Essential metabolic reactions are shaping constituents of metabolic networks, enabling viable and distinct phenotypes across diverse life forms. Here we analyse and compare modelling predictions of essential metabolic functions with experimental data and thereby identify core metabolic pathways in prokaryotes. Simulations of 15 manually curated genome-scale metabolic models were integrated with 36 large-scale gene essentiality datasets encompassing a wide variety of species of bacteria and archaea. Conservation of metabolic genes was estimated by analysing 79 representative genomes from all the branches of the prokaryotic tree of life. We find that essentiality patterns reflect phylogenetic relations both for modelling and experimental data, which correlate highly at the pathway level. Genes that are essential for several species tend to be highly conserved as opposed to non-essential genes which may be conserved or not. The tRNA-charging module is highlighted as ancestral and with high centrality in the networks, followed closely by cofactor metabolism, pointing to an early information processing system supplied by organic cofactors. The results, which point to model improvements and also indicate faults in the experimental data, should be relevant to the study of centrality in metabolic networks and ancient metabolism but also to metabolic engineering with prokaryotes. If we tried to list every known chemical reaction within an organism–human, plant or even bacteria–we would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular species–prokaryotes–to understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships . Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design. Mycoplasma genitalium, Haemophilus influenza, Bacillus subtilis and Escherichia coli found that it is essentiality, not expressiveness, that drives gene strand bias [B. subtilis, a species known to have an active riboflavin salvage capability. The authors of the DEG database have also conducted a pair of integrative analyses on large-scale essentiality data. The first concluded that there are less essential genes inside than outside genomic islands, and some of those are related with virulence [E. coli essentiality data where it was proposed that essential genes are more evolutionarily conserved than non-essential genes [Prokaryotes are the simplest contemporary life forms known, and nevertheless are characterized by an immense complexity. The debate on the features of such complexity and its breadth in the primordial life forms has been around for years –3, and wand bias . Later, and bias . Functioirulence . The secirulence  added toal genes . Luo andal genes . The stuGenome-scale metabolic models (GSMs) are large curated repositories of metabolic data for individual species that expand possibilities of analysis of cellular physiology . Apart fComparative genomics is commonly used to find core essential genes for several species, and being based on the key evolutionary notion of orthology, to infer genes present in common ancestors . EvolutiIn parallel with genetic comparisons, genome-scale metabolic models allow for functional comparisons at the level of metabolic capacities (reactions). Building up on this methodological advantage, in this study, 36 experimental genome-scale essentiality assays were integrated with simulation results from 15 genome-scale metabolic models to reveal common patterns of essentiality. To this analysis the screening of full genome sequences of 79 prokaryotic species was added in order to find core conserved functions in prokaryotic biology. It is expected that this knowledge on the minimal metabolic functions of prokaryotic cells can not only help untangling the fundamental complexity of cellular systems but also, by building up on the concept of orthogonalization of metabolic modules , here anFor all essentiality predictions performed in this study, 15 genome-scale metabolic models were chosen based on curation, validation, and comparability of the nomenclature of metabolites and reactions. These comprise 7 prokaryotic phyla, including one archaea. Ten of these models include more than 20% of the total of ORFs of the corresponding species. -1 h-1 to allow for the import of all transported metabolites .All GSMs were collected in SBML format and then parsed to model an environmental condition corresponding to rich media: all original exchange reactions in the model were set to a maximum uptake limit of -20 mmol gDWFlux Balance Analysis (FBA) ,47 was uFor comparison of the essential reactions calculated for the 15 GSMs, some nomenclature inconsistencies were resolved: standardization of suffixes used in reaction IDs, removal of unnecessary or redundant indications of reversibility and species names allocated to reactions and other redundant tags. Irrelevant and irregular characters such as dashes were filtered out of the nomenclature  were used as queries and annotated to the subsystems of the latest E. coli model [To analyse the conservation and infer ancestry of all the genes annotated in metabolic subsystems of GSMs, a local protein blast was performed against representative genomes of all the 35 prokaryotic phyla with at least one fully sequenced quality genome in the NCBI genome database (version June 2015). For this task, translated genomes were selected and downloaded for 53 unique species of prokaryotes for which an available GSM could be found; to these, 26 representative genomes for phyla not modelled with GSMs were added. This totalled in 79 translated genomes representing the 35 fully sequenced phyla in the current tree of life of prokaryotes (see li model . The thrW) was calculated for each subsystem m, as:For assessing the conservation of essential reactions and essential genes in each metabolic subsystem, the weighted sum of essentiality  minus the number of times it was found not essential (N) in all experimental assays datasets:The score sum of experimental essentiality for each individual gene All statistical analyses were performed using R . Hierarchical clustering was performed using the ‘pvclust’ R package  with binTo analyse the validity of the essentiality results on a large scale, the different models were clustered based on single-reaction essentiality predictions and the different datasets available on DEG  were cluC. beijerinckii and P. putida. H. pylori and S. oneidensis show up in the same cluster, but not together with the rest of the Proteobacteria, but the two high-level clusters  are not statistically supported . The lower number of available exchange reactions in the models of H. pylori, S. oneidensis and P. putida  compared with the models for other Proteobacteria  points to a justification for these results, as less exchanges cause more reactions in the network to be essential. C. beijerinckii’s model is also very restricted with regards to exchange reactions, with only 19 metabolic drains available.In the case of the simulated essentiality , stronglAcinetobacter baylyi, dataset II of E. coli K12, three Salmonellas, one Shewanella and one Francisella. Others are the cluster of Bacteroidetes, the cluster with all three datasets of M. tuberculosis and the cluster of the alpha-proteobacteria, Sphingomonas and Caulobacter. The datasets of Pseudomonas aeruginosa PAO1 and Salmonella enterica subsp. Enterica serovar Typhimurium str. 14028S cluster together likely due to not being saturated genome-wide gene-essentiality screens . Discrepancies regarding results for each individual model are not only related with differences in the metabolic network but are also dependent on the formulations of the biomass equation (e.g. the biomass equation in Klebsiella pneumoniae’s model lacks cofactors) [For the majority of the models, the most essential subsystem is that of cofactor and prosthetic group biosynthesis. Nearly 48% of the essential reactions in the simulations with the GSM of ubsystem . Severalfactors) .). The total of reactions and genes in each subsystem  also varies significantly. To test for independence of the totals of essentials from the sizes of the subsystems, we performed a Fisher’s exact test, and for the majority of subsystems the null hypothesis of dependence was rejected .To validate the predictions of essentiality of metabolic subsystems obtained with GSMs, each experimentally essential gene in DEG was annotated according to its function, using the same system used in GSM’s. This system see  covered Fig 3Synechocystis there are twelve essential reactions related with fatty acid biosynthesis all encoded by the same gene, fabZ (sll1605 in the model), a gene that is essential in rich medium experimentally, but in that case, counted only once. Similarly, all of the twenty essential fatty acid synthase reactions in the model of M. tuberculosis are encoded by only 3 different genes: Rv1663 and Rv1662 or Rv2940c. There might also be some lack of integration in nomenclature of the reactions in these subsystems in the different GSMs that should not contribute significantly as all models follow the same nomenclature scheme, which was still manually curated for the essential reactions.In K. pneumoniae. Although there are differences in the models regarding the capacity to uptake aromatic amino acids, this does not justify the observed differences in terms of essentiality. The notable difference between the model of K. pneumoniae and the others is that it lacks cofactors and prosthetic groups in its biomass equation. Those three reactions correspond to the three last steps in the synthesis of chorismate, which is part of the shikimate pathway, which connects central metabolism with aromatic amino acids metabolism. However, this pathway is also the route taken to synthesize several other compounds in the cell, including quinones and folates [Klebsiella, which is likely the cause for the difference in the results as discussed in [To further explore essentiality at the level of genes and reactions, the conservation of essentiality across models and experimental datasets was analysed for each reaction and gene in each metabolic subsystem. Strikingly, no reaction was essential in all the models analysed. Three reactions annotated within aromatic amino acids metabolism  were essential in all models except for  folates , which aussed in  and abovB. subtilis and D. ethenogenes—glucosamine-1-phosphate N-acetyltransferase, phosphoglucosamine mutase and UDP-N-acetylglucosamine 1-carboxyvinyltransferase. Acetyl-CoA carboxylase, related with membrane lipid metabolism, is essential in 12 of the 15 models. One reaction not assigned to any subsystem, the HCO3- equilibration reaction, was essential in 11 of all 15 models.Reactions essential in several models annotated within the cofactor and prosthetic group biosynthesis subsystem are dihydrofolate synthase, essential in 13 out of the 15 models, and dihydrofolate reductase and NAD kinase, both essential in 12 models. These are related with the biosynthesis of folates and the phosphorylation of NAD to produce NADP. Two reactions involved in the salvage pathways of nucleotides–the biosynthesis of GDP and dTTP—were also essential for 14 models. Three reactions related with the biosynthesis of cell wall components are essential in all models except for S. oneidensis, To overview the relationship between modelling and experimental results, the conservation of essentiality for each set of results was compared. The inset plot in E. coli impairs growth [E. coli aroC mutant in M9 minimal medium can be rescued [Escherichia coli or any other of the working set. Transporters for these compounds or others that might compensate in rich media for lethal phenotypes in minimal media remain to be integrated in the genome-scale metabolic models and further explored.Interestingly, regarding the three highly essential reactions in modelling results related with chorismate biosynthesis, these were not found as significantly essential in experimental data. It is known that in minimal media the knock-out of chorismate synthase (aroC) in s growth . The non rescued . PABA anE. coli in Again, the subsystem of cofactor and prosthetic groups metabolism has the highest number of reactions appearing as essential in more datasets in experimental data, in accordance with modelling data. Dihydrofolate synthase and reductase (highest ranking in modelling) are essential in 11 and 25 experimental datasets, respectively, indicating that either several organisms can overcome the lack of both enzymes by intermediate pathways not yet modelled, or that the experimental assays have produced false negatives  and csd, a cysteine desulfurase, essential in 7 datasets and involved in the formation of Fe-S clusters, also annotated in category E.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 TableE. coli K12.The list includes genes conserved in all 79 prokaryotic genomes analysed. Essentiality is given as the number of datasets in DEG (out of 36) in which each gene is essential. The description is that of the corresponding annotated ORF in the genome of (PDF)Click here for additional data file.S2 TableE. coli K12. Biosynthesized cofactors were manually retrieved from the detailed information available in the Metacyc database.Essentiality is given as the number of datasets in DEG (out of 36) in which each gene is essential. Conservation is given as the percentage of the 79 genomes where a significant homolog for this gene was found. The description is that of the corresponding annotated ORF in the genome of (PDF)Click here for additional data file.S1 FilePresence-absence matrix of essential reactions in the 15 GSMs used.(XLSX)Click here for additional data file.S2 FileLists of original subsystem classifications and corresponding translation for all models and all reactions.(XLSX)Click here for additional data file.S3 FileList of genes with corresponding scores of essentiality and conservation.(XLSX)Click here for additional data file.


