Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines  from predicted annotation features  to the metabolic network-based analysis of symbiotic communities . While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases. Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. These bacteria are important for the brown algal host e.g., by promoting its growth and by providing defenses against pathogens  platform . Both the list of producible metabolites and the selected microbial communities were compared for all tested annotation pipelines. The detailed description of the code and data used for the above steps is available on GitHub: https://github.com/ElhamKarimi/Metabolic-predictions_different-Pipelines.The SBML files were utilized to predict potential metabolic complementarity between the algal host and the bacteria using the MiSCoTo tool . Based o2-acceptor, H2-donor). Finally, for each graph, strongly connected components were found with the Networkx strongly_connected_components function. Then, for each metabolic network, the ratio between the size of the largest strongly connected component and the total number of metabolites was computed.To gain insight into the quality of metabolic networks generated based on the different pipelines, dead-end metabolites were computed for each pipeline using Memote . FurtherIn addition to these indirect measures, we randomly chose 100 reactions that were exclusively found in one pipeline (20 per pipeline) for manual curation. Protein sequences of the bacterial genes that led to the prediction of these reactions were aligned with the Swissprot/Uniprot database using blastP and were then classified as high confidence , low confidence , or false .All statistical analyses were carried out using RStudio (v.1.1.463). Differences in the number of annotation features per pipeline were determined using an ANOVA followed by a Tukey’s HSD (honestly significant difference) test, considering each genome as a replicate. Nonmetric multidimensional scaling (NMDS) was performed on a presence/absence matrix comprising all reactions for each genome/annotation pipeline. The Bray-Curtis index was used as a measure of quantification of compositional dissimilarity between genomes annotated by different pipelines. To verify if the annotation pipeline had an impact on the reaction, an ANOSIM test (number of permutations = 9999) was performed using the Vegan package . UpSet d81 bacterial genomes were annotated using the Prokka, RAST, IMG, MaGe, and DFAST pipelines, and the number of predicted features (Coding Sequences (CDSs), Enzyme Commission (EC) numbers, hypothetical proteins) are shown for each genome and pipeline in Prokka predicted more EC numbers than the other pipelines, regardless of the e-value cutoff used. The precision of the predictions was similar in Prokka, IMG, MaGe, and DFAST with 85, 88, 84, and 85% of all EC numbers being complete. The only outlier was RAST, which provided only complete EC numbers . We did,Actinobacteria by DFAST were lower compared to other pipelines, and CDS prediction for Firmicutes by IMG were slightly higher. Finally, we examined three indirect measures of quality for the network reconstructions: the percentage of dead-ends, i.e., metabolites which can be produced but are not consumed; orphan metabolites, i.e., metabolites that are consumed but not produced in the metabolic networks; and the ratio between the size of the largest strongly connected components and the total number of metabolites, a direct estimate of the “connectedness” of the network. The percentage of dead-end metabolites was similar across all pipelines: 30.35% for Prokka, 29.66% for IMG, 29.17 for DFAST, 29.02% for MaGe, and 28.4% for RAST. The percentage of orphan metabolites was also similar: 30.63% for Prokka, 30.49% for IMG, 29.81% for DFAST, 30.37% for MaGe, and 29.48% for RAST. Lastly, this was also true for the ratio between the size of the largest of strongly connected components and the total number of metabolites: 26% for Prokka, 28% for IMG, 29% for DFAST, 27% for MaGe, and 29% for RAST.In terms of predicted metabolic reactions, IMG predicted the highest numbers (4.2% more than average) closely followed by Prokka with default settings (3.1%), while DFAST annotations resulted in the fewest reactions (7.7% fewer than average). Similar patterns were also obtained when running the analyses separately for the different bacterial phyla , except p < 0.0001 in both cases), but confirm that the effect was stronger for phylum (R = 0.93) than for annotation pipeline (R = 0.41).To determine how similar networks based on different annotation pipelines were, the Bray-Curtis dissimilarity, a unidimensional distance measure between two matrices or networks, was calculated for all network comparisons. Non-Metric Multidimensional Scaling (NMDS) was then used to display the resulting distance matrix in the two-dimensional space. In the resulting graph  the distTo further explore the differences in the metabolic network reconstructions and to highlight the specificities of each annotation pipeline we aimed to determine the conserved and unique content between the genome annotations from these different pipelines. This was done using the UpSetR package. Besides generating lists of reactions specific to different subsets of pipelines, this package also generates an UpSet diagram , which dTo get an overview of the dominant metabolic functions specific to the networks based on the different pipelines, we examined pipeline-specific metabolic reactions at the level of the pathways they participate in. In total, 180 pathways comprised the reactions specific to Prokka, and among them, 28 were constituted of >50% Prokka-specific reactions, including nitrobenzene degradation (PWY-5637) I, taurine degradation I (PWY-1263), albaflavenone biosynthesis (PWY-5887), and novobiocin biosynthesis (PWY-7287). Similarly, for MaGe the specific reactions could be associated with 127 pathways, and 11 were constituted of >50% specific reactions, including nitroethane degradation (PWY-5355), sulfite oxidation I (PWY-527), and sulfite oxidation IV (PWY-5326). For RAST 99 pathways including 6 pathways >50% complete presented the specific reactions, e.g., acetaldehyde biosynthesis II (PWY-6330) and trypanothione biosynthesis (TRYPANOSYN-PWY). IMG-specific reactions represented 52 pathways with only 4 >50% complete, including tRNA splicing II (PWY-7803) and aldoxime degradation (P345-PWY). Finally, reactions predicted specifically by DFAST corresponded to 42 pathways with only Nitrogen fixation II (PWY-7576) and Fatty acid biosynthesis (PWY-5970) being >50% complete .To evaluate the accuracy of the annotation pipelines, manual curation of 20 randomly selected reactions that were unique to each pipeline was carried out. DFAST has the highest number of high confidence assignations (10) followed by Prokka and MaGe (6), IMG(4), and RAST(1). RAST, on the other hand, did not produce any annotations that could be clearly identified as false, followed by Prokka (1), DFAST (2), IMG (3), and MaGe (3). In Prokka, increasing the stringency of the e-value cutoff did not affect the proportions of high confidence, low confidence or false reactions. In DFAST, although the correct reaction was associated with the gene, we observed discrepancies between the product name of genes and the EC number associated in 3 cases. In MaGe and IMG we found 1 and 3 predicted proteins without any similarity in the database and classified them as low confidence .To assess how these differences in the draft metabolic networks impacted the function of the predicted metabolism of algal–bacterial holobionts, we next examined the list of metabolites that could be produced by the algal metabolic network when combined with the 81 draft bacterial networks for each annotation pipeline . As shown in Pseudomonas 9AG, was included in minimal communities only when they were calculated from merged data from all annotation pipelines. Most strains were jointly selected by two or more pipelines, but even Prokka and IMG, for which minimal communities were based on the smallest subset of bacteria  had only six strains in common. However, we also found commonalities between the selected communities: all communities comprised at least one representative of the Actinomycetales (Actinobacteria), Bacillales (Firmicutes), Rhizobiales (Alphaproteobacteria), Sphingomonadales (Alphaproteobacteria), and Pseudomonadales (Gammaproteobacteria). Lastly, one strain, Maribacter strain 151 , was consistently selected in all data sets regardless of the annotation tool  to produce the highest proportion of high-confidence prediction, as well as the most connected networks in terms of dead-end metabolites. The overall high proportion of dead-end metabolites can be explained by the facts that (i) during the reconstruction process we discarded reactions without genetic support (gene association), and that (ii) metabolic networks underwent no or very little curation. Furthermore, the genomes analyzed here belong to non-model organisms, and hence their metabolism is more difficult to reconstruct than in well-established models.Given these differences, a key question is how reliable functional analyses based on these draft metabolic networks are, and how much their results change according to the annotation pipeline employed. Here our results may seem contradictory at first: on one hand, variability at the level of producible metabolites was low; on the other hand, during the selection of microbial communities based on metabolic complementarities, even these small differences in the draft metabolic networks resulted in largely different consortia. These small differences, whether they are erroneous or missing annotations, accumulate across the 81 annotated genomes and are particularly likely to impact the overall set of producible metabolites and hence community selection, as any possible cooperation with an added value to the host is selected. Indeed, although small, differences in the producible metabolites require complementing the bacterial community with strains that specifically enable the production of these metabolites. Overall, however, the low number of pipeline-specific metabolites indicated that all of the selected consortia were functionally similar. Furthermore, we have shown that a given minimal bacterial community (in our case one generated with Prokka data), also yields similar producible metabolites regardless of the annotation pipeline used for the generation of the metabolic networks. This means that the different selected bacterial consortia are likely able to fulfill the same or similar metabolic roles in our metabolic model. This phenomenon has previously also been described in natural alga-associated bacterial consortia  and was Regarding the use of metabolic complementarity as a criterion to select microbial communities, this implies that, as also confirmed experimentally by Another application of metabolic complementarity using different annotation pipeline could also be in the context of metagenome analysis, on Metagenome Assembled Genomes (MAGs) of uncultured bacteria. In the long run, continued and extensive experimental validation of bioinformatic predictions in both a culture and a metagenomic context will be key to evaluate and refine the pipelines  and need10.7717/peerj.11344/supp-1Supplemental Information 1The list of genomes used in the study and their properties (S1), the parameters used for genome annotation (S2), the EC numbers (S3) and reactions (S4) predicted in each genome/annotation pipeline, the pipeline-specific reactions (S5) and pathways (S6), the results of the manual curation for 100 reactions (S7), the metabolites producible by the cooperation of algal network and bacterial networks with each of the pipelines , and the results of the detailed outcomes of the ANOVA test corresponding to Fig. 1 (S10).Click here for additional data file.10.7717/peerj.11344/supp-2Supplemental Information 2The graphs show the deviation of each genome annotated with everyone pipeline from the mean across all pipelines in %.Click here for additional data file.


