated with changes in intracellular and extracellular conditions to maintain the homeostasis of the metabolic network. Therefore, the dynamic regulation of gene expression requires more flexible regulatory promoters . Around li cells . A de noectively . Promoteectively . Dozens revisiae . A randorevisiae . Recentloumarins . Inducibachieved . As coumArabidopsis roots [Arabidopsis mutation f6'h1 can successfully produce coumarin derivatives, such as scopoletin, fraxetin and sideretin [Enzyme engineering is also important to improve the yield of natural products. CCoAOMT is a key enzyme for the production of feruloyl CoA by catalyzing caffeic acid, and it is also involved in the biosynthesis of lignins and scopoletin in is roots . Previouis roots . F6'H [4is roots  or C2'H is roots  is involideretin , 50, 51.O-methylation of N-acetylserotonin (NAS) was improved by 9.5 fold [p-hydroxybenzoate hydroxylase from Pseudomonas aeruginosa, variant for hydroxylating 3,4-dihydroxybenzoic acid  into gallic acid (GA) via structure-based protein engineering approach was reported [Methylation and hydroxylation are the two important reactions in scopoletin biosynthesis. The yields of coumarins are generally low, which is due to the low activity of the methyltransferases and hydroxylase to a great extent. Modification of methyltransferases by rational protein engineering enhanced their binding capacity with substrates, and activity of 9.5 fold . In termreported . Multiplp-coumaric acid and caffeic acid as well as ferulic acid due to the weak substrate specificity, and the specific substrate affinity can be increased by engineering of 4CL. In the cell factories for the production of umbelliferone, virtual screening based on the binding energies was used for the selection of 4CL, and the site-specific mutagenesis-based protein engineering approach indicated that double mutation of Q272H and F267L may improve the yield of umbelliferone [Many factors, such as substrate specificity and enzymatic activity of key enzymes, affect conversion and production efficiency. Nowadays, artificial intelligence and automated platforms, such as bioinformatics and machine learning, help in the engineering and optimization of enzymes. The mining new enzymes can be accelerated with the help of a computational method called AlphaFold 2 . This emliferone . TherefoPetroselinum crispum and F6'H1 from Arabidopsis thaliana, the engineered strain achieved 27.8 mg/L scopoletin using ferulic acid as substrate [Ipomoea batatas with glutathione S-transferase (GTS) and expressing 4CL from Oryza sativa in E. coli, the yield of scopoletin increased to 79.5 mg/L using ferulic acid as a substrate [S. cerevisiae using the heterologous expression [Arabidopsis thaliana and 4-coumarate coenzyme A ligase (4CL) from Petroselinum crispum significant improved the production of scopoletin in S. cerevisiae. Experimental screening was conducted with a devise of different lengths, and forward and reverse linkers. Different linkers between the two fusion expressed genes also affect the conversion. The linker of (Gly-Gly-Gly-Gly-Gly-Gly-Ser)4 showed a 3.3-fold increase in scopoletin production and a final titer of 3.42 mg/L.Heterologous expression is fundamental in the production of natural products in microbial cell factories. In order to improve the production, the strategies about heterologous expression include overexpression of key genes, fusion expression of sequential enzyme genes, expression of key with specific locations and so on. With the co-expression of 4CL from ubstrate . By fusiubstrate . Ferulicpression . Fusion E. coli compartmentalized the cytosolic space into regions of high and low enzyme density and led to a significant enhancement of α-farnesene production [Organelle localization of enzymes are also important to improve the product yield. Enzymes can be localized in different organelles, including the endoplasmic reticulum (ER), lipid body (LB) and peroxisome, and so on. Compartmentalization in cellular organelles can isolate synthetic pathways from competing pathways, and provide a suitable environment for biosynthesis. Synthetic pathways targeting to peroxisomes increased the production of fatty-acid-derived fatty alcohols, alkanes and olefins . Condensoduction . The locoduction . Due to pheA) and o-aminobenzoic acid synthase (trpE) catalyze the side reactions, and they are knocked out to improve carbon flux of tyrosine, which is the precursor for umbelliferone biosynthesis [Carbon metabolic flux regulation is important for improving the efficiency of microbial cell factories for natural products. Carbon metabolic flux can be regulated by the inhibition of side reactions, genomic disturbance and screening, and so on. The metabolic pathways of coumarin synthesis are usually long pathways with dozens of reactions. The side reactions would reduce the metabolic flux to the target products. It is crucial to reduce metabolic competition between target products and byproducts , 61–63. ynthesis .E. coli W3110 can only accumulate 5.54 g/L pyruvate, but accumulate large amounts of byproducts including lactate, acetate and formate. In order to improve the accumulation of pyruvate in the strain, the FLP/FRT gene editing technology was used to knock out the genes encoding lactate dehydrogenase (ldhA), pyruvate oxidase (poxB), pyruvate formate lyase (pflB), phosphotransacetylase (pta), and acetate kinase A (ackA), which led to accumulate 20.9 g/L pyruvate [l-lactate dehydrogenase (ldhA) and acetyl-CoA acetyltransferase (atoB) were deleted individually by CRISPR-Cas9, and the yield of adipic acid was increased from 49.5% to 61.7% and 68.5% theoretical yield in shaken flasks [Vibrio fischeri and Enterococcus faecalis was used to control the synthesis of medium-chain fatty acid pathway enzymes and the expression of the endonuclease MazF, ultimately altering the global distribution of cellular metabolic resources and the production of medium-chain fatty acid increased by 5.4-fold in a 5-L fermenter [Furthermore, gene editing, gene circuits , and othpyruvate . Based opyruvate . During n flasks . Gene ciermenter . When CRermenter .p-coumaric acid, caffeic acid, and ferulic acid are also commonly found in plants. With rapid developments in synthetic biology, the design and optimization of microbial cell factories has gradually opened up for natural aromatic product synthesis, applying a new approach for the synthesis of coumarins from renewable sources like lignin or its derived aromatics. Despite the efforts made, cytotoxicity induced by the accumulation of aromatic products remains a continuous challenge that cannot be ignored. Further optimization to improve the tolerance of microbial cell factories has become an essential step for industrial production. In addition, the tolerance of engineered strains can be enhanced by means of adaptive laboratory evolution and genomic rearrangement techniques, along with other methods for finding key tolerance elements and targets. Furthermore, the biosynthesis processes in microbial cell factories should be based on the concepts of economic benefit and low-carbon development. In conclusion, with developments in synthetic biology, the emergence and integration of more and more advanced technologies and genetic tools will accelerate the development of more efficient microbial cell factories and realize feasible industrial production for natural aromatic products.Natural aromatic products, such as coumarins, are a group of chemicals of great industrial importance . Within


We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for  Microbial cell factory development using metabolic engineering seeks to obtain high levels of products of interest through genetic modification of microorganisms. Natural cells use complex regulatory networks to preserve robust growth and endure environmental changes by dynamically adapting cell metabolism . These rConsidering the metabolic network dynamics allows better analysis of the sensitivity of the metabolites or fluxes of interest to the optimal enzymatic intervention points under different environmental situations. Dynamic network models, from grey-box to black-box ones, of scales ranging from a subset of pathways to genome-scale, have been used to this end . The optOnce the optimal signals to be feedback and the intervention points are obtained, the problem of designing and tuning the proper dynamic feedback regulation biomolecular controller remains. Achieving robust optimal production in microbial cell factories requires considering the dynamic regulation of the pathway of interest. Dynamic feedback regulation constitutes a very interesting strategy to construct pathways with the ability to self-tune upon changing environmental conditions and to overcome many of the ongoing challenges faced in metabolic engineering . For exaDespite the growing number of reported successful cases, engineering dynamic feedback control strategies in biological applications remains a major challenge . Model-bHere, we consider the design and tuning of a biomolecular controller for the dynamic feedback regulation of a merging metabolic pathway. Since we restrict to a single metabolic pathway, determining the dynamic regulation topology, i.e., the feedback variable and the intervention point, could be made by simple inspection and previous knowledge of the system. In this metabolic motif, two substrates, the primary precursor and an essential metabolite, are converted to an intermediate product which is subsequently transformed into a target product. The secondary essential metabolite plays an additional role in cell metabolism in many practical situations. Therefore, it is subject to environmentally-induced fluctuations. Over-expressing the enzyme that synthesizes this secondary metabolite or redirecting the flux towards it is not feasible in cases where its accumulation is toxic for the cell, leading to growth inhibition. This is the situation encountered in applications like the production of phenylpropanoids of industrial interest, e.g., naringenin .in vivo implementation of the system. This gives us more information than the simplistic models of the antithetic biocontroller used in the literature that do not consider fundamental aspects like:• non-linearities in the promoters. In the simplest models, the expression of proteins and sigma factors is always proportional to the number of transcription factors, i.e., there is no saturation of the promoters.• formation of the antithetic complex, and the unbinding reaction of the complex.