Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based)  E. coli strains with different specifications for growth-coupled production of 2,3-butanediol to demonstrate the functional and performance benefits of our methodological enhancements.The targeted modification of metabolic networks, e.g. for designing microbial cell factories or to combat cancer cells, is often supported by computational methods. The framework of Minimal Cut Sets (MCS) uses a constraint-based approach to determine a minimum set of reaction deletions in a metabolic network that enforce desired phenotypes according to user-defined specifications. In this work we generalize the MCS approach by introducing several new features making it suitable for a broader range of applications. Among other extensions, the new features support (1) the combination of multiple strain design specifications at once and thus more precise metabolic network tailoring, (2) the optional addition of alternative substrates or of heterologous reactions in combination with reaction deletions, and (3) an improved direct computation of gene-based intervention strategies also exploiting new compression rules for gene-reaction-enzyme relationships. We use the example of designing  PLOS Computational Biology Methods paper.This is a Metabolic engineering of cell factories has become an essential technology for developing bio-based production processes as sustainable alternatives to conventional petrochemical syntheses . Variousconstrained MCS introduced the option to demand the conservation of desired behaviors 13] togetN and the matrix T for the target region are used with their transposed version  are given in their primal representation directly expressing Eqs  and primal (r) variables are interconnected via the binary zi variables. Each zi is linked with vi via two other binary variables . Effectively, cuts are marked by the zi that are equal to 1, which happens if the associated dual variable vi of a reversible reaction i is non-zero or if vi of an irreversible reaction i is positive . The smallest MCS can be found by a mixed-integer linear optimization problem (MILP) with an objective function that minimizes the number of cuts:The blocking of the target region is addressed in the first two rows of the matrix in  via a duion (cf. ). The asive part ) but we sing Eqs , 2) and andN andsing Eqs . The conC is given by C = {i | zi = 1}. Furthermore, multiple MCS solutions  can be found by adding integer cut constraints to the MILP for a previously found solution. If Ck denotes the set of reaction indices that are knocked-out in the k-th MCS, then the integer cut constraint readsAs mentioned above, the computed MCS C, but not necessarily a minimal (irreducible) cut set, i.e. C might contain redundant knockouts. However, by applying, in a second step, objective function  MCS will be identified. Still, this MCS is not necessarily an MCS with the fewest number of interventions. Nevertheless, a larger sample of MCS can be quickly generated in this way, which is often faster than always demanding to find the cardinality-minimal MCS in the next iteration.A new MILP optimization based on –13) is  is 13) ifunction  to a conzi = zp,i = zn,i = 0, or completely be removed from the system together with vi and the corresponding columns in the matrix in , their indicator variables can be fixed to atrix in . FurtherSo far, only one target and one desired region can be considered in . This isT1 and t1 also include the constraints :T1r≤t1rs). Constraint-based models often hold constraints that already exclude the zero-flux vector as feasible solution . If this is not the case, we must explicitly exclude the trivial flux vector, e.g. by demanding a minimum substrate uptake rate for the target region. The second target region can thus be expressed by the three inequalitiesThe second target region comprises anaerobic flux vectors with product per substrate yields below a threshold For the example, we also need to specify two desired regions: The first expresses that a high biomass yield, above a specified threshold We then also need to ensure that, under anaerobic conditions, where high product yield is guaranteed due to the elimination of low-yield solutions in the second target region, some minimum growth is still possible. This gives rise to the second desired region for which we can use Eqs  and 10)10) in coIt is important to notice that the desired systems 1 and 2 cannot be jointly represented by a single desired system because their union is not convex (the same holds for the two target regions).T1, t1), , …, . Likewise, we allow the definition of arbitrary many desired regions represented by , , …,. In the MILP formulation  and two desired  regions reads:As a generalization, we allow in the following the definition of arbitrary many target regions, each defined by inequalities posed by an appropriate pair of matrix and vector  always unity in , the MCSMost computational strain optimization algorithms use combinations of (gene or reaction) knockouts to enforce desired phenotypes. However, some dedicated methods have been developed for biased strain design techniques to also allow addition of heterologous reactions alongside reaction deletions, to further enlarge the search space for network design ,33,34. Sknockout of a reaction i is marked by (zi = 1) and implies costs in the objective function  addable reactions are included in the original network and every reaction is then marked as either “deletable”, “addable” or “non-targetable”. In contrast to deletable reactions, where the function , addablefunction  (and anafunction ) for addi is the standard case, indicated by (zi = 0), while the incorporation of a reaction into the network is marked by (zi = 1) and therefore leads to increased costs in the objective function  leads to a functional separation of the two substrates: S is then used for ATP and product synthesis while U is needed as biomass precursor.With the extensions of multiple target regions (extension 1) and possible addition of reactions (extension 3), we are now able to compute suitable MCS that exploit substrate co-feeding, which adds another dimension for strain design strategies. Co-feeding of substrates has been used in metabolic engineering, for example, to enhance the productivity of a designed strain –37, to cYP/(S+U) ≤ 0.4. We could then mark rU,up as addable reaction and try to search for both classical one-substrate strain designs (using only S) as well as substrate co-feeding strategies (using S and U). If U would only act as optional substrate and the excretion of U was not possible, then co-feeding strategies could be straightforwardly identified by marking the uptake of U as addable reaction. However, in this example with U also being a potential product, this alone would not be sufficient to find suitable co-feeding solutions. The MCS algorithm would seek to eliminate all flux states with yields inferior to YP/(S+U). Yet, the cycle of the sink pseudo-reaction rU,ex and an activate uptake reaction rU,up can carry high fluxes and inflate the denominator of the yield function, making it impossible to disrupt all flux distributions with yields below the threshold. Allowing the deletion of rU,ex would affect the correct identification of single substrate strategies because the algorithm would then be able to generate solutions that delete the secretion reaction rU,ex although it is non-targetable.Generally, scenarios with multiple substrates require a suitable redefinition of the yield to properly account for both substrates. In our example we would consider the combined yield A solution to this dilemma is the definition of two target regions. The first target polyhedron describes undesired solutions for the “classical” one-substrate usage where U is not taken up:T1r ≤ t1. The second target region specifies undesired solutions for the case where U is taken up, using the combined yield term. Importantly, in the definition of this second target region, only the relevant cases where U is not excreted are considered. Hence, with the defined target regions 1 and 2, the irrelevant case with an active cycle in rU,up and rU,ex is simply not included in the set of target vectors:These constraints can be reformulated to bring them into the required form rBM ≥ 0.1), the enumeration of strain designs with the introduced MCS algorithm extended with multiple target regions and addition of reactions will return both solutions . A translation of reaction deletions to corresponding gene deletions is often possible, however, there are also complicated relationships, where this translation is not straightforward, for example, when an enzyme or enzyme subunit is involved in several reactions. Hence, for the application as strain design tool, MCS should deliver suitable combinations of gene interventions (gene MCS), which induce the desired metabolic phenotype by indirectly targeting the operation of certain reactions  or . (2016) . The fun. (2016)  can now . (2016) ,27 metholgorithm , which hE. coli, S. cerevisiae and P. putida. Here, benchmark calculations showed a clear benefit of the new compression rules for GPR associations. The analysis of the computation results for E. coli showed that the new features allow the identification of qualitatively new strain designs that could not be generated with existing methods. Previous experimental works favored the use of microaerobic conditions for 2,3-BDO production in E. coli  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Kiran Raosaheb Patil, Ph.D.Associate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors extend the already existing CellNetAnalyzer for the computation of MCSs in metabolic networks. The two major additions are the integration of constraints allowing to describe contradicting conditions of the network, e. g. aerobic and anaerobic growth, and compression rules which reduce the size of the network, while including gene rules and therefore not only reactions. <o:p></o:p>They show the improvements of their method by applying it to the core and genome-scale version of E. Coli. Their computational time is significantly reduced and they were able to compute MCSs in the genome scale network which was not possible before.