The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of 13C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO Howeverproducts , such asproducts , ethylenproducts , ethanolproducts , or omegproducts .2 limitations , allows for the characterization of in vivo fluxes of central metabolism in photosynthetic organisms , with the goal of capturing light-limited, light saturated and light-inhibited growth , a “pseudo-flux control coefficient” (PFCC), To further quantify the relationship between metabolic fluxes and individual enzyme abundances, the Pearson correlation coefficient and associated reaction . Based oi, around a reference state (–2 s–1 steady state. The non-linear fitting method (Synechocystis (p > 0.1), as it was not possible to perform any accurate non-linear fitting between J and E.In metabolic control analysis, a flux control coefficient, ce state . Typicalce state , and do g method  was usedg method , as theyhocystis . Intereshocystis , meaninghocystis , p > 0.113C labeling kinetics wherein downstream metabolites had higher 13C labeling than their precursors at the steady state condition. A proposed explanation for this phenomenon is that CBB cycle metabolites may be subjected to metabolite channeling, a phenomenon where enzymes are organized into complexes that pass metabolites to each other, thereby potentially increasing the local concentration resulting in higher enzymatic reaction rates , and significantly higher when light was either limited or inhibiting growth.Our results reinforce these observations, as we consistently found lower and RUBP . The preand RUBP  (Ma et aand RUBP . In gene–1 h–1 (assuming 0.0412 mol C gDW–1 based on the WT biomass equation). In all tested conditions, as expected, the majority of carbon (∼95%) was fixed from RuBisCO activity as opposed to other CO2 incorporating reactions such as PEP carboxylase. In general, it is difficult to statistically differentiate between small CBB cycle fluxes due to relatively large error bounds   , of whiclass II) . PKT catlass II) . The ballass II) .Synechocystis 6803, but the use of proteomics over transcriptomics represents an iterative improvement. The use of proteomics and fluxomics together presents an opportunity to examine correlations between fluxes and enzyme abundances. Based on statistical correlation analysis  between those conditions despite a similarly labeled RUBP   . To a lectively) . SimilarBacillus subtilus to enhance N-acetylglucosamine synthesis  expression was relatively insensitive to light level, a result corroborated in a recent proteomic study  and the small protein CP12, forming a multi-enzyme complex that appears to be conserved in both cyanobacteria and plants . This coactivity . Of thesr plants . Curiousic study . This coproteins .During light-inhibiting conditions, the production of radical oxygen species becomes significant and may promote PRK/CP12/GAPDH formation similarly to a low light or dark condition as reflected by the lower growth rate. Therefore, under light-inhibiting conditions where oxidative stress could be high, the formation of PRK/CP12/GAPDH complex can be an additional way to explain the reduced flux through PRK and GAPDH. The PRK/CP12/GAPDH complex has much lower activity as compared to the dimeric PRK or GAPDH alone . Based oSynechocystis central metabolism. In comparison with earlier INST-MFA flux maps performed under several different light conditions  was provided by a cold LED light . The light spectra is provided as a 730 0.1 to 0.7. 13C was introduced to the bioreactor after OD730 reached at least 0.6.PC media  with no 13C into cellular metabolism  were removed and quenched at separate time intervals  into 40 mL pre-chilled 60% methanol in a dry ice-ethanol bath and rapidly pelleted at 8000 g, −20°C and then extracted. The MATLAB-based software INCA v1.8  allows for estimation of flux distribution throughout photoautotrophic central carbon metabolism by tracking the transient incorporation of tabolism . NaH13CONCA v1.8  was usedNCA v1.8 . Flux es4OH (2 and then reconstituted in 60 μL ultrapure water. An QTRAP 5500 (AB Sciex) linked to HPLC (Shimadzu) was used to identify central carbon metabolites using an established ion-pairing method (Pellets were extracted using 500 μL chloroform/methanol (3:7 v/v) under −20 °C for 2 h followed by two rounds of 500 μL 0.2 M NH4OH . The uppg method  using a Immediately prior to labeling experiments, a 20 mL aliquot of culture was withdrawn and pelleted for use in non-targeted proteomics. Cell pellets were washed twice in 20 mM PBS and then stored at −80°C until sample preparation for proteomic analysis. Cell pellets were re-suspended in 400 μL of 100 mM ammonium bicarbonate containing 1 mM phenylmethylsulfonyl fluoride and homogenized at high pressure  at 6500 rpm for 90 s (Bertin Technologies SAS). Homogenate was transferred to new tubes and centrifuged at 13,500 rpm for 15 min at 4°C. The supernatant was transferred to a new tube and treated as a soluble fraction, and the pellets were treated as insoluble fraction. Proteins in the soluble fractions were precipitated overnight at −20°C using 4 volume of cold (−20°C) acetone and precipitated proteins were pelleted by centrifugation at 13,500 rpm for 15 min at 4°C. The soluble and insoluble fractions were dissolved in 8 M urea at room temperature for 1 h with continuous vortexing. The protein concentration was determined by Bicinchoninic Acid BCA assay and 50 μg of the protein  was used for proteomics sample preparation. Samples were incubated in 10 mM dithiothreitol at 37°C for an hour for reduction followed by incubation in 20 mM iodoacetamide for an hour at room temperature in the dark for alkylation. Samples were digested using Trypsin/LysC protease mix at 1:25 (enzyme to substrate) ratio at 37°C. After 3 h of digestion, additional Trypsin/LysC protease mix was added at 1:50 (enzyme to substrate) ratio and digestion was allowed to proceed overnight at 37°C. Peptides were desalted using C18 micro spin desalting columns . Eluted peptides were dried and re-suspended in 3% acetonitrile/0.1% formic acid to a final concentration of 1 μg/μL and 1 μL (1 μg) was loaded used for LC-MS/MS analysis.Peptides were analyzed in an Dionex UltiMate 3000 RSLC nano System coupled on-line to Q Exactive Orbitrap High Field Hybrid Quadrupole Mass Spectrometer  as described previously . BrieflySynechocystis sp. PCC 6803 database downloaded from the NCBI1. We edited the following parameters for our search: precursor mass tolerance of 10 ppm; enzyme specificity of trypsin/Lys-C enzyme allowing up to 2 missed cleavages; oxidation of methionine (M) as a variable modification and carbamidomethylation (C) as a fixed modification. False discovery rate (FDR) of peptide spectral match (PSM) and protein identification was set to 0.01. Proteins with LFQ >0 and MS/MS  ≥2 were considered as true identification and used for further downstream analysis. In total, 1,202 proteins were detected in at least one of the four tested conditions.LC-MS/MS data were analyzed using MaxQuant (version 1.6.3.4) against the 2  intensities were used. The intensities of the soluble and insoluble fractions were combined for each replicate. InfernoRDN2  was usednditions .https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp), under accession number MSV000087721.The original contributions presented in the study are publicly available. This data can be found here: the MassIVE repository (NY designed and conducted the isotopic labeling experiments, analyzed all the data, and wrote the manuscript. UA conducted the proteomic study including sample preparation, data acquisition, and analysis, and edited the draft manuscript. JM assisted with the experimental design, evaluated the experimental results, edited the manuscript, and acquired funding. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Clostridium acetobutylicum DSM 1731.The intracellular ATP level is an indicator of cellular energy state and plays a critical role in regulating cellular metabolism. Depletion of intracellular ATP in (facultative) aerobes can enhance glycolysis, thereby promoting end product formation. In the present study, we examined this s trategy in anaerobic ABE (acetone-butanol-ethanol) fermentation using atpAGD encoding the subunits of water-soluble, ATP-hydrolyzing F1-ATPase, the intracellular ATP level of 1731(pITF1) was significantly reduced compared to control 1731(pIMP1) over the entire batch fermentation. The glucose uptake was markedly enhanced, achieving a 78.8% increase of volumetric glucose utilization rate during the first 18 h. In addition, an early onset of acid re-assimilation and solventogenesis in concomitant with the decreased intracellular ATP level was evident. Consequently, the total solvent production was significantly improved with remarkable increases in yield (14.5%), titer (9.9%) and productivity (5.3%). Further genome-scale metabolic modeling revealed that many metabolic fluxes in 1731(pITF1) were significantly elevated compared to 1731(pIMP1) in acidogenic phase, including those from glycolysis, tricarboxylic cycle, and pyruvate metabolism; this indicates significant metabolic changes in response to intracellular ATP depletion.Following overexpression of C. acetobutylicum DSM 1731, depletion of intracellular ATP significantly increased glycolytic rate, enhanced solvent production, and resulted in a wide range of metabolic changes. Our findings provide a novel strategy for engineering solvent-producing C. acetobutylicum, and many other anaerobic microbial cell factories.In The online version contains supplementary