The existence of three such processes is demonstrated, and a metabolic flux model is developed to explore and characterize them. The model is consistent with a slow return of carbon from cytosolic and vacuolar sugars into the Calvin–Benson cycle through the oxidative pentose phosphate pathway. Our results provide insight into how carbon assimilation is integrated into the metabolic network of photosynthetic cells with implications for global carbon fluxes.Photosynthesis metabolites are quickly labeled when  Camelina sativa leaf metabolites to test models of 12C washout when the CO2 source is rapidly switched to 13CO2. Fitting exponential functions to the time course of CBC metabolites, we found evidence for three temporally distinct processes contributing to the labeling but none for metabolically inactive pools. We next modeled the data of all metabolites by 13C isotopically nonstationary metabolic flux analysis, testing a variety of flux networks. In the model that best explains measured data, three processes determine CBC metabolite labeling. First is fixation of incoming 13CO2; second is dilution by weakly labeled carbon in cytosolic glucose reentering the CBC following oxidative pentose phosphate pathway reactions, which forms a shunt bypassing much of the CBC. Third, very weakly labeled carbon from the vacuole further dilutes the labeling. This model predicts the shunt proceeds at about 5% of the rate of net CO2 fixation and explains the three phases of labeling. In showing the interconnection of three compartments, we have drawn a more complete picture of how carbon moves through photosynthetic metabolism in a way that integrates the CBC, cytosolic sugar pools, glucose-6-phosphate shunt, and vacuolar sugars into a single system.When isotopes of carbon are fed to photosynthesizing leaves, metabolites of the Calvin–Benson cycle (CBC) are rapidly labeled initially, but then the rate of labeling slows considerably, raising questions about the integration of the CBC within leaf metabolism. We have used 2-h time courses of labeling of  The reactions involve both gluconeogenesis and the nonoxidative reactions of the pentose phosphate pathway (PPP)  of photosynthesis is the source of nearly all carbon in the biosphere. COay (PPP) . Since tay (PPP) , and cony (PPP) 5. Many oty (PPP) 5.2, a series of reactions involving three organelles and amino acid metabolism is initiated that results in 3/4 of the carbon first lost to 2-phosphoglycolate being returned to the CBC.Another set of reactions comprise the photorespiration pathway. When rubisco fixes oxygen instead of CO2, CO2 is released by a process originally called dark respiration in the light (LR)  work provides detailed support , in MFA, are low  . A stati feeding  pointed  support . However support  and are These anomalies indicate that we do not fully understand how the CBC is integrated into the metabolic network of photosynthetic cells. To explore them, we have extended a previously published dataset of leaf isotope labeling  to 2 h, 12CO2 to 13CO2, a semilog plot of 12C levels for the CBC intermediates RUBP, PGA, E4P, S7P, GAP, dihydroxyacetone phosphate (DHAP), and FBP (Dataset S1) shows three straight lines . This indicates that we can fit our metabolite labeling data directly to polyexponential models and, by using model selection techniques to find the model that best describes our data, relate this to an underlying network architecture.Following a switch from ht lines A. This pe scales , 24. IntSI Appendix, Fig. S2) shows the results of fitting the measured 12C levels in the aforementioned aggregated CBC intermediates of Camelina sativa to models with one, two, or three exponential components (corresponding to one to three processes controlling labeling kinetics). We evaluated which model best describes the data, using four statistical model selection criteria. Each represents a different measure of overfitting and approach to model selection. All four statistical criteria support the existence of three exponential components in the CBC labeling time course (Dataset S2), consistent with their high rates of interconversion and turnover during photosynthesis, resulting in rapid “mixing” of carbon between them.Nonlinear regression and model selection strongly support the existence of three distinct processes controlling the labeling of CBC intermediates but do not support inactive pools. e course C, corres13C labeling and argues against inactive metabolite pools. To elucidate these pools and their interconnectivity, we now model carbon metabolism by 13C isotopically nonstationary MFA.Our data are best described by a triexponential model without constants  injection of 12CO2 into the internal CO2 pool to simulate an unknown source of older C being broken down; and 4) addition of 12C triose phosphate to the plastid triose phosphate pool to simulate entry via the triose phosphate transporter from an unknown source in the cytoplasm . To do so, we increased the time span and range of metabolites over which labeling was measured and updated our previously developed metabolic model to include reversibility of several reactions for which there is biochemical evidence (Dataset S3). We assessed these alternatives comparing sum of squared residuals (SSR), a measure of the goodness of fit between modeled and measured data  entrred data . However12C entry from intact unlabeled glucose via the OPPP shunt at a rate of 1.9 μmol⋅g−1 FW⋅h−1, with the best SSR improvement from 1,340 to 1,126. The second possible 12C entry flux is 0.3 μmol⋅g−1 FW⋅h−1 from the triose phosphate transporter from an unknown source in the cytosol, with a decrease in SSR from 1,340 to 1,273. The third possible 12C entry is from starch turnover flux of 0.5 μmol⋅g−1 FW⋅h−1, with a decrease in SSR from 1,340 to 1,300. We also tested other models with variations in starch metabolism to test 1) whether addition of reactions representing starch turnover to the metabolic model meaningfully improves the agreement between the measured and simulated labeling and other flux data and 2) whether the fitting of such models indicates biologically significant fluxes through starch turnover. We tested six such models with different representations of how starch turnover might act to influence the carbon fluxes and expected labeling patterns. Other models were tested in which either the whole starch pool or an intermediate pool (such as might represent either oligoglucans or short- versus long-term starch pools) can turn over while maintaining the measured net starch accumulation rates.The data were consistent with 12CO2 entry flux of 0 with no change of SSR .No unknown source of older C being broken down is indicated, with e of SSR . This reSI Appendix, Table S4). If inactive metabolite pools cause the lack of complete labeling, then, at later time points, for example, in G6P, only M0 and M6 should be observed. However, the amount of M0 could account for only one-third of the 12C in G6P at 2 h . We suggest that the high amount of M0 comes from a large metabolic pool, such as fully unlabeled glucose that enters the CBC at a low rate.In our study’s later time points, anomalously high values for fully unlabeled isotopologues (M0) were found well after the singly labeled (M1) isotopologues had decayed to very low levels . Since tSI Appendix, Fig. S3; labeling kinetics for other metabolites are shown in SI Appendix, Figs. S5 and S6). At 60 min, the glucosyl and fructosyl moieties of sucrose contained 49% and 46% 13C, respectively . Sucrose recycling through invertase and fructokinase yields F6P that would distribute between sucrose resynthesis and G6P, but this alone is insufficient to account for a prolonged dilution flux. By contrast, at 60 min, glucose and fructose were only 12% and 20% labeled with 13C, respectively , consistent with previous evidence that vacuolar sucrose turns over due to invertase activity .In light of the above results, we included sucrose recycling and sugar vacuole pool transport reactions in the model with known biochemical reactions that can mediate such slow turnover processes of sucrose/glucose/fructose. Inclusion of sucrose recycling and sugar vacuole pool reactions markedly reduced overall SSR from 1,340 to 968 and reduced individual SSRs for labeling in the least well-fitted metabolites F6P, ribose 5-phosphate (R5P), G6P, ADPG, and UDPglucose (UDPG) . While intermediates of the CBC, photorespiration, and starch and sucrose biosynthesis pathways showed substantial 13C labeling, the tricarboxylic acid (TCA) cycle intermediates, and most amino acids derived from them, showed very little labeling after 120 min. The flux map is consistent with previous reports of low TCA fluxes and operation of the OPPP shunt as a source of LR (Dataset S4). Exchange fluxes are less well defined, especially for reactions with modest net fluxes.13C MFA is complicated by the presence of multiple subcellular pools of serine and glycine and the multiple reactions and interconversions that they can undergo in different compartments . The value obtained (0.31) was used as input to the MFA model instead of relying on fitting the labeling measured in glycine and serine . Using measurements of serine, glycine, 2PG, and glycerate without compartmentation, Ma et al. .The inclusion of inactive metabolite pools was made in previous studies to account for the persistence of unlabeled carbon in CBC intermediates , 17, 18.SI Appendix, Fig. S6) compared to previous studies . Our model included three inputs of carbon into the CBC: 1) 172 μmol⋅g−1 FW⋅h−1 by carboxylation by rubisco, (2) 75 (25 × 3 carbons per glycerate) μmol⋅g−1 FW⋅h−1 returned from photorespiration, and 3) 35 (7 × 5) μmol⋅g−1 FW⋅h−1 returned from the G6P shunt. The carbon paths in photorespiration and the G6P shunts require an extra 110 (75 + 35) carbon atoms to be processed for 172 carboxylations, adding more than 50% to the required flux through reactions in the CBC.A key finding from this study is that the kinetics of the CBC is best described as a function of three interconnected processes, as indicated both by our modeling analysis of the time course of eriments  and by oIn previous work, we allowed only a stromal shunt . When we2 in photorespiration at a rate of 25 μmol⋅g−1 FW⋅h−1 and, from the G6P shunt, at a rate of 7 μmol⋅g−1 FW⋅h−1. The rate of glucose entry into the shunt was estimated to be about 5% of the rate of net CO2 fixation. The cost of the shunt is three ATP per glucose. Therefore, this shunt would increase the energy requirement for CO2 fixation from three ATP and two NADPH to ∼3.15 ATP and two NADPH  (The model includes release of COixation) , 32.The cost of the G6P shunt may be offset by benefits of refilling the CBC when intermediates fall during transients in light or other factors . This ha13C labeling time course data, we can test models of the general structure of the underlying network and corroborate or contradict assumed or proposed MFA models. This attests to the possible utility of these kinds of