<o:p></o:p>Overall impression:<o:p></o:p> I think the authors made a considerate improvement for the computation of MCSs in genome-scale metabolic networks. However, I think further tasks have to be executed to fully show the general applicability of their method. I am especially concerned about their claim to be able to add new reactions to their metabolic network which they do in fact “by hand”, thus this feature is not provided by their method but is part of the input which is created by the user.<o:p></o:p> Major:<o:p></o:p>- Extension 3: Where does the additional reactions come from?https://doi.org/10.1186/1752-0509-6-30 where the KEGG-data base was used.<o:p></o:p>I do not really see a “proof of concept” for the addition of reactions. Adding new reactions to the network “by hand” is changing the input, thus this is not part of the introduced method or a new “computational” addition. I would rather like to see an automated way, similar to how it was done here  - I would like to see a wider application, not only for the two E. Coli models. Maybe the authors can apply their method to all metabolic networks from the BiGG model data base, in order to see what the benchmarks are and the limits (regarding the sizes of the networks) of their method. Right now they claim to introduce a new toolbox, but do not show the overall applicability of it.<o:p></o:p> - For me the major achievement here is not that their method is faster, but that they were able to compute MCSs in the genome-scale E. Coli which was not possible before. They should emphasize this more. Also by applying it to other genome-scale networks and show where they are able to compute MCSs and the non-decompressed version not.<o:p></o:p> - Is the computation of MCSs using CellNetAnalyzer the same as the non-decompressed column in table1? If yes, please clarify. If not, the authors should compare their method with CellNetAnalyzer and/or other state-of-the-art MCSs methods.<o:p></o:p> - page 17: The authors claim that decompressing the MCSs computed in the compressed network may lead to higher number of interventions per MCS. Thus they do not compute MCSs for the original network. Can they elaborate what that means? For example is a MCS of size 1 in the original network always a MCS of size 1 in the compressed network ? It could happen that a lot of “small” MCSs in the compressed network are actually too large for wet lab applications, thus computing those does not seem to be efficient. Can they compare the size of the compressed MCSs to the size of the MCSs in the original network the compressed MCSs relate to? How do they make sure that all MCSs of desired size are found?<o:p></o:p> - I would rather like a different structure of the article. I would like to first read the overall results, thus the improvement of their method, and the extensive description of the rules and methods afterwards.<o:p></o:p>  Minor:<o:p></o:p> p_i is set to 0? Would this imply that reaction i is always set to zero (or non-zero). Would you still be guaranteed with a true MCS?<o:p></o:p>- Extension 2: What happens if  - What does CSOM stand for? Abbreviation is not introduced <o:p></o:p> - No introduction of “flux vector”  .<o:p></o:p> https://doi.org/10.1093/bioinformatics/bty1027 on p3-4<o:p></o:p>- cite  -state (inconsistent spelling)<o:p></o:p>- steady state vs. steady - p5: MCS abbreviation was already introduced<o:p></o:p> Y?<o:p></o:p>- Formula (5): What is  P and S on page 7? Is it O2 up or just some general substrate product? What is X? What is mu?<o:p></o:p>- what is   -in Formula (13): 0.25z_{1,p,i} + 0.25 …. <= zi: Do you mean ‘=’ instead of ‘<=’? Otherwise this is redundant or?I find the equations really hard to read because of how they are presented. Can you put the (in)equality signs all in the center, such that they are aligned?<o:p></o:p>Do you need z_{1,n,i} = 0 for irreversible reactions? Shouldn’t this be in Ar<=b (as claimed before)?<o:p></o:p> https://doi.org/10.1093/bioinformatics/btv217 can be also formulated for several desired and undesired phenotypes. However, not for those which interdict each other (e..g aerobic and anaerobic maximum growth). Please clarify.<o:p></o:p>- What is the difference between constrained MCSs and the first extension? As far as I understood, cMCSs in  - I would like to have more explanation on how to derive formula (9) (page 6). And it should be in a better readable format. What size is the identity matrix?<o:p></o:p> - Extension 3: DOI 10.1007/s10295-014-1576-3 add to references<o:p></o:p> - page 9: MCS3 and MCS4 are part of the WT too, thus the additional reactions are not necessary here, or?<o:p></o:p> - page 10, first sentence, typo: additionAL candidates<o:p></o:p> - page 10: Why can exchange reaction not be deleted? And if so, is this integrated in the MILPs? What if a reaction which is directly coupled to an exchange reaction is deleted?<o:p></o:p> - page 14: rule 2 and rule 3 can be merged. If a gene is essential for an essential reaction it should be marked as protected. Or am I missing something? Isn’t rule 2 contained in rule 3?<o:p></o:p> - table 1:<o:p></o:p>            - EColiCore2, first column “No compression”, rows “# MCS found for compressed network”       and “# MCS found after decompression”: Why 6025 and 6015? Shouldn’t it be the same?<o:p></o:p>            - what is row “Reactions”? At the top the number of reactions is 502 (core), resp 2715      , but the row contains higher numbers for the non-compressed network. Please     explain. .   Same for the number of             species.            - I am missing a legend which explains the rows and columns. It is also confusing that the rows   say “compressed network”, even though the columns distinguish between compressed and     decompresses network too. Seems to be redundant and is confusing.<o:p></o:p> - It is sometimes hard to distinguish between examples and actual constraints for the programs. Can the authors clarify this more? Maybe introducing an “example-environment”.<o:p></o:p>Reviewer #2: The MCS methodology has been one of the major breakthroughs derived from the field of Systems Biology. The extensions of the MCS approach presented by Professor Klamt and colleagues are really interesting and valuable for different practical applications. In my opinion, this article deserves publication in Plos Computational Biology. However, I have some comments that, in my opinion, could improve the manuscript.1- For readers not familiar with the MCS approach, the relationship between variables in the dual problem and constraints in the primal could be described in pages 5-6.2- The objective function for the first extension (multiple targets) could be defined after Eq. (13). By the way, this new approach is really elegant. However, the number of variables is double. In Table 2, you describe the computation time in the case of 2,3-BDO and ATP maintenance. Is it worthy in terms of computation time? Why not resolving 2632*2 (max/min ATP maintenance reaction) LPs to filter those satisfying ATP maintenance?3- Why the number of MCSs found after decompression in EcoliCore2 is different to the uncompressed case (6015 vs 6025)?4- I like a lot your approach to gene MCSs. It is clear the effect of GPR compression. However, it would be nice to compare your approach with the one presented in Apaolaza, 2019 in order to calculate synthetic lethals.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see  16 Jun 2020AttachmentReview_Response_final.docxSubmitted filename: Click here for additional data file. 30 Jun 2020Dear Dr. Klamt,We are pleased to inform you that your manuscript 'An Extended and Generalized Framework for the Calculation of Metabolic Intervention Strategies Based on Minimal Cut Sets' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Kiran Raosaheb Patil, Ph.D.Associate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The manuscript has significantly improved. I especially want to thank the reviewers for adding two more applications of their method and for answering my questions and clarifying my confusions. Also, the explanation and notation of the formulas is much more clear now.I have no further comments.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as  Bacillus methanolicus MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make B. methanolicus a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. Intriguingly, the only substrates known to support B. methanolicus growth as sole sources of carbon and energy are methanol, mannitol, and, to a lesser extent, glucose and arabitol. Because fluxomics provides the most direct readout of the cellular phenotype, we hypothesized that comparing methylotrophic and nonmethylotrophic metabolic states at the flux level would yield new insights into MGA3 metabolism. In this study, we designed and performed a 13C metabolic flux analysis (13C-MFA) of the facultative methylotroph B. methanolicus MGA3 growing on methanol, mannitol, and arabitol to compare the associated metabolic states. On methanol, results showed a greater flux in the ribulose monophosphate (RuMP) pathway than in the tricarboxylic acid (TCA) cycle, thus validating previous findings on the methylotrophy of B. methanolicus. New insights related to the utilization of cyclic RuMP versus linear dissimilation pathways and between the RuMP variants were generated. Importantly, we demonstrated that the linear detoxification pathways and the malic enzyme shared with the pentose phosphate pathway have an important role in cofactor regeneration. Finally, we identified, for the first time, the metabolic pathway used to assimilate arabitol. Overall, those data provide a better understanding of this strain under various environmental conditions.IMPORTANCE Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry. It has successfully been used to identify new pathways  and/or nuclear magnetic resonance (NMR). With NMR, carbon isotopomers are determined; hence, positional information is provided. With MS, isotopologues are identified. A metabolic model is then used to fit these measurements to theoretical data that are simulated by optimizing reaction flux values from the metabolic model. Assuming mass balance, if the measurements are coherent and the topology of the metabolic model is correct, the experimental and simulated data will converge, yielding the estimated reaction fluxes. Finally, 13C-MFA provides a flux map: a predicted snapshot of the metabolic fluxes through the organism of interest during the experiment, i.e., its metabolic state. 