material available at 10.1186/s12934-021-01639-7. Clostridium acetobutylicum is a model anaerobic bacterium and well-known for industrial production of organic solvents including acetone, butanol and ethanol (i.e. ABE fermentation). ABE fermentation involves typically two physiological phases, acidogenesis and solventogenesis. During the acidogenic phase, the bacterial cells produce energy via glycolysis, acetate and butyrate generation and ATP synthase, whereas during the solventogenic phase, the cells use glycolysis and ATP synthase for solvent production and energy metabolism, meanwhile re-assimilate the extracellular accumulated acids to generate solvents and survive acid stress [C. acetobutylicum.The fluctuation of petrol fuel supply and associated environmental issues place critical challenges to global economy, thereby motivating alternative of microbial production of biofuels and chemicals from renewable feedstocks , 2. Closd stress . TherefoC. acetobutylicum [1Fo-ATP synthase uses the transmembrane proton potential to synthesize ATP from ADP and it is a major ATP producer in a cell [1Fo-ATP synthase consists of a membrane integrated proton channel Fo and a cytoplasmic ATP hydrolase (ATPase) F1. Increasing the proportion of free-form F1 by reducing the expression of Fo or enhancing the expression of F1 could promote ATP hydrolyzation, reduce the intracellular ATP level, and consequently induce glycolysis. Previous studies demonstrated that in many aerobic cell factories decreasing intracellular ATP level forced the cells to maintain a higher substrate uptake rate, promoted glycolysis to regenerate NAD+, and therefore enhanced fluxes towards end products. Specially, deletion of the Fo-encoding genes atpFH disrupted oxidative phosphorylation and led to a two-fold increase of glycolytic flux in Escherichia coli [1 α, β and γ subunits remarkably decreased intracellular ATP/ADP ratio and resulted in 2.7, 3.0, and 1.2-fold increased glycolytic flux in E. coli, Lactococcus lactis and Lactobacillus plantarum, respectively [1 unit also increased the glucose consumption in Bacillus subtilis [Corynebacterium glutamicum [Glycolysis is a well-conserved metabolic pathway in nearly all types of organisms, and it is subject to regulation of many factors . Transcrutylicum . At metautylicum , such asn a cell , 8. The hia coli . Overexpectively –12. Incrsubtilis  and Coryutamicum . All theC. acetobutylicum DSM 1731 via overexpression of native F1-ATPase genes. As a result, we observed a remarkably enhanced glucose utilization as well as a substantially improved solvent production. Genome-scale metabolic modelling was employed to examine the metabolic changes at the network level. Overall, we have demonstrated that the strategy of intracellular ATP depletion can be applied in anaerobic cell factories such as C. acetobutylicum, to enhance glycolysis and improve end product formation.In the present study, we significantly decreased the intracellular ATP level of 1-ATPase is the water-soluble component of the F1Fo-ATP synthase and catalyzes ATP hydrolysis. It consists of α3β3γδε subunits (atpAGDHC) in C. acetobutylicum DSM 1731. Previous studies reported that the combination of α, β and γ showed the strongest ATPase activity [1-ATPase genes (atpAGD) were cloned and expressed in strain DSM 1731 with a constitutive thiolase promoter using plasmid pITF1  displayed a lower ATP level compared to its vector control 1731(pIMP1) during the entire batch fermentation. The ATP level in strain 1731(pITF1) peaked (35 µmol gDW−1) at 14 h, and rapidly declined to undetectable level thereafter; whereas the ATP level in 1731(pIMP1) peaked (61 µmol gDW−1) at 20 h and slowly decreased to 40 μmol gDW−1 at 30 h  compared to vector control throughout the initial 30-h fermentation ; whereas only 162 mM glucose was utilized by vector control strain 1731(pIMP1)  was achieved by 1731(pITF1) compared to vector control (9.03 mmol L−1 h−1) in the first 18 h. Similarly, the specific glucose utilization rate at 0–12 h was higher in 1731(pITF1) than that in 1731(pIMP1)  quickly declined to its minimum level, while control strain 1731(pIMP1) still remained a moderate glucose utilization rate till 27 h  was 17.2 at 18 h, which is 22.8% higher and 9-h earlier than that of 1731(pIMP1) (14.0 at 27 h)  was substantially higher than that of control at the beginning of fermentation  but remained similar from 6 h onwards  at 48 h of the batch fermentation  at 12 h, respectively, which were 54.4% and 33.5% less than those in control  were 10.3 and 16.7 mM, respectively, whereas the corresponding concentrations were 17.0 and 23.0 mM in 1731(pIMP1). Notably, an earlier re-assimilation of acetate and butyrate is evident in 1731(pITF1) compared with 1731(pIMP1) , 14.5% and 5.3% higher than those of 1731(pIMP1), respectively , representing 3.3%, 22.5% and 33.6% increases compared to those in control  was constructed for C. acetobutylicum DSM 1731 using genome annotation and literature. The model contained 766 genes, 775 metabolites and 1003 reactions, and it was used to compute the metabolic fluxes in strains 1731(pITF1) and 1731(pIMP1) during ABE fermentation from 0 to 30 h  were enhanced in acidogenic phase, and then substantially declined to low levels from 12 h onwards  might result in an alleviation of allosteric inhibition of PFK and PYK, thereby enhancing the activities of these two enzymes and increasing the overall glycolytic activity in acidogenic phase. Simulation results using model iCac20 indicate that 1731(pITF1) might have an increased flux through pyruvate ferredoxin oxidoreductase (POR) and an enhanced hydrogen production via hydrogenase (FDXNH) in acidogenic phase  produced abundant NADH, which then likely increased hydrogen formation. Furthermore, the tricarboxylic acid cycle (TCA) fluxes including those through citrate synthase (CS), aconitase (ACONT), isocitrate dehydrogenase (ICDHyr), were ~ 2.0 to 7.4-fold higher in 1731(pITF1) than those in vector control at 0–12 h, respectively, but were sharply declined to low levels thereafter  , the glycolytic fluxes of 1731 than that in control at 0–18 h, but was remarkably low at 24 and 30 h. Similarly, in 1731(pITF1) the fluxes through solventogenic reactions including acetoacetate decarboxylase (ADC), CoA transferases (COAT1/COAT2), alcohol dehydrogenases (ALCD and BUTOH) were significantly higher than those in 1731(pIMP1) at 0–12 h of fermentation.  started to decline, while the corresponding fluxes in 1731(pIMP1) achieved high levels.Within ABE fermentation pathway, the flux through thiolase (ACACT) was 1.3 to 2.1-fold higher in strain 1731(pITF1Fo-ATPase in 1731(pITF1) was 1.9 to 6.0-fold higher than 1731(pIMP1) at 0–6 h, but rapidly decreased at the following timepoints. The average NADH and ATP turnover rates remained at approximately 1.8–2.3 and 1.7–1.9 fold higher in 1731(pITF1) compared to 1731(pIMP1), respectively , the ATP hydrolysis flux (ATPM) was at a 24% higher level at 0 h, but remained at a similar or even lower level at the following time points compared to that in vector control, suggesting an initially enhanced ATP hydrolysis activity induced by overexpression of F1-ATPase subunit genes. Overall, our flux balance analysis (FBA) results indicate that overexpression of F1-ATPase genes resulted in a significant impact on bacterial metabolism not only ABE fermentation, but also many other pathways including glycolysis, TCA cycle and energy metabolism.In energy generation Fig. d, the ATely Fig. d, but de1) started to reutilize acids at 12 h, approximately 6-h earlier than vector control strain 1731(pIMP1)  and 18 h in 1731(pIMP1)  , ORP displayed a sharp decrease to its minimum value after 12 h and was remained at approximately − 340 mV till 27 h; whereas in 1731(pIMP1), ORP level slowly decreased to its lowest value at 30 h . Furthermore, alcohol (i.e. ethanol and butanol) formation is one of the major electron sinks during solventogenesis of C. acetobutylicum [1) was much higher than that of control in the first 12 h of fermentation  remained unchanged  were 2 and 3-fold higher than those in control, respectively  in concomitant with the significantly decreased intracellular ATP level.In ABE fermentation, solventogenesis starts along with re-assimilation of extracellularly accumulated acetate and butyrate, rising extracellular pH, decreasing culture oxidoreductive potential (ORP) . Strain P1) Fig. a. Togeth0 h Fig. b, indicautylicum , 22. TheC. acetobutylicum recently attracts extensive interests from both academic and industry owing to the limited supply of petroleum fuel and mounting environmental concerns [adhE1) [C. acetobutylicum; hence, inactivation of butyrate production might impair energy production and result in slow growth and low cell density [C. acetobutylicum.The biological production ABE process using anaerobe concerns , 24. Manconcerns , overexp [adhE1) , and scr [adhE1) ; however density . Overexp density . Enhanci density ; this maSaccharomyces cerevisiae for ethanol fermentation [E. coli for lactate production [1-ATPase enforced ATP hydrolysis, without directly competing glycolytic metabolites or diverging glycolytic fluxes to other pathways; it is thus more suitable for metabolic engineering ABE fermentation pathway in C. acetobutylicum.Different strategies were applied in aerobic microorganisms to reduce intracellular ATP level and reinforce glycolysis; these include construction of ATP futile cycles by overexpression of pyruvate carboxylase and phosphoenolpyruvate carboxykinase in entation , and oveoduction . NotablyC. acetobutylicum strain DSM 