statistical tools in constraint-based modeling, and we believe advancement in this area could encourage use of MFA models to gain insight into photosynthetic metabolism.The use of multiple statistical tests specifically designed for model selection and the comparison of nested model series shows the potential for improvement of statistical rigor in this important aspect of MFA modeling. Although MFA software packages like INCA  can repoDataset S3. The reversibility of reactions in the CBC has been corrected. Reactions present in the previous models, representing inactive pools for all the CBC intermediates, ADPG, UDPG, 2PG, phosphoenolpyruvate, and glycerate have been removed. The inactive pools for alanine, glycine, and serine have been retained because of their compartmentation complexity. Reactions newly added in this study, including cytosolic OPPP shunt, sucrose recycling reactions, and sugar vacuole pool reactions, explain the longstanding puzzle of the slow labeling phase of CBC intermediates and the overabundance of fully unlabeled isotopologues. These improvements to the metabolic network have largely eliminated the need for hypothesizing inactive pools. In showing the interconnection of these three compartments, we have drawn a picture of how carbon moves through photosynthetic carbon assimilation in a way that integrates the CBC, cytosolic sugar pools, the glucose-6-phosphate shunt, and vacuolar sugars into a single system.This new model improves on previous efforts on several fronts. Comparisons of the model in this work with previous models , 18 are LR made using 12CO2 emission into a 13CO2-containing atmosphere  provided clear-cut evidence against a biologically significant contribution of starch turnover to labeling patterns or carbon balances in central metabolism.In the past, starch recycling was proposed as a possible source . We have•13C, but the last 10 to 20% of labeling is much slower . Entry of carbon from relatively unlabeled free sugars into active pools accounts for the preponderance of M0 isotopologues.•To achieve acceptable fits, previous MFA studies assumed large metabolically inactive pools of central metabolites including metabolites of the CBC –18. HoweThe abundance of M0 over M1 isotopologues was confirmed here. If metabolically inactive pools explained the lack of complete labeling, then the M0 isotopologues should account for all the unlabeled carbon atoms. However, using G6P as an example, 2.9% of the molecules were fully unlabeled, but this accounts for only about one-third of the missing label . The exception is SBP as reported in Arrivault et al.  based on gas exchange data . Fatty acid synthesis and LR were constrained (but not fixed) based on previous measurements (•ov/cv), in MFA are low  has some label at the time LR is assessed. The results presented here provide a framework for more detailed LR measurements. Measuring LR is a very difficult task but very important for understanding global carbon cycles -MS had the following changes: Samples for reverse-phase liquid chromatography-tandem mass spectrometry were analyzed by an ACQUITY UPLC pump system (Waters) coupled with Waters XEVO TQ-S ultra-performance liquid chromatography tandem mass spectrometry (Waters) by the method described in ref. −1. The oven temperature gradient was: 40 °C (1-min hold), increased at 40 °C/min to 150 °C, then a 10 °/min to 250 °C, then a 40 °C/min to 320 °C, and finally held at 320 °C for 4.5 min. CI was used, and the mass scan range was 150 amu to 650 amu with step size 0.1 amu. The ionization source temperature was set at 300 °C, and the transfer line temperature was 300 °C.Mass spectrometry for anion exchange LC-MS/MS and GC-EI-MS were carried out using the methods described in ref. n = 1,000). Extra sum-of-squares, cross-validation, Akaike information criterion (AIC), and Bayesian information criterion (BIC) model selection criteria were evaluated for all models and model comparisons, and the Bonferroni–Holm multiple testing correction was applied for the P values generated by the extra sum-of-squares hypothesis testing (SI Appendix, Supplemental Information Text T1 and Supplemental Methods T2.A nonlinear ordinary least-squares algorithm implemented in the Python package SciPy was used to fit models 1 to 7 C to our  testing –50. FurtSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File


FMR1 gene are generally unstable, often expanding to a full mutation (>200) in one generation through maternal inheritance, leading to fragile X syndrome, a condition associated with autism and other intellectual disabilities. To uncover the early mechanisms of pathogenesis, we performed metabolomics and proteomics on amniotic fluids from PM carriers, pregnant with male fetuses, who had undergone amniocentesis for fragile X prenatal diagnosis. The prenatal metabolic footprint identified mitochondrial deficits, which were further validated by using internal and external cohorts. Deficits in the anaplerosis of the Krebs cycle were noted at the level of serine biosynthesis, which was confirmed by rescuing the mitochondrial dysfunction in the carriers’ umbilical cord fibroblasts using alpha-ketoglutarate precursors. Maternal administration of serine and its precursors has the potential to decrease the risk of developing energy shortages associated with mitochondrial dysfunction and linked comorbidities.Fifty-five to two hundred CGG repeats  in the 5′-UTR of the  FMR1) gene contains CGG repeats that can expand to >200 copies (full mutation), causing fragile X syndrome, a condition frequently co-diagnosed with autism and other intellectual disabilities [The fragile X mental retardation 1 (bilities .Adult PM carriers are at higher risk of developing the late-onset fragile-X-associated tremor/ataxia syndrome (FXTAS), characterized by progressive intention tremors, gait ataxia, deficits in executive function and memory, peripheral neuropathy, and parkinsonism ,5. ChildMitochondrial dysfunction (MD) is at the center of many disorders, especially those affecting neurological systems, due to the heavy reliance of neurons on mitochondrial ATP . In thisFMR1-encoded fragile X mental retardation protein (FMRP) seems to have a strong neurodevelopmental role based on its high prenatal expression followed by its decline after birth in mice [As the  in mice , the putt (33) = −1.493; p = 0.143; d = 0.389; unequal variances F = 16.13; p < 0.0001). The age of the mothers carrying a non-carrier fetus ranged from 25.5 to 41.8 y (34 ± 4), slightly older than those carrying a PM fetus, who ranged from 17.8 to 42.1 y  = −2.597; p = 0.0138; d = 0.751; unequal variances F = 2.334; p = 0.035). The fetal repeats, as expected, were significantly different between groups, ranging from 20 to 44 for the non-carrier group, and from 55 to 157 for the carrier group  = 9.908; p < 0.0001; d = 2.938; unequal variances F = 13.75; p < 0.0001). Most  of the transmitted PM alleles were unstable  supernatants obtained from 52 pregnant carrier women who had undergone amniocentesis for prenatal fragile X diagnosis. All mothers carried a male fetus who had inherited either a PM or a non-mutated fragile X allele . Due to fined in ). As expn = 30; 17 ± 1 and n = 22; t (50) = -0.838; p = 0.406; d = 0.228; Samples were taken from female carriers at gestational periods ranging from 13.3 to 21.2 weeks, with no statistical differences between the two groups  and X-linked proteins (fetus and mother) in the AF proteome , allowedTo test the separation of the diagnostic groups and predict outcomes based on the omics data, we used two approaches (supervised and unsupervised learning models) within the artificial intelligence and machine learning fields. For the first approach (supervised), classification problems  use an algorithm to accurately assign test (labeled) data into specific categories . In our case, we used the classification algorithm named partial least squares-discriminant analysis PLS-DA; A,C. For Pathway analysis of a joint AF metabolome–proteome revealed upregulation of aminoacyl-tRNA biosynthesis, as well as the metabolism of the following amino acids: sulfur-containing (Cys and Met), branched-chain , aromatic , and Arg and Pro. The downregulated pathways included, among others, energy supply (glycolysis and TCA cycle); metabolism of Ala, Gly, Ser, Thr, Asp, and Glu; the pentose phosphate pathway; glutathione metabolism; and pantothenate and coenzyme A biosynthesis .Conditions of increased oxidative stress were incurred by the downregulation of the pentose phosphate pathway B, glutatImpaired glycolysis was gathered by the lower expression of GAPDH and the downstream enzymes of the glycolytic pathway . This analysis resulted in 34 modules , severalIt could be argued that the association between maternal age and dizygosity incidence—leading to age-dependent epigenetics  or the association between the genetics of dizygotic twinning and cerebral asymmetry—may have influenced the results presented here. However, support for the non-concordance of the biological outcomes between dizygotic twins was performed by principal components analysis . For thiTaken together, these results point to a general decline in cellular energy metabolism in carriers, encompassing both glycolysis and oxidative phosphorylation linked to deficiencies in the Ser and Gly biosynthetic pathways. This is supported by the major decline in the levels of Asp, Glu, and Ser —amino acNotably, PGK1 coordinates glycolysis with the TCA cycle  , autophaIt could be claimed that the impact of a non-essential amino acid such as Ser on cellular metabolism is negligible; however, its role is critical during CNS neurodevelopment, based on its low blood–brain barrier permeability , and theFurther independent support for these findings was provided by the significant overlap of dysregulated pathways between the metabolic footprints observed in AF from fetal carriers and those produced by stressors relevant to energy management  and proteotoxicity , mitochondrial uncouplers and inhibitors, and ER stress . A majorTo prevent bias-corrected estimates of model performance , the prep < 0.01), with an accuracy of 77.78%.A receiver operating characteristic curve for the predictive performance of the prenatal PM metabolic footprint was built utilizing two-thirds of the AF samples (randomly selected) and the top variable in importance (VIP) predictors from both proteomic and metabolomic data , and thep < 0.01 and < 0.05 for proteomics and metabolomics; When the model was built with either the proteomics data of all AF samples, with proteomics from fibroblasts used as holdout samples , or the FMR1 CGG expansion, impacted the Ser pathway. We reasoned that if glycolysis-derived Ser was critical in providing AKG for the TCA cycle in the PM, then mitochondrial outcomes of non-carriers with a halted Ser biosynthesis should result in mitochondrial outcomes like those of the PM. Conversely, if Ser biosynthesis were the rate-limiting step for providing AKG, then mitochondrial outcomes in