13C-MFA flux maps are conceptually similar to those from flux balance analysis (FBA), a purely in silico method that, from a metabolic model , computes the optimal reaction flux distribution to maximize an objective defined in terms of metabolite production, often designed to model cell growth. Importantly, 13C-MFA flux maps are estimates based on experimental data, whereas FBA flux maps are purely in silico predictions, which can be confirmed by gene deletion analysis or 13C-MFA, for example.In the last decade, pathways , improvepathways , and, mopathways  such as pathways –8. BriefBacillus methanolicus MGA3 is a Gram-positive bacterium that was first isolated in the 1990s from freshwater marsh soil samples after an enrichment culture on methanol at 55°C  and mannitol (C6H14O6) are the best-known carbon sources for this strain (5H12O5) is significantly slower  and two putative arabitol phosphate dehydrogenases (AtlD and AtlF) H-dependent reduction. A number of potential sugars  and sugar alcohols  were also tested as the substrates, but no significant activity  was detected. The kinetic parameters measured for AtlD and AtlF reduction using either Xyl5P or Ribu5P as the substrate are summarized in mK of 0.07 ± 0.03 mM for AtlD with Xyl5P as the substrate and NADH as cofactor was 2.5 times lower than the value obtained for AtlF (0.18 ± 0.05 mM). With Ribu5P as the substrate, the mK for AtlD was 17 times lower (1.21 ± 0.42 mM) (Vmax for AtlD with Xyl5P as the substrate and NADH as cofactor was 2.7 times higher than with Ribu5P as the substrate (0.49 ± 0.06 U mg−1) and 12 times higher than for AtlF with Xyl5P (0.11 ± 0.01 U mg−1) (mK (0.01 ± 0.01 versus 0.11 ± 0.09 mM). The affinity and NADPH-dependent activity (0.24 ± 0.06 U mg−1) could only be determined with AtlD and Xyl5P, as no significant activity was detected for any of the other reactions. Liquid chromatography-mass spectrometry (LC-MS) analyses were performed to confirm the formation of arabitol phosphate in the enzyme reactions catalyzed by AtlD. Although arabitol 1-phosphate and arabitol 5-phosphate could not be distinguished, arabitol phosphate was clearly produced with both Xyl5P and Ribu5P as the substrates (see The operon responsible for arabitol assimilation in nd AtlF) , which and AtlF) , 32 (seend AtlF)  Fig. 1)B. methan0.42 mM) , and for U mg−1) . NADH waates see .10.1128/mSystems.00745-20.5TEXT S1Text S1, PDF file, 0.5 MB.Phylogeny of AtlD and AtlF. Download Copyright © 2020 Delépine et al.2020Delépine et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in vitro and that AtlD and AtlF both prefer Xyl5P. This suggests that arabitol is mainly assimilated through the arabitol 1-phosphate pathway. However, assimilation via arabitol 5-phosphate cannot be excluded, since AtlD can also use Ribu5P, albeit with reduced efficiency, as shown both by the kinetic parameters , were inspected manually and then used with additional NMR and physiological data to fit a model of B. methanolicus’s central metabolism  tabolism .Some isotopologues of 6-phosphogluconate (Gnt6P) (M + 2 and M + 3) could not be quantified, and the flux through the oxidative pentose phosphate pathway (PPP) could not be accurately resolved. Hence, to exploit these partial data and estimate the flux through the oxidative PPP, we applied the ScalaFlux approach . Using sB. methanolicus was grown in batches on three different carbon sources. For each cultivation, the growth, consumption, and production rates were determined. Results are given in −1) is slightly higher than reported in a previous proteomic study of B. methanolicus MGA3 (0.40 h−1) (B. methanolicus PB1 (0.32 h−1)   or for t.32 h−1) . Biomassd values .B. methanolicus MGA3 is known to overproduce glutamate at up to 50 g/liter  may operate in vivo. To confirm whether either or both arabitol assimilation pathways are operative in B. methanolicus, as suggested for E. avium   via PTS-mediated uptake and phosphorylation to arabitol 1-phosphate followed by oxidation to Xyl5P with AtlD as the major dehydrogenase  . Intereslation”) . This inrogenase .B. methanolicus and rule out the hypothesis of additional catabolism through arabitol 5-phosphate and Ribu5P derived from our enzymatic analyses  cycle that condenses formaldehyde (For) and ribulose 5-P (Ribu5P) into hexulose 6-P (Hex6P)  . The reg (Hex6P) . Interes (Hex6P) . The uti (Hex6P) , 45—is oet al.’s  study ofntration , and we ntration .1 acceptor under those conditions. As expected, the nonoxidative PPP was more oriented toward carbon assimilation on arabitol than on mannitol. On arabitol, the ribulose-phosphate 3-epimerase’s flux (rpe) was in the opposite direction, and the ribose-5-phosphate isomerase’s absolute flux (rpi) flux was almost 2 times higher , whereas arabitol is converted to Xyl5P as discussed above. However, on those carbon sources, the nonoxidative PPP was operating in the opposite direction than when on methanol , which is higher  and C ins higher . Simulats higher . Howevers higher  and the s higher .glpXP) on the pBM19 plasmid, whereas there is only one transaldolase gene (taC) in the chromosome. Proteomic  and ta (associated with taC) carried a comparable flux; thus, both variants may be active  cycle and glyoxylate shunt , the reported expression levels suggest that this pathway does not carry a high flux under mannitol or under methanol growth conditions  was observed on methanol than on mannitol and arabitol    and C.Overall, as reported before, the TCA cycle is mainly active under nonmethylotrophic conditions, and the glyoxylate shunt was inactive under our conditions. The novelty is the noncyclic operation of the TCA cycle under methylotrophic conditions.B. methanolicus balances its redox and energy needs, we used the estimated fluxes from the 13C-MFA to infer production and consumption rates of ATP, NADH, and NADPH, as shown in 13C-MFA is constrained by carbon balance and the distribution of the isotopic tracer, but unlike flux balance analysis, it is typically not constrained by cofactor balance. We used the estimated fluxes from the MFA and the expected stoichiometry of the associated reactions to assess the absolute rates of cofactor production and consumption. In the absence of specific measurements, we used the biomass requirements of Bacillus subtilis  than on mannitol and arabitol  because of the conversion of methanol into formaldehyde by methanol dehydrogenase . This suarabitol . It was Overall, nonmethylotrophic growth on mannitol and on arabitol share the same features despite their associated different growth rates (with the notable exception of the contribution of the oxidative PPP for NADPH production). On methanol, however, we observed more differences in the origin of cofactor production, which clearly highlight a different metabolic state. Importantly, the linear detoxification pathways share with the PPP and the malic enzyme an important role in cofactor regeneration.B. methanolicus MGA3 provides three snapshots of its metabolic states for growth on methanol, mannitol, or arabitol. Isotopic data are consistent with prior knowledge of MGA3 methylotrophy, showing greater flux in the RuMP cycle than in the TCA cycle. The 13C-MFA provided new insights related to the utilization of cyclic RuMP versus linear dissimilation pathways and between the RuMP cycle variants; finally, the characterization of the arabitol assimilation pathway was completed using enzymatic data. In future studies, these validated flux maps will be used as references for constraint-based modeling to validate genome-scale model predictions. Overall, the information provided in this work and previous omics studies on B. methanolicus metabolism can be used to improve design strategies for new strains . For instance, the comparison between methanol and arabitol might help identify key control steps in the metabolic network that allows shifting from a cyclic  to a linear  mode of operation. The experimental path outlined here likely leads to B. methanolicus becoming a viable alternative to existing cell factories.In summary, this MFA of B. methanolicus wild-type MGA3 (ATCC 53907) strain was used for metabolic flux analyses. Strains used for cloning and expression are described below in “Strains and culture conditions” and are listed in The (i) Culture conditions and parameters. For the carbon source methanol, two batch cultures were performed in 0.5-liter bioreactors  with a working volume of 0.40 liters coupled to a Dycor ProLine Process mass spectrometer . The culture medium per liter was 3.48 g Na2HPO4·12H2O, 0.606 g KH2PO4, 2.5 g NH4Cl, 0.048 g yeast extract, 1 ml of 1 M MgSO4 solution, 1 ml of trace salt solution, 1 ml of vitamin solution, 0.05 ml Antifoam 204, and 150 mM methanol. The trace salt solution per liter was 5.56 g FeSO4·7H2O, 0.027 g CuCl2·2H2O, 7.35 CaCl2·2H2O, 0.040 g CoCl2·6H2O, 9.90 g MnCl2·4H2O, 0.288 g ZnSO4·7H2O, and 0.031 g H3BO3. The vitamin solution per liter was 0.10 g d-biotin, 0.10 g thiamine-HCl, 0.10 g riboflavin, 0.10 g pyridoxine-HCl, 0.10 g pantothenate, 0.10 g nicotinamide, 0.02 g p-aminobenzoic acid, 0.01 g folic acid, 0.01 g vitamin B12, and 0.01 g lipoic acid. The precultures were grown in two 0.5-liter shake flasks containing 150 ml of the preculture medium and inoculated with cryostock of B. methanolicus wild-type MGA3 cells. The cultures were grown overnight at 50°C under shaking at 200 rpm and used to inoculate the reactors. The aeration rate of 1 vvm (volume of gas per volume of liquid per minute) was controlled by a mass flow meter , and partial O2 pressure (pO2) was maintained at >25% throughout all cultures. Temperature, pH, and stirring speed were maintained at 50°C, pH 6.8 (with KOH 1 M), and 800 rpm, respectively. The N2, O2, Argon, CO2, 13C-CO2, and methanol concentrations in the bioreactors off-gas were measured on-line with the mass spectrometer.13C-methanol  was added to the cultures at an optical density of 600 nm (OD600) of 2.5. Growth curves are available in To perform the pulse of tracer, 100 mM (ii) Quantification of cells and supernatant NMR analysis. For determination of the dry weight of cells, a conversion factor of 0.389 g/liter (dry weight) of cells per OD600 unit was used. Supernatant samples were taken to analyze substrate methanol consumption as well as by-product formation by subtracting 1 ml of culture and centrifuging it at 13,000 × g for 60 s. Thereafter, supernatant was collected and stored until analysis at −20°C. Supernatant analysis was performed by 1D 1H NMR at 292°K, using a 30° pulse and a relaxation delay of 20 s, with an Avance 800 MHz spectrometer . Deuterated trimethylsilyl propionate (TSP-d4) was used as an internal standard for quantification.