1731, F1Fo-ATP synthase is encoded by operon atpIBEFHAGDC. Previous studies has revealed that the membrane-bound proton channel Fo is formed by three subunits with the stoichiometry ab2c10–15, while the soluble F1 component consists of subunits α3β3γδε [1Fo-ATP synthase catalyzes ATP synthesis using the energy released from proton influx via oxidative phosphorylation; whereas F1 component has the secondary function of hydrolyzing cytosolic ATP through the action of a rotational mechanism. Overexpression of F1 subunit genes atpAGD  compared to control may be due to the hydrolyzed ATP was compensated by higher glycolytic (e.g. PYK) and acidogenetic fluxes   Fig. a, c whicC. acetobutylicum uses glycolysis, acid fermentation and membrane-bound F1Fo-ATP synthase to produce ATP. In the present study, the significantly reduced ATP level resulted in substantially increased glucose consumption, fast growth  had a 22.8% increase at maximal cell density compared to vector control 1731(pIMP1) and a significantly improved solvent volumetric productivity which is necessary to develop effective continuous butanol production [The traditional batch ABE fermentation suffers from low cell density and low reactor productivity, this could be due to the rapid accumulation of toxic byproducts  and/or the insufficient energy supply for bacterial growth . Anaeroboduction .adc, ctfA/B and adhE) [1) started acid-assimilation and solventogenesis 6-h earlier than vector control. In addition, with metabolic modelling, we identified many elevated metabolic fluxes during acidogenesis and early induction of solventogenic fluxes in 1731(pITF1)  was significantly improved with better cell growth and higher solvents titer, yield, productivity. High cost is a main limiting factor of commercializing ABE fermentation. The substrate consumption and solvent extraction account for > 30% of total fermentation cost [Clostridial ABE fermentation involves two physiological stages, namely acidogenesis and solventogenesis. In acidogenic phase, cells grow exponentially with the production of acetate and butyrate, thereby causing a dramatic decline of extracellular pH. Previous studies revealed that many factors including low pH, large amount of undissolved organic acids and significantly altered energy charge (ATP/ADP ratio) could induce the expression of solventogenic genes (e.g. nd adhE) , 31. In F1) Fig. . Overallion cost . The hig1-ATPase, we significantly reduced the intracellular ATP level in C. acetobutylicum industrial strain DSM 1731. The overexpression strain 1731(pITF1) exhibited higher cell density, enhanced glycolytic rate, early onset of solventogenesis, significant changes of many metabolic fluxes, and importantly, much improved solvent production than its vector control. Our study, for the first time, demonstrates that the glycolytic rate can be manipulated via altering ATP level in C. aceotbutylicum. This strategy can also be employed for metabolic engineering of other anaerobic fermentations.By overexpression of FC. acetobutylicum strain DSM 1731 and its derivatives  broth at 37 °C with vigorous shaking at 220 rpm [E. coli JM109 was employed for gene cloning. Shuttle vector was methylated by transforming E. coli ER2275 bearing plasmid pAN1. Ampicillin (100 μg mL−1) and erythromycin (50 μg mL−1) were used for screening and maintaining of plasmids whenever necessary. Cell growth was monitored by measuring the optical density at 600 nm (OD600) with a UV/Vis 2802PC spectrophotometer . 220 rpm . E. coli1 component genes, atpAGD (SMB_G2901-SMB_G2903), and thiolase promoter (Pthl) were amplified from DSM1731 genome using primers atpAGD-1: 5′-CGCGGATCCATGAACATAAAACCTGAAGAGATAACTTCA-3′, atpAGD-2: 5′-CCGGAATTCTTAGCTTTCCATCATTTTTTTAGCTTT-3′, thl-1: 5′-CGCGTCGACTATATTGATAAAAATAATAATAGTG and thl-2: 3′-CGCGGATCCTTCTTTCATTCTAACTAACCTCCTA. The fragments were then jointed and cloned into the shuttle vector pIMP1 [1. Insertion of DNA sequence was confirmed by sequencing. Plasmids pIMP1 and pITF1  was added for defoaming purpose. The reactor medium  was inoculated with 300-mL preculture of early log-phase (OD600 ~ 2.0). Broth pH was monitored by pH meter (Mettler-Toledo) and controlled above 5.0 by supplementation with 6 M ammonia automatically. Broth oxidoreductive potential (ORP) was measured by real-time ORP electrode (Mettler-Toledo). Extracellular metabolites from three independent bioreactors including glucose, butanol, ethanol, acetone, butyrate and acetate were determined by high performance liquid chromatography  [Three independent batch fermentations were performed in two 7.5-L BioFlo 110 fermentors  containing 3.0 L clostridium growth medium (CGM) , 39. AnaCA, USA) , 40.g for 1 min. The cell pellets were homogenized immediately in 200 μL ice-cold 7% perchloric acid and incubated on ice for 10 min. After centrifugation at 15,000×g for 5 min, the supernatant was collected and neutralized using 50 μL 3 M potassium hydroxide, 100 μL 0.4 M Tris and 50 μL 3 M potassium chloride. The mixture was vortexed thoroughly and subjected to another centrifugation at 15,000×g for 5 min to remove residual precipitate. The supernatant was then transferred to another sterile centrifuge tube for HPLC analysis. An ion-pair reversed-phase HPLC method was used to determine the intracellular adenine nucleotides level according to previous work [−1.Intracellular ATP and ADP were extracted with perchloric acid method as described previously  with minous work , 43. Agi+ were conducted as preciously described [g for 10 min at 4 °C. Then, 200 μL supernatant was collected and neutralized to pH 7.5–8.0 by adding 0.4 M hydrochloride acid. For NAD+, 400 μL ice-cold 0.4 M hydrochloride acid was added to 1 mL broth. The mixture was incubated at 50 °C for 10 min and centrifuged at 15,000 × g for 10 min at 4 °C. Then, 200 μL supernatant was collected and neutralized to pH 7.2–7.4 using 0.4 M potassium hydroxide. The neutralized samples were immediately used for NADH and NAD+ determination. The spectrometric enzymatic cycling assay was applied with slight modifications. The assay mixture contained 2 mL buffer , 400 μL phenanziniummethylsulfate (PES) (4 mg mL−1), 400 μL thiazolyl-blue (MTT) (5 mg mL−1), 70 μL ethanol, and 40 μL alcohol dehydrogenase (60 U mL−1). After addition of 50 μL neutralized sample into reaction buffer followed by a brief vortexing, absorption was examined using spectrophotometer for 10 min at 570 nm. Mass balance was calculated by summing up carbon amounts of metabolite products and biomass, and compared with glucose consumption. Specific carbon partition was calculated by normalizing the carbon molar amount of a produced metabolite with the carbon molar amount of the consumed glucose. For biomass, its molar carbon per gram dry weight (36.8 mM C gDW−1) was calculated by FBA (see below) with glucose uptake rate set as − 10 mmol⋅gDW−1⋅h−1.The extraction and determination of intracellular NADH and NADescribed  with sli−1, 2.5% of growth associated maintenance (40 mmol gDW h−1) according to previous modeling effort [The model construction started with collecting genome annotation (GCA_000218855.1) from GenBank and obtaining metabolic reactions from KEGG (Kyoto Encyclopedia of Genes and Genomes)  and BioCg effort .600 and external metabolite molar concentrations were employed to estimate the specific rates. First, a shape-preserving piecewise cubic Hermite function from shape language modelling (SLM) MATLAB toolbox  was used for data fitting of OD600 values and metabolite molar concentrations, followed by a numerical differentiation to compute the changing rates . Then the specific growth rates (µ), specific glucose consumption rates (rglc), and specific product secretion rates (rproduct) were calculated using equationsDW is calculated by 0.34 × OD600 as previously described [iCac20 with up to 10% variations. The optimal specific growth rate (µ*) was firstly calculated using FBA, and then the solution space was sampled with 1000 points using artificially centered hit-and-run (ACHR) algorithm [µ*. The obtained feasible solutions were adjusted by applying loopless FBA (ll-FBA) method [t-test was conducted to determine the differentially changed metabolic fluxes with FDR  adjusted P < 0.05. Metabolite turnover rates were calculated by summing up all the incoming or outgoing fluxes of the metabolite i [Our FBA is based on an assumption of pseudo steady state where the intracellular metabolite concentrations are invariant but the extracellular metabolite concentrates are variable; the exchange fluxes of extracellular metabolites were estimated from the batch fermentations and used as the constraints of FBA. Specifically, the ODescribed , 51 (Addlgorithm  with the) method  to avoidbolite i  using eqAdditional file 1: Fig. S1. Plasmid construction and overexpression of atpAGD genes in strain DSM1731. Fig. S2. NADH/NAD+ ratio of 1731(pIMP1) and 1731(pITF1) in anaerobic ABE fermentation. Fig S3. OD600 and the calculated specific growth rates of 1731(pIMP1) and 1731(pITF1). Table S1. Strains and plasmids used in this study. Table S2. Comparison of fermentation results after 48 h fermentation between engineered C. acetobutylicum strain and control strain.Additional file 2: Table S1. Specific metabolite consumption or production rate obtained by data fitting. Table S2. Specific growth rates obtained by data fitting. Table S3. Calculation of mass balance. Table S4. Average metabolic fluxes of 1731(pITF1) and 1731(pIMP1) in 0–30 h fermentation. Table S5. Mean and SD values of the computed NADH turnover rates for 1731(pIMP1) and 1731(pITF1) at 0, 6, 12, 18, 24 and 30 h.