Streptomyces lividans is an appealing host for the production of proteins of biotechnological interest due to its relaxed exogenous DNA restriction system and its ability to secrete proteins directly to the medium through the major Sec or the minor Tat routes. Often, protein secretion displays non-uniform time-dependent patterns. Understanding the associated metabolic changes is a crucial step to engineer protein production. Dynamic Flux Balance Analysis (DFBA) allows the study of the interactions between a modelled organism and its environment over time. Existing methods allow the specification of initial model and environment conditions, but do not allow introducing arbitrary modifications in the course of the simulation. Living organisms, however, display unexpected adaptive metabolic behaviours in response to unpredictable changes in their environment. Engineering the secretion of products of biotechnological interest has systematically proven especially difficult to model using DFBA. Accurate time-dependent modelling of complex and/or arbitrary, adaptive metabolic processes demands an extended approach to DFBA.S. lividans, Adaptive DFBA can overcome the limitations of traditional DFBA to reproduce experimental data on plasmid-free, plasmid bearing and secretory protein overproducing S. lividans TK24, and can yield useful insights on the behaviour of systems with limited experimental knowledge such as agarase or amylase overproduction in S. lividans TK21.In this work, we introduce Adaptive DFBA, a novel, versatile simulation approach that permits inclusion of changes in the organism or the environment at any time in the simulation, either arbitrary or interactively responsive to environmental changes. This approach extends traditional DFBA to allow steering arbitrarily complex simulations of metabolic dynamics. When applied to Sec- or Tat-dependent secretion of overproduced proteins in S. lividans, improving our understanding of the underlying processes. Adaptive DFBA is versatile enough to permit dynamical metabolic simulations of arbitrarily complex biotechnological processes.Adaptive DFBA has allowed us to overcome DFBA limitations and to generate more accurate models of the metabolism during the overproduction of secretory proteins in  Streptomycetes possess distinctive characteristics that make them an appealing model system for engineering overproduction of biotechnologically interesting products: they are capable of producing a large array of antibiotics and other compounds of interest, and interestingly, large amounts of extracellular proteins ’ (upper limit) or ‘[low]’ (lower limit). Only reactions whose limits are to be changed need to be listed. Values must be specified at every time point.Since under experimental conditions exchange rates are typically measured only at specific time points, we considered the possibility of specifying only these values and providing various interpolation mechanisms. However, since implementing any imputation scheme is easy in R, we considered best to leave interpolation to the user.A function that takes as arguments the model, the current concentrations, the last previously computed fluxes and the time step, and which returns a new model. The function may use these values to implement an imputation/interpolation scheme as in the matrix approach, as well as interactive changes in response to the environmentAny reaction limit can be modified at any time point using one of two forms:A data frame of predefined changes: a table of changes to apply at each time point to the substrate concentration. Since it works as a differential engine, a zero means no change to current concentrations, a positive value an addition and a negative value a removal of metabolites.A function that will take as input the current concentrations and return the new concentrations to be used. Using a function provides the additional control needed to implement feed-back intervention in response to dynamic system changes.We assume that this will be used typically to model external interventions on the system, such as predictable changes  and feed-back controlled actions in response to metabolite changes (such as a sensor/actor system in a controlled fermentation that injects or depletes nutrients when concentrations cross a given threshold). Again, there are two possibilities:At any time point, the concentration of each metabolite is checked against the substrate uptake rate to avoid overusing non-existing resources. In the original DFBA algorithm, the direction of each reaction was predefined by the initial limits. This is sensible when nutrients are added only at the onset and biomass can only grow, but is unsatisfactory when any concentration or exchange limit can change at any time during the simulation or a metabolite is excreted and later consumed (and vice versa). In Adaptive DFBA, the lower rate of an exchange reaction is modified to avoid over-consumption only if the reaction is explicitly an uptake reaction at the current time step.The classic DFBA algorithm uses FBA to solve the metabolic problem at each step. The result of FBA is not uniquely defined , 7, 8. WSolution of the FBA system of equations relies on an auxiliary ODE solver library. We checked GLPK, CLP, LP-SOLVE and CPLEX on different computer architectures. All of them should produce the same or approximately similar (within rounding error bounds) results. However, in our experience, we found that LP-SOLVE gave different results in several of the calculated simulations, GLPK failed to run in only one specific combination of simulation parameters, operating system and architecture (whose origin we could not track), CLP worked correctly in all cases but was significantly slower than GLPK, and CPLEX was tested only in the free, academic version, which accepts a limited number of equations: for systems within this limit, it was the fastest method, but we could not check it with larger systems.Uncertainty in FBA may be reduced using optimal fluxes calculated through Minimization of the Total Flux (MTF), leading to a hopefully more parsimonious solution. We have implemented MTF as an optional alternative to FBA, and found that it leads to a system evolution that is practically equivalent to that obtained by FBA. Since MTF computation time is larger, FBA has been kept as the default option, and MTF is offered on demand.Both, FBA and MTF compute a single value for each metabolic flux at each step. It would be more useful to obtain an estimate of flux variability limits. We have tested as well implementing Flux Variability Analysis (FVA) calculations at each time step to obtain stepwise flux limits for each reaction. FVA results in a huge increase in computational time that would only be justified under special situations. We are currently exploring the best approaches to definitively integrate FVA within the algorithm.After the system has been solved, biomass content is calculated using the actual Biomass reaction instead of the objective function. This allows study of the evolution of a system using an objective other than (and possibly conflicting with) Biomass. The Biomass calculated is combined with the computed fluxes to calculate new concentrations for the next step as in the traditional scheme.We have ensured that the return of Adaptive DFBA remains fully compatible (and thus can substitute it seamlessly) with DFBA, easing its integration in existing simulation schemes. Additional information can be obtained using increasing verbosity levels.During simulations, both fluxes and metabolite concentrations computed at each step were collected for further statistical analysis, their distributions were checked for normality using the Anderson-Darling and Shapiro-Wilk tests, prior to selection of subsequent analyses. Correlation and regression analyses were used to study the relationship between all possible pairs of same-type variable distributions: we considered the Pearson product-moment, Spearman Rank and curvilinear regression tests, which were applied to all possible pairs of variables. Curvilinear regression was used to fit quadratic and cubic polynomial equations.To identify which variables may have a stronger influence on desired outcome variables, we built models where each desired outcome was initially modelled from all existing variables, and used three feature extraction methods to select the most significant contributors: forward selection, backward selection and Boruta’s algorithm  were applied to determine the relative variable importance in explaining each outcome sought. For the application of Boruta’s algorithm, variables were divided in groups of 20, repeatedly applying selection and regrouping until there remained 20 or less significant variables which were subject to a last selection iteration. At each step, critical judgement based on metabolic understanding was used to supervise automatic selection.Similarities and differences in time-dependent distributions were studied comparing either whole or partial timelines. Group comparisons were carried out using ANOVA and Kruskal-Wallis, followed by Welch modification of Student’s t-test or the Mann-Whitney/Wilcoxon test. Metabolites whose evolution was clearly different were selected to build time-dependent predictive models that might be used to differentially steer simulations, fitting their time-dependent evolution to either linear, quadratic or cubic models. The parameters for the resulting models were then compared with the experimental data using two non-nested model comparison methods, the Cox test and the David-Mackinnon J test.Streptomyces lividans, in the presence of conflicting and uncorrelated objectives.We have exploited Adaptive DFBA to model heterologous protein overproduction and secretion in For simplicity, in all subsequent simulations, we have used the following scheme: system interventions have been reduced to the minimum, time step was set at 1 h, constraints were defined only at a few key times , and values at intermediate points were imputed using linear interpolation, corresponding to multi-phase linear regression , 31. DesStreptomyces lividans TK24 strains growing on a complex