(iii) Sampling and MS analysis of intra- and extracellular pool sizes. When the cultures reached an OD600 of 2, metabolome samples were collected using the optimized method described in reference Escherichia coli, cultivated on 99% [13C6]glucose  as an internal standard  and extracellular (B) pools of key metabolites of the central metabolism of Copyright © 2020 Delépine et al.2020Delépine et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00745-20.2FIG S2m/z values for arabitol 5-phosphate (https://doi.org/10.1073/pnas.1423570112). Download FIG S2, TIF file, 0.1 MB.Detection of formed arabitol phosphate in AtlD-catalyzed enzyme reactions using LC-MS. Chromatograms of detected pentitol phosphates (top) and their respective LC-MS spectra (bottom) of enzyme reactions using Xyl5P (A) and Ribu5P (B) as the substrates. The peaks for the given compounds were identified by characteristic mass spectra and retention time using standards for Xyl5P and Ribu5P or by comparison to previously reported Copyright © 2020 Delépine et al.2020Delépine et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the (iv) Sampling and MS analysis of labeled metabolites. Label enrichments in the intracellular metabolites were followed after performing a pulse of 100 mM 13C-methanol at an OD600 of 2.5. Whole broth  and culture filtrate  were sampled to indirectly track label incorporation in the intracellular metabolites. Specifically, 13 WB and 3 CF samples were collected in 3.5 min from each bioreactor. Exact sampling times can be seen in d-glycerate (G3P) plus phosphoglycerate (PGA), citrate (Cit), aconitate (Aco), fumarate (Fum), malate , and succinate (Suc). After manual peak integration, the raw peak areas were corrected for the contribution of all naturally abundant isotopes using IsoCor software  Mass balance. Experimental data consistency of the measured rates was verified using standard data reconciliation procedures under the elemental mass balance constraints  Culture conditions. For the carbon sources mannitol and arabitol, the culture medium composition remained unchanged. The experiments were performed in 500-ml baffled shake flasks using 40 ml of medium, and cells were grown at 50°C and 200 rpm. Precultures contained yeast extract and 15 mM unlabeled mannitol or arabitol. They were inoculated as stated previously and grown overnight. The precultures were used to inoculate triplicate main cultures to an OD600 of 0.05 after centrifugation and resuspension in medium without yeast extract. The medium to perform the 13C MFA contained 15 mM [1-13C]mannitol, 15 mM [5-13C]arabitol, or 15 mM a mixture of 10% [1-13C]arabitol and 90% [2-13C]arabitol . An experimentally determined conversion factor of 0.22 g/liter (dry weight) of cells per OD600 unit was used. Growth curves are available in 13C-isotopologues.(ii) Measurements of proteinogenic amino acids  Mannitol (or arabitol) cultures were sampled around 10 h (or 30 h) once they reached an OD600 of 1.3. The pellets obtained from the cellular extract were hydrolyzed 15 h at 110°C with 500 μl 6 N HCl. Samples were evaporated and washed twice with 500 μl of ultrapure water, evaporated to dryness, resuspended , and diluted  for the mass spectrometry analysis.2O and solvent B was 0.1% formic acid in acetonitrile at a flow rate of 250 μl/min. The gradient was adapted from the method used in reference Amino acids were separated on a pentafluorophenyl (PFP) column . Solvent A was 0.1% formic acid in Hm/z) in full-scan mode, with the following source parameters: capillary temperature, 275°C; source heater temperature, 250°C; sheath gas flow rate, 45 AU (arbitrary unit); auxiliary gas flow rate, 20 AU; S-lens radio frequency (RF) level, 40%; source voltage, 5 kV. Isotopic clusters were determined by extracting the exact mass of all isotopologues, with a tolerance of 5 ppm. Experimental carbon isotopologue distributions (CIDs) of alanine, glycine, valine, serine, threonine, phenylalanine, aspartate, glutamate, histidine, isoleucine, leucine, lysine, arginine, tyrosine, proline, and methionine were obtained after correction of raw MS data for naturally occurring isotopes other than carbon, using IsoCor (High-resolution experiments were performed with an Ultimate 3000 high-performance liquid chromatography (HPLC) system  coupled to an LTQ Orbitrap Velos mass spectrometer  equipped with a heated electrospray ionization probe. MS analyses were performed in positive Fourier transform MS (FTMS) mode at a resolution of 60,000 (at 400 g IsoCor . However(iii) NMR measurements. Concentrations in supernatants were measured by 1D 1H NMR at 290°K using a 30° angle pulse, and a presaturation of water signal was applied during a relaxation delay of 8 s. TSP-d4 was used as an internal standard for calibration and quantification.13C-isotopologues MS analysis, which were redried and suspended in 200 μl D2O (0.1% DCl). The positional isotopomer distribution of alanine C-2 and C-3 was extracted from the analysis of 13C-13C couplings in two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) experiments  (The measurement of isotopomers and specific enrichments of targeted biomass components were performed using the same samples used for proteinogenic amino acids and eriments . The carF-TOCSY) .1H spectra using TopSpin 3.5 .NMR spectra were collected on an Avance III 800 MHz spectrometer  equipped with a 5-mm z-gradient QPCI cryoprobe. Every acquisition, 1D, 2D, and absolute quantifications were performed on 1D (i) Metabolic flux analysis software. All simulations needed for metabolic flux analysis (MFA) on methanol, mannitol, and arabitol were performed with influx_si software v4.4.4  Metabolic models. We designed the models to cover central carbon metabolism and biomass needs of B. methanolicus. influx_si uses a nonstandard legacy file format (FTBL) to encode the metabolic network and the associated atom-atom transitions. This format centralizes the metabolic network with all biological measurements, i.e., metabolite pool sizes, fluxes, and carbon isotopologue distribution. Consequently, we developed distinct model files for each carbon source. Models share the same nomenclature and the same general topology between them, which is displayed in 2 pool was explicitly modeled within the system to allow for reincorporation of tracer via CO2. Amino acid synthesis pathways were modeled as part of biomass needs along with important precursors. The biomass equation was borrowed from a Bacillus subtilis genome-scale model  were also exploited. No acetate production was observed. Because some isotopologues of Gnt6P (M + 2 and M + 3) could not be quantified, these isotopic measurements could not be included in the influx_si model; hence, the flux through the oxidative PPP could not be accurately resolved. To exploit these partial data and estimate the flux through the oxidative PPP, we applied the ScalaFlux approach  and isotopologue profiles  through the influx_si optimization process. Only the measurement of the COapproach , which a(iii) Quality checks and flux conversion. Experimental data were fitted to our models as described above. For each culture replicate, we performed a Monte Carlo sensitivity analysis (n = 100) on the fit to assess its robustness to small variations around the fitted values. We also performed a chi-squared goodness-of-fit statistical test to ensure that simulated data for each biological replicate were significantly close to experimental data. All tests were significant with a significance level (α) of 0.05 (https://fairdomhub.org/data_files/3269?version=2). For convenience, we provide figures of measured versus simulated data points , mannitol (B), and arabitol (C) conditions. Each data point refers to the proportion of one of the isotopologues from the carbon isotopologue distribution (CID) of a metabolite used to constrain the models, i.e., either metabolites from central metabolism (A) or proteinogenic amino acids (B and C) as described in Materials and Methods. On the Copyright © 2020 Delépine et al.2020Delépine et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the On mannitol and arabitol, the precision of the fluxes through malate dehydrogenase , malic enzyme , and pyruvate carboxylase  was not satisfactory. We thus applied a flux minimization analysis  includedSince both the substrate uptake rates see  and the (i) Strains and culture conditions. In this study, Escherichia coli DH5α (E. coli BL21(DE3) (E. coli strains were routinely cultivated at 37°C and 180 rpm in lysogeny broth (LB) or on LB agar plates supplemented with 100 μg ml−1 ampicillin and 0.5 mM isopropyl-β-d-thiogalactopyranoside (IPTG) when relevant.oli DH5α  was usedL21(DE3) . A summa(ii) Recombinant DNA work. Molecular cloning was performed as previously described  with primers P192, P193, P194, and P195 (escribed  using prand P195 . All clo(iii) Overproduction and purification of AtlD and AtlF. Plasmids for protein production using E. coli BL21(DE3) were constructed on the basis of pET16b  and are presented in atlD or atlF gene was PCR amplified from B. methanolicus MGA3 genomic DNA using the primers P192 and P193 or P194 and P195, respectively  Arabitol phosphate dehydrogenase enzymatic assays. Determination of purified AtlD and AtlF activities in the reductive reaction using Xyl5P or Ribu5P as the substrate were performed as previously described (escribed . The assB. methanolicus MGA3 genome NZ_CP007739.1. https://fairdomhub.org/data_files/3269?version=2.Gene loci mentioned throughout the text are from NCBI annotation of