• dilution rate of all the species due to cell growth. It is known that the dilution destroys the perfect adaptation property of the antithetic biocontroller, introducing a steady-state error. As we comment later, this forces us to use more than one objective to optimize.In previous work, we considered the problem of designing a dynamic regulation topology for the production of naringenin while coping with fluctuations in malonyl-CoA, the secondary essential metabolite . This woMultiobjective optimization has already been demonstrated to be an appropriate tool for characterization of gene circuit parts , and forWe first show our approach using a simple illustrative pathway that captures the essential topological features of merging metabolic pathways. We use a feedback regulation strategy encompassing a simple TF-based biosensor to obtain readouts of the product and a simplified model of the biomolecular antithetic controller. In this case, the final titer of the target product and the robustness to fluctuations in the secondary metabolite are evaluated. Then, we consider a detailed model of the metabolic merging pathway of naringenin, the biocontroller, and the extended biosensor of naringenin production that we previously introduced in . In thisP from a precursor substrate S1 and a secondary substrate S2. The reaction is catalyzed by the enzyme E. This metabolic pathway can be described using the following model:To illustrate our approach’s broad scope and usefulness, we first study a basic metabolic pathway that contains the main common features of a typical merging motif. As shown in Si and P are the amount of substrates and product. X is the number of cells in the population. S1 is the primary substrate, and S2 is the secondary substrate. The first order dilution term represents the effect of cell growth on the amount of substrates and products, being μ the specific growth rate. Xmax accounts for the maximum growing capacity of the population. The metabolic fluxes are given by the kinetic terms:S1 has constant rate S2 is normally available at non-limiting amount. The flux E is the amount of enzyme catalyzing the pathway, kcat is the enzyme catalytic rate and Where E. As its expression level is independent of any metabolite in the pathway, the production of P is affected in the presence of a sudden change in the availability of the secondary substrate S2, as shown in the right plot of In the case of static pathway regulation  closer to its previous value  and binds the anti-σ molecule to form an inactive complex, effectively reducing the amount of free σ. The resulting dynamics of the amount of free σ molecules is:The acting molecule P expressing the anti-σ molecule as a function of the product amount. A constitutively expressed Transcription Factor (TF) (equation omitted for brevity) binds to the product P inducing the expression of the anti-σ molecule. The dynamics of the amount of anti-σ molecules is:Next, a TF-based biosensor detects the product P decreases, so does the amount of expressed anti-σ, thus increasing the amount of free σ molecules and, this way, up-regulating the expression of the enzyme E. Next, we consider the optimal tuning of the gene circuit parts composing the antithetic biocontroller and the TF-based biosensor.When the amount of P together with reducing the production loss after a perturbation on the level of the secondary metabolite S2, as illustrated in P in the bioreactor and the desired target value (J1). For the second one, we focus on the production loss (amount of product expressed per cell) after a perturbation on S2 (J2). The corresponding expressions for both objectives to be jointly minimized are:Punperturbed(T) is the product amount at the end of the experiment (time T), K is a conversion constant from the amount of product to titer, Pperturbed(T) is the amount of product after a perturbation in the secondary metabolite. As J1 describes the difference between the desired target titer and the actual one, lower values of J1 correspond to larger titers. On the other hand, J2 is related to the loss in production after a perturbation. Therefore, small values of J2 correspond to low production loss after a perturbation. That is a better rejection of the perturbation on the secondary metabolite.To characterize the trade-offs between reaching the desired titer target for E, a1; the dissociation constant between σ and the enzyme promoter, kd20; and the expression strength for anti-σ, kσa. Additionally, the specific growth rate, μ, was included as a decision parameter to account for the dependency of the results on cell growth.Next, we selected the biosensor and biocontroller set of parameters to be tuned. We took into account to what extent these parameters can be changed in the biological implementation of the system at the lab. Thus, we considered the set: expression strength for the enzyme J1, J2. The resulting solutions are all equally optimal in the sense of Pareto  subject to biologically plausible bounds on the values of the parameters . Yet, there is no monotonous trend in neither the anti-σ expression strength kaσ nor in the E enzyme expression strength a1.A detailed inspection of the library of controller and biosensor pairs obtained  reveals Altogether these results suggest that strategies for fine-tuning the trade-off between target titer error and production loss in dynamic pathway regulation are complex and impossible to obtain by simple inspection even for a simplified case, rendering the use of the multiobjective optimization methodology not only helpful but necessary.Naringenin is a flavonoid compound predominantly found in grapefruits and oranges. It has been reported