medium . The experimental data has been originally published elsewhere as figures [The systems modelled correspond to  figures .4+ with glucose, glucose exhaustion and some amino acid uptake rates evolve similarly to the experimental observations, however, other rates such as growth arrest and L-alanine excretion and later uptake are not reproduced.DFBA and Adpative DFBA models are illustrated in Additional file To properly model growth and L-alanine exchange, they had to be steered during the simulation using Adaptive DFBA, which permitted forcing early excretion of L-alanine and releasing this constraint later in the simulation.S. lividans TK24 harbouring the multi-copy plasmid pIJ486 (model S.lividans-TK24-pIJ486), the plasmid imposes a strain that can be detected as a growth delay  in published growth curves [Adapting glucose uptake to its concentration in the medium using Adaptive DFBA allowed simulation of gene-regulated adaptation to nutrient depletion, making it available for a longer time. Additionally, at low levels of glucose uptake, an alternate C source is needed, requiring L-alanine to be switched from excretion to uptake: only controlling both switches could growth be maintained. Use of L-alanine resulted in a corresponding reduction in NHh curves , 28.S. lividans TK24 pIJ486 overproducing Sec-secreted mTNF-α (model S.lividans-TK24-mTNFα) are presented in Fig. Simulation results for To match experimental rates Fig.  we had tS. lividans TK21 grown in a complex medium (NMMP complemented with mannitol and casamino acids), over-expressing secretory proteins cloned in the multi-copy plasmid pIJ486 and secreting them through the minor Tat or the major Sec secretion routes, using model proteins that have been shown to display secretion patterns clearly dissociated from growth [S. lividans TK21 behave similar to those of S. lividans TK24, when grown on similar media [We now proceed to show the use of Adaptive DFBA to explore the metabolism of systems with limited knowledge: first, we analyse m growth , 29. Prear media , 29, andS.lividans TK21(pAGAs5) was generated transforming S. lividans TK21 with the multicopy plasmid pIJ486 carrying the gen dagA encoding agarase from S. coelicolor and its regulatory region (model S.lividans-TK21-DagA). Tat-dependent secretion typically starts by the end of exponential growth, when the cells enter the stationary phase, increasing when nutrients become scarce in the medium. In the case of agarase, there is also some reduced early secretion detectable during the second exponential growth phase [Strain th phase , 29. HenDFBA was unable to produce a proper simulation: besides the previous issues, if the lower limit on protein secretion was set to zero to accommodate early lack of agarase secretion, it was never secreted ignoring any upper limit, as agarase overproduction competes with growth. On the other hand, setting the lower limit to a higher value to force late secretion would also force early production, with an unrealistic initial evolution of the system, and leading to early termination of the simulation due to exhaustion of the C source  of 80,000 H,000 ATP , 30. InsS. lividans TK21 (pAMI11) [amlB gene encoding α-amylase from S. lividans under the control of its own promoter (model S.lividans-TK21-amlB). Simulation requires considering a secretion pattern clearly different from that of mTN-α: α-amylase is maximally secreted at the exponential phase, and progressively less during the stationary phase until it becomes undetectable, making DFBA constant rates inappropriate   was gene1  was ge4+ and L-alanine was more evident during the stationary phase  (model S.lividans-TK24-CelA) grown in a different, C-limited minimal medium containing glucose and casamino acids [S. lividans model to accommodate overproduction and secretion of cellulase-A, and to use the new experimental conditions. As in previous cases, traditional DFBA failed to reproduce experimental observations  between uptake of some amino acids and protein production could be identified, consumption of amino acids is not enough to explain protein production as secretion may be detected after amino acids have been consumed or may be absent in their presence  are consistently among the most significant variables for both proteins.Feature selection was applied to models initially including all metabolites to select the most important variables in the model , so only Mann-Whitney U test results were finally considered. Visualization with heat-maps , implem2 on pH , 33), ac2 on pH , 29, impt cycles ), or detThese modifications permit unprecedented arbitrary complexity in steering simulations to model dynamic regulatory, genomic or even epigenomic changes with Adaptive DFBA.Users may choose FBA or MTF to solve the metabolic problem at each step. We have tested several combinations of ODE solvers and architectures. To summarize, GLPK provides the best cost-benefit trade-off, and if it fails to run on a specific calculation , then CLP (which is slower) or CPLEX provide suitable alternatives.It has been shown that poor cell growth can be associated with increased secretion of endogenous and heterologous products , 34, andWe have shown several examples of DFBA limitations when applied to secretory protein overproduction and how to overcome those using Adaptive DFBA, reporting the simplest models that still gave good results in each case. More complex parameter-tuning and pre-processing (e.g. growth estimation using sigmoid functions or rate approximations using non-linear functions) can be easily implemented in R. Using Adaptive DFBA we could enforce specific rates such as growth, L-alanine, C-source or protein secretion and explore which limits are compatible with observed experimental behaviour.Even with minimal intervention in the system constraints and a coarse time step, different cellular responses and their metabolic associations could be identified using Adaptive DFBA: harbouring the pIJ486 multicopy plasmid or overexpressing secretory proteins resulted in changes to the dynamic consumption of various nutrients in a delicate balance. As in previous metabolomics studies , both thS. lividans showed an initial preference for using amino acids as the main C and N sources, resulting in an initial increase in NH4+ levels and sustained saccharide concentration, coincident with previous observation in batch and fed-batch cultures [4+ became preferential sources of C and N, and L-alanine was excreted to the medium in large quantities [4+ and was associated to the switch to the stationary phase . These data also yield useful information to interpret previous observations of Tat- or Sec-dependent secretory protein overproduction using glucose or mannitol using S. lividans TK21 [cultures , 28. As antities . This swans TK21 , 28, 29.Rhodothermus cellulase-A have a larger, size dependent, metabolic footprint and lead to faster amino acid uptake than overexpression of α-amylase from S. lividans or agarase from the closely related S. coelicolor, suggesting mutual adaptation of metabolism and protein composition in S. lividans.The size and composition of the overproduced secretory protein affects metabolic patterns: interestingly, heterologous proteins like mouse mTNF-α and The availability of a versatile method to model complex systems paves the way for exploring time-dependent interactions. FBA-based studies consider a single point in time, associations are heavily dependent on the magnitude of fluxes and their differences and necessarily ignore time-dependent changes.Having access to dynamic simulation data opens the possibility of exploring time-related associations, such as identifying fluxes or metabolites that display a highly correlated, anti-correlated or independent time behaviour, clustering reactions by flux patterns, grouping and ranking them functionally and investigating associations to specific targets.Our results suggest that using only roughly approximate limits for key reactions may be enough to obtain acceptable predictive models. While these limits may be provided by educated guesses, proper modelling should rely on statistically sound multivariate models, as no single metabolite could be completely associated to growth or protein secretion. Selecting an appropriate metabolite combination becomes then the most important issue. All three methods gave relatively consistent results, however, in our hands, Boruta’s Random Tree based approach seems better suited than traditional feature extraction methods to identify biologically meaningful variables.2O2 , L-alanine, mannitol, other amino acids and, surprisingly, four metallic ions  whose association with secretion had never been noticed before. Coincidentally, a recent study [S. lividans grown in minimal medium (MM) had a low yield in mRFP protein secretion, whereas growth in CM/glucose had a high yield, on the opposite side of the spectrum, only paralleled by NB medium . The only qualitative differences between MM and CM/glucose are the presence in CM/glucose of CuSO4 and CoCl2. Furthermore, that a carefully controlled, minimal medium like CM/glucose, containing only glucose and various ions, can match in efficiency a very rich, complex medium like NB, further supports their key role. While the relevance of these ions has passed unnoticed to date, these experimental findings strongly validate the use of deductions based on methods of feature extraction from simulated calculations.Application of feature extraction methods identified relevant associations with Hnt study  reportedWe used heatmaps to identify metabolites whose time-dependent evolution differed significantly and which could be selected as targets for elaborating roughly predictive time-dependent models for use in the specification of approximate exchange limits. Although these methods may be used to obtain approximate predictions, further work is clearly needed to construct better predictive models. A potentially promising approach should likely consider the dependence of each metabolite on all other relevant ones to build multivariate models.Adaptive DFBA expands the field of metabolic systems whose time-dependent evolution can now be