Inflammation is a complex biological response to injuries, metabolic disorders or infections. In the brain, astrocytes play an important role in the inflammatory processes during neurodegenerative diseases. Recent studies have shown that the increase of free saturated fatty acids such as palmitic acid produces a metabolic inflammatory response in astrocytes generally associated with damaging mechanisms such as oxidative stress, endoplasmic reticulum stress, and autophagic defects. In this aspect, the synthetic neurosteroid tibolone has shown to exert protective functions against inflammation in neuronal experimental models without the tumorigenic effects exerted by sexual hormones such as estradiol and progesterone. However, there is little information regarding the specific mechanisms of tibolone in astrocytes during inflammatory insults. In the present study, we performed a genome-scale metabolic reconstruction of astrocytes that was used to study astrocytic response during an inflammatory insult by palmitate through Flux Balance Analysis methods and data mining. In this aspect, we assessed the metabolic fluxes of human astrocytes under three different scenarios: healthy , induced inflammation by palmitate, and tibolone treatment under palmitate inflammation. Our results suggest that tibolone reduces the L-glutamate-mediated neurotoxicity in astrocytes through the modulation of several metabolic pathways involved in glutamate uptake. We also identified a set of reactions associated with the protective effects of tibolone, including the upregulation of taurine metabolism, gluconeogenesis, cPPAR and the modulation of calcium signaling pathways. In conclusion, the different scenarios studied in our model allowed us to identify several metabolic fluxes perturbed under an inflammatory response and the protective mechanisms exerted by tibolone. Astrocytes are the most abundant cells in the human brain. In the last years, it has been shown that they are of paramount importance for different essential functions in central nervous system (CNS). For instance, the homeostatic regulation of the central nervous system , tissue Different works have shown that astrocytes are key mediators in the brain lipid homeostasis and B-oxidation of fatty acid . InteresIn this aspect, tibolone is a synthetic steroid , with esIn that sense, genome-scale metabolic reconstructions are a compilation of all the stoichiometric reactions and pathways that can describe the entire cellular metabolism of an organism . In receHaving in account the importance of astrocytes for brain inflammation, and the promising effects of tibolone for astrocytic and neuronal protection , we deve1 and further enriched with metabolic information obtained from KEGG  were constrained proportionally to the mean gene expression reported for genes included in Gene-Protein-Reaction (GPR)  associatFlux balance analysis is a linear optimization method for simulating the metabolic reactions of a cell or an organism that allows the identification of the set of reactions involved in the production of a biological response within the metabolic model . The metThe systems of mass balance equations at steady state are defined by:2, setting the generic human biomass reaction included in RECON 2.04 as default, and each one of the reactions described in This expression seeks to maximize or minimize an objective function, which can be any linear combination of fluxes to obtain a flux for each reaction, indicating how much each reaction contributes to the objective function . The FBAflux Differences function calculates the fold change for each common reaction between metabolic scenarios.The measurement of flux change for each reaction between metabolic scenarios is a task generally carried out manually and oriented directly toward the research objective. However, at system level this process can become laborious. The Fold change is a measure that describes how much a quantity changes going from an initial to a final value. The implemented algorithm in the flux Differences function is described in Eq. 1:Here, the function takes as argument two valid models for the “sybil” R package and a customizable threshold value to filter functions to be reported.In this aspect, we chose an arbitrary threshold value greater or equal to 2-fold times for reactions with an absolute change between the unconstrained and constrained metabolic scenarios, as reported in previous models .50) value for all the objective functions included in 50 values were calculated through a robustness analysis performed using uptake of palmitate (‘EXhdca(e)’ in RECON 2.04) as control and 1000 points in the range from 0 to 1 mMgDW–1h–1 for each objective function. Uptake values where each objective function reached IC50 was selected and subsequently averaged. Moreover, the modeled inhibitory effects are in congruence with the reported damaging effects of palmitate in astrocytes (To test the protective effects of tibolone during metabolic inflammation in astrocytes we defined three different metabolic scenarios: (1) A “healthy” scenario, where the rate of palmitate uptake was set freely by the optimizer. This scenario emulates the normal conditions of astrocytes metabolism , withouttrocytes . Finallytrocytes , and tentrocytes  not inclFlux differences for each reaction between optimized scenarios were measured using the fold change as described in the following equation:Enzymes involved in pro-inflammatory and anti-inflammatory responses during palmitic acid damage and upon tibolone treatment were identified through a sensitivity analysis as follows: Pro-inflammatory enzymes are those that catalyze reactions that allow the increase of the objective function value when knocked out. Anti-inflammatory enzymes are those associated with reactions that have a fold-change greater or equal to 2, and that once being knocked out reduces the objective function value. Tibolone associated enzymes are those that catalyze reactions that produce a total inhibition of the metabolic effects of tibolone when knocked out.We reconstructed an FBA based astrocytic tissue-specific model composed by 1262 unique genes, 1956 metabolites and 2747 biochemical reactions of which 1607 were intracellular reactions, 60 were exchange reactions, and 1080 were transport reactions . ReactioBased on the enzymatic classification , 33.2% oThe reactions included in the model are associated with 113 metabolic pathways reported in the KEGG database . Almost -1h-1) under normal conditions using DMEM medium as metabolic supply3. This result is in agreement with the study of Our metabolic simulation predicts a slow growth rate for astrocytes , is synthesized and stored in neurons but is hydrolyzed in glial cells  experienced a fold change of −6.85 when knocked out . This trin vitro and in vivo models including lung cells, leucocytes, rat macrophages and neuronal cells (In our “Tibolone treatment” scenario, tibolone affected the flux rate of 948 reactions in comparison with the inflammatory scenario. We found important metabolic changes associated with the activation of several protective pathways in astrocytes . These ial cells , gluconeal cells , calciumal cells . Interesal cells . These ral cells .L-asparagine, L-glutamate and the release of L-glutamine when compared with the inflammatory scenario (The “tibolone treatment” scenario also increased the demand for L-aspartate and in turn, decreased the demand for scenario . The redscenario . In thisscenario . Howeverscenario . Againstscenario . Howeverscenario , suggestscenario . Interesscenario . In thisscenario . There iscenario .Based on a sensibility analysis performed over 289 reactions associated with tibolone and estradiol-derived compounds, we identified a set of four reactions that after being individually knocked out, completely blocked tibolone effects in our model . The ideIn this work, we developed a tissue-specific metabolic network for human astrocytes that was simulated under three different scenarios. The model allowed us to identify the metabolic changes between a healthy, an inflammatory and a tibolone treatment scenario. In our model, the adverse effects associated with the increase of palmitic acid uptake in astrocytes were described based on exchange fluxes, metabolite production, and metabolic pathways perturbed under the inflammatory response. Moreover, this model was consistent with previous experimental studies showing that tibolone exerts multiple protective effects against inflammation, oxidative stress and metabolic dysregulation (Our results suggest that tibolone exerts its protective effects through multiple mechanisms, including the reduction of neurotoxicity mediated by L-glutamate in astrocytes, the activation of inflammatory modulators like PPAR gamma, and the increase in the metabolism of antioxidative molecules like taurine. We also found a tibolone-associated increase in biomass growth rate, which is similar to previously reported studies of the tumorigenic effects exerted by this compound in breast cancer . On the Finally, a sensitivity analysis performed through constrained-based modeling approaches and FBA allowed us to recognize two possible reactions with their associated enzymes, susceptible to be knocked out in order to reduce the inflammatory perturbations of palmitic acid in astrocytes.In summary, constraint-based models are valuable tools for the study of the protective effects mediated by pharmacological molecules in astrocytes, and provide a detailed insight into high-throughput data analysis. Further experiments are needed in to confirm the involvement of tibolone in the regulation of inflammatory mediators in astrocytic animal and cellular models.The datasets generated for this study are available on request to the corresponding author.DO and AP developed the theory, performed the model, contributed to the design and implementation of the research, the analysis of the results, and writing of the manuscript. CM-J and DO performed the simulations and wrote the manuscript with input from all authors. DO, GB, AP, and JG designed the model and the computational framework and analyzed the data. All authors discussed the results and contributed to the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Xanthomonas phaseoli pv. manihotis (Xpm) is the causal agent of cassava bacterial blight, the most important bacterial disease in this crop. There is a paucity of knowledge about the metabolism of Xanthomonas and its relevance in the pathogenic process, with the exception of the elucidation of the xanthan biosynthesis route. Here we report the reconstruction of the genome-scale model of Xpm metabolism and the insights it provides into plant–pathogen interactions. The model, iXpm1556, displayed 1,556 reactions, 1,527 compounds, and 890 genes. Metabolic maps of central amino acid and carbohydrate metabolism, as well as xanthan biosynthesis of Xpm, were reconstructed using Escher (https://escher.github.io/) to guide the curation process and for further analyses. The model was constrained using