to have many pharmacological properties, including anti-dyslipidaemic, anti-obesity and anti-diabetic . FlavonoL-tyrosine precursor (see μM in E. coli) (The naringenin pathway has four enzymatic steps from the rsor see . The thiE. coli) . MoreoveL-tyrosine to naringenin . Lt is the number of molecules of L-tyrosine, pC is p-coumaric acid, pA is p-coumaroyl-CoA, Nc is naringenin chalcone, and N is the target metabolite naringenin. Next, we assumed that the fluxes Vj follow the Michaelis-Menten kinetics , and Kmj is the Michaelis-Menten constant for each substrate. The enzyme kinetic parameters, detailed in For each reaction, the corresponding flux is kinetics , and theMa level caused by changes in the cell will act as a perturbation signal. We considered a basal value of Ma in the mid-range of values reported in the literature   . When kai—cell in the population, the kinetics of the enzyme-catalyzed reactions involved in this extended pathway were modeled using the set of rate Di is the number of molecules of Dihydrokaempferol, Ka is kaempferol, the flux VDi obeys the Michaelis-Menten kinetics Q is the constitutively expressed QdoR protein. All the parameters are listed in For every σ and anti − σ factor proteins. The σ factor activity is controlled by the anti − σ factor that binds to and keeps the σ factor sequestered. The anti − σ is only released and de-repressed in response to the QdoR transcription factor. The dimer formed by the LuxR protein and AHL lactone activates the PLuX promoter, inducing the synthesis of σ factor. The externally added concentration of AHL acts as the desired reference input for naringenin production. We do not assume the AHL concentration needed to set the desired value for naringenin must be equal to this one—implying an unnecessary metabolic burden—but simply proportional. Free σ factor binds to the P20 promoter to activate expression of the naringenin chalcone synthase CHS, which subsequently converts p-Coumaroyl-CoA and malonyl-CoA into the naringenin precursor. In other words, the CHS enzyme represents the controller output signal.The antithetic controller used for the dynamic regulation of naringenin production is depicted in σ ⋅ aσ is the amount of molecules from the generated complex after σ sequestration, σ and aσ are the factor and its cofactor, respectively. All the parameters are listed in Considering the same assumptions as those to derive the TF-based biosensor model, the dynamics of the antithetic controller for every cell is given by the following set of equations:R is the number of molecules of LuxR, A and Ae are the intra and extracellular AHL molecules, respectively, the term x is the number of cells. The parameters are also listed in As in , the desσ factor increases leading to an increased expression of the enzyme CHS. This results in a slight increase in the naringenin production. However, as seen in Using the set of preliminary parameters in Escherichia coli (E. coli).Having developed a detailed and realistic model of the pathway dynamic regulation, we optimally tuned the controller and biosensor components of the dynamically regulated metabolic pathway that produces naringenin in −1.We first established a baseline production pathway for naringenin, with the basal level of the CHS enzyme provided by a constitutive promoter. On top of this, the feedback control loop regulates the total level of expression of CHS to give a robust response to the fluctuations in the secondary co-substrate malonyl-CoA availability and drive the production of naringenin up to the target industrially relevant value of 1 g Lunp and Npert are the amount of naringenin before and after the malonyl perturbation at the time T, respectively, and J3 is an indirect measure of the frequency and damping factor of the transient in the antithetic biocontroller (see Methods for a detailed description).Next, we used our multiobjective optimization approach to find a library of optimal biocontroller and biosensor pairs for the dynamic regulation of the naringenin pathway. As in the previous example, we aim to determine controller and biosensor designs that allow reaching a target titer of naringenin production while minimizing the production loss after perturbations in the secondary metabolite. However, the extra dynamics introduced by the extended biosensor used in this case and the ones introduced by using a more realistic model of the biomolecular antithetic controller must be considered. These extra dynamics force us to consider the transient behavior of the regulated feedback loop in the design process to evaluate the overall stability properties of the designed system. Thus, we defined the following three objective functions to be minimized:σ and the CHS enzyme, pσa and pH; the plasmid copy numbers σ anti-σ complex dissociation rate, k−c; and the dissociation constant between σ and the CHS enzyme promoter kd20. The majority of these parameters are also easy to tune in the real implementation in the lab. Additionally, as in the simplified example before, we also considered the growth rate, μ, as a decision parameter. We computed them as the optimal solutions of the multiobjective problem min . The details of the optimization problem can be found in the Methods section and details on the obtained solutions can be found in the Supplementary Material, including the list of the solutions in A preliminary parameter sensitivity