the PM should respond favorably to the supplementation of AKG precursors. As a proof of concept, we tested these options by utilizing umbilical cord fibroblasts (UCFs)—tissues reflecting prenatal periods as close as possible to those of the AF—from non-carriers and carriers (two for each). Compared to non-carriers, UCFs from carriers presented mitochondrial hypofunction. This was characterized by a lower fraction of the maximum mitochondrial capacity to synthesize ATP , lower Inhibition of the endogenous synthesis of Ser in non-carrier UCFs with WQ-2101 (inhibitor of PHGDH ) resulteFurther confirmation on the contribution of this metabolic pathway in UCFs was obtained through metabolomics analysis . UCFs frWhen the media were supplemented with the cell-permeant AKG, cells from carriers and from non-carriers with or without the PHGDH inhibitor responded similarly, showing increased ATP:AMP ratios and moderate impact on the glycolytic flux, but with significant improvements in the TCA cycle. Cells from non-carriers treated with the PGDH inhibitor resulted in a significant buildup of glucose 6-phosphate and 3-phosphoglycerate, thereby confirming a block in the branch that leads to the formation of AKG, Ser, and Gly from 3-phosphoglycerate. Similar metabolomics results were obtained when both Gln and AKG were used to supplement the media with glucose, indicating that the greatest effect was achieved by the cell-permeant AKG, as shown by the mitochondrial functional outcomes C.These results are consistent with those obtained with AF in which the disease gene pathway analysis run with the joint input of metabolites and proteins indicated deficiencies in the AKG dehydrogenase and Ser biosynthetic pathways  may present different degrees of severity, as is usually seen with mitochondrial disorders, further complicated by the phenotypic threshold effect ,61,62; hFMR1 repeat sizing was performed by PCR analysis using AmplideX PCR CE/FMR1 , and the products were separated by capillary electrophoresis with Applied Biosystems 3130. The AGG interruption patterns were determined using either AmplideX PCR CE/FMR1 at the New York State Institute for Basic Research in Developmental Disabilities , or Xpansion Interpreter . Changes in allele repeat length on transmission were determined by comparison of parental and transmitted alleles in parallel PCR capillary electrophoretic analyses. An unstable transmission was defined as a change of at least one repeat from parent to child [Fifty-two pregnant women, all carriers of the PM, gave consent for any AF samples remaining after prenatal fragile X diagnosis to be used for research. All available demographic and genetic data are shown in to child .Metabolomics and proteomics analyses of 54 AF samples, external validation cohorts constituted by dermal fibroblasts and plasma samples obtained from non-carriers and carriers , and UCFg, washed twice in RPMI media without FBS, and resuspended in RPMI supplemented respectively with (1) 4 mM Glucose, (2) 2 mM Glutamine, (3) 3 mM DMAKG, and (4) a combination of the four. Viability was evaluated by trypan blue assay with a TC20 counter (Bio-Rad). For all cell lines, viability was 97–99%.Fibroblasts (P4) obtained from the umbilical cords of 2 controls  and 2 PM carriers  were thawed in medium consisting of a 1:1 ratio of AmnioMAX-C100 and RPMI-1640 basal medium supplemented with 10% fetal bovine serum , 1× penicillin/streptomycin (Gibco), and 1% non-essential amino acids (Gibco), as previously described [Oxygen consumption was immediately evaluated in intact cells using a Clark-type oxygen electrode , as previously described . ATP-linSpectral counting was utilized to quantify the proteins from mass spectrometry analysis. Both protein and metabolite levels were normalized to pooled samples from pregnancies of non-carrier fetuses; the features were normalized by utilizing the quantile and then auto scaled (mean-centered and divided by the standard deviation of each variable). Combined pathway analysis was performed using MetaboAnalyst . The thrpO2 between weeks 15 and 17 is <10 mmHg , but also has the potential to affect other biosynthetic pathways relevant to neurodevelopment . It could be argued that mild prenatal changes in mitochondrial function would not result in fetal metabolic changes in utero, as this environment is considered “hypoxic”. However, a wealth of studies reported embryonic lethality at equivalent gestational ages for homozygous knockouts of respiratory chain subunits , able t = 0.9 M ). HoweveWhile decreasing energy provision by downregulating the Ser biosynthetic pathway limits macromolecule synthesis to reduce the load of ROS-mediated damage to misfolding-prone proteins, this mechanism also puts a brake on CNS cell growth and differentiation at a critical stage of brain development. The encouraging results of Ser supplementation to treat individuals with PHGDH deficiency , ALS 7676, and A


Most importantly, it highlights the necessity for model validation.This viewpoint devises recommendations for future studies utilizing  Within the plant sciences, two studies utilizing this technique to characterize carbon metabolism have been published so far. Here, I examine these studies carefully. Readers unfamiliar with 13C-INST-MFA will obtain a critical understanding of the method and its findings. Readers working with 13C-INST-MFA are recommended to enter a phase of model validation to devise clear-cut protocols enabling robust estimations of specific fluxes.13C isotopically non-stationary metabolic flux analysis (13C-INST-MFA) is an emerging technique for estimations of metabolic fluxes and pool sizes. Within the plant sciences, two studies utilizing this technique to characterize carbon metabolism have been published so far. Here, I examine these studies carefully. Readers unfamiliar with 13C-INST-MFA will obtain a critical understanding of the method and its findings. Readers working with 13C-INST-MFA are recommended to enter a phase of model validation to devise clear-cut protocols enabling robust estimations of specific fluxes.13C-INST-MFA, a list of coded reactions specifies by which routes carbon can move from labelled or unlabelled sources through metabolic networks into sinks , fructose bisphosphatase , and fructose-bisphosphate aldolase . Lastly, mesophyll chloroplasts reportedly lack enolase (enolpyruvate (PEP) is likely to be infeasible, and fatty acid biosynthesis probably relies on PEP import from the cytosol. Future studies are encouraged to implement more realistic reaction networks representing carbon metabolism with all its intrinsic restrictions and freedom. Incorporation of cytosolic glyceraldehyde-3-phosphate dehydrogenases and phosphoglycerate kinase may enhance the utility of the model since these reactions proposedly constitute a central hub in leaf energy metabolism  while maintaining flux ratios. Additionally, study 1 fixed the absolute flux into starch (νStarch) and flux ratios between sucrose and amino acid biosynthesis according to physiological measurements. Similarly, study 2 fixed absolute fluxes into starch, sucrose (νSucrose), and amino acid biosynthesis, and the ratio of Rubisco oxygenation to carboxylation. This practice is potentially problematic since it may affect modelled flux ratios. Additionally, the necessity for constraints poses an important question. If large fluxes need to be fixed , can one rely on 13C-INST-MFA to return credible results for unconstrained fluxes including smaller fluxes of interest (νOPPP)? Thus, future studies are encouraged to present models without constraints alongside constrained models to show that data-driven flux estimation is feasible. Ideally, 13C data should drive the estimations with a minimum of imposed constraints.INCA allows users to constrain fluxes and pool sizes, for example based on independent physiological measurements or theoretical considerations. Users may specify constants or intervals, or choose not to impose any constraints. In all models (both studies), net COo starch , νStarchνO/νC was constrained to be between 0.2 and 0.25. Modelling returned a νO/νC ratio of 0.2, and an RL and νOPPP of 5.2 μmol CO2 g–1 FW h–1 and 4.6 μmol CO2 g–1 FW h–1, respectively. When left unconstrained, modelling returned a physiologically unrealistic νO/νC ratio of 0.09, and an RL and νOPPP of 12.1 μmol CO2 g–1 FW h–1 and 10.5 μmol CO2 g–1 FW h–1, respectively. This indicates negative correlations between νO/νC and RL and νO/νC and νOPPP . Hence, fixing νO/νC at values >0.2 may cause RL→0 and νOPPP→0. Note that under normal growth conditions, νO/νC ratios of 0.34 are common  forward and reverse fluxes of all reactions and (ii) pool sizes of all metabolites specified in the reaction network  by the Laisk method which returned an RL estimate of 9.3 μmol CO2 g–1 FW h–1 . Thus, these estimates are statistically different at the 0.05 significance level. Additionally, the model estimate for νOPPP in chloroplasts was compared with an estimate of flux through the cytosolic OPPP  can be utilized to test the method by leaving them unconstrained and comparing modelled and measured values. Additionally, νO/νC ratios may help to test the method since several alternative methods can provide independent estimates  and modelled accordingly by including a dilution term for each metabolite (accounting for apparently constant offsets between measured and modelled 13C enrichments). Alternatively, labelling lags may be explained in combination by breakdown of weakly labelled cytosolic sucrose into glucose and fructose, phosphorylation by hexokinase and fructokinase, and reinjection of glucose-6-phosphate-derived carbon into chloroplasts via a cytosolic OPPP not shown in 13C enrichments of metabolites of the Calvin–Benson cycle, and starch and sucrose biosynthesis 1 h into 13CO2 labelling of Arabidopsis thaliana rosettes  to fully cytosolic (x=1). Interestingly, plastidial metabolites are more strongly 13C labelled than cytosolic metabolites. Metabolite distribution explains 55% of the labelling variability . Since it is not apparent why sizes of metabolically inactive pools would correlate with plastid–cytosol metabolite distribution, this corroborates the idea of injection of weakly labelled carbon into cytosolic metabolism. Future 13C-INST-MFA studies are encouraged to further explore this by expanding their reaction networks by sucrose breakdown pathways and a cytosolic OPPP. Additionally, sucrose, glucose, and fructose are large carbon pools with significant vacuolar contributions (In as 13CO2 . Both st13C-INST-MFA returns reliable flux and pool size estimates is not available. Therefore, the field is recommended to enter a phase of validation of the complex models used in 13C-INST-MFA to devise clear-cut protocols enabling robust estimations of specific fluxes.To date, evidence that


Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements  were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source.  Phenyl urea herbicides are among the most widely used herbicides for weed control in several crops  through their pre- or post-emergence applications. These substances interrupt electron transfer in photosystem II, leading to the formation of reactive oxygen species and resulting in cell damage . The incArthrobacter, Bacillus, Comamonas, Pseudomonas, Sphingobium, and Variovorax are able to degrade/transform linuron in various environments either in isolation or as part of a consortium  and gene clusters which are responsible for the degradation of linuron and dichloroaniline in SRS 16 has been described in detail with the support of proteomic studies . DCA further degrades to metabolites that can be consumed in the central metabolism of the microorganisms. However, partial degradation of linuron produces chloroanilines that are more toxic than linuron itself. Bacterial genera such as nsortium . Variovo studies . Based o studies , VariovoProcessing genomic information into a metabolic model is increasingly used as a route for generating a predictive tool to elucidate and manipulate cellular biochemical activity. Genome-scale metabolic modeling has been proven as an efficient approach to decode the genomic and functional information for a specific phenotype by investigating the gene–protein interactions on a cellular level . To consRalstonia eutropha H16, a microorganism with multiple potential applications in environmental biotechnology, was used to analyze its metabolic characteristics  to characterize active metabolic processes at different growth stages. The matching of predictions with bacterial response indicates the importance of modeling to enhance the success rate of bio-inoculants in complex systems  and estimate their potential usage as biostimulants in order to harness the full potential of indigenous degraders. SRS 16 was selected due to the availability of a fully sequenced genome and extensive biochemical knowledge of its full pathway in linuron degradation – a pollutant that belongs to a large contaminant group of the phenyl urea herbicides. A genome-scale metabolic model of SRS 16 was constructed following automatic and manual protocols and was applied to explore the stimulating effect of different supplements through iterative simulations Compounds pointed by simulations as strong enhancers of degradation were further validated through wet laboratory experiments.The importance of these models as a tool for rapid biodegradation and for the development of effective bioremediation strategies is recognized  and demoeristics . The gen systems . Such st systems . Xu et adegrader . The effin vitro . The preVariovorax sp. SRS 16 was reconstructed by following a bottom-up approach as elaborated by As a first step, a draft model of Variovorax SRS 16 in additional genomic annotation databases including BiGG , allowing us to depict cellular processes based on cellular reconstructions . Briefly linuron  and 120  linuron .Dynamic modeling was used for the prediction of the profile of consuming metabolites typical to the biomass increase and linuron degradation across time. To this end, we simulated the behavior of our metabolic model across time. The simulation process follows  and is iSimulations were carried out until a state where time cycles did not lead to an increase in biomass was reached. Initial concentration values for all metabolites were set to a fixed amount of 50 units . The upper bound of the exchange reactions was set to 1,000 units to allow the secretion of all the exchange metabolites. Reversible non-exchange reactions’ lower bound was −1,000 units, and for non-exchange non-reversible reactions, it is 0 units; the upper bound for non-exchange reactions was 1,000 units. At each growth cycle, the generation of biomass is updated on the basis of flux flow in the biomass reaction. The algorithm assumes that media components are available to all growing cells with equal probability. The growth rate and the flux of substrate (consumed or secreted) associated with the model in the given media conditions are calculated at every time point, where, in each cycle, we try to maximize the biomass of each member of the community and then we fixate the biomass and minimize the uptake of metabolites of each member while maintaining the max biomass found. The cycles of predictions were carried out until the saturation point appeared in the biomass production (no growth recorded reflecting the exhaustion of given resources). Starting with one bacterial cell, the flux balance model was used to predict the uptake of carbon and nitrogen sources (including linuron) across time.All model simulations were done on an Intel i7 quad-core server with 32 GB of memory, running Linux. The development programming language of our simulators was JAVA, and our linear programming software was IBM CPLEX.Variovorax sp. strain SRS 16  was revived from stock cultures stored in glycerol at −80°C. The purity and authenticity of the strain were checked by 16S rRNA gene sequencing. The minimal medium . Linuron degradation was measured through HPLC with standard procedures. Briefly, 1 ml of sample was taken and centrifuged at 10,000 × g for 5 min. The supernatant was filtered through a 0.22 μm PTFE syringe filter and transferred to the HPLC vials for the detection of residual linuron. Linuron and DCA were analyzed by using Agilent 1100 HPLC . Detection of linuron was done at 240 nm using the external calibration method (sensitivity 0.5 mg L–1). The mobile phase of 70% methanol at a flow rate of 1 ml min–1 with a reverse-phased  of 250 mm length × 4.6 mm inner diameter with particle size 5 μm was used for separation.All the inoculated flasks were incubated at 25°C (120 rpm) for 7 days. Bacterial growth and linuron degradation were monitored at definite intervals  from the day of inoculation. For bacterial growth, 200 μl was taken, and OD was measured at 600 nm by using Infinitep < 0.05 in SPSS v19.Linuron biodegradation and biomass buildup experiments were carried out in biological triplicates. The effect of supplements was statistically analyzed by performing repeated-measures ANOVA at Variovorax sp. strain SRS 16 is about 7.7 Mb in size, including its chromosome (5.7 Mb) and four plasmids with an approximate size of <1 Mb each  to the synthesis of a new cell and is similar to the previously published genome-scale reconstruction of Escherichia coli strain K-12  (Rhodoferax ferrireducens (from the same family Comamonadaceae) – 15.6% (E. coli str. K-12 (The reconstructed metabolic network presented here covers 19.2% of the open reading frames (ORFs) present in the genome . The covherichia  (29%), iacterium , and iPA (25.8%) . It is,  – 15.6% . Total ntr. K-12 . A detaitr. K-12  in SupplVariovorax SRS 16 and is supported in part by molecular evidence. Notably, none of the reactions in the modified ortho-cleavage pathway was identified by automatic reconstruction. Alternatively, we also included two hypothetical pathways , Pathways 2 and 3 , 40% of the linuron was degraded at the 11th simulation round. The predictions stratify a group of enhancers with variable degrees of linuron degradation in the 11th iteration (>40–100%), in comparison to non-enhancers . All the carbon sources and several amino acids  were classified as non-enhancers vs. nitrogen sources that are predicted to expedite degradation at various degrees, with some variations depending on the simulation pathways used. Overall, considering the different pathway options, the rate of degradation broadly follows the same trend where most amino acids act as moderate enhancers, where on the 11th round, 50–75% of linuron is degraded. Fast enhancers include glutamine and asparagine . Though groups of enhancers vs. non-enhancers are similar when considering both simulation pathways (The SRS 16 linuron degradation behavior under the influence of specific carbon (succinic acid) and nitrogen  sources was extensively studied and reported . Here, wpathways , differep < 0.05). Degradation results (p < 0.05). Aniline and methionine had a non-significantly stronger enhancement effect in comparison to MS and were classified into the non-enhancers group (p < 0.05 repeated-measures ANOVA), consistent with simulations in Pathways 2 and 3.Seven metabolites representing a range of predicted degradation enhancement potential and biochemical characteristics  were sel results  are over results , with siThe results provide an insight that some supplements (associated with nitrogen metabolism) have a strong enhancing effect that helps in the conversion of linuron into potential cellular building blocks.Variovorax SRS 16, a member of a genus reported as an efficient degrader of phenyl urea herbicide, is widely known for its ability to mineralize linuron  would be beneficial to track the molecular processes considering different degradation scenarios.Thus, the simulation predictions reflect the complexity of cellular processes and the balance between different metabolic pathways contributing to growth. The simulations provide multiple optimal solutions for the internal fluxes involved in central metabolism and hence are not sufficient for deciphering the mechanism by which environmental (media dependent) conditions affect linuron degradation. However, the integration of simulation data with ‘omics data  can reduce the solution space and shed light on the fine-tuning of cellular activity, considering the need to balance between myriads of constraints. The model provides a platform for future sequential studies through the integration of metabolomic and transcriptomic data   relies on a detailed description of linuron degradation at strain level . Variovopathway) . Alternapathway) . In the pathway) . Here, tP. aurescens TC1. The current study demonstrated that genome-scale modeling allows the optimization of microbial activity, leading to herbicide biodegradation. The integration of predictive biology helped to screen the effect of the higher number of supplements on the degradation by SRS 16. Such supplement-derived amendment might reflect exchanges in the indigenous community where the degrader can be supported by non-degrader species  and save resources to identify the optimal solutions for a specific phenomenon. The association of genome-scale metabolic models with the omics approach can help to unravel the hidden facts of metabolism . Thus, tThe original contributions presented in the study are included in the article/KD, RZ, ZR, and SF designed the study and drafted the manuscript. RZ constructed the metabolic model. KD participated in model construction, led the design and analysis of computational and experimental results, and wrote the manuscript. SM participated in computational data analysis. NK and DB performed the wet laboratory experiments. BÖ, RA, and HE analyzed and reviewed the results. All authors carried out writing and improvement of manuscript.BÖ was employed by the Leibniz Institute DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany. The remaining 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.