the RNA-seq data of a mutant of Xpm for quorum sensing (QS), and these data were used to construct context-specific models (CSMs) of the metabolism of the two strains (wild type and QS mutant). The CSMs and flux balance analysis were used to get insights into pathogenicity, xanthan biosynthesis, and QS mechanisms. Between the CSMs, 653 reactions were shared; unique reactions belong to purine, pyrimidine, and amino acid metabolism. Alternative objective functions were used to demonstrate a trade-off between xanthan biosynthesis and growth and the re-allocation of resources in the process of biosynthesis. Important features altered by QS included carbohydrate metabolism, NAD(P)+ balance, and fatty acid elongation. In this work, we modeled the xanthan biosynthesis and the QS process and their impact on the metabolism of the bacterium. This model will be useful for researchers studying host–pathogen interactions and will provide insights into the mechanisms of infection used by this and other Xanthomonas species. Manihot esculenta Crantz) is one of the most important crops in Africa, Asia, and around the world due to its drought tolerance, its ability to grow on acidic soils and in low nutrient conditions, and its high tolerance against several pests and diseases , previously known as Xanthomonas axonopodis pv. manihotis  and X. citri (Xci)   has been elucidated in silico and confirmed through experimental verification , caused by anihotis , is the anihotis . The maianihotis , 1986. Se stress . The difas citri . In thisri (Xci) . These tis (EPS) . An addiding Xpm , althougding Xpm . AdditioXpm  approach . Among pXcc , Pseudom. tomato , Pectobaotovorum , and Ralnacearum .Xpm at the genome scale was developed. The model was integrated with RNA-seq data from rpfCGH mutants of Xpm. Differential gene expression of mutant strains was used to construct context-specific metabolic models (CSMs) using the COBRA approach. This metabolic model is an approach to understand the mechanisms of metabolic pathways directly or indirectly related to the quorum sensing and xanthan biosynthesis processes of Xpm. Specifically, we aimed at determining the metabolic map of carbohydrate utilization pathways in Xpm as a first step to understand its nutritional relationship with the host plant. In addition, we hypothesized that the xanthan biosynthesis route has an energetic cost that the bacterium must pay in order to survive in the environment and cause disease.In this study, the metabolic model of Xpm . These were used to retrieve candidate metabolic reactions from three databases: MetaNetx  numbers were extracted from the annotation file of MetaNetx , KEGG  in order to determine the number of reactions in common between the reconstructions and the level of agreement with the modelSEED reconstruction. Network gaps were identified and filled using the GapFind and GapFill algorithms . Finallypathways . Forty-opathways  and inclXpm genome, a BLAST search  reported in BiGG database was performed. In the case of the xanthan biosynthesis pathway, the backbone of the pathway was constructed based on the existing map reported for Xcc available in MetaCyc and KEGG and previously modeled  of biomass and xanthan corresponds to the addition of the metabolites xanthan and biomass:Xcc (We tested both objective functions: biomass and biomass with xanthan. This proportion of xanthan and biomass (80–20%) was experimentally determined in Xcc . FinallyXpm strain CIO151 were used in this study: (1) CIO151 strain transformed with the empty vector pBBR1-MCS5 (Xpm CIO151 EV) and (2) a double recombinant mutant previously generated .Two strains derived from rated M.  lacking –1) for plating serial dilutions of the liquid cultures. The cells were plated at 4-h interval time-points between 0 and 24 h. Because the wild-type and the mutant strains showed the same growth rate, we selected 18 h as the time for mid- to late-exponential phase for the RNA extraction.In order to determine the time-point to perform the RNA extraction, a common exponential growth phase among strains was established by means of a growth curve. The growth assay was performed using NYG complex medium  for liquid cultures and NYGA  and a lysis buffer solution consisting of 4 M LiCl, SDS 2%, 0.5 M EDTA (pH 8), and 1 M Tris–HCl (pH 8). The cells were then vortexed, and a solution of chloroform/isoamyl alcohol (24:1) was added prior to a second vortexing and centrifugation step to separate the different phases. Two overnight precipitation steps were carried out: the first with 4 M lithium chloride and the second one with 3 M sodium acetate (pH 5.2). RNA quality was confirmed using a Bioanalyzer . Two independent extraction processes were performed with different colonies for each strain to account for two biological replicates. Subsequent procedures were performed at the Beijing Genome Institute (Hong Kong): rRNA depletion was performed with the Ribo-ZeroTM rRNA Removal Kit from Epicentre , and RNA-Seq was performed through the Illumina HiSeq 2000 platform. The libraries generated were paired-end (100-bp read length) and strand specific  was used with the 7500 FAST (Applied Biosystems) thermal cycler according to the manufacturer’s instructions; melting curves were used in each run to detect and discard experiments with non-specific amplifications. gyrB was selected as a housekeeping gene for validation because it showed low variability compared with rpoB, rpoD, and atpD genes (data not shown). The Pfaffl method  for each CSM using both biomass and biomass and xanthan as objective functions was performed.The iMAT algorithm , implemein vitro growth of the mutant and the wild type in Phi ϕ liquid media for a period of 28 h at 28°C with shaking at 200 rpm was performed. The cell populations were determined by measuring their colony-forming units (CFU) in dilution plating (growth in LPGA solid media for 48 h at 28°C) at different points of the Phi ϕ liquid culture . The composition of plating LPGA solid media was 5 g/L of yeast extract, 5 g/L of peptone, 5 g/L of glucose, and 15 g/L of agar. Rifampicin antibiotic (100 μg/ml) was added to the media.In order to compare the growth rate of the two strains, testing of the Z-score. The Ward method was used to calculate the linkage matrix based on Euclidean distance . Therefore, values below the median are colored purple and above the median red. When in the reaction rule two or more genes are strictly necessary (AND Boolean rule), the mean of the abundance was calculated. When in the reaction rule two or more genes code for the same reaction (OR Boolean rule), the sum of the abundances was calculated. The thickness of the lines is proportional to the abundance of the transcripts involved in the given reaction.Pathways for carbohydrate source assimilation, glycolysis, central metabolism, xanthan biosynthesis, and amino acid metabolism were constructed in Escher . The maphttps://github.com/davidoctaviobotero/Xpm_metabolic_model.All the code, maps, and metabolic models used have been uploaded in GitHub at Xpm CIO151, iXpm1556, had 1,556 reactions after curation, of which 155 were transport reactions and 90 were exchange reactions. The model included 1,527 metabolites, 890 genes associated with reactions, and 452 reactions  that could be assigned to 113 metabolic pathways of KEGG .The metabolic model of  of KEGG . Figure Xpm: pentose-phosphate and Embden–Meyerhof–Parnas. Moreover, a carbohydrate source connected with the ED pathway, xylose, was found, which was previously reported as carbohydrate source in Xcc and as being involved in xanthan production (The Entner–Doudoroff (ED) pathway, important in carbohydrate utilization metabolism and EPS production, also had a notable amount of reactions assigned. Two other main carbohydrate utilization pathways were found in oduction . Figure Xpm. For example, sucrose has been reported to be imported -dependent phosphotransferase system (PTS). Eleven nitrogen sources are predicted to be exchanged in the Xpm metabolic model. Three of them constituted inorganic forms and nine were organic (amino acids). L-arginine, L-lysine, and L-tryptophan . In the case of L-glutamine ABC transporter, evidence of glutamine transport at the genomic and the transcriptomic level was found in our model, however, there is no evidence in the literature for Xpm. BLAST of the glutamine transport ATP-binding protein of Escherichia coli against Xpm genome significantly hit several proteins previously annotated as ABC amino acid transporters. Other nitrogen sources transported in Xanthomonas are L-aspartate, L-glutamate, L-methionine, and L-proline . The metabolic reconstruction of this pathway shows that all genes present in the gum cluster of Xpm are required for the last steps of xanthan biosynthesis. Furthermore, a strong connection between the reactions of the xanthan pathway and the carbohydrate utilization pathways was found through the branched reactions catalyzed by phosphoglucomutase, mannose-6-phosphate isomerase, and 1-deoxy-D-xylulose 5-phosphate synthase. Finally, a modified objective function of biomass with xanthan production was used to simulate the trade-off between biomass and xanthan biosynthesis. Xcc experimentally measured proportions of biomass (80%) and xanthan (20%) are used together as the objective function was 0.493 h–1. This value is in the same order of magnitude as the experimental value for Xpm  (–1)   and in a–1) (–1) . The difrpfCGH caused an alteration in the expression of 1,553 genes, out of which 99% corresponded to upregulated genes by QS pathway , mainly hrcS and hpa3, were also downregulated in Xpm CIO151 ΔrpfCGH-EV. These results suggest that DSF-QS could be a positive regulator of the T3SS, as observed for HrpG, a master regulator of this system.In order to elucidate the biological and the molecular processes affected by RpfCGH, we conducted a Fisher enrichment analysis for the DEGs using Blast2GO. Due to the low number of downregulated DEGs, no enrichment analysis was done for these genes. The altered molecular functions for rocesses , S2. BesOut of the many RpfCGH-upregulated genes, we detected many interesting targets with hypothetical roles in phosphorylation sensor and transduction pathways for further investigation, some of which share a c-di-GMP phosphodiesterase activity (HD-GYP domain), just as RpfG. This could enable them to start different signaling cascades triggered by second messengers like c-di-GMP.Xanthomonas and with roles that might prove useful during the infectious process were selected for validation using qRT-PCR. Prior to the validation process, four previously reported housekeeping genes were tested between our samples, and gyrB was selected as the preferred endogenous control.Genes previously not associated with pathogenesis in Xpm CIO151 ΔrpfCGH-EV and Xpm CIO151-EV, a subset of putative sensor proteins (PhoB and a methyl-accepting chemotaxis protein) and a subset of regulatory proteins  were selected for validation, using gyrB as the endogenous control  were associated with reactions with evidence at the transcript level. This validated 1,268 (81%) of the reactions constructed in our model at the transcription level. A total of 219 metabolic genes covered in the model were differentially expressed: 211 upregulated and eight downregulated by the Rpf-dependent QS pathway . Importa+ transhydrogenase was inactive in the mutant of QS. The differentially expressed reactions mainly belong to pathways related with the biosynthesis of antibiotics and amino acids, carbon and secondary metabolism, and nicotinate and nicotinamide metabolism . Seven gum genes  showed lower levels of expression in the rpfCGH mutant when compared with the wild-type strain. However, guml and gumBCEJ show smaller differences between the two strains. In EPS metabolism, xanthan production genes were repressed in the Showing these changes on a metabolic map allows one to identify highly expressed reactions across metabolic pathways. 