analysis of the biological parts from the biocontroller and the biosensor revealed six parameters to be tuned in the optimization process : the traJ3, related to the transient characteristics, can be clearly seen. This objective is represented by the size of the circles that correspond to each of the solutions in the Pareto front. Notice that reducing the production loss can be achieved at the cost of increasing the titer error as also seen before in the previous example. But in this case, an increased capacity to reject perturbations also increases the number of oscillations in the response, and thus corresponds to a less marginally stable configuration of the controller.The Pareto front resulting from the optimization is shown in a in J3 (with solution b as representative) has too an under-damped and lengthy transient, which could be unacceptable in some cases. Extreme solutions like (c) which has the lowest production loss or (d) with the smallest titer error may be of interest in particular cases when one objective has more practical importance than the others.However, unlike in the previous example, in which the oscillations were no taken into account in the optimization process, now it is possible to obtain a compromise design  shown in a arises as a better compromise with less under-damped response. A detailed inspection of the library of controller and biosensor pairs obtained (The trade-off is evident in the case of solutions obtained  again reσ ⋅aσ) complex. Depending on the available biological parts, one implementation can be more feasible than another, increasing the importance of having such a variety of elements in the library.Taking a deeper look into the obtained library, we can also make an interesting observation: different combinations of parameters result in similar performances. This is nothing more than another evidence of the inherent robustness obtained with negative feedback control. For example, devices 5, 6, and 7 from the library  the al steps  that reaθ =  is the decision vector that contains the decision variables for multiobjective optimization; J(θ) is the objective vector and K(θ), L(θ) are the inequality and equality constraint vectors, respectively, Θ. The MOP (4.2) has a set of solutions whose values in the Pareto front are function of the decision variables defined as the Pareto Set ΘP. Each solution in this set corresponds to an optimal objective vector in the Pareto Front JP. All solutions in the the Pareto Set are Pareto-optimal non dominated solutions, that is, they differ from each other in the objectives trade-off each one represents.As referred in , a MultiJ(θ) has to be defined to solve the problem presented in 2.1. We maximized the desired target titer for product P while minimizing the perturbation effects on the substrate S2 dynamics. The objective functions J1 and J2 were defined before in θ used for our optimization are θ =  with their corresponding lower and upper bounds as detailed in Here, the objective vector Hence, the MOP in (4.2) can be stated as:v is the Avogadro’s number, x(T) is the number of cells at time T, and σ factor used to detect its zero-crossing and obtain the number of oscillations of the σ before the malonyl perturbation at the position k. Additionally, the constraints vector K(θ) set two significant limitations for the antithetic controller performance in this pathway:For the dynamic regulation of the naringenin pathway, we defined three objective functions to tune both antithetic controller together with the biosensor:As we said in For the rest of the parameters, a sensitivity analisys was performed in previous work. Evidently, using different values of the these parameters may have an impact on the resulting behavior of the system, however this does not invalidate the whole methodology we are presenting here.via a basic bound mechanism in the objective space  with their decision variables θ by illustrating two graphs. The first graph contains each objective, where its Y-axis is the p-norm ‖J(θ)‖p of the objectives vector, and the X-axis corresponds to each objective value Ji(θ). The second graph shows ‖J(θ)‖p with respect to every θ, so a given solution will have the same y-value in all graphs.Choosing the preferable solution according to designer’s criteria takes place in an  Toolbox  from Mat Toolbox 2 as the@ 3.80 GHz. First, we defined two sets of model parameters known as nominal parameters for each system. Then, we computed the number of molecules of each species from every i − cell in the population over time. These data allow us to obtain the performance, robustness and stability of the biosensor and the antithetic controller from each system. Finally, we tuned the biosensor and the biocontroller for optimal dynamic pathway regulation following the multiobjective approach. For the merging metabolic pathway, the sp-MODEx evolutionary algorithm evaluated 1,000 the cost function, using 125 generations and taking 1 h for a simulation time. For the naringenin pathway dynamic regulation, the sp-MODEx evaluated 10,000 times the cost function, using 199 generations over 8 h of simulation time.All simulations of both the merging metabolic pathway and the naringenin pathway were performed in Matlab, using a 4 Core processor, 16 GB RAM https://github.com/sb2cl/molecular-biocontroller-tuning.All the scripts of the simulations and optimization can be found in the Github repository