analysed, extending DFBA applicability to deal with uncorrelated objectives, dynamically adapting and unforeseeable systems, and improving predictive power in situations with reduced experimental data.Our analyses may certainly be improved. Possible enhancements to Adaptive DFBA include additional fine-tuning, further exploring the effect of other key metabolites identified thanks to these simulations, modelling the role of relevant post-secretory mechanisms such as the effect of protein folding and modification, the role of external proteases in degrading misfolded proteins  to accouS. lividans secretion via the major Sec or the minor Tat secretion routes.We have shown the advantages of the Adaptive DFBA approach with its descriptive application to reproduce and explore experimental data, and its exploratory utility for modelling Our work not only opens the way for advanced and versatile simulation of arbitrarily complex metabolic systems, but also allows exploration of time-dependent relationships that were not easily accessible until now due to the difficulty in modelling arbitrary systems with DFBA.Project name: AdaptiveDFBAProject Home Page:https://github.com/jrvalverde/AdaptiveDFBAArchived version: 08-Feb-2019 DOI:10.5281/zenodo.2560170Operating System: Platform IndependentProgramming Language: ROther requirements: an ODE solver  and its R binding, R Sybil packageLicense: GPL/EU-GPLAny restrictions to use by non-academics: NoneAdditional file 1: Table S1. Sample commands to reproduce the calculations. Figure S1. Simulation of wild-type S. lividans TK24. Figure S2. Simulation of S. lividans TK24 pIJ486. Figure S3. Simulation of S. lividans TK24 pIJ486 overproducing Sec-secreted mTNF-α. Figure S4. Simulation of overproduction of Tat-secreted agarase by S. lividans TK21 pIJ486. Figure S5. Simulation of overproduction of Sec-secreted α-amylase by S. lividans TK21 pIJ486. Figure S6. Simulation of S. lividans TK24 pIJ486 overproducing Sec-secreted cellulase-A. Figure S7. Sample correlations between amino acid and agarase exchanges. Figure S8. Sample correlations between amino acid and amylase exchanges. Figure S9. Variable importance computed after Boruta’s method. Figure S10. Partial heatmap of Mann-Whitney’s U P-values. Figure S11. Examples of quadratic regression fits. (DOCX 5348 kb)


Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO2 with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO2 fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO2 levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO2. Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions . Our results suggest that cells growing at high CO2  have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO2-heterotrophic and high CO2-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO2 fixation, biomass accumulation, and bioenergy production.The increase in atmospheric CO This has strongly contributed to climate change and global warming leading to a severe environmental crisis  to adapt to their environment. These mechanisms increase photosynthetic productivity by scaling up the levels of inorganic carbon over the environmental levels of CO2  as compared to high CO2 (1% to 5%) (Significant advances have been made in order to elucidate the  factors . For exantration . Under C% to 5%) . Moreove% to 5%) . In this% to 5%) .C. reinhardtii shows three CO2 acclimation states: high concentration (>5% [CO2]), low concentration (0.04%–0.03% [CO2]), and very low concentration (<0.01% [CO2])  . Therefotrations . In thisaptation . Therefoaptation . All thee carbon .C. reinhardtii  Identification of metabolic pathways and membrane transport reactions based on the genomic annotation, enzyme homology, and experimental observations. This step used databases such as KEGG, ModelSEED, TCD, and TransportDB, among others; (ii) Development of equations for biomass components based on physiological data or comparison between similar species; (iii) Identification of incomplete pathways and transport reactions of missing metabolites through semi-automated methods, such as reverse engineering, or automated methods, such as pFilling . In thisnhardtii , which rnhardtii , KEGG  were used as additional constraints in the dynamic model. Then, based on the growth conditions, we constructed two Chlamydomonas kinetic models: (i) heterotrophic growth and (ii) mixotrophic growth.Kinetic parameters associated with substrate consumption at different growth conditions  concentration [g∕L], t is time [h], μm is maximum specific growth rate [1∕h], Ks is the acetate saturation constant [g∕L], Ki acetate inhibition constant [g∕L], Yx is yield constant on acetate m is the maintenance energy coefficient.In this work, we used the Haldane model, which includes terms to describe the effects of a nutrient on the growth rate at high concentrations . \documeThe behavior of microalgal cultures was described with a set of nonlinear algebraic-differential equations deduced from mass balance considerations for both liquid and gaseous phases, assuming well-mixed conditions, and following the Haldane model .CDIC] is dissolved inorganic carbon concentration [mol∕L], KsCDIC is the CDIC saturation constant [mol∕L], KiCDIC is the CDIC inhibition constant [mol∕L], Mx is the C-mole mass, NCO2 is the volumetric mass transfer rate for CO2kLaCO2 is the volumetric mass transfer coefficient [1∕h], P is the pressure HCO2 is the CO2Henry constant [Pa∗m3∕mol], CO2 molar fraction in the output gas, and K1 and K2 are equilibrium constants.FBA assumes that metabolic networks achieve a steady state that is constrained by the stoichiometry and that they keep on self-adjusting and optimizing in a changing external environment. This method then uses a mathematical model to simulate the network self-adjustment . Here, w2 fluxes were changed in order to determine the impact of the different tested CO2 concentrations supply at the steady state of cell metabolism. The biomass function was exactly the same as described in our previous study  distribution 23.9.5 using FBA by solving the linear programming problem with constraints set as follows: LB ∈ Rn, UB ∈ RnThen, COus study . The LBsS is the stoichiometric matrix, v is the flux vector, LB is the lower bound, UB is the upper bound, c is a vector of zeros that sets the objective function, m is the number of metabolites, n is the number of reactions, and 2 levels were identified by FBA. In that way, we were able to determine candidate metabolic pathways of relevance for a CO2 response.where biomass) . FinallyLinear program (LP) problems arising in FBA can have multiple solutions with the exact same optimal value for the objective function and still satisfy all the constraints. The number of these solutions determines the dimensionality of the alternative optimal space. These variables can be identified using flux variability analysis (FVA), where the flux of each reaction in the network is maximized and minimized, one at a time, while the optimal value is obtained from FBA . Here, tFVA was then used to identify blocked reactions in a metabolic network under a given environmental and growth condition. Blocked reactions were reactions with both a minimum and maximum flux value equal to zero under the examined condition. The relevance of identifying blocked reactions was: (i) that they could be used as a basis for improving the quality of metabolic network reconstruction by helping to identify gaps, or (ii) that they could significantly reduce the search space of FBA by pre-setting their value to zero .vi -min vj) differed by >10−6 were defined as loop reactions , and αare weights. As described above, the loopless condition generates an LP problem. The driving force of each reaction was then indicated by the vector of continuous variables (Gi) .This quantity can be thought of as being analogous to the ΔG of each reaction. As a loop is entirely defined by the direction of the flux distribution ) using the FVA. Subsequently, these reactions and the metabolites involved are expressed in matrix form, and the null space of the matrix is calculated. Each thermodynamically infeasible cycle (TIC) is thus detected with the reactions involved. The TICs are solved through manual revision of reversibility or irreversibility of the reactions involved, removal of repeated reactions, or as a last option, the blocking of some reactions. Finally, this procedure is repeated until the null space is zero; thus, the GSM reconstructed model is cured  distribution 23.9.5. Computation for obtaining the null space of S was performed in MATLAB® version 2015b  followed by manual correction of reversibility in the involved reactions according to the databases sources.The loop law states that around a metabolic loop all the thermodynamic driving forces must add up to zero. In other words, a net flux cannot exist around a closed cycle in a network at steady state . By usineactions . The looribution , if NxGiis cured . Here, t®  distribution 23.9.5 by setting the following: zi is the extracellular metabolite concentration (based on the components of the culture media), zi,0 and X0 are initial conditions for extracellular metabolite and algal concentrations, respectively, X is the algal concentration, μ is the specific growth rate, cj is the reaction weight, t0 and tf are the initial and the final times, respectively, and G is the number of intervals in which the time is discretized.A variant of FBA, called dynamic flux balance analysis (DFBA), allows a description of the transience of metabolism. DFBA can be implemented in two ways: by a dynamic optimization approach (DOA), which requires solving a nonlinear program (NLP), and by a static optimization approach (SOA), which requires solving an LP . On one Chlamydomonas reinhardtii strain CC503 cw92mt+  was cultivated in tris-acetate-phosphate (TAP) medium , salts (25 ml) , trace elements (1 ml) 6Mo7O24 4H2O, 0.11 g 100 ml−1; EDTA 5 g 100 ml−1), acetic acid (1 ml), and tris (2.42 g/L) at 27 °C in heterotrophic and mixotrophic growth conditions, under different CO2 concentrations , in a R’ALF Plus bioreactor  under continuous illumination. Cell growth, bioreactor experiments and counting of cells