5. The models showed small differences in number of reactions, compounds, and genes. When the reactions between the two models were compared, only 32 and 26 reactions were unique for Xpm CIO151 EV and Xpm CIO151 ΔrpfCGH-EV, respectively , they were not excluded from the metabolic model of the mutant by iMAT. However, the abundances of transcripts for the majority of genes in the gum cluster were half for Xpm CIO151 ΔrpfCGH-EV when compared to Xpm CIO151 EV . Finally, FBA of each CSM was used to calculate the distribution of fluxes of each model. The fluxes of each reaction were grouped by hierarchical clustering , S18. A Xpm was reconstructed at the genome scale. The main features of this model were depicted and simulated in silico, especially those related with quorum sensing and xanthan production. Bacterial growth using an alternative objective function of biomass with xanthan was modeled, exposing the implications of this polymer in resource allocation processes. The dynamics of the mechanism of xanthan production and its connection to carbohydrate utilization pathways were reconstructed and modeled in Xpm, showing a trade-off between biomass and xanthan production. The model shed light onto the transport mechanism for several carbohydrate sources . Furthermore, data and analyses of differential expression assays of a quorum sensing mutant were used for understanding this bacterial mechanism in Xpm. The visualization of transcript abundance of these data on Escher maps helped to understand some differences at the metabolic level. These included the xanthan biosynthetic route, as well as carbohydrate and central metabolism, notably the balance of NAD(P)+. Furthermore, RNA-Seq data were used to construct and analyze the CSMs of the Xpm wild type and a mutant of quorum sensing. A flux balance analysis of these CSMs showed differences in the flux of the reactions for the two strains studied here. Some of the main metabolic pathways identified as altered by quorum sensing were amino acid and nitrogen metabolism, as well as fatty acid elongation. Finally, other important contributions to the metabolic modeling of Xpm were three manually curated maps of central carbohydrate metabolism, xanthan production, and amino acids that will serve as visualization tools for Xpm metabolism and that of other Xanthomonas.In this study, the metabolic model of Xcc and Ralstonia solanacearum, which have used a similar approach to model pathogenicity, with a modified objective function  can be explained by the divergence of sugars into the xanthan route from the ED pathway. This could be related to the cycles of growth vs. generation of aggregates in the vascular system or at the epiphytic stages of the bacterium in the plant. When the bacterium is growing, xanthan production decreases, and when it is forming aggregates and colonies, the production of xanthan is increased in detriment of growth . This trfunction . FurthergumD was reported to be important for pathogenicity, EPS production, and epiphytic survival of Xpm  maximizes the number of reactions to be included in each CSM based on the transcript levels, the method does not restrict the flux levels based on these data in order to improve the metabolic simulations in the FBA. In the future, fluxomics data  and otheXcc . AnotherXcc  or incluXcc  can be uXcc .Xcc, the DSF family of QS signals requires branched amino acid precursors . This highlights the alteration of amino acid metabolism by quorum sensing. In ecursors , whose soduction . We foun+ were differentially expressed, including reactions for carbohydrate metabolism and amino acid biosynthesis. NAD(P)+ transhydrogenase, an enzyme reported as the major source of NADPH in E. coli , some of them previously reported as regulated by QS in other genera of bacteria + transhydrogenase and the carbohydrate pathway reactions in the QS mutant.Several enzymes related with reactions that influence the balance of NAD(P) E. coli , was dow species . In the bacteria . Quorum bacteria . In our Xpm, such as transport of fumarate, citrate, trehalose, which were suppressed, and D-mannose (via PEP:Pyr PTS), which was induced in the QS mutant. Trehalose is connected to the carbohydrate utilization pathways through the D-glucose-6-phosphate, and it can influence xanthan metabolism. The reactions affected in the trehalose metabolism included the transformations of maltose to trehalose and of trehalose-6-phosphate to glucose-6-phosphate. In contrast, fumarate and citrate are directly connected to the central metabolism. It has been proposed that quorum sensing regulates carbohydrate uptake and utilization pathways in bacteria and slows down central metabolism, functioning as a metabolic brake   . This transporter was first reported in X. phaseoli  . The eviphaseoli , and it Xpm. RpfCGH is involved in the upregulation of several genes within the Rpf-dependent QS pathway; a global expression pattern that agrees with previous results published by Guo + transhydrogenase had a lower expression in the QS mutant, affecting the NADPH levels in a DSF-dependent manner. All the genes and reactions related to the pathways studied here can be used as a starting point for gaining new insights in Xanthomonas metabolism.We present here the metabolic model of in vitro and in planta, will expand the knowledge of the interaction between Xpm and cassava. This work is just the first step to a fully comprehensive understanding of the metabolism of Xpm as a plant pathogen through a systems biology approach. Our work poses more questions in the metabolic status of the bacterium before and during in planta growth, which can be addressed using our metabolic model and experimentation. For example, how does Xpm balance importation and metabolism of organic acids to produce xanthan and multiply in the plant? What is the effect of pyruvate dehydrogenase in the production of xanthan in Xpm? It would be important to improve and refine the metabolic model and to integrate other types of omics data and physiological information, including in planta studies. We also expect that this metabolic model will serve to improve knowledge of other plant pathogens belonging to the Xanthomonas genus.The metabolic model reconstructed here and the mathematical modeling performed using pathogenicity factors such as xanthan, along with the integration of more information from experiments, both https://github.com/davidoctaviobotero/Xpm_metabolic_model – a detailed list is available in The datasets generated for this study can be found in the NCBI under the projectID PRJNA598165 and SRA codes SRX7570863–SRX7570866. All related code, models and maps can be downloaded from GitHub: Xpm and advised all the computational analyses. MJR designed, performed, and analyzed the xanthan maps and the computational simulations. DB designed and performed the computational analyses, models, and maps, integrated the metabolic model and the experimental results, and elaborated the first draft of the manuscript and the corrections. All authors contributed to writing the manuscript and approved its final version.AB, SR, AG, and DB designed the study. AR advised the metabolic analyses. AB, ARC, MR, and VB-G designed and performed the experimental procedures and the analyses for RNA-seq and elaborated the first draft of the transcriptional analyses. AG, BP, and JM designed and supported COBRA approach for The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Saccharomyces cerevisiae strain and a biodiesel-producing Yarrowia lipolytica strain. Synthetic pathways diverging from the distinctive cellular metabolism of these yeasts guide future directions for product-specific engineering strategies for the sustainable production of non-native chemicals on an industrial scale.Yeasts are promising industrial hosts for sustainable production of fuels and chemicals. Apart from efficient bioethanol production, yeasts have recently demonstrated their potential for biodiesel production from renewable resources. The fuel-oriented product profiles of yeasts are now expanding to include non-native chemicals with the advances in synthetic biology. In this review, current challenges and opportunities in yeast engineering for sustainable production of non-native chemicals will be discussed, with a focus on the comparative evaluation of a bioethanol-producing  Microorganisms have gained significant attention as cell factories for the sustainable production of fuels and chemicals, providing great opportunities for a bio-based economy in the post-petroleum era. Of the microbial cell factories, yeast has long been used as a proven industrial producer of fuels and chemicals. The robustness in a wide range of environmental conditions, the ease of separation from the culture medium, and no phage contamination related issues make the yeast more attractive industrial cell factory