Escherichia coli as an example of metabolic engineering. These principles also apply to fermentation of lignocellulosic sugars to other chemicals that are currently produced from petroleum.The finite nature of fossil fuels and the environmental impact of its use have raised interest in alternate renewable energy sources. Specifically, nonfood carbohydrates, such as lignocellulosic biomass, can be used to produce next generation biofuels, including cellulosic ethanol and other nonethanol fuels like butanol. However, currently there is no native microorganism that can ferment all lignocellulosic sugars to fuel molecules. Thus, research is focused on engineering improved microbial biocatalysts for production of liquid fuels at high productivity, titer, and yield. A clear understanding and application of the basic principles of microbial physiology and biochemistry are crucial to achieve this goal. In this review, we present and discuss the construction of microbial biocatalysts that integrate these principles with ethanol-producing  Availability of inexpensive fossil fuels, such as petroleum, natural gas, and coal, has positively transformed human culture and the world economy. However, the rate of increase in the use of this limited resource by an expanding population is causing environmental changes at the global level  to higher molecular weight products, and resist inhibitors generated during chemical processing of biomass , ferment diverse soluble saccharides and sugars , ferment gases CO, H to higheass Fig. . New micS. cerevisiae dominates alcoholic fermentation of food and feed carbohydrates owing to its specific physiological characteristics  and its corrosive nature. To overcome this limitation, fermentation of sugars to next-generation liquid fuels  requires a new class of microbial biocatalysts that, in addition to the desired physiological characteristics of yeast, have properties required for cost-effective production of these chemicals. In this review, the principles of microbial physiology that govern the production of chemicals as primary products of anaerobic metabolism at high titer and yield are discussed. The aim is to provide a physiology-based roadmap for designing and developing microbial biocatalysts with ethanol as the model product due to its simplicity. These basic tenets apply to the design and construction of microbial biocatalysts for production of other next-generation fuels at high titer, yield, and productivity.2. Of this total, fossil fuel sources  contributed 80% and renewable fuels  provided only 5% of the total (EIA-DOE). These energy reserves (fossil or renewable) are derivatives of photosynthesis that capture atmospheric CO2 and reduce it to various redox states utilizing water and solar energy , the common organic energy source used by biological systems, releases 2,81 MJ/mol energy upon complete combustion to 6 CO2 (ΔHc) and the presence of oxygen in the molecule reduces the energy content of glucose by about 33% compared to an analogous fully reduced six carbon compound, hexane 6. It should be noted that although hydrocarbons are produced by microorganisms by fermentation of glucose, the redox state of carbon in all products combined cannot exceed that of starting glucose-C. This dictates that for every product that is more reduced than the feed glucose, there is another co-product that is more oxidized than glucose in the final product profile of an anaerobic microorganism. In the fermentation of glucose to ethanol (Reaction 3O0.5)2 (redox state of −2) than glucose (CH2O)6 (redox state of 0) and this redox difference is offset by the co-product CO2 (redox state of +2) that is more oxidized than glucose. In 2017, 85% of energy consumed in the United States of America was derived from oxidation of carbon to COrgy Fig. . Carbon 2. However, fermentation of glucose to ethanol (ΔHc of −1.37 MJ/mol × 2) conserves about 97% of the energy in glucose  and is the most efficient conversion of energy from glucose to a fuel molecule to date. Other aliphatic alcohols such as butanol  due to the co-product, CO2 from outside to balance the overall redox stoichiometry. For example, glucose can be hydrogenated to sorbitol and hydrogenolysis of sorbitol can yield hexane at high yield with H2 as the reductant  but not necessarily with low energy yield. This lower product yield on a mass basis  is a result of anaerobic metabolic pathway limitations  and productivity .YATP), defined as the amount of cells produced per mole of ATP hydrolyzed to ADP and Pi, and is about 10.5 g dry biomass/mol ATP for E. coli and other bacteria   Payne, . However2 concentration, eliminating terminal oxidase by mutation, overexpressing appropriate metabolic pathway(s) toward the desired product, etc. . This diversion of carbon to products results in lower ATP yield and cell growth with an associated increase in glycolytic flux to compensate for this reduction in ATP production  is also lower compared to an aerobic culture  and very little energy (∼70 kJ/mol glucose) is available to support growth. Taking a ΔG° value of −31.5 kJ/mol for ATP hydrolysis to ADP and inorganic phosphate   and declines with declining growth rate at late-exponential and stationary phase of growth  compared to yeast with an ATP yield of 2 per glucose , glycolysis (conduit), a set of reactions that convert the activated sugar to pyruvate while generating ATP and reductant, and conversion of pyruvate to product(s) (sink), such as ethanol to maintain redox balance Fig. . These t2) is −73.8 kJ/mol (Reaction RTlnK and the temperature at 25°C and the gas constant (R) at 8.31 J/(deg mol), the rate constant of this overall reaction (K at standard reaction conditions) can be calculated to be 77.6.From a thermodynamic/kinetic point of view, the specific productivity is dependent on the ΔG  of the overall reaction, glucose to product. The calculated ΔG° value for the conversion of medium glucose to 2 ethanols  from the cytoplasm. By lowering the ATP yield during glucose fermentation from 2 to 1 as the case with Z. mobilis, the rate constant can be greatly improved with a higher glycolytic flux  also plays a critical role in supporting high specific productivity and high yield   high-density cultures transferred to fermentation conditions have low ATP requirement, like stationary phase anaerobic cultures. As an example, Clostridium thermobutyricum growing anaerobically and producing butyrate as a primary product, maintains a specific productivity of about 1.1–1.3 g/(g DCW hr) during the growth phase  Payne, . A possiOne of the unanswered problems of industrial fermentation is sustaining high volumetric productivity of late-exponential growth phase cultures to stationary phase and to high cell density cultures for high product titers to lower residence time in industrial fermentations. One approach is to engineer the microbial biocatalyst for high ATP yield during growth phase and low ATP yield at the end of growth to support high rate of product production at high cell density. It has been demonstrated that functional ATP futile cycles (as well as ATPases) do increase productivity of cultures  in United States in 2018 , is produced using this microorganism. This can be attributed to its optimized metabolic pathway from sugars to ethanol, in addition to having several other industrial advantages . This strain contained two foreign genes; pdc and adhB encoding pyruvate decarboxylase (PDC) and alcohol dehydrogenase II (ADH-B), respectively, from Z. mobilis  (Table These two enzymes constituted a new ethanologenic pathway that increased the ethanol fraction in the fermentation products of e) Table . PDC catE. coli strains KO3 and KO4 used pyruvate and derivatives of pyruvate  as electron acceptors and the concentrations of the enzymes, PDC and ADH, are crucial to direct the carbon to ethanol and away from production of lactate and other fermentation products. Although strain KO11, a frd mutant of strain KO4, that lacks fumarate reductase and thus could not produce succinate as a fermentation product, fermented glucose to ethanol at almost 100% conversion efficiency when cultured in rich medium, the PDC and ADH levels were found to be limiting for ethanol productivity and growth when this strain was shifted to mineral salts medium to lower industrial production cost  . High sugar concentrations can induce osmotic stress that limit productivity due to lower growth rate unless the cell compensates for this by producing osmoprotectants. Osmotic stress is introduced by an imbalance in water activity across the cell membrane by high solute concentration in the immediate vicinity of the cell membrane compared to the cytoplasm. If the sugar is rapidly consumed by the fermenting cell at a rate that is higher than the rate of diffusion to the cell membrane surface, the osmotic stress exerted by that sugar in the bulk medium could be minimal. This suggests that a sugar that is rapidly fermented, such as glucose, will exert less of an osmotic stress on the cell than a sugar that is fermented at a lower rate, such as xylose, at the same molar concentration  exerts osmotic stress on ethanologenic E. coli is readily applicable to other microbial biocatalysts designed for production of other biofuels. The main difference between ethanologenic and other microbial biocatalysts is the change of the “Sink” from ethanol to butanol or other primary product of choice. Adaptive metabolic evolution of the synthetic microbial biocatalyst for high productivity and titer is an essential component in the development process of an effective microbial biocatalyst as this allows the selection of “best fit” for biofuel production. Physiological insights from genome scale analysis of these adapted microbial biocatalysts can be combined in the development of microbial biocatalysts with higher productivity.The physiological information gained during the development of an effective ethanologenic E. coli are being adapted to grow in the presence of acid hydrolysis derived inhibitors  or by mild acid treatment at elevated temperatures  in which NADH is used for reduction of furfural and NADPH is used for biosynthesis reduced the severity of this inhibition  starts only after the preferred sugar is exhausted from the medium or is growth rate-limiting in a medium containing two or more C-sources. Almost all secondary sugars require one or more enzyme-catalyzed steps before the metabolic intermediate can enter the EMP-pathway and production of these enzymes, including transport, accounts for the observed lag before consumption of the second sugar commences (Diauxie)  or lower medium concentration, glycolytic flux is apparently operating at a lower than the maximum achievable level and xylose provides the additional glycolytic intermediates to maximize glycolytic flux.These results suggest that glucose by itself supports the highest glycolytic flux in ethanologenic E. coli strains by increasing the level of PDC/ADH  acts as a valve to remove the excess DHAP to optimize overall glycolytic flux  and lower  parts of glycolysis and is derived from dihydroxyacetone-phosphate (DHAP). This imbalance can lead to accumulation of DHAP and methylglyoxal synthase  Baker, , it is r1.1O0.35)10], compared to sugars (CH2O)n would dictate a lower yield or require input of a reductant such as H2 in the production of energy rich fuel molecules, like ethanol, (CH3O0.5)2 making lignin unattractive for fuel production. However, lignin can and continues to serve as a source of various fine chemicals and aromatics including vanillin  than glucose (CH2O), the additional reductant can be used for synthesis of other primary products of fermentation that are more reduced than ethanol, such as butanol, at close to the theoretical yield   where supply overwhelms demand. Significant amount of methane that has a greater global warming potential than CO2-limited environment while aerobic metabolism is utilized for production of several other chemicals  but the concentration of O2 at each cell surface and this needs to exceed the rate of consumption (respiration) by the microorganism. Due to the difficulty of maintaining uniform dissolved O2 concentration in large industrial tanks, localized anaerobic/O2-limited microenvironments develop leading to fermentation-derived co-products. These co-products, such as acetate, in addition to contaminating the product of choice, also has the potential to inhibit growth and productivity of the microbial biocatalyst. It is anticipated that metabolic engineering strategies for production of all bulk chemicals, including alternate fuel molecules, will include the advantages of anaerobic industrial fermentation to minimize cost of production of the desired chemical at the highest productivity and yield.As discussed above, primary products of fermentation are derived in an anaerobic or OMicroorganisms play critical roles in the global biogeochemical cycles. They are involved in our energy cycles by supporting plant growth and thus biomass production, by degrading biomass to sugars through enzyme hydrolysis and by fermenting sugars to products. Based on the principles derived by Darwin more than 150 years ago, the primary metabolic activity of all living systems is to support the highest rate of growth of the organism in the specific environment, and thus, it stands to reason that in a closed anaerobic system, the highest productivity would be for the terminal product of this metabolic activity. Production of potential fuels, such as ethanol, is dependent on a balanced redox state, without which high rates of growth and productivity are not possible. An understanding of the innate physiological principles of microbial energy production/consumption and redox balance is crucial for optimal metabolic engineering of appropriate microbial biocatalysts to extend the catalog of fermentation products that can serve as liquid transportation fuels. The material presented here is intended to stimulate our thinking toward development of improved biocatalysts for cellulosic ethanol that can withstand the inhibitors generated during acid hydrolysis of biomass, ferment the multitude of sugars derived from the biomass efficiently without product inhibition and toxicity and also producing glycan hydrolases that can lower or eliminate the need for commercial enzymes, preferably using fermentation reactions rather than aerobic processes. The design of these microbial biocatalysts would be based on the physiological requirements described above and will employ the tools of synthetic and systems biology with the aim to produce next generation bio-based fuels and chemicals at commercially viable productivity, titer, and yield.