were performed as previously described in The ) medium : phospha2 and acetic acid as a carbon source) and mixotrophic (10% CO2 and acetic acid as carbon sources) growth conditions. Then, the total biomass was determined based on dry weights following the method described in our previous work  charges, disruptions in the balance of water consumption and production in some reactions, the oxygen requirement for growth, and the presence of the same reaction twice but one of them was the reverse reaction, among other factors. Initially, our model consisted of 3554 reactions and 2,342 metabolites, in contrast to iCre1355, which has 2,394 reactions and 1,845 metabolites  and the continuous input of CO2 into the media.Cell growth and substrate consumption profiles were obtained in batch culture under heterotrophic and mixotrophic conditions in order to utilize them as restrictions for the dynamic model  and 4. pn source . A lag pn source . Likewisnditions . The inc+ charge and water consumption in the entire network. A total of 187 loops were identified and many cycles were corrected and eliminated to finally generate a metabolic network curated without any thermodynamically infeasible cycle.The guarantee of a robust and sensitive model to predict metabolic fluxes and to use in subsequent dynamic simulations required the correction of all TICs, along with a balancing of HC. reinhardtii under photoautotrophic (using light and CO2 as energy and carbon sources), heterotrophic (using acetate as carbon source), and mixotrophic  growth conditions. This new sensitivity analysis implemented FBA and DFBA to describe new pathways using transcriptomic data, which provided a reference for optimizing biomass production upon different growth conditions. The predictions were performed with maximization of biomass. The reactions we have found showed significant variation depending on the CO2 concentration and confirmed effects on different metabolic pathways, such as glycerolipid synthesis, amino acid synthesis, glycolysis, carbohydrate catabolism, and carbon fixation, among others  have the ability to increase their biomass production.The magnitude of the fluxes for glycine transport from chloroplasts to mitochondria, serine transport from mitochondria to chloroplasts, and production of hydroxy pyruvate, glycerate, and 3-phosphoglycerate in the chloroplast showed positive values. These routes contribute to the maximization of biomass production because they are related to photorespiration processes . FBA revreported . MoreoveC. reinhardtii to environmental changes in CO2 concentrations from a metabolic dynamics perspective. For this reason, we performed a sensitivity analysis to provide a reference for optimizing biomass production in response to variations in heterotrophic, photoautotrophic, and mixotrophic growth conditions by changing the carbon sources available in the extracellular medium. We also wanted to test the effects of changing the initial concentration of inorganic compounds, such as photons and water, in the extracellular medium to promote the photosynthesis process and biomass production. Some reports on the energy requirements for photoautotrophic growth of C. reinhardtii indicated that, at high light supply, the biomass yields decrease due to light saturation effects  considering the metabolic abundance. Metabolites and abundance were shown to be significantly affected by changes in CO2 concentration level. Our dynamic modeling agreed with these results since it showed a significant increase of flux in metabolic routes including the TCA cycle, glycolysis/gluconeogenesis, and amino acid biosynthesis at high CO2 concentrations, as confirmed by the experimental abundance of metabolites involved in these pathways. Our data suggested that high CO2 concentration may activate mechanisms that are able to control carbon fixation.We performed a canonical discriminant evaluation of the experimental data to compare with our dynamic modeling results. The canonical discriminant analysis allows evaluation of the presence of significant differences between groups of a set of measured variables in order to classify them. 2 concentrations and low values for reaction fluxes at low CO2 concentrations). This is in agreement with the experimental results of metabolic abundance that showed opposing behavior between the two CO2 concentrations. This suggests that our proposed metabolic network model is highly sensitive and predictive of environmental changes in both steady-state and dynamic conditions.The dynamic model predicted the profile only for the extracellular metabolites, so this required obtaining the fluxes of the reactions that mainly contributed to the experimentally observed metabolic abundance in order to make appropriate comparisons. The metabolomic data primarily showed the presence of amino acids and few sugars; therefore, to compare these data with the results predicted by our model, we primarily analyzed reactions involved in amino acid biosynthesis. 2 concentrations, in agreement with previous reports of carbon uptake mechanisms in C. reinhardtii , NAD-dependent malate dehydrogenase (MDH3) did not show a differential behavior. These results are in agreement with our steady-state and dynamic simulations, which showed abundant gene transcripts in crucial genes involved mainly in carbon uptake mechanisms and the TCA cycle. Furthermore, they were related to the variation in differential growth conditions, which was confirmed in the fluxes of these reactions (including transport and exchange reactions) in our current reconstruction model . The experimental results agreed with steady-state and dynamic simulations, showing abundant gene transcripts in crucial genes involved in carbon uptake mechanisms and the TCA cycle. Our proposed metabolic network model can, therefore, provide complementary and high-value perspectives on transcriptional changes, metabolic abundance, and biomass production under different environmental conditions. Finally, up to now, no reports exist regarding dynamic flux balance analysis in C. reinhardtii. Consequently, our results and predictions can improve the understanding of the biological response of C. reinhardtii, as a dynamic entity, to environmental changes. This is of great interest given the biotechnological potential of this organism for CO2 fixation, biomass accumulation, and bioenergy production.In this work, we have significantly improved the previous models, obtaining a new robust genome-scale metabolic network model for the microalga a priori effects of several perturbations on the culture media, limiting factors, metabolic pathways, potential mutations, biomass production, lipids accumulation, and so on. Thus, our model represents a useful contribution to the field since it will allow to propose, analyze and evaluate different conditions affecting biological systems from a dynamic point of view. This is of relevance as biological systems are highly dynamic entities affected by a wide variety of changes in the environment, as a part of an adaptation process.Model predictions with FBA allows the description of different metabolic states regarding the variations in the growth conditions. In addition, the dynamic approaches have revealed a robust model with a high predictive capacity, which is a useful tool to further analyze 10.7717/peerj.5528/supp-1Figure S1Click here for additional data file.10.7717/peerj.5528/supp-2Figure S2Click here for additional data file.10.7717/peerj.5528/supp-3Figure S3Click here for additional data file.10.7717/peerj.5528/supp-4Data S1Click here for additional data file.10.7717/peerj.5528/supp-5Data S2Click here for additional data file.10.7717/peerj.5528/supp-6Data S3Click here for additional data file.


The liver—a central metabolic organ that integrates whole-body metabolism to maintain glucose and fatty-acid regulation, and detoxify ammonia—is susceptible to injuries induced by drugs and toxic substances. Although plasma metabolite profiles are increasingly investigated for their potential to detect liver injury earlier than current clinical markers, their utility may be compromised because such profiles are affected by the nutritional state and the physiological state of the animal, and by contributions from extrahepatic sources. To tease apart the contributions of liver and non-liver sources to alterations in plasma metabolite profiles, here we sought to computationally isolate the plasma metabolite changes originating in the liver during short-term fasting. We used a constraint-based metabolic modeling approach to integrate central carbon fluxes measured in our study, and physiological flux boundary conditions gathered from the literature, into a genome-scale model of rat liver metabolism. We then measured plasma metabolite profiles in rats fasted for 5–7 or 10–13 h to test our model predictions. Our computational model accounted for two-thirds of the observed directions of change (an increase or decrease) in plasma metabolites, indicating their origin in the liver. Specifically, our work suggests that changes in plasma lipid metabolites, which are reliably predicted by our liver metabolism model, are key features of short-term fasting. Our approach provides a mechanistic model for identifying plasma metabolite changes originating in the liver. The liver is the primary organ responsible for metabolizing drugs and toxicants, a process collectively known as xenobiotic metabolism. This function makes the liver highly susceptible to injury and potential failure . Currentin vivo studies during which the rats underwent short-term food deprivation for up to 13 h. We used a recently published algorithm to integrate the measurements with a rat metabolic network model, and predicted the direction of change in extracellular metabolite concentrations resulting from a perturbation of metabolic fluxes in the network . The rats were fed with Formulab Diet 5001  and given water Three types of measurements, plasma metabolite profiles, liver gene expression, and stable isotope tracer-based metabolic flux profiles, were made at one or two time points in three experimental studies. The three studies described here were