Metabolic syndrome (MetS) is a significant factor for cardiometabolic comorbidities in people living with HIV (PLWH) and a barrier to healthy aging. The long-term consequences of HIV-infection and combination antiretroviral therapy (cART) in metabolic reprogramming are unknown. In this study, we investigated metabolic alterations in well-treated PLWH with MetS to identify potential mechanisms behind the MetS phenotype using advanced statistical and machine learning algorithms.n = 100) defined according to the International Diabetes Federation (IDF) consensus worldwide definition of the MetS or without MetS (n = 100). The untargeted plasma metabolomics was performed using ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS/MS) and immune-phenotyping of Glut1 (glucose transporter), xCT (glutamate/cysteine transporter) and MCT1 (pyruvate/lactate transporter) by flow cytometry. We applied several conventional approaches, machine learning algorithms, and linear classification models to identify the biologically relevant metabolites associated with MetS in PLWH.We included 200 PLWH from the Copenhagen Comorbidity in HIV-infection (COCOMO) study. PLWH were grouped into PLWH with MetS  differed significantly between PLWH with and without MetS . The majority belonged to amino acid metabolism (43%). A consensus identification by combining supervised and unsupervised methods indicated 11 biomarkers of MetS phenotype in PLWH. A weighted co-expression network identified seven communities of positively intercorrelated metabolites. A single community contained six of the potential biomarkers mainly related to glutamate metabolism. Transporter expression identified altered xCT and MCT in both lymphocytic and monocytic cells. Combining metabolomics and immune-phenotyping indicated altered glutamate metabolism associated with MetS in PLWH, which has clinical significance. With the introduction of combination antiretroviral treatment (cART), people living with HIV (PLWH) have experienced a dramatic increase in life expectancy, with a concomitant decline in AIDS-defining morbidity and mortality . A simulWith the advancement of technology, high-throughput untargeted metabolomics became attractive as a novel tool to identify large numbers of metabolites and investigate molecular mechanisms of disease phenotypes that are not usually included in routine biochemical analyses . The useHere, we aimed to identify a metabolomic signature of MetS in HIV-infected individuals to reveal key aspects of the underlying pathophysiology. This may help to improve the understanding of the pathogenesis of this disease and potentially identity new targets for the treatment of this condition and provide better quality of life including healthy aging. To our knowledge, this is the first study to apply several conventional and advanced bioinformatics algorithms to analyze the metabolic alterations associated with MetS in PLWH using untargeted metabolomics.p-value = 0.329) and cART exposure . Larger prevalence of CD4 nadir < 200 cells was found among PLWH with MetS .PLWH was stratified according to the presence of MetS syndrome . Clinical characteristics of PLWH are summarized in n = 285, 32%), xenobiotics including food components  and amino acid metabolism   of which the majority were amino acids   . The rel33, 43%) . We next33, 43%) . The net33, 43%) .n = 21, using the feature selection by Boruta) (n = 877) that was submitted after scaling. A model using the two first components showed a significant coefficient of determination (R2 = 0.58) and coefficient of prediction (Q2 = 0.2). Using the predictive Variable Importance in Projection (VIP) vector, the top 30 crucial metabolites for this model were extracted  was run on data with two classes: PLWH with and without MetS. The top-ranking metabolites ( Boruta)  showed s Boruta) , and an  Boruta) . PLS-DA xtracted . LIMMA ixtracted . A UMAP xtracted . Seven oxtracted . γ-glutaxtracted . The RF xtracted . Interesxtracted . The fivxtracted  with an We thus tested whether there was an association between the biomarkers and MetS status corrected for BMI. All biomarkers were significant (FDR < 0.05) after BMI and treatment correction  and 2. P+ T-cells and non-classical monocytes in PLWH with MetS compared to without MetS  and significantly different metabolites in PLWH with MetS indicates negative co-relation of monocytes subpopulations of Glut1 with pyruvate level and CD8+ T-cells and classical monocytes with lactate level . Therefore, we investigated key metabolites from other pathways of central carbon metabolism: glycolysis, pyruvate metabolism, and the TCA cycle . No chanout MetS . PLWH wiout MetS . For xCTte level . These fTo establish the metabolic shifts  associated with MetS in PLWH, a weighted co-expression network analysis was performed using all detected metabolites and all patients. In this network, the nodes were individual metabolites, and edges were modeled by the positive coefficients of correlation between two metabolites . The resulting network consisted of 867 nodes and 45379 edges. Leiden partitioning highlights seven communities of positively intercorrelated metabolites  top. AnaIn this study, using a consensus approach of traditional biostatistics and advanced machine learning algorithms, we first identified altered amino acid metabolism as a central characteristic of PLWH with MetS with glutamate metabolism as a key metabolic pathway in this phenotype. Second, a weighted co-expression network analysis indicated the interaction between lipid metabolism and amino acid metabolism that could modulate glutamate metabolism as a coordinated metabolic alteration in PLWH with MetS. Finally, immune phenotyping of the transporter expression identified that both lymphocytic and monocytic cells exhibited increased levels of glutamate to cysteine transport, indicating elevated glutaminolysis in PLWH with MetS compared to PLWH without MetS.The role of amino acid metabolism in the development of metabolic dysfunction has been extensively studied in the general population, but it is still unclear in the context of HIV infection. The most consistent findings describe an essential role for the three branched-chain amino acids (BCAA)  in the development of cardiometabolic diseases, in particular, obesity and diabetes . In our In the present study, PLWH both with and without MetS had higher concentrations of several metabolites involved in glutamate metabolism compared to uninfected controls. Although the sample size of uninfected controls is small, these findings support a previous study that showed higher glutamate in PLWH than uninfected controls . The assin vitro studies showed that the inhibition of glutaminolysis affects ROS homeostasis in cancer cells , and one linear classification model (PLS-DA) to identify the most important metabolites associated with MetS in PLWH using R packages ropls and RF, respectively [For clinical data, Welch’s ectively . Both thectively . For RF,http://stitch.embl.de/). Association analysis was performed using R and python 3. First, metabolites with low variance were removed. Then, pairwise Spearman correlations between all metabolites were performed. Only correlations with FDR<0.05 were conserved. The distribution of correlation coefficients was plotted to evaluate the size and connection strength of the networks. Accordingly, a weighted co-expression network for significant positive associations for all samples was constructed using the python module igraph (https://igraph.org/python/). Random networks with the same dimensions were also constructed and network properties analyzed. Leiden algorithm was used for partitioning the network using the python module leidenalg [To highlight the most enriched pathways in PLHW with MetS group compared to PLHW without MetS, metabolites with differential abundance  were submitted to Ingenuity Pathway Analysis software (IPA) . The top 13 enriched pathways and associated metabolites were extracted, represented as a Sankey plot using R package ggalluvial and network using Cytoscape version 3.6.1 . Metaboleidenalg . The aven = 31) and without MetS (n = 27) we performed immunophenotyping of peripheral blood mononuclear cells (PBMCs) for transporter expression of glucose transporter (Glut1), monocarboxylic acid transporters (MCT-1), and glutamate/cystine transporter xCT on T-cells (CD4+ and CD8+) and monocytes  as described by us recently [In a subset of samples PLWH with MetS (recently . All celSupplementary FiguresSupplementary Tables


Synthetic biology has brought about a conceptual shift in our ability to redesign microbial metabolic networks. Combining metabolic pathway-modularization with growth-coupled selection schemes is a powerful tool that enables deep rewiring of the cell factories’ biochemistry for rational bioproduction. Escherichia coli and Pichia pastoris3. Their realization required the steady-state maintenance of appropriate concentrations of intermediate metabolites to allow for the activity of these non-native metabolic routes4. Moreover, an equally important rewiring of central metabolism in E. coli led to a non-oxidative glycolysis5. The resulting strain could catabolize one glucose equivalent to three acetyl-CoA molecules, instead of the two obtained via the canonical glycolysis. Non-traditional hosts can now also be engineered by harnessing the enabling tools of synthetic biology, as illustrated by the implementation of a linear glycolysis in the soil bacterium Pseudomonas putida, which fully replaces the native Entner–Doudoroff catabolism6. The combination of rational engineering and adaptive laboratory evolution (ALE) has been crucial to achieve these deep metabolic reconfigurations. The success of such studies shows a surprising degree of metabolic plasticity, even in core routes, which challenges the established textbook knowledge describing central metabolism as a static network.The field of metabolic engineering has entered the next level of maturity, shifting from looking at metabolism as a rigid structure to a rather flexible and versatile network. Recently, several groundbreaking studies focused on central assimilation pathways have demonstrated an unprecedented level of control over core metabolic networks. Examples of this sort aimed at establishing alternative assimilation pathways in microbial workhorses, e.g., implementing the autocatalytic Calvin–Benson–Bassham and ribulose monophosphate cycles in product · L−1 or gproduct · L−1), rates (gproduct · gCDW−1 · h−1 or gproduct · L−1 · h−1), and yields (gproduct · gsubstrate−1 or gproduct · gCDW−1), are the benchmarks of product formation efficiency7. One way to increase TRYs is engineering microbial metabolism to funnel carbon flow into the target bioproduction pathways. Traditional