The liver metabolizes a variety of substances that sometimes interact and regulate each other. The modeling of a single cell or a single metabolic pathway does not represent the complexity of the organ, including metabolic zonation (heterogeneity of functions) along with liver sinusoids. Here, we integrated multiple metabolic pathways into a single numerical liver zonation model, including drug and glucose metabolism. The model simulated the time-course of metabolite concentrations by the combination of dynamic simulation and metabolic flux analysis and successfully reproduced metabolic zonation and localized hepatotoxicity induced by acetaminophen (APAP). Drug metabolism was affected by nutritional status as the glucuronidation reaction rate changed. Moreover, sensitivity analysis suggested that the reported metabolic characteristics of obese adults and healthy infants in glucose metabolism could be associated with the metabolic features of those in drug metabolism. High activities of phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphate phosphatase in obese adults led to increased APAP oxidation by cytochrome P450 2E1. In contrast, the high activity of glycogen synthase and low activities of PEPCK and glycogen phosphorylase in healthy infants led to low glucuronidation and high sulfation rates of APAP. In summary, this model showed the effects of glucose metabolism on drug metabolism by integrating multiple pathways into a single liver metabolic zonation model. The liver performs hundreds of vital functions such as metabolism of exogenous and endogenous compounds, bile production, and maintenance of blood levels of many substances, including albumin, glucose, amino acids, and vitamins . AnatomiN-acetyl-p-benzoquinone imine (NAPQI) by cytochrome P450 2E1 (CYP2E1), which is detoxified and eliminated through glutathione (GSH) conjugation. In adults, glucuronidation and sulfation play a major role in APAP metabolism, and approximately 5% of the substrate is converted into NAPQI (Acetaminophen (APAP) is one of the most commonly used over-the-counter medications; however, its overdoses can lead to hepatotoxicity and acute liver failure . APAP isto NAPQI . Sulfatito NAPQI . GSH is to NAPQI .Glucuronidation accounts for the majority of APAP metabolic pathways in healthy adults . Interesin silico approaches as complementary or promising alternatives to existing in vivo and in vitro approaches. The first mathematical model for APAP metabolism  with NumPy library . The blood concentrations of glucose and APAP under different conditions, such as fasting, feeding, and moderate- and over-doses, were determined according to previous reports . The dynamic simulation reproduces the time-dependent transitions of metabolic concentrations but requires many parameters. For instance, the reversible Michaelis–Menten rate equation (commonly used for dynamic metabolic models) requires at least four parameters per enzyme reaction, leading to hundreds of parameters to construct a single metabolic model. Due to the limits of experimental data availability, optimizing many parameters is difficult. Meanwhile, MFA does not require any kinetic parameters. However, as it assumes that the entire system is at a steady state, a simple MFA cannot simulate any temporal changes in metabolite concentrations. Therefore, we used a hybrid approach in this study to compensate for the shortcomings of each method . Brieflyx is the variable vector representing metabolite concentrations (n × 1), S is the stoichiometry matrix (n × m), and v is the reaction rate vector (m × 1), which is a function of x. We divided the system into two modules: dynamic and static. The dynamic module contains key reactions that are bottlenecks of dynamic behaviors. The static module contains fast, non-bottleneck reactions. We divided v into reaction rates in the dynamic module (vd) and the static module (vs). We divided x into the variables only associated with dynamic module reactions (xd) and those with one or more static module reactions (xs). We divided S into three matrices: Sd,d, Ss,d, and Ss,s. Sd,d is the stoichiometry matrix for the dynamic module variables and dynamic module reactions, Ss,d is for the static module variables and dynamic module reactions, and Ss,s is for the static module variables and static module reactions. Our model has 13 variables and 27 reactions in the dynamic module and 21 variables and 20 reactions in the static module. The incorporation of the new symbols allows the previous equation to be written as:In general, the dynamic behavior of a metabolic pathway is modeled by the following differential equation.d,d, Ss,d, and Ss,s values are given. vd is a set of kinetic rate equations such as mass actions and Michaelis-Menten rate equations, i.e., vd is a function of xd and xs. Thus, the right-hand side of the upper equation can be calculated.STo calculate the right-hand side of the following equation, we assume the static module is at a steady state at each time step in a simulation. Thus, the following equation becomes:vs to satisfy the previous equation. Instead, we can obtain vs, by the following equation:s,s. vs in the second equation with xd and xs.In the over-determined case (including our model), there is no solution space for c module . ReplaceSulf), oxidation (vCYP450), glucuronidation (vUGT), and GSH conjugation (vGSHT) were zero. In contrast, metabolite concentrations and reaction rates related to glucose metabolism changed in response to the blood glucose level and reproduced zonation patterns with the values consistent with the liver (GK), fructose-2,6-bisphosphatase (vFBP2), aldolase (vALD), glyceraldehyde-3-phosphate dehydrogenase (vGAPDH), and pyruvate kinase (vPK) compared with the periportal region, and the positive lactate dehydrogenase reaction rate was indicative of glycolysis promotion and lactate (Lac) production (FBP1) and phosphoenolpyruvate carboxykinase in the cytosol , and glucose-6-phosphate phosphatase in the endoplasmic reticulum  compared with the pericentral region (PEPCKmito) was higher in the pericentral region  glucose concentration and the change of glycogen concentration over time was investigated to further validate the glucose metabolism model . The gluSulf), oxidation (vCYP450), and glucuronidation (vUGT) (Sulf) was higher in the periportal region than that in the pericentral region, while vCYP450 and vUGT showed the opposite tendencies. A prominent increase of protein adducts leading to hepatotoxicity was observed in the pericentral region due to low GSH concentration and sulfation activity. Indeed, over-administration of APAP resulted in GSH depletion. Overall, our model was consistent with the existing observations in the pericentral region (Cellular APAP concentration increased with its administration in the constructed model . After 2n (vUGT) . The ratl region .Finally, the contribution of each APAP metabolic reaction was investigated . Accordimax) of PEPCK and G6P by 50% in our model and investigated the effects on APAP metabolism. Interestingly, increased PEPCK and G6P activities influenced APAP metabolism in only two cases out of eight (bold letters in It is widely known that obese and healthy individuals have different enzyme activities. For example, PEPCK and G6P activities are increased in obese individuals . Thus, wInfants have low PEPCK and glycogen phosphorylase activities and high GS activity compared with adults . SulfatiIn this study, we integrated multiple metabolic pathways including drug and glucose metabolism into a single numerical liver zonation model and validated it with the results and observations of previous reports in a semi-quantitative manner. Then, we investigated the possible crosstalk between those pathways. The model showed the effects of glucose metabolism on drug metabolism in different contexts, which indicated the importance of integrating multiple metabolic pathways.The glucose metabolism model consisting of glycolysis, gluconeogenesis, and glycogen metabolism was determined by adopting and modifying previous rate equations . APAP mein vivo observation and demonstrates the advantage of simultaneously considering multiple metabolic pathways.The model showed that drug metabolism was affected by nutritional status . The nutin vivo observations and the complementarity of in vivo and in silico approaches. We also investigated the case of healthy infants in our model by adjusting some of the glucose metabolism enzymatic activities to reproduce healthy infant characteristics according to previous reports (Our integration model was also useful for proposing hypothetical insights into the mechanisms of metabolic features involving glucose- and drug metabolism. Increased activities of PEPCK and G6P in glucose metabolism are reported in obese patients . Meanwhi reports . The adj reports . Althougin vivo observations in drug metabolism in obese patients. To overcome these limitations, the modification of boundary conditions and parameters in drug metabolism based on actual measurements may lead to better prediction. The contribution of fatty acid metabolism is another challenge, especially for the better reproduction of energy metabolism in obese patients (in vivo and in vitro experiments, contributing to a better understanding and prediction of complex biological systems (The study has some limitations. The model successfully reproduced periportal and pericentral characteristics of metabolic zonation regarding many metabolites and reaction rates in APAP and glucose metabolisms. Nonetheless, such validations were performed by comparing the present results with the results and observations of previous studies in a semi-quantitative manner, and we performed these validations using a limited number of conditions regarding glucose and APAP blood concentrations. The model contains only 13 zonation-dependent kinetic parameters , and metpatients . To undepatients . Conside systems .To conclude, we integrated multiple metabolic zonation models into a single numerical human liver model and validated it with the results and observations of previous reports in a semi-quantitative manner. This model showed that changes in the enzyme activities of glucose metabolism affected APAP metabolism, and the crosstalk effects were dependent on liver zonation and nutritional status. These results showed the importance of incorporating multiple metabolic networks in a single zonation model.