the vehicle control groups of a larger study involving three different toxicants. The vehicle for each toxicant was different due to their differing physical and chemical properties. The time points also varied slightly because of the differences in their toxicity in the larger study. Catheter implantation surgery was performed 7 days before each experiment, as previously described . Rats wead libitum but not food. Then, at 12 p.m. (5 h group) or 5 p.m. (10 h group), after blood collection, animals in Studies 1 and 2 were anesthetized by intravenous injection of sodium pentobarbital through the jugular vein catheter and immediately subjected to laparotomy. The same procedures were performed at 2 p.m. (7 h group) or 8 p.m. (13 h group) in Study 3. After laparotomy, the liver was dissected and frozen using Wollenberger tongs precooled in liquid nitrogen. The collected plasma and liver samples were stored at -80°C until use for further analyses.Two time points were selected for sampling tissue and blood after vehicle administration in each of the three studies analyzed in the present paper: they were 5 h and 10 h for Studies 1 and 2, and 7 h and 13 h for Study 3. The administered vehicles and their dosages were polyethylene glycol at 6 ml/kg, corn oil at 2 ml/kg, and saline at 2 ml/kg in Studies 1, 2, and 3, respectively. Following blood collection, animals were given vehicle by oral gavage at 7 a.m. and moved to a new housing cage, where they were given access to water 2H2]water (99.9%) was delivered subcutaneously to enrich total body water to 4.5%. A glucose prime (80 mg ⋅ kg-1) was dissolved in the bolus. Post-awakening, at 1 p.m. or 4 p.m. , rats were connected to sampling and infusion lines and placed in bedded containers without food or water. Following the bolus, glucose was administered as a continuous infusion (0.8 mg ⋅ kg-1 ⋅ min-1) into the systemic circulation through the jugular vein catheter for the duration of the study. Sodium [13C3]propionate (99%) was delivered as a primed (110 mg ⋅ kg-1), continuous (5.5 mg ⋅ kg-1 ⋅ min-1) infusion starting 120 min after delivery of the [2H2] water bolus. All infusates were prepared in a 4.5% [2H2] water-saline solution unless otherwise specified. Stable isotopes were obtained from Cambridge Isotope Laboratories . Blood glucose was monitored  and donor erythrocytes were infused to maintain hematocrit throughout the study. Three blood samples (300 μl each) were collected over a 20-min period following 100 min of [13C3]propionate infusion. Arterial blood samples were centrifuged in EDTA-coated tubes for plasma isolation, and the three 100-μl plasma samples were stored at -20°C prior to glucose derivatization and gas chromatography-mass spectrometry (GC-MS) analysis. Rats were rapidly euthanized through the carotid artery catheter immediately after the final steady-state sample was collected.At 7 a.m. on the day of the study, rats in all three studies were administered vehicle  by oral gavage. Then, after food and water were removed, they were anesthetized with isoflurane at 12:50 p.m. (Studies 1 and 2) or 3:50 p.m. (Study 3). Subsequently, a 200-μl arterial blood sample was collected through the carotid artery catheter to determine the natural isotopic abundance of circulating glucose, after which a bolus of glucose infusion rate and rat weights. Flux estimates for the steady-state samples were averaged to obtain a representative set of values for each rat.A detailed description of the provided . Brieflyprovided . The reaprovided . The aven scans. The scan range varied slightly between methods but generally covered 70–1,000 m/z.Sample preparation was carried out at Metabolon, Inc. in a manner similar to a previous study . BrieflyMetabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra, and curated by visual inspection for quality control using software developed at Metabolon. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards .n × 105 instances of MS count data by random sampling with replacement, where n is the number of animals. Then, for each metabolite in the given study, we calculated n × 105 fold-change values from the synthetic data sets generated in the previous step. We pooled these fold-change values across studies for a given metabolite, and calculated 105 sample means, which constitute the bootstrapped distribution of the mean fold-change. To obtain the 99% confidence interval of the mean fold value for each metabolite, we identified a percentile-based confidence interval from the bootstrapped distribution of the mean fold-change value, which excluded values above the highest 0.5th percentile and those below the lowest 0.5th percentile. A metabolite was determined to have significantly increased or decreased if both bounds of the 99% confidence interval of its mean fold-change value were above or below the value 1. All statistical analyses were performed in MATLAB®R2017b . We have provided MATLAB code for this analysis in the We performed statistical analysis to identify metabolites that changed significantly with the duration of fasting. The raw data consisted of MS counts for each metabolite detected in a given plasma sample. We imputed any missing values with the minimum observed value for each metabolite. We then computed distributions of fold-change values for each metabolite and pooled them across the three studies to resolve changes during short-term fasting above experimental and biological noise. From these pooled distributions, we calculated 99% confidence intervals for the mean fold-change values of each metabolite using the percentile approach . BrieflyTotal RNA was isolated from the liver, using TRIzol Reagent  and the direct-zol RNA Mini Prep kit . The isolated RNA samples were then submitted to the Vanderbilt University Medical Center VANTAGE Core  for RNA quality determination and sequencing. Total RNA quality was assessed using a 2100 Bioanalyzer . At least 200 ng of DNase-treated total RNA with high RNA integrity was used to generate poly-A-enriched mRNA libraries, using KAPA Stranded mRNA sample kits with indexed adaptors . Library quality was assessed using the 2100 Bioanalyzer (Agilent), and libraries were quantitated using KAPA library Quantification kits (Roche). In Study 1, pooled libraries were subjected to 75-bp single-end sequencing according to the manufacturer’s protocol . In contrast, in Studies 2 and 3, the respective pooled libraries were subjected to 150-bp paired-end sequencing on Illumina NovaSeq 6000 and 75-bp paired-end sequencing on Illumina HiSeq 3000 according to the manufacturer’s protocol. Bcl2fastq2 Conversion Software (Illumina) was used to generate de-multiplexed Fastq files.Analysis of RNA-seq data consists of two stages: (1) determination of transcript abundance and (2) determination of differentially expressed genes. We determined transcript abundance from Fastq files, consisting of raw sequence reads, using a recently published software tool Kallisto . Using KiRno, which contains 2,325 genes and 5,620 metabolites in 8,336 reactions and eight compartments connected by Gene-Protein-Reaction rules, and is capable of simulating 327 liver-specific metabolic functions -lactate:ferricytochrome-c 2-oxidoreductase, which was determined to be non-existent in mammalian systems. Additionally, we added 90 transport and 105 exchange reactions to iRno to improve its coverage of exchangeable metabolites that were detected in plasma metabolite profiles in the present study. The updated iRno contains 2,325 genes and 5,709 metabolites including 3,201 unique metabolites in 8,534 reactions including 595 exchange reactions in eight compartments. iRno.We first updated a recently published functional rat genome-scale network reconstruction unctions . The updevidence . For insin vivo measurements in rats undergoing short-term fasting  by minimizing the weighted sum of fluxes across all reactions for each condition and metabolite, while satisfying the steady-state mass balance and a defined optimal fraction of maximum network production flux capability (νopt) to produce a metabolite as shown below:Transcriptionally inferred metabolic biomarker response (TIMBR) is a recently published method developed for predicting changes in extracellular metabolites due to gene expression changes under defined physiological operating conditions by integrating those changes into genome-scale network reconstructions see , in contS is the stoichiometric matrix. We included boundary conditions for uptake and secretion rates into the algorithm by fixing the respective lower (νlb) and upper bounds (νub) of the metabolite exchange reactions (νex), as shown in Eq. (2). Similarly, we integrated measurements from 13C-labeled tracer studies for some of the central carbon metabolism fluxes into the TIMBR algorithm by constraining the lower and upper bounds of the respective reactions in the model (νmfa) (Eq. 3).where ν is a vector of reaction fluxes and Xraw) from 5 to 7 h (X5-7) and 10 to 13 h (X10-13) time points (Eq. 4), and then calculated the TIMBR production scores (Xs) as the z-transformed scores across all exchangeable metabolites (Eq. 5).Using this method, we determined the relative production scores for all metabolites (SE) = 7 g, n = 35] fed standard chow under conscious unrestrained conditions. The fractional contribution of glycogen to liver glucose production (48%) at 5–7 h was reported to be invariant to rat strain, body weight, state of anesthesia, and measurement technique . During fasting, the liver produces glucose by synthesizing it from glycerol, lactate, and amino acids, as well as by breaking down glycogen. echnique . The remechnique . The repechnique  in varioOverall glucose output progressively decreased by 30% from 5 to 7 h until 13 h of fasting. Much of this reduction was due to a decrease in the flux of glycogenolysis, whose fractional contribution to glucose output decreased from 48% at 5 h to 2.3% at 13 h of fasting . Thus, tThe major conclusions from the central carbon flux data  were thap < 0.01). Of these, 39 metabolites were represented in the rat metabolic network model (iRno) as exchangeable between liver cells and the extracellular space or plasma. We