metabolic engineering strategies for improving production pathway capacities include overexpression of required pathway enzymes for a desired product and knockout of enzymes competing for metabolites with the production pathway. These genetic engineering interventions are typically followed up by screening strain performances; such traditional approaches have been extensively reviewed elsewhere8. In this study, we discuss the rationale behind (re-)emerging strategies for improving cell factories and propose exploiting metabolic versatility to advance the state-of-the-art.The degree of control we can now exert over metabolic networks can be exploited for designing next-generation cell factories. The societal impact of industrial biotechnology depends significantly on our ability to “teach” microbial hosts how to efficiently convert a substrate into a product of interest. TRY values, i.e., titers  of interest. In other words, combination of rational design, growth-coupled selection, and ALE will pave the way for screening and improving enzyme and pathway variants, which can be transformed into a microbial production platform. The coupling of metabolite biosynthesis to growth has been harnessed as a successful bioproduction strategy already for very long, especially in anaerobic fermentations. In the last two decades, design of knockout strains to couple production of new molecules to growth has been extensively explored, both by modeling and experimental approaches14. Yet, growth-coupled selection of pathway modules is still a relatively underrated approach for metabolic engineering.We advocate that metabolic fluxes can be rewired by employing rationally designed selection strains as an alternative to traditional metabolic engineering approaches. In literature, such an approach is usually described as “growth-coupled selection”16. Then, to assess these modules in vivo, we can test and optimize them into dedicated microbial selection strains. When no module is present, these strains depend on supplementation of additional nutrients for synthesizing one or more biomass precursors17  rate, which can be approximated by assessing the growth rate of the selection strains, and (ii) yield, for which the highest biomass concentration of the selection strain can serve as proxy  cycle7. By iterating DBTL cycles, microbial strains are improved in their TRY values. Product formation must be measured, and many research endeavors have significantly improved the accessibility and the rapidity of high-throughput, -omics pipelines for metabolites determination30. However, the type and number of samples to prepare, analyze, and interpret can render the analytic step a bottleneck in some DBTL cycles32. Only in very few cases product formation is measurable via readily available, cost-effective readouts, such as, e.g., colorimetry33.While entering its third decade of life as an applied discipline34. In the “build” phase, the selection strain will be generated and the module(s) of interest (and variants) will be plugged into the strain. In the “test” phase, the different strains will be cultivated under one or more selective conditions, and biomass formation will be used as a proxy for in vivo quantification of modules’ rates and yields , while still providing straightforward and useful data about the performance of biological parts. Moreover, it is a standard process that can be adapted to different needs, such as testing novel assimilation routes, alternatives for rate-limiting enzymes in the central metabolism, or synthetic production pathways  could be optimized by using this pipeline. One exemplary study that used such growth-coupled approach for upstream pathway optimization tackled the production of N-hexanol in E. coli37. To optimize the upper part of the pathway towards C6-precursors formation, the authors coupled NAD+ regeneration under anaerobic conditions to the formation of hexaonic acid. This served as a base to implement the whole pathway towards n-hexanol. In summary, use of growth-coupled selection could optimize production by performing early DBTL cycles and later continue with classical product screening-based DBTL cycles. This approach demonstrated to be beneficial, as the number of variants to be screened via the later classic DBTL could be limited.We argue that this adapted “growth selection-based” DBTL concept Fig.  improvesays Fig. . One exa38. In a biofoundry, rapid prototyping and optimization generally focuses on the production pathway of a cell factory. However, due to their value as high-throughput infrastructure, they are finding alternative applications to the original DBTL pipeline for biomanufacturing39. Because of its technical simplicity, growth-coupled selection is an interesting setup for streamlining the DBTL method in biofoundries. As an intellectual exercise, here we describe how the biofoundry pipeline could be adapted based on this very approach  or allow for the selection of the fastest growers in direct competition cultures . Such a pipeline can be employed for screening from single enzymes up to entire pathways. We argue that one can follow this method for the assessment of candidate modules needed for substrate assimilation, as well as testing alternatives for debottlenecking rate-limiting enzymatic steps within the central carbon metabolism. A complementary utilization of such infrastructures could include selecting microbial platforms that are naturally endowed with production traits of interest  and then engineering alternative assimilation routes for abundant, renewable substrates . Alternative uses of these infrastructures could include testing libraries of enzyme variants that are rate limiting in central carbon metabolism or introducing synthetic pathways that better connect key metabolic nodes important for microbial fitness. In conclusion, the use of growth-coupled selection in combination with pathway modularization and evolution represents an accessible, highly valuable opportunity for expanding the boundaries of industrial biotechnology by creating superior cell factories for novel bioprocesses.Biofoundries are high-throughput infrastructures used for analysis and optimization of biological systems via DBTL cyclesach Fig. . For thi


The process depends on local interactions and is underpinned by rock–paper–scissors dynamics in which populations of cells with streamlined genomes beat TEs, which beat non-streamlined genomes, which beat streamlined genomes, in continuous and repeating cycles. Streamlining is maladaptive to individual cells, but improves lineage viability by hindering the proliferation of TEs. Streamlining does not evolve in sexually reproducing populations because recombination partially frees TEs from the deleterious effects they cause.Eukaryotes and prokaryotes have distinct genome architectures, with marked differences in genome size, the ratio of coding/non-coding DNA, and the abundance of transposable elements (TEs). As TEs replicate independently of their hosts, the proliferation of TEs is thought to have driven genome expansion in eukaryotes. However, prokaryotes also have TEs in intergenic spaces, so why do prokaryotes have small, streamlined genomes? Using an This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’. In general, prokaryotes have small streamlined genomes where up to up to 90% of DNA is host-essential ,2. By coTo understand the relationship between TEs and genome architecture, it is necessary to consider mechanisms underpinning TE persistence. Theory predicts that, in asexual organisms, the long-term fate of TEs within a given lineage is extinction ,17. OppoAlthough TEs can become linked to ecologically relevant genes and thus confer direct benefits to their hosts, the majority incur measurable fitness costs ,29. MargHere, we present a coevolutionary model of TEs and their host genomes. The model explicitly considers hosts with a genome that contains stretches of coding DNA and non-coding DNA. Host genomes can be infected by TEs via uptake of extracellular DNA (eDNA). Once infected, TEs replicate within genomes, where integration into essential genes results in cell death and lysis. Lysis liberates TEs back into the eDNA pool. Our model shows that TEs can drive the evolution of genome streamlining in asexual organisms. The process depends on local interactions and is underpinned by rock–paper–scissors dynamics in which populations of cells with streamlined genomes beat TEs, which beat non-streamlined genomes, which beat streamlined genomes, in continuous and repeating cycles. Genome streamlining does not evolve in sexually reproducing populations because recombination partially unlinks TEs from the deleterious effects they cause. Together, our findings provide support for a previously unrecognized role of TEs in the evolution of genome streamlining.. 2 (a)https://github.com/bramvandijk88/selfishDNA) and the explorable model (https://bramvandijk88.github.io/cacatoo/TEs_streamlining/).To understand how the interaction between TEs and cells shapes genome architecture, we present an individual-based model of co-evolving TEs and host genomes packaged within cells. We first focus on simple bacteria-like cells, which engage in HGT via environmental pools of DNA, but later extend the model to encompass sexual reproduction. A brief overview of the model is given below. For more insights into the workings of the model, please see §4, the published code (a). Elements are represented as a linear sequence and can be exchanged and recombined through different mutational processes (b). For example, single gene duplications may result in redundant gene copies, subsequent gene inactivation may result in the generation of non-coding DNA, and further deletions/duplications may expand or reduce the amount of non-coding DNA.Individuals are simple cells that carry a genome with three distinct genetic elements: (i) ten host-essential genes (type A–J), which are necessary for survival/reproduction of the host, (ii) TEs, which are slightly costly to the host and (iii) non-coding DNA, which provides no function, but also carries no cost a. Elemenrocesses b. For exa) is lethal (fitness = 0.0).We assume that a maximally streamlined genome, i.e., a genome encoding only one copy of each essential gene, has the same fitness as a genome that has multiple gene copies and large stretches of non-coding DNA. Fitness differences arise via differences in TE-abundance and site of TE insertion. Insertion of TEs into non-coding DNA or redundant copies of host-essential genes are relatively harmless, incurring a fitness cost of just 0.005 per TE. Insertion into essential genes . When a grid point is empty, competition occurs between up to eight cells from the neighbouring grid points with the winner being chosen at random, but weighted by fitness. The genome of the winning cell is replicated (with mutations) and the daughter cell is placed on the empty grid point.