Following a renewed commitment to build a sustainable bioeconomy, there has been a heightened shift in interest from the production of petroleum based chemicals to biomanufacturing. To make biomanufacturing competitive with chemical synthesis, it is important to develop microbial cell factories that can efficiently utilize cheap and readily available feedstocks. These feedstocks are then metabolized to yield value-added products in high titres that can be easily scaled up to commercial quantities. Advances in synthetic biology have paved the way for efficient microbial design and improvement to reduce the cost and time of feedstock bioprocessing. These cell factories can further be optimized by fine-tuning relevant metabolic pathways utilizing the iterative and systematic Design-Build-Test-Learn (DBTL) cycle . CurrentIn this research topic, we highlight a broad range of reviews and original research covering several applications of genomic strategies  being apWongsurawat et al. where the authors provide a comparative study of transcriptomic analyses using direct RNA sequencing (dRNA-seq) or direct cDNA sequencing (dcDNA) reads. While dRNA-seq is cheaper and simpler to perform, dcDNA-seq provides a higher accuracy as reads are longer.Transcriptomic data is essential in understanding global gene expression of microbial hosts under different condition . This daGao et al. demonstrate the production of friedelin  in Saccharomyces cerevisiae by integrating the endogenous pathway genes of friedelin into S. cerevisiae. To increase the friedelin yield, competing pathways were knocked out by applying the CRISPR/Cas9 system. Using this genomic strategy, Gao et al. were able to significantly increase the yield of friedelin produced which is the highest reported titre in yeast to date. It will be interesting to see future work on the production of such triterpenoid compounds from cheap feedstocks such as lignocellulose.As mentioned, development of genome editing tools continues to play a key role in genome-wide engineering of microbial hosts for the production of value added compounds. S. cerevisiae are unable to utilize most of the sugars in hydrolyzed lignocellulose. Xylose which is abundant in lignocellulose cannot be metabolized as a sole carbon source by S. cerevisiae unless a non-native xylose isomerase and xylose reductase pathway is engineered in this yeast  metabolism in yeast where the focus is on engineering S. cerevisiae to assimilate these polymers to produce bioethanol. Both extracellular utilization through cell surface engineering and intracellular transport followed by polymer degradation have been exploited. In some cases, engineering S. cerevisiae to express non-native xylanases from bacteria and other fungi has been reported to result in a non-specific metabolic burden. Hence, complementing rational engineering with adaptive laboratory evolution presents a promising genomic strategy for efficient yeast engineering to convert complex lignocellulosic sugars into bioethanol.Although lignocellulose is cheap and readily available, industrially relevant microbial hosts such as is yeast . To addrChen et al. highlight the potential application of genome engineering tools such as CRISPR in diatoms. Furthermore, the first sequenced genome of a diatom is discussed which has made it possible to build genome-scale metabolic models for diatoms. In order to fast track the development of diatoms as suitable microbial cell factories, future work should go into developing high quality genome annotations essential for building more comprehensive metabolic models in diatoms. In well-studied organisms like E. coli and S. cerevisiae, genome-scale metabolic models have played a key role in guiding metabolic engineering strategies . Thus, metabolic engineering of diatom cell factories will benefit from such metabolic models.Lastly, expanding the repertoire of high-value compounds produced by microbial cell factories will require exploiting non-conventional microbial hosts in order to tap into the unique biochemistries of their metabolic pathways. In that regard, microalgae such as diatoms offer excellent opportunities that have not been fully explored. Having an innate ability to produce large amounts of lipids , diatomsWe conclude by emphasizing that the articles presented in this research topic provide a glimpse of the role of genome-wide tools in making microbial cell factories more efficient at producing high titres of desired biochemicals. We anticipate that these studies will inspire the development of new genomic tools as well as the improvement of existing tools for microbial strains design and engineering. Ultimately, improved microbial performance will enhance biomanufacturing and fast track the efforts towards creating a sustainable and environmentally-friendly society.


Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research. Metabolic errors and perturbations play an important role in a variety of human diseases, including cancer , neuroloMetabolomic workflows identify and quantify metabolites in these complex intracellular networks ,13. TradMetabolic reaction rates, also known as metabolic fluxes, more accurately gauge cellular metabolic activity. Overall, the fluxome is constrained by the metabolic inventory of the cell and the associated stoichiometry. Furthermore, the rate of each metabolic reaction depends on several upstream parameters including gene expression and regulation, enzyme concentration, enzyme phosphorylation state, and the concentrations of metabolites associated with the reaction. Therefore, metabolic flux measurements capture the net interplay of the transcriptome, proteome, regulome, and metabolome to offer a comprehensive and dynamic representation of cell metabolic state ,17,18,19Metabolic fluxes can be classified into extracellular fluxes, which cross the cell membrane, and intracellular fluxes, which do not cross the cell membrane. The effects of extracellular fluxes, such as glucose uptake rate or cellular (biomass) growth rate, are observable in the extracellular medium. Therefore, these fluxes can be directly measured by tracking changes in extracellular metabolite concentrations or biomass over time.13C-glucose) , where n is the number of carbons in the metabolite  to fully labeled metabolites with all labeled carbons . Ex. Ex20]. 13C] glucose provides information on glucose contribution to the TCA cycle, while labeled glutamine provides information on α-ketoglutarate reductive carboxylation and gluconeogenesis  glucose is ideal for measuring this branch point, as glucose produces M+2 lactate when it is metabolized through glycolysis and M+1 lactate as it is metabolized through the PPP  glucose is the most precise scheme when only considering PPP and glycolysis in rat hepatoma cells, while 100% [U-13C] glucose offer superior precision for TCA cycle and anaplerotic pathways. An approximate 51:44:5 ratio of [1-13C]/[U-13C]/unlabeled glucose was used to optimize precision for all pathways [13C] glucose was determined to provide the most precise estimates for glycolysis, PPP, and overall metabolism, while [U-13C] glutamine was better suited for the TCA cycle  glucose/[U13C6] glucose and [3-13C] glucose/ glucose/[U13C5] glutamine were identified as optimal, non-redundant tracer mixtures to study glycolysis and TCA cycle in non-small cell lung cancer cells  glucose and  glucose increased flux precision by 18-fold when compared to the reference labeling scheme of 81% [1-13C] glucose. In comparison, non-parallel individual labeling only achieved approximately 5-fold precision score increases  glucose, allowing discrimination of PPP vs. glycolytic flux. This aspect of NMR is especially helpful to determine which atoms in a metabolite are labeled, especially when multiple tracers are used simultaneously  glucose. Potential dilution sources include additional carbon influxes  and intracellular carbon use . In these cases, dilution sources provide unlabeled carbons that propagate throughout the network and contribute to net metabolite flux. In other cases, dilution sources carry no net carbon flux since there is an equal rate of breakdown and synthesis, such as when there is an exchange of labeled and unlabeled amino acids that dilutes the labeled amino acid pool but does not change net carbon flow. Dilution reactions are included in iMFA models to account for unlabeled carbon sources that enter the metabolic network and decrease the metabolite labeled fraction. A key sign of dilution is a high abundance of an [M+0] label in metabolites that should otherwise be fully labeled, such as in G6P in cells provided with  propionate, and a labeled glucose tracer  [13C] propionate is metabolized into succinyl-CoA and subsequentially oxaloacetate but avoids oxidative pathways. [U-13C] propionate derivatives then pass through phosphoenolpyruvate carboxylase, gluconeogenesis, pyruvate cycling, and the TCA cycle. Together, propionate and deuterium labeling can be integrated to understand the relationship between TCA cycle fluxes and glucose production Glucose and analyzed using INST-MFA. Flux maps showed that that iPSC-CMs downregulated glycolytic and lipid synthesis fluxes and further unveiled pyruvate metabolism as a distinguishing feature between both groups. Pyruvate was primarily metabolized into lactate in 2D cultures, while 3D cultures directed pyruvate towards the mitochondria. Finally, ATP production was calculated by multiplying the number of ATP molecules in relevant fluxes by respective flux measurements. This analysis showed that ATP production rates were higher in 3D cultures and that a larger portion of ATP was generated from mitochondrial metabolism  glucose and [U-13C5] glutamine. Both high lactate cultures were also labeled with [U-13C3] lactate. Data from all experiments were integrated into iMFA models. High lactate lowered glucose consumption, lactate secretion, and anaplerotic rates. However, high lactate also promoted malate-aspartate shuttle activity, which transports reducing agents into the mitochondria, as well as fatty acid synthesis. Through iMFA, the authors were able to integrate large, complex datasets into comprehensive quantitative models and show that iPSCs have the metabolic flexibility to incorporate lactate into biosynthetic substrates for proliferation, regardless of glucose availability  glucose-labeled brown adipose tissue treated with a β3-AR, a β3-adrenergic receptor agonist used to mimic cold exposure. INST-MFA revealed that brown adipose tissue TCA activity increased with agonist treatment as pyruvate contribution to the TCA cycle through pyruvate dehydrogenase (PDH) and pyruvate carboxylase (PC) both increased glucose, 50% [U-13C6]glucose, and 20% unlabeled glucose provide optimal flux resolution for glycolysis/PPP in platelets, while 25% [1-13C]acetate, 25% [2-13C]acetate, and 50% unlabeled acetate was the ideal acetate mixture to resolve the platelet TCA cycle. Platelets were collected and isolated from whole blood, washed, and incubated with either vehicle control or thrombin. INST-MFA demonstrated that at rest, glucose consumption fuels glycolysis while acetate is the sole source for TCA cycle. Upon thrombin activation, platelets undergo systemic increases in metabolic fluxes and increase glycogen catabolism by 3-fold. Furthermore, INST-MFA showed that acetate consumption remained constant and increased TCA cycle fluxes were the result of redirecting glycolytic flux into the mitochondria. While total PPP flux increased due to increased overall glucose consumption, activated platelets lowered the proportion of glucose that entered the PPP by half. Through INST-MFA, the authors quantified absolute and relative flux changes in platelets for the first time. They also demonstrated that iMFA can be utilized to study metabolism in suspended cells [INST-MFA was also recently used to characterize metabolic differences between resting and activated platelets. Optimal labeling schemes