compared our model predictions for the direction of change with fasting to those for the 39 metabolites, 33 of which showed an increase and 6 of which showed a decrease.Plasma metabolites changed after short-term fasting . Given tWe also compared the significant changes in plasma metabolites observed in the present study to those reported in the literature on short-term fasting in the rat . In termReports on large-scale data on plasma metabolite changes during a short-term fast, the number of biological replicates required to resolve them, and their sensitivity to the type of vehicle administered, do not exist in the literature. The number of metabolites measured in Studies 1, 2, and 3 were 569, 645, and 633, respectively, where the vehicle administered to the rats was different for each study. The metabolite fold-change values needed to be pooled across the three studies to resolve metabolite changes above experimental and biological noise during short-term fasting. The sum total of unique metabolites measured in the plasma in all three studies was 824 , of whicAmong the 193 significantly changed metabolites , 104  were measured in all three studies, which indicated the overall reliability of the data on exchangeable metabolites. Similarly, among the 39 significantly changed metabolites, 35 (90%) were measured in all three studies, which indicated the reliability of the metabolite data against which our model predictions were compared. Of the remaining four, N-carbamoylaspartate was measured in Study 3, acetylcarnitine in Study 1, inosine in Studies 1 and 2, and isocitrate in Studies 1 and 3.Of the 216 metabolites represented in Metabolite pathway annotations showed that lipids, amino acids, and cofactors and vitamins account for 49%, 23%, and 4% of the 824 metabolites, respectively, which indicated that lipid metabolites constituted the single largest category. Among the 193 metabolites that changed significantly, lipid metabolites again constituted the single largest group at 58%. The fraction of significantly changed lipid metabolites among all lipid metabolites was also highest at 28%, when compared to changes in other major pathways (19% or less). These results underscore the significance of lipids during short-term fasting.iRno. Out of the 2,325 genes in iRno, which were annotated with NCBI gene identifiers, 2,240 had 2,258 ENSEMBL gene identifiers that were used to annotate our transcriptomic data, with several genes mapping to than one ENSEMBL identifier. Based on the criteria of a false discovery rate of less than 0.1 and a biological effect size cutoff of 0.6 (corresponding to the 90th percentile), we found no statistically and biologically significant change in the expression of metabolic genes mapping to iRno in Studies 1 and 3 except for 100 genes in Study 2. Therefore, we did not use any differential gene expression-based weights in our implementation of the TIMBR algorithm to predict plasma metabolite changes. Gene expression changes in the liver during short-term fasting in all three studies  revealediRno using the TIMBR algorithm. We then used the TIMBR algorithm to compute a TIMBR score, whose positive or negative sign indicated the tendency of a metabolite to increase or decrease in the plasma, respectively, owing to changes in the liver metabolic network demand induced by fasting. The TIMBR predictions agreed overall with the metabolite changes observed here; TIMBR scores accurately predicted five out of six depressed, and 20 out of 33 elevated metabolites (2(fold-change) values, and corresponding TIMBR scores  and late (after 10–13 h of fasting) time points, with abolites . A summaR scores  revealedThe results in iRno, currently the most comprehensive genome-scale model of rat metabolism, instantiated with physiological flux bounds pertinent to the liver, was tested for satisfying defined liver-specific metabolic functionalities  was lower than that of the overall data set (49%). However, the fraction of lipid metabolites among the 39 significantly changing metabolites was higher at 46%, which is consistent with the trend in lipid metabolite fractions observed in the overall data set. Therefore, metabolite changes mapped to the network model are not biased by their limited coverage.A limitation of our modeling analysis was the restricted coverage of metabolites exchanged between the plasma and liver cells. Additional curation of in vivo central carbon fluxes derived from our tracer-infusion studies under short-term fasting conditions coupled with literature data from several studies during short-term fasting that sets the overall metabolite uptake and secretion fluxes of the liver , lipids , and amino acids  demonstrated the capability of the model to describe liver metabolic functions, and provided estimates of contributions of liver metabolism that agreed with metabolite changes observed in those pathways. In particular, lipid metabolite changes emerged as indicators of changes in liver metabolism, which were characterized both experimentally and computationally with sufficient statistical significance.Finally, the estimated model accuracy in predicting bile acids and bile pigments . Metabolomics data sets generated for this study are provided in RNA-seq datasets generated for this study can be found in the Gene Expression Omnibus repository (accession numbers KV conceived the short-term fasting analysis, analyzed RNA-seq and metabolomics data, computed model predictions, and wrote the manuscript. VP curated the rat metabolic network model. MW and MR performed metabolic flux calculations based on isotope labeling measurements. SE performed all of the animal studies, including catheterization surgeries. IT processed plasma samples for metabolic flux analysis. TO collected and analyzed all of the blood samples. RP contributed to RNA extraction from tissue and purifications. JR helped to conceive and supervise the study, and helped to edit the manuscript. MS conceived the study, supervised and carried out the experiments on rats to generate the raw data, and helped to write the manuscript. JY conceived the study, supervised the metabolic flux analysis, and helped to write the manuscript. AW conceived and supervised the study, and helped to edit and write 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.


The gut microbiota has been neglected in precision medicine for a long time and is considered as the virtual endocrine organ of the human body    and KEGG metabolic pathway; furthermore, the functional and metabolic annotations of different genes were informed. Intestinal microbiota from cirrhotic patients contained a significant reduction in functional diversity compared with healthy patients, with an enrichment in material  transport and metabolism, energy production and conversion. In terms of metabolites, there was a distinct abundance of nitrogen, branched-chain amino acids, glutathione, and lipids, with a remarkable deficiency in the metabolites of bile acid and aromatic amino acid . A core-metaproteome database containing 119 proteins, specific to the gut microbiota of cirrhotic patients, was discovered. More than one third of the proteins from this database were assigned to material transport and metabolism; moreover, fourteen proteins had remarkably enhanced expression and seven proteins had unique expression in patients compared to the controls. In two metabolic pathways, which were map 00290  and map00770 (pantothenate and coenzyme A biosynthesis), the number of cirrhosis-unique proteins increased as the disease got worse, and in patients with a CTP score of C, the unique proteins almost coverd the entire metabolic pathway , and unsaturated fatty acid, as well as decreased concentrations in both amino acid and sugar alcohol. Moreover, cirrhotic patients contained abnormal, and differential microbiota-metabolite correlations   probiotics compete with pathogens for essential nutrients; (b) probiotics compete with pathogens for adhesion sites; (c) signaling of immune cells by probiotics can lead to the secretion of cytokines, thereby targeting the elimination of pathogens; and (d) probiotics can attack pathogenic organisms by releasing bacteriocins. Prebiotics are usually fiber compounds that are not easily digested by the human body and can be used as a substrate to stimulate the growth or activity of beneficial bacteria in the intestine. Fermentation is the main mechanism of action of beneficial bacteria using prebiotics in human gut. At present, research on prebiotics has mainly focused on the effects of prebiotics on In a normal human gut, the microbiota participates in the degradation of amino acids from dietary intake, with the production of nitrogen-containing compounds, among which the most important nitrogenous waste product is ammonia. The N-containing compounds are transported to the liver through the portal vein, where more than 80% are metabolized through the urea cycle pathway and/or excreted immediately Bajaj, . The iniThis study summarized the cirrhosis-specific metabolic network of intestinal microbiota, illustrating that the abnormal gut microbiota metabolic pathway resulted in the excessive production of ammonia, which could not be eliminated completely by the damaged liver and crossed the blood-brain barrier, and might facilitate the acquisition of hepatic encephalopathy. In the future, many objects will be recruited to further verify and improve the cirrhosis-specific metabolic network of intestinal microbiota in this study. Other than this, an intestinal micro-ecological treatment method related to liver cirrhosis needs to be formulated according to the improved metabolic pathway. Consequently, actively regulating the balance of the intestinal microbiota community and metabolism will have a significant effect on preventing the deterioration and improving the prognosis of the disease. Our research will provide guiding significance for cirrhosis clinically in the future.JZ and XW designed research. XW, JZ, XJ, XZ performed research. HL, WL, and RF contributed analytic tools. XW, JZ, XJ, XZ, and HL analyzed data. XW, JZ, XJ and JY wrote the paper.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.