φ) is high, host-level selection struggles to prevent accumulation of TEs and ultimately hosts are driven extinct. When φ is low, TEs replicate too infrequently to compensate host-level selection and degradation from the eDNA pool. Ultimately, coexistence of TEs and hosts requires the possibility that TEs infect naive (uninfected) lineages. The model assumes that TEs can transpose to the host chromosome after uptake via eDNA. Although such transposition events from naked DNA have been shown to occur [TEs replicate independently of host genomes. If the rate of transposition  of the healthy population, die. Dead cells lyse, spilling fragments of genome into the environment giving rise to a pool of eDNA that can be taken up in a transformation-like process by the next generation of cells. Uptake happens at a fixed rate (u = 0.01) and integration occurs with the same rate φ that determines transposition within genomes. Each time TEs replicate, there is a small chance that the φ-parameter changes. Taken together, the model contains multiple levels , with mutation and selection operating on each level. It is important to note that while spatial structure has special relevance to communities forming biofilms [After the reproductive phase, non-viable cells plus a small fraction φ) and degree of genome streamlining . All other parameters are defined in Two important factors affecting the stable maintenance of cells and TEs are the degree of genome streamlining and rate of TE transposition. The manner in which these two properties interact depends on the scale of interactions, and particularly on whether or not interactions are confined to near-neighbours. To explore parameter space, mutation rates were first set to zero, and simulations performed over a range of fixed values of transposition rate  (white points). TEs are unable to persist in cells that have highly streamlined genomes , while cells containing less streamlined genomes are susceptible to extinction by TEs (red triangles).As shown in figure 2a depend on the interplay between three factors: (i) genome streamlining, (ii) lethal mutations through TE insertion and (iii) the abundance of TEs in the  eDNA pool. In cells containing streamlined genomes, infection by TEs is likely to be lethal and thus there is little opportunity for the TEs to increase in frequency (figure 2b). At the other extreme, cells with non-streamlined genomes are less likely to be killed by TE infection. This allows TEs to increase in abundance, first within cells, and second in the eDNA pool. The latter then contributes to further amplification of TE abundance via HGT. As the TE load increases there is increasing chance that all cells become infected and from this state, there is no possibility of recovery (figure 2d).The conditions for coexistence shown in figure 2c) is essential for long-term survival of both TEs and cells. This pool of uninfected cells decreases in frequency when infected by nearby TE-carrying strains, but can increase in frequency by recolonising vacant niche space made available by extinction events (white arrows in figure 2c). Uninfected cells are then available for reinfection, resulting in a time-dependent cyclical process with chaotic waves . The critical factor for maintenance of both TEs and hosts is the time-to-extinction (and subsequent cell death and lysis) after infection. If this is too rapid (as happens with highly streamlined genomes), then TEs have little opportunity to amplify within genomes and eventually go extinct. If extinction is too slow, then all cells become infected and all host cells eventually go extinct.Maintenance of uninfected cells , thus demonstrating the central importance of spatial structure and local interactions.Another important factor determining coexistence is the interaction range. If the infection of one strain readily infects another cell, the uninfected pool of cells is reduced over time. As evident from figure 2 (c)An intriguing finding from the above analysis is that streamlined genomes are resistant to invasion by TEs. This is evidently a lineage-level effect. To individual cells with streamlined genomes, infection by a TE is invariably lethal. In other words, what is costly to the individual appears beneficial at the lineage level. A central issue is whether this apparent example of altruism can evolve de novo.φ) (see §4). Cells in the initial host population are all identical, carrying 10 host-essential genes and 30 non-coding positions, and were locally inoculated with TEs (in the middle of the grid).To this end, we introduced TEs into an evolving population of cells containing  non-streamlined genomes. Mutations occur after each replication step, modifying genome size and genome content of hosts, as well as transposition rates of TEs , genomes expand in size. This is a consequence of TE amplification, but also entails an increase in the number of host-essential genes and non-coding elements. After expansion, a period of genome streamlining occurs (episode II). During this phase, a decrease in the number of TEs and non-coding DNA is observed. The decrease in TE-abundance does not reflect a decrease in the transposition rate (φ), which instead increases over evolutionary time . Thus, TEs do not adapt to their host by becoming less infectious. Eventually, TEs and the amount of non-coding DNA reach a stable equilibrium (episode III), where genomes are comprised primarily of host-essential DNA (figure 3b). Concurrent with genome streamlining is a decrease in vacant niche space (black areas in figure 3c), which reflects decreased TE-driven extinction events and therewith an increase in the total population size . In the steady state, most of the population consists of uninfected host cells, with occasional bursts caused by TE infection .Data in figure 3a shows, for each extinct lineage, the number of TEs that accumulate from the time of TE infection until extinction. Lineages of cells with non-streamlined genomes  persist for longer after infection and liberate many more TEs into the eDNA pool compared to lineages of cells with streamlined genomes (generations 1800–2100). The histograms in figure 4b show that streamlined genomes go extinct rapidly (no more than a few generations), and as a consequence only produce a few TEs (figure 4c). Thus, although streamlined genomes produce fewer progeny in the short term, they eventually shape an environment in which they thrive. Moreover, after non-streamlined genomes have succumbed to bursts of TE infection, streamlined genomes are free to invade the space freed by cell lysis (figure 4c).As evident in the ecological simulations above , the timFigure 4 (d)c indicate an important role for spatial structure. To test this directly, we repeated the simulations, but assigned individuals to a random position after each round of competition and reproduction. Starting from conditions that are ecologically viable according to The data shown in figure 3 (e)c).The evolution of genome streamlining appears adaptive in that it results in the elimination of TEs—at least temporarily—from local populations. But to individual cells, genome streamlining is clearly maladaptive: infection of cells with streamlined genomes is invariably lethal. The evolution of genome streamlining thus appears to be attributable to selection at the level of lineage viability, with those lineages comprised of streamlined genomes outcompeting lineages with less streamlined genomes  DNA at the insertion site. When b = 0, however, TEs are assumed to insert precisely in between two genes, avoiding DNA damage . In the latter case, genome streamlining does not evolve . However, we found that genome streamlining always evolves when b > 0, although genome streamlining evolves very slowly when the risk of DNA damage is low .To test the hypothesis that lineages of cells containing streamlined genomes gain a lineage-level benefit that derives directly from the lethal effects experienced by individual cells, we modified the above simulations. Specifically, we included a parameter TEs and their hosts nonetheless persist through continuous waves of infection and recolonization of available niche space. This result demonstrates that the evolution of genome streamlining  is drive (f)c seems counterintuitive. However, understanding emerges from examination of the eco-evolutionary dynamics , combined with observation of the evolution of non-streamlined genomes in the absence of TEs. Starting with the latter, data in electronic supplementary material, figure S6 show that non-streamlined genomes replace streamlined genomes in the absence of TEs. This occurs in part as a consequence of duplication bias , but also because genome expansion generates multiple copies of essential genes that confer mutational robustness. Thus, in the absence of TEs, selection favours larger genomes.Given that cells containing streamlined genomes drive TEs extinct, the persistence of TEs shown in figure 3In data from evolutionary simulations , TEs areCells with streamlined genomes thus beat TEs, which beat non-streamlined genomes, which beat streamlined genomes, and so on, in a cyclical game of rock–paper–scissors . The lon (g)As TEs require transfer to new linages to persist, simulations in which DNA uptake is disabled result in TE extinction . However, TEs in nature can also persist in populations through sex and recombination. Our model was therefore modified to incorporate sexual reproduction by disabling DNA uptake and implementing a simplified form of sexual reproduction. In these populations, competition for vacant space is determined by sampling two individuals from the local neighbourhood (weighed by their fitness), and their genomes recombined via a single cross-over event (see §4). Importantly, this process allows TEs to infect new lineages without transposition, removing the risk of lethal mutations. These sexually reproducing ‘eukaryotic' populations . In asexual populations, cells with streamlined genomes die during the infection step, thus immediately blocking further TE amplification. With recombination, however, TEs can infect new lineages without risk of immediately killing the host, irrespective of the level of genome streamlining. Although subsequent transposition may still render the host inviable, the host cannot prevent a TE from infecting its genome, thus allowing the TE to replicate at least once. The fact that genome streamlining does not occur in these sexual populations suggests that streamlining in asexual populations evolves to prevent transposition . 