were determined a priori by applying INCA’s software tracer simulation function to a previously curated platelet metabolic model . Simulated cells . Togethe13C5] glutamine labeling showed increased glutamine enrichment of acetyl-CoA and [M+5] citrate in treated cells, suggesting a shift towards reductive TCA metabolism of glutamine. iMFA enabled the investigators to determine that cigarette smoke treated cells reversed isocitrate dehydrogenase (IDH) flux from oxidative to reductive carboxylation, a finding which would have been difficult to identify from labeling data alone. iMFA also showed an increased flux of malic enzyme, which contributes NADPH for lipogenesis, as well as increased dependency on de novo lipogenesis rather than exogenous lipid uptake for cellular biomass  citrate, TCA intermediate patterns were indefinite and showed evidence of direct metabolism in both compartments. The initial tracer analysis interpretation required the simplifying assumption that TCA intermediates were present for only one TCA cycle turn. iMFA, which can account for multiple TCA cycle turns, unveiled significant cytosolic citrate metabolism via ATP-citrate lyase and IDH1, and that exogeneous citrate must be metabolized to α-ketoglutarate before entering the mitochondria. Thus, by taking into account multiple TCA cycle turns, iMFA uncovered compartmentalization of exogenous citrate metabolism  glucose and [U-13C] glutamine tracers quantified the metabolic impact of infecting an amniocyte-derived cell line with adenovirus type 5 under both exponential growth and growth arrest conditions. iMFA estimated that cells in exponential growth increased reductive carboxylation and PPP fluxes upon infection. iMFA also demonstrated that cells in both exponential growth and growth arrest convert citrate to cytosolic acetyl-CoA after infection, which contributes to lipogenesis. This further suggests that infection may promote metabolic alterations that support host acetylation, a mechanism that adenoviruses use to control gene expression during infection. Through these studies, iMFA highlighted infection-induced metabolic changes that support adenoviral replication, which is important for therapeutic targeting, as well as strategies to increase virus production for viral and gene therapy vectors [iMFA has also been used to identify and quantify metabolic changes during infection. Viruses hijack host cell metabolism to drive flux towards pathways that are beneficial for viral production . iMFA us vectors . 13C] glutamine and [U-13C] glucose was used to evaluate how clinically relevant mitochondrial DNA alterations disrupt central carbon and amino acid metabolism. Cells with mutant mitochondrial DNA decreased oxygen consumption, glucose entry into the TCA cycle, and glutamine-derived citrate. Initial iMFA analysis confirmed that mutant cells increased glucose and glutamine uptake, but the metabolic byproducts were shuttled out of the cell as lactate and glutamate instead of contributing to mitochondrial metabolism. SLC7A11, a glutamine-cystine antiporter, was upregulated in mutant cells. iMFA was used to analyze wild-type cells overexpressing SLC7A11 to explore how this transporter impacted metabolism. iMFA identified enhanced glucose-derived pyruvate flux into the TCA cycle in SLC7A11 overexpressing cells via cystine-mediated production of α-ketobutyrate. Thus, increased cysteine and decreased glutamine metabolism restore glucose oxidation to compensate for mitochondrial DNA mutations  glucose and [U-13C] glutamine revealed similar glycolytic profiles but slight differences in fumarate and proline labeling between control and cells with diminished FH activity. iMFA unveiled that diminishing fumarate hydratase decreased glucose-derived pyruvate flux into the mitochondria, which lowered TCA cycle activity. Cofactor consumption/generation were included in the iMFA model, and ATP was calculated by using a phosphate-oxygen (P/O) ratio calculation in which each NADH generated 2.3 moles of ATP. This analysis revealed that NADPH consumption and NADH production were reduced in FH-diminished cells, suggesting that the altered redox balance may have caused decreased proline synthesis from glutamine. This analysis also demonstrated that FH-deficiency shifts ATP production towards glycolysis, allowing for increased resistance to mitochondrial inhibitors  glucose, [U-13C] glucose, and [U-13C16] palmitate labeling data from LKB1 expressing or non-expressing A549 and H460 human lung cells was integrated into an iMFA model. The results indicated that LKB1 increased mitochondrial metabolic flexibility, enabling the cells to oxidize pyruvate, glutamine, and fatty acids. This change was primarily driven by increased flux through reactions fueling the TCA cycle  and increased flux through the TCA cycle itself   glucose and [U-13C] glutamine. MDVs, specifically alanine labeling, suggested that paclitaxel decreased glycolytic flux while maintaining oxidative PPP flux. iMFA demonstrated that paclitaxel-treated cells increased TCA cycle, malate dehydrogenase, and pyruvate carboxylase pathway activity to compensate for reduced glycolytic flux. ATP levels, which were calculated from the sum of substrate-level phosphorylation events and NADH P/O ratios  glucose or [U13-C5] glutamine and added to pancreatic ductal adenocarcinoma cell cultures (PDACs). Metabolites from CAFs, PDACs, and extracellular vesicles were collected at each experimental time point and analyzed via GC-MS. The resulting MDVs were fit into a custom iMFA model that included terms for extracellular vesicle cargo packaging, secretion, and cargo release into recipient cells. Exo-MFA was used to calculate the rate of metabolite transfer into recipient PDACs and the intracellular PDAC fluxes. Using this approach, the authors found that TCA metabolic fluxes increased six hours after extracellular vesicle exposure, likely due to lactate, glutamine, and TCA intermediate cargo delivery. This work demonstrates that iMFA can be expanded to estimate quantitative information about multicellular metabolic interactions  glucose, [2H2] water, and [13C3] propionate. Flux analysis demonstrated that glycogen contribution to hepatic glucose production diminished with fasting while glycerol and phosphoenolpyruvate contributions increased. The initial iMFA model assumed that CO2 was not reincorporated in metabolism, an idea supported by previous in vivo NMR work but contradicted by a GC-MS study  glucose infusion. [M+6] glucose was higher in liver-specific pyruvate carboxylase knockout mice, implying decreased hepatic gluconeogenesis. An iMFA model revealed that whole body gluconeogenesis was maintained in liver-specific pyruvate carboxylase knockout mice, suggesting an extrahepatic glucose source [This combined catheterization/iMFA approach was also used to determine how in vivo glucose production changed in hepatic AMP-activated protein kinase (AMPK) knockout mice with treadmill exercise. iMFA showed that AMPK knockout decreased glycogenolysis and hypoglycemia in exercising mice but had a negligible effect on TCA cycle and anaplerotic fluxes . Similare source . In vivo iMFA was also used to study how diet may impact nonalcoholic steatohepatitis (NASH) progression, which is believed to result from oxidative stress . Mice we13C-MFA (DMFA) is an emerging non-steady state approach; however, DMFA faces unique experimental and computational challenges, since metabolite concentrations, MDVs and fluxes that vary with time greatly complicate flux estimation [While non-stationary iMFA can account for systems that are not at isotopic steady state, both standard and non-stationary iMFA require that the system is at a metabolic steady state. However, this assumption cannot be met for every system, especially for cases in which temporal flux changes are of interest. Mammalian cells change their metabolic activity over time, especially during differentiation, activation, and proliferation . Dynamictimation . One DMFtimation ,140. Thetimation , and thetimation . Further13C6 glucose [A recent proof-of-concept study used B-spline curve fitting to estimate non-steady state glycolytic fluxes from isotopomer data acquired from adipocytes treated with insulin and labeled with  glucose . The aut13C iMFA in E. coli at steady state [iMFA typically focuses on a subset of metabolic pathways, with most studies described in this review focused on glycolysis and TCA and sometimes the PPP. However, exclusion of pathways deemed to be non-essential or difficult to measure can bias the results, since metabolic pathways are highly interconnected. Genome scale models comprehensively represent all metabolites and reactions in the biological system to offer a true systemic perspective. Several factors limit iMFA model expansion to the genome scale, including lack of information on atom transitions and compartmentalization, as well as experimental design challenges . One study state . Flux raSynechocystis 6803 (729 reactions and 679 metabolites) and Synechococcuss 2973 (700 reactions and 667 metabolites) using INST-MFA [Synechococcus elongatus 2973 model, resulting in up to 15-fold acceleration in computation time. Other studies use an alternate approach known as known as two-scale MFA, which uses data from iMFA to constrain flux predictions in a genome-scale model [This model was modified to study transient metabolism in the model cyanobacterium INST-MFA ,145. GenINST-MFA ,145. AltINST-MFA . This aple model ,148,149.13C MFA, which runs a second optimization after standard iMFA to find the solution that minimizes the sum of all the reaction fluxes [One possible issue with genome-scale models is that large metabolic networks with limited experimental data can produce large flux variabilities. A potential solution is parsimonious n fluxes . Parsimon fluxes .E. coli co-culture system, in which one strain was an upper glycolysis knockout and the other strain was a PPP knockout [Co-culture studies are vital for physiologically relevant model systems, since different cell types are in constant metabolic communication with one another. For example, blood–brain barrier models best recapitulate barrier integrity and function when endothelial cells are cultured alongside other brain cells such as astrocytes and neurons . Experimknockout . The modThe advancement of mathematical and computational frameworks has enabled iMFA to be applied to complex, compartmentalized mammalian cells, facilitating studies focused on human physiology and disease. The strength of iMFA lies in its ability to generate flux maps from complex metabolomic data, allowing end users to quantify metabolic differences among cell states. iMFA can highlight systemic metabolic shifts in multiple pathways yet also assist in identifying crucial nodes and specific enzymatic targets. Furthermore, iMFA can be a powerful tool in generating new hypotheses or complementing other -omics techniques. iMFA has been especially useful in resolving TCA or mitochondrial features such as pyruvate transport dynamics, upregulated redox-associated fluxes, unconventional shuttles, and alternative metabolic programming events. Many of these findings cannot be intuited from isotope labeling or smaller-scale fluxomic methods, owing to the complexity of metabolism. We envision that iMFA will continue to yield new insights into metabolic states in human health and disease, especially as larger models and dynamic approaches are developed. Most current in vitro iMFA applications are in cancer while in vivo studies have only examined hepatic gluconeogenesis thus far. However, iMFA has great potential in iPSC differentiation, rare inherited metabolic disorders, and diseases related to metabolic dysfunction such as cardiovascular disease, diabetes, and obesity. As iMFA software and tutorials continue to develop, iMFA approaches should be increasingly accessible for biomedical researchers and lead to new insights into metabolic mechanisms in disease.