Novel synthetic biology tools or multi-step bioprocessing  are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding  have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses  to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and  As the fields of synthetic biology have advanced, biological production has become a promising option for manufacturing various chemicals and pharmaceuticals. In principle, any compound with a defined synthesis pathway could be produced from cheap feedstock by an engineered microbial culture. Unfortunately, while recombinant microorganisms have been generated to produce some molecules, their productivity rarely reaches sufficient levels for commercialization . One keycis,cis-muconic acid) [E. coli strains which together produce high-value chemicals such as flavan-3-ols [Microbial consortia can break up metabolic load among partners –13. In tic acid)  as well ic acid) . Synthetan-3-ols , curcumian-3-ols , and antan-3-ols . CurrentDesulfovibrio vulgaris and Methanococcus maripaludis was developed for methane production from organic acids [Microbial communities are ubiquitous in natural environments and are key players in global carbon and nutrient cycles . Severalic acids . The paric acids .Fig. 2InPichia stipites and immobilized Zymomonas mobilis together for ethanol production) [Salmonella enterica evolved to secrete methionine, a costly amino acid, for an E. coli strain, while the E. coli evolved novel secretion of sugar to feed S. enterica [Due to the complex interactions and dynamics of species within a community, consortia maintenance and stability is crucial for any successful applications Fig. . Differeduction) . Fifth, duction) . Moreoveenterica , 25.2 and cellulosic biomass. Synthetic consortia composed of bacteria and fungi are a particularly promising method for utilizing agricultural wastes to achieve waste-to-fuel/waste-to-food processes [Microbial communities have been extensively studied for microbial ecology and waste treatment. Industrial biotechnology has applied the same principles to increase bioproduction from cheap feedstocks such as COrocesses .Saccharomyces cerevisiae was engineered to carry mini-cellulosomes to give cells the ability to simultaneously break down and ferment cellulose to ethanol. However, this engineered strain only achieved a low titer of ethanol (~ 1.8 g/L) [Clostridium thermocellum, can be a CBP platform for ethanol production, but they have low ethanol yields and resistance to genetic modifications. To overcome drawbacks associated with single-organism CBP, synthetic co-cultures are developed, where cellulose is fed into the system and the product of interest is synthesized using two different organisms: a cellulose degradation module that secretes cellulases and a product synthesis module that consumes the resulting glucose to make the product . Trichoderma reesei and Rhizopus delemar can also be coupled to convert cellulose to fumaric acid (6.87 g/L and 0.17 w/w yield) without supplemental cellulase enzymes [Consolidated bioprocesses (CBP) aim to directly convert lignocellulosic biomass to biofuel. Attempts have been made to engineer a super bug that can convert cellulose to ethanol or other fuels. In one attempt, 1.8 g/L) . In contuct Fig. . For exa ethanol . More reoculture . CBP for3.73 g/L . Anotheroduction . The fun enzymes . In anot enzymes .Fig. 3CoE. coli and Saccharomyces cerevisiae has been developed, wherein E. coli  generate acetate as waste product; S. cerevisiae consumes the acetate and reduces its inhibition of E. coli growth [S. cerevisiae strains can ferment glucose–xylose–arabinose mixtures [For cellulosic feedstock, xylose derived from the hemi-cellulose component cannot be used by model yeast platforms. To address this, a xylose-fed co-culture of i growth . Similarmixtures .2, and CO). For example, Citrobacter amalonaticus can assist Sporomusa ovata to produce acetic acid from carbon monoxide [2 has great potential. Algae, including both prokaryotic cyanobacteria and eukaryotic microphytes, can fix inorganic carbon for bio-production. However, algal growth and genetic tool development have lagged far behind that of classic heterotrophic hosts like E. coli and S. cerevisiae [Synechococcus elongatus PCC 7942, which heterologously expresses the proton-sucrose symporter cscB. Under conditions of osmotic stress, this cscB+ strain irreversibly secretes up to 85% of fixed carbon as sucrose [E. coli, Bacillus subtilis, and S. cerevisiae), soil bacteria , and diazotrophs (Azotobacter vinelandii) to produce a variety of products without additional sugar input [Haematococcus pluvialis with B. subtilis demonstrated a coupling of oxygenic photosynthesis with respiration, such that neither external CO2 nor O2 supplies were necessary, suggesting that some of the overhead costs associated with autotrophs could be reduced in a properly-configured production system [Chlorella minutissima with E. coli can increase neutral lipid production and improve biodiesel quality [2-to-fuel technologies.Consortia can serve as platforms for utilization of C1 substrates  is produced via a two‐step fermentation process to reduce costs and increase product quality. In the second step, a mixed fermentation consisting of a production strain (Ketogulonicigenium vulgare) and a companion strain (e.g. Bacillus spp.) is used to convert l‐sorbose to 2‐keto‐l‐gulonic acid and ascorbic acid [K. vulgare and the companion strain have been studied, and it was found that the companion strain secreted metabolites to support K. vulgare growth  while K. vulgare can also inhibit growth of the companion strain by lysis of companion cells .Interacting partners can be designed to maintain stable interactions through detoxifying inhibitory substances. For instance, toxic CO . Co-cultbic acid . The intO-glucoside from glucose, which is functionally impossible via monoculture. Another advantage of microbial consortia is that the use of multiple species in one pathway can ensure that all genes are properly expressed [+/NADH and NADP+/NADPH) or active building blocks . While it is difficult to maximize every module simultaneously in a single strain, DoL can facilitate the functional expression of the complete pathway. With all the modules fully potentiated and the inter-module interference minimized by spatial segregation, DoL greatly improves pathway efficiency. A successful demonstration of such a method is the high-titer production of oxygenated taxanes in a S. cerevisiae–E. coli consortium. S. cerevisiae was used to express a P450 taxadiene hydroxylase , and E. coli was engineered for efficient taxadiene production. This co-culture generated 33 mg/L oxygenated taxanes, which were undetectable in either E. coli or yeast monoculture harboring the entire pathway [The introduction of long-step biosynthetic pathways in a single bacterial strain can cause severe metabolic burden due to the overwhelming consumption of cellular build blocks and ATP for enzyme synthesis. In contrast, dividing the pathway into multiple strains in a synthetic consortium can split the overall metabolic burden among the constituent strains, with each strain bearing significantly lower stresses  Fig. 4)4). Such xpressed . In the xpressed . Additio pathway . Divisio pathway , which i pathway . TheoretE. coli and S. cerevisiae, S. cerevisiae must be grown with acetate to avoid producing toxic ethanol [E. coli may form two distinct cell populations with acetate cross-feeding) [T. reesei and engineered E. coli consortia form stable cooperator–cheater dynamics [Culturing microbial workhorses as a community faces several key limitations. First, many microbial community systems are dynamic and cannot achieve long-term production stability. Applications of consortia systems require identifying both an optimal inoculation ratio and an ideal product induction time. Nevertheless, the stability of the co-culture is expected to decrease in large volume and long-term fermentation processes due to spatiotemporal dynamics inside of reactors , incompa ethanol . Since a ethanol . Further ethanol  Fig. 4)E. coli afeeding) . In mutufeeding) . For exadynamics . Interesdynamics .2, thus moving carbon into the desired metabolic pathway. A similar concept has been used to combine hybrid inorganic and biological organisms for solar-to-chemical production [Current community-based biomanufacturing mainly leverages natural systems and focuses on utilization of recalcitrant or cheap feedstock. In these desired platforms, metabolic interactions  can be robustly evolved. Still, there are few industrial applications based on the division of biosynthesis pathways among different species. For future DoL developments, a few approaches are promising. First, biosensors can be used to monitor and control cell sub-populations. In addition to quorum sensing for population monitoring, an RNA riboswitch-based biosensor module has been developed to provide real-time screening for overproducing cells in co-cultures . Biosensoduction , 60.E. coli and photoheterotrophic Rhodopseudomonas palustris cross-feed carbon (organic acids) and nitrogen (ammonium) to form a stable co-culture. Monod-based differential equations have been developed to reveal fermentation conditions for stabilizing mutualism [E. coli can be inhibitory and thus decrease carbon utilization efficiency and population equilibrium. For molecular-level understanding, functional genomics and high-quality metabolic modeling are needed to decipher cellular regulatory networks and integrated functions. For consortia, functional analysis can rely on typical microbiome tools, including targeted cell population analysis (such as 16S rRNA), metagenomic sequencing (DNA is recovered in an untargeted manner), metatranscriptomics , and metaproteomics . Microfluidic cell sorting can assist in the separation of individual species to study their DNA–RNA–Protein profiles under a community system. Moreover, measurement of metabolites within community systems can monitor the global community outcome and provide a snapshot of the general physiological state of microbes [There are many tools being developed to study consortia and DoL applications. Kinetic modeling is the most common method to analyze nutrient cross-feeding and microbial interactions . For exautualism , 62. Themicrobes , 63.Candidatus Endobugula sertula” and identify their metabolic deficiencies [Desulfovibrio vulgaris/Methanococcus maripaludis chemostat system [Clostridium cellulolyticum and the solventogenic Clostridium acetobutylicum, an adapted dFBA was used where the key parameters required are km and Vmax for cellulose solubilization and cell metabolism, as well as starting biomass concentrations for the two co-culture strains to set the input conditions for the growth dynamics [Mycoplasma genitalium [To date, methods applicable for analyzing metabolic fluxes between two species  are still rare. Currently, genome-scale models (GSMs) predict the feasible fluxes based on stoichiometry of the metabolic reactions, cellular objective functions, and constraints –68. Co-cciencies . Flux bat system . The moddynamics . dFBA cadynamics . This redynamics . In paradynamics , 75. Recdynamics . In addinitalium . Anothernitalium . Togethe13C-metabolic flux analysis (MFA) can measure intracellular carbon and energy fluxes. As a simple approach, MFA can treat co-culture as a single system and determine the bulk fluxomes of this system using traditional 13C-MFA approaches via labeling in protein-derived amino acids. This bulk MFA only describes the system as a whole, without metabolic exchanges. The full separation of metabolites from individual species is required to reveal subpopulation fluxes and interactions between two species. Separation of subpopulation is technologically difficult, even with fast cell-sorting. However, a “reporter protein” synthesized by a specific cell type can store 13C-fingerprints to investigate the microbial community. For mixed culture MFA, instead of detecting the 13C labeling patterns in amino acids from the total cellular protein, 13C patterns in amino acids from the reporter proteins can provide labeling information for each individual species. For example, photosystem I is a reliable reporter protein to probe symbiotic interactions in algae-heterotrophic bacterial communities [E. coli mutants. The reporter method quantitatively resolved the expected mutant-specific phenotypes down to subpopulation fractions of ~ 1% [13C-MFA has been developed to measure intracellular metabolic fluxes and inter-species metabolite exchange for complex microbial communities. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques, which can recover metabolic fluxes in the same way as through the standard amino acid-based 13C-MFA [13C-MFA is proposed by the Antoniewicz group, which determines metabolic flux distributions without the need for physical separation of cells or species-specific products via measurement of isotopic labeling of total biomass and elegant mathematical calculations. This approach can simulate both fluxes and the relative population size of each species in a mixed culture as well as inter-species metabolite exchange [munities . Specifimunities , 80. For of ~ 1% . Similar 13C-MFA . Anotherexchange .Division of labor via mixed cultures can theoretically reduce metabolic burden to enable a system to utilize recalcitrant feedstock or generate products which require long-step heterologous pathways. However, microbial consortia represent complex and dynamic systems that are difficult to operate. Community based strain engineering, metabolic modeling, and flux analysis tools are rapidly developed recently, which may facilitate the wide application of DoL concept for biomanufacturing.