than bacteria. With recent advances in synthetic biology, the product profiles of yeasts have been expanding to include those that have never been produced naturally, such as cannabinoids, tropine, and amorphadiene . Given tSaccharomyces cerevisiae is one of the most widely developed cell factories for the production of fuels and chemicals. Industrial production of ethanol, the native product of S. cerevisiae, is in operation worldwide to supply alcoholic beverages and biofuels  terpenoids, (ii) polyketides, (iii) fatty acid-derived chemicals, and (iv) others, based on the core intermediate used as a precursor, namely acetyl-CoA, malonyl-CoA, and DHAP, which are key intermediates and diverge into branched pathways in the cellular metabolism of yeasts, such as the mevalonate or shikimate pathways  has been reported through protein engineering and enhanced precursor supply  contributed to improved metabolic flux through an isoprene synthetic pathway  from Populus alba along with an enhanced pentose phosphate pathway  is a colorless and flammable compound, and the main monomer of natural rubber. In r supply . Specifi pathway . There arpenoids . The iso pathway . The iso pathway .S. cerevisiae has been reported to be 1.68 g/L through overexpression of the genes involved in the MVA pathway, tHMGR and IDI1, and the use of truncated geraniol synthase (GES) (Catharanthus roseus (CrGES) showed the highest efficiency in geraniol production, which was further improved by truncation of a plastid targeting signal sequence required in its source organism. In addition, the researchers fused the ERG20ww (F96W/N126W), the modified FPPS solely generating GPP .Geraniol  is one of the most widely used monoterpenes as a food additive, cosmetic ingredient, pesticide, and gasoline alternative. The highest titer of geraniol in se (GES) . Of the ting GPP , into Crting GPP . There a5.3 mg/L  and 6.965.3 mg/L , respect E. coli  has beenS. cerevisiae has been reported through the construction of a functional biosynthetic route based on a plant-derived dehydrogenase and cytochrome P450  and 8-hydroxycopalyl diphosphate (8-OH-CPP), respectively, were overexpressed in the strain harboring a sclareol synthase (SCLS), a class I diterpene synthase from Salvia sclarea. The engineered strain was further modified by disrupting the function of the genes selected from a heterozygous deletion screening experiments; this resulted in a 40-fold increase in the sclareol titer (750 mg/L). To our knowledge, the production of sclareol in Y. lipolytica has not been previously reported. In E. coli, a high titer (1.5 g/L) of sclareol was reportedly produced by simply introducing a heterologous pathway that converts GGPP into sclareol from S. sclarea  from Catharanthus roseus along with homologous squalene monooxygenase (ERG1) and tHMGR. To increase the production titer of betulinic acid, these authors performed fed-batch fermentation with excess ethanol, which supports the increased supply of acetyl-CoA and NADPH for triterpene production. When combined with nitrogen-limitation conditions, metabolic flux could be directed toward betulinic acid synthesis and thereby increased the titer and yields of betulinic acid. In Y. lipolytica, betulinic acid titer of 51.87 mg/L has been reported during flask fermentation by adapting multimodular engineering strategy ; this resulted in the production of betulinic acid with a titer of 51.87 mg/L. Given that this titer was obtained from flask fermentation, the production performance of Y. lipolytica could be further improved when combined with process engineering approaches applied to S. cerevisiae. There are limited reports on the betulinic acid production in E. coli possibly due to the antibacterial properties of betulinic acid  under the control of various promoters with optimized gene expression levels  turned a previously engineered lipid over-producing strain of Y. lipolytica into a superior β-carotene-producing strain. A comparable β-carotene titer has been achieved by E. coli , fungi , and plants (type III). Polyketides exhibit various bioactive properties such as anticancer, antifungal, and antiviral, and serve as important resources for pharmaceutical development . Within Gerbera hybrid, is a simple polyketide used as a chemical building block for a range of value-added products such as sorbic acid, hexanoic acid, and acetylacetone  was reported from the shake-flask culture of an engineered strain expressing a designed TAL reporter  by introducing a heterologous phosphoketolase (PHK)-based pathway. Optimization of carbon flux through interconnecting points between glycolysis and the AAA pathway was also critical for high-titer ρ-coumaric acid production (ρ-Coumaric acid is an aromatic amino acid (AAA) that serves as a starting material for numerous high-value biochemicals such as flavors, fragrances, nutraceuticals, and pharmaceuticals. Recently, a recombinant diploid QL60 strain of nditions . This de culture . This reoduction .de novo production of naringenin from Y. lipolytica, with the highest titer among those using S. cerevisiae and E. coli. In this study, they introduced the tyrosine-based ρ-coumaroyl-CoA- naringenin pathway and enhanced the metabolic flux from acetyl-CoA to malonyl-CoA through the overexpression of ACC1 and PEX10 , implying the potential of S. cerevisiae as a production host of naringenin as demonstrated in resveratrol production (discussed in the next paragraph). In E. coli, 421.6 mg/L of naringenin was produced in flask culture condition  or the serial reaction of chalcone synthase (CHS) and chalcone isomerase (CHI), respectively. With the introduction of a phenylalanine-based ρ-coumaroyl-CoA—route, boxylase  along wideletion . As mentnditions . Under sveratrol . In E. cnditions ; Table 1 pathway , 2017.Fatty acids are essential compounds for sustaining cell membranes. With the help of synthetic biology, fatty acids are transformed into non-native chemicals with a wide range of applications. As fatty alcohols and fatty esters have been frequently discussed in previous reviews , we focuS. cerevisiae strain was developed by adapting a type I bacterial fatty acid synthesis pathway. Through the directed evolution of TPO1 transporter and laboratory adaptive evolution, the engineered strain with improved MCFA tolerances produced almost 1 g/L of MCFAs  production. Although the MCFA content (45%) in its total lipids was agreeable, the titer was insignificant, possibly because its FAS systems evolved toward a highly efficient LCFA production system  through metabolic engineering approaches along with enhancing the tolerance toward MCFAs  and Methylobacterium. However, these native producers still have drawbacks to be served as a production host for PHB commercialization due to their intracellular lysis of PHB, poor tolerance toward harsh environments, restriction of metabolic substrates, and unfamiliar genetic characterization  in E. coli in improving PHB production as a rebalanced carbon and nitrogen metabolism with increased precursors and energy levels. By introducing a PHB synthesis pathway based on PhaA, PhaB, and PhaC from C. necator  led to the high production of muconic acid, resulting in a muconic acid titer of 20.8 g/L during pH-controlled fed-batch fermentation. In E. coli, co-fermentation of glucose and xylose resulted in 4.09 g/L of muconic acid in a shake-flask condition , even with relatively minimal engineering. The higher titers in terpene production by S. cerevisiae were often based on subcellular compartmentalization using mitochondria or peroxisomes, implying the critical role of proper intracellular structures. The potential of Y. lipolytica in triterpene production could be improved by adapting the engineering strategy of compartmentalization applied to S. cerevisiae. In the strain development for the biosynthesis of β-carotene and lycopene in S. cerevisiae and Y. lipolytica, balanced enzyme expression levels played a critical role in enhancing production titer, especially at the rate-limiting steps mediated by crtI and crtE, the two main engineering targets. Even with limited attempts, Y. lipolytica has shown greater titer in the production of β-carotene, naringenin, and TAL by taking advantage of the high accessibility of acetyl-CoA over S. cerevisiae and E. coli. As more engineering tools are developed, and thus more sophisticated tuning of gene expression would be possible, Y. lipolytica could be used as a promising industrial host for the production of tetraterpene such as β-carotene. Moreover, polycistronic gene expression systems, which allow equivalent expression and regulation of multiple genes by one promoter in bacterial hosts, have been recently investigated and successfully developed for fungal hosts such as S. cerevisiae (Based on success as a proven industrial cell factory for biofuel production, yeasts are transforming into a cell factory for the production of non-native chemicals. In addition to the benefit from their robustness, yeasts offer ease of reactor operation and cell separation as well as low energy consumption with lower fermentation temperature . By explrevisiae . Thereforevisiae . Thus, nS. cerevisiae and Y. lipolytica, respectively. The comparison of the potential of these yeasts in the production of non-native chemicals presented can serve to identify the production host of choice. By taking advantage of their distinctive cellular metabolism and characteristics as eukaryotic systems, these yeasts offer great potential for the industrial production of various non-native chemicals. Recent technological developments, such as high-throughput screening methods, are accelerating the application of synthetic biology to develop and upgrade yeast cell factories in a more efficient manner. Through target-specific engineering strategies with an expanded carbon source portfolio and optimized fermentation conditions of a single/co-culture system, yeasts could be successfully transformed into favorable industrial cell factories for the production of a wide range of non-native chemicals.In this review, we discussed the current status of non-native biochemical production by model and non-model yeasts of JK and PH wrote and edited the manuscript. S-ML provided conception of this review and edited the manuscript. All authors contributed to the article and approved the submitted manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.