3in silico coevolutionary model of TEs and host genomes. The model reveals an interesting interplay between genome streamlining (the amount of coding DNA) and TE-abundance. Selection initially favours cells with expanded genomes, because additional genome space reduces the chance that transposition has deleterious effects. However, while adaptive at the level of individual cells, cells with expanded genomes provide opportunity for the population of TEs to increase in the lineage of descendent cells to the point where extinction of the lineage becomes inevitable. When the environment is spatially structured, such extinction events are localized, enabling the more persistent (streamlined) genomes to recolonize vacant niche space. We found that the resulting coexistence of TEs and hosts is remarkably stable, which can be explained by rock–paper–scissors interactions [Here, we have presented an ractions ,43,44, sractions . FinallyInterestingly, genome streamlining is maladaptive at the individual cell level, but is selectively favoured because of benefits that accrue to lineages of cells. This is analogous to abortive infection, a well-studied mechanism that protects cellular collectives against bacteriophages . EarlierE. coli and Pseudomonas fluorescens SBW25 [The TEs in our model are based on insertion sequence (IS) elements, a particular yet common class of TEs. TEs are assumed to be autonomous (they encode their own transposase function), show no notable bias in insertion site preference, and move both vertically and horizontally. Not all TEs are marked by these characteristics. For example, REPIN sequences  take up many intergenic spaces in ns SBW25 ,17. Howens SBW25 . The patin silico models concern relevance to the natural world. However, there are reasons to assume the likelihood of legitimate connections, particularly given the spatially structured nature of microbial populations, the abundance of TEs and pervasiveness of HGT. Evidence could be sought by interrogation of genome sequences from a set of phylogenetically related strains sampled at precise spatial and temporal scales. Such a future project would be a major challenge, however, as our model abstracts away from the precise magnitude of those spatial and temporal scales. For example, although the ecological processes illustrated in our study are reminiscent of biofilms, the importance of local extinctions and recolonization is perhaps better captured by processes on a larger scale, such as microbial populations growing on segregated food particles with limited exchange. An alternative possibility is to compare data on the relationship between genome size and TE-abundance derived from the analysis of diverse genome sequences with theoretical predictions of this relationship at equilibrium. The latter can be derived from our model populations by analysis of the genomes of all viable cells present at the end of the simulations. The data, shown in electronic supplementary material, figure S7, indicate a strong positive correlation between genome size and TE-abundance only under conditions where TEs cause harmful effects. Precisely such a relationship has been previously reported [Questions unanswered through use of reported  for IS-ereported ,50, whicreported .For illustrative purposes, we have deliberately not included other mechanisms that are known to result in genome streamlining. For example, alternative hypotheses for the different structures of prokaryotic and eukaryotic genomes are differential energy budgets , deletio. 4The model implemented in this study is an individual-based model (IBM) of the coevolution of TEs and their host. The primary goal of the model is to first explore the conditions under which autonomously replicating TEs can coexist with their host, and how the insertion of TEs into coding DNA shapes the composition of the host genome.φ (see §4d), and serve no purpose to their host. Besides TEs, simple genomes contain host-essential genes and non-coding DNA (see §4a). The exact proportion of non-coding to coding DNA may vary through mutations (see §4b) that occur when cells replicate after local competition has occurred (see §4e). As a consequence of mutation, each TE can also have a unique transposition propensity (φ). In short, the model contains multiple levels, describing both the ecology and the evolution of TEs and their host genomes.The model is composed of a  spatial grid, which is a computationally efficient way of modelling local interactions. On this grid, three types of entities can reside: (i) simple cells with genomes packaged within them, (ii) naked DNA from prior generations that is taken up by cells, and (iii) TEs that can replicate within genomes and transfer horizontally via the eDNA pool ( (a)a–j). We assume that these essential functions are performed when an essential gene with function i is present at least once (ie > 0). Carrying multiple copies of these genes does not directly impact fitness (if), which we assume in order to deliberately avoid selecting for streamlining genomes simply due to reduction in costs. However, a genome that lacks one of these essential functions has fitness zero, meaning it cannot (or can no longer) compete for reproduction and dies in the next time step. The second type of genetic elements we consider are TEs, which self-replicate within genomes. The total number of TEs (T) confer a small cost (c) to the host. The fitness of the host then becomesIndividuals carry a genome that encodes a linear sequence of genetic elements. We assume that cells need to perform 10 essential functions, and therewith there exist 10 essential genes . (b)φ with a uniform step size (0.1) up or down.Mutations happen every time genetic elements are replicated, and change the gene content and genome size of individuals. When cells reproduce, their genomes are scanned from left to right, allowing each genetic element to undergo mutations once. Changes can be applied to the genetic element itself, or they can be the start site of a large-scale deletion, duplication, or inversion of multiple genes. These large-scale events enable the rearrangement of gene order, and also ensure that genomes do not grow indefinitely in the absence of a deletion bias . Single  (c)d. Moreover, cells that are not viable (or no longer viable due to transposon-induced mutations) also die. Before being removed from the grid, dead cells spill their DNA into the environment. This DNA is uniformly fragmented into pieces of 3 to 8 genetic elements each. These stretches of DNA can either degrade (with rate q), or be taken up by future generations (with rate u).Every time step, a small subset of the host population stochastically dies with probability  (d)j. Every time step a cell survives, each TE in its genome gets an opportunity to replicate one with rate φ·j, where each TE can have a potentially different φ-parameter. The second process by which TEs spread is by means of a transformation-like process. Living cells take up naked DNA derived from prior generations with rate i, after which TEs can integrate into the host chromosome with the same rate φ. We assume that both transposition events (i.e. after uptake or during the cell's lifetime) occur at random positions in the chromosome, and can cause the inactivation of genes at the insertion site (lightning symbols in figure 1a). When a gene at the site of transposon-insertion codes for one of the host-essential functions, this event is lethal for cells (unless another copy of that gene is still active). Transposons inserting into non-coding DNA never have a (direct) damaging effect.The dynamics of TEs occurs through two distinct processes. The rate at which TEs replicate during the lifetime of a cell is set by parameter In the main text, we also test what happens when inactivation of genes at the insertion site was removed. For these populations, TEs are always inserted ‘next to' rather than ‘into' the genetic element at the insertion site. Note that whether TEs insert ‘into' or ‘next to' genetic elements at the insertion site does not influence their transposition dynamics but only the potential damage that transposition may cause.Our model assumes that a TE can transpose directly after the uptake of DNA. Although transposition events from naked DNA have been shown to occur  by using (e)i wins this competition (Ri) is determined by the individual's fitness (fi), divided by the total fitness of all competitors (fTOTAL) plus a constant ε. The latter constant ensures that a single individual does not win by default, and ensures that it is unlikely for any individual to reproduce when all competitors are unfit.Each time step, competition happens for unoccupied grid points. Up to eight cells in the direct (Moore) neighbourhood compete proportional to their fitness. The relative chance that individual  (f)To distinguish the process of HGT from sex and recombination, we implemented a simple mode of sexual reproduction in our model. For this, two competitors are sampled proportional to their fitness (see above), and their genomes are recombined. Because the genes in our model have no sequence identity to infer homology, we cannot model actual homologous recombination, so we instead assume a simple cross-over event that occurs in the middle of the two genomes. The resulting (haploid) genome undergoes mutations in the same way as a clonally reproduced genome would in the base model. These sexually reproducing populations do not take up environmental DNA. (g) —TEs can  coexist with their host genome •Requires sufficient HGT (or sex) for TEs to jump to new lineages •Requires local extinctions such that healthy lineages can invade empty space  •DNA diffusion is low, so that TEs can only infect local strains (and not the entire population at once) —The model remains computationally feasible •Large-scale duplications and deletions are assumed, so that genomes do not grow indefinitely  •The system size is set to the minimal size where local extinctions, wavefronts, and multiple strains can occur simultaneously.Because our model is an abstraction of biological processes, it is not trivial to estimate the precise values that would be realistic/accurate. We however found that, given parameters that allow for host/TE coevolution, our main results are robust to the precise values of parameters. Apart from the parameter sweeps presented throughout this study, we therefore chose to parameterize the model by finding  parameter values where:Our evolutionary simulations were performed with the above-mentioned conditions in mind. Other variables  are allowed to evolve. See  (h)The individual-based model presented in this study is a C++ extension of Cash (Cellular Automaton simulated hardware), originally written in C by R.J. de Boer and A.D. Staritsk. All analyses were done in R, using the packages ggplot2 , dplyr .