INST-MFA entails fitting a (large) system of coupled ordinary differential equations, with metabolite pools and reaction fluxes as parameters. Here, we focus on the Calvin-Benson cycle (CBC) as a key pathway for carbon fixation in photosynthesizing organisms and ask if approaches other than classical INST-MFA can provide reliable estimation of fluxes for reactions comprising this pathway.Flux phenotypes from different organisms and growth conditions allow better understanding of differential metabolic networks functions. Fluxes of metabolic reactions represent the integrated outcome of transcription, translation, and post-translational modifications, and directly affect growth and fitness. However, fluxes of intracellular metabolic reactions cannot be directly measured, but are estimated via metabolic flux analysis (MFA) that integrates data on isotope labeling patterns of metabolites with metabolic models. While the application of metabolomics technologies in photosynthetic organisms have resulted in unprecedented data from First, we show that flux estimation with the labeling patterns of all CBC intermediates can be formulated as a single constrained regression problem, avoiding the need for repeated simulation of time-resolved labeling patterns.Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii under different growth conditions.We then compare the flux estimates of the simulation-free constrained regression approach with those obtained from the classical INST-MFA based on labeling patterns of metabolites from the microalgae Our findings indicate that, in data-rich scenarios, simulation-free regression-based approaches provide a suitable alternative for flux estimation from classical INST-MFA since we observe a high qualitative agreement ( While metabolite concentrations and contents can be measured at cell- and (sub)compartment-specific level , reactioglobal MFA approaches is well established even for the isotopic nonstationary state and implemented in toolboxes like INCA  and related canonical processes, namely starch and sucrose syntheses. Estimating fluxes in the CBC and other downstream processes in metabolic networks of photosynthetic organism in photoautotrophic growth conditions necessitates the usage of isotopically nonstationary MFA (INST-MFA). This is the case since under photoautotrophic growth conditions, feeding 13CO13C-MFA, INCA  that can be regarded as a constraint-regression problem in which we minimize the chi-square goodness-of-fit measure for the change in MID concentration over time as well as external fluxes. The minimization is performed under the constraint that the entire system, represented by stoichiometric matrix with 2.2Chlamydomonas reinhardtii and Chlorella sorokiniana under low light , and in Chlorella ohadii under LL and extreme illumination levels . Like in Treves et al.  FBP, ADPG and UDPG were the only metabolites not at steady state  as well as flux values for starch and sucrose synthesis reactions . This approach was possible since all MIDs included in the ODEs were measured  and 520.6 (149 degrees of freedom), respectively, and were not considered statistically acceptable. Since the relative error in measurements under EIL is not significantly lower than in the other algae, there is no experimental basis for omitting the last time point, as there was for C. ohadii under LL. Our previous modeling efforts with INCA did not find acceptable fit for the data of C. ohadii under EIL  of bootstrap samples were within the acceptable range . For reactions PFP/FBA  the sum of flux through the entire CBC was the highest for . ohadii . For a dC. reinhardtii and C. sorokiniana. Interestingly, the Spearman correlation of the flux distribution for C. ohadii to that of either C. reinhardtii or C. sorokiniana was smaller , followed by C. reinhardtii (66s) and C. sorokiniana (6min). In line with the observed turnover of RuBP, we found that turnover of FBP was the fastest in C. ohadii (15s), followed by C. reinhardtii (22s) and C. sorokiniana (31s). However, while SBP exhibited the fastest turnover in C. ohadii (18s), C. sorokiniana (24s) showed similar turnover as C. ohadii, followed by C. reinhadtii (49s) with the slowest turnover for SBP. Together, these findings indicated that C. ohadii has more efficient photosynthesis in comparison to C. reinhardtii, indicated by higher carboxylation rates in comparison to C. reinhardtii, while metabolites pool sizes are in the same order of magnitude for both algae. In addition, it can be also concluded that C. ohadii has more efficient photosynthesis in comparison to C. sorokiniana, indicated by similar carboxylation rates for C. ohadii in comparison with C. sorokiniana, despite pool sizes in C. ohadii which are one order of magnitude smaller than those observed for C. sorokiniana . Hence, we investigated if the difference of flux estimates may be due to variance in metabolite pool sizes used in INCA and measured pool sizes integrated in the SFCR approach. While the estimated total pool size from INCA matched the measured content for ADPG, RuBP and UDPG, estimates of pool sizes for F6P, FBP, G1P and G6P were different from mean measurements by at least one order of magnitude . We found that the flux estimates obtained from the original time series and the time series with 3The availability of flux estimates for reactions in a metabolic network provides several possible applications. In scenarios in which fluxes are compared between conditions for the same organism, reactions with differential fluxes can be seen as main drivers for the differential behavior for a related trait at a higher level (e.g. growth). Further, in scenarios in which fluxes of the same pathway or network are compared across organisms , the reactions with differential fluxes between organisms, can indicate points of intervention to drive the high-level trait towards a desired biotechnological goal. To this end, the fluxes estimated from integration of labeling data can be integrated in genome-scale metabolic networks as constraints in various constraint-based approaches. However, obtaining flux estimates for photosynthetic organisms, particularly in plants, remains challenging due to the large experimental efforts as well as the related computational problems associated with the relevant mathematical formulations.C. reinhardtii and C. sorokiniana; however, it could be further refined by experimental knowledge in later studies. For C. ohadii, the compartmentation predicted in INCA indicates larger cytosolic than chloroplastic pools for T3P, FBP and G6P. Note that compartmentation values obtained from INCA correspond to those obtained from the pseudo-reaction added for compartmentation effects. Furthermore, we found differences between the compartmentation ratio from the ratio of estimated pool sizes used in INCA  and obtain flux estimates as well as consider multiple modeling scenarios which we discuss here. Interestingly, the SFCR formulation allowed us to investigate compartmentation via a simple sampling approach, significantly reducing the time for parameter estimation. The sampling approach assumed that the ratio between cytosolic and chloroplastic pool is between 0.1 and 0.9. This assumption is in line with predictions from INCA  total pool sizes, (2) sizes of pools actively involved in photosynthetic processes, (3) pool compartmentation, and (4) constraints on boundary fluxes to the modeled system.C. sorokiniana and no sugar production for C. reinhardtii and C. sorokiniana  differ by an order or magnitude, result in no acceptable fit for Another possibility is that only a fraction of the measured pool actively participates in photosynthesis, due to various reasons . RepeatLastly, it is known that boundary fluxes have a large effect on the flux estimates and often lead to numerical issues, which has prompted the usage of local approach that motivated our work. Rescaling the experimental exchange fluxes used by division with factor 100 results in starch and sucrose synthesis rate estimated from SFCR that match the observed pattern, underlining the issue of numerical problems. The rescaling of the exchange flux leads to a good qualitative agreement between flux estimated for individual algae from SFCR and INCA indicated by  studies .Our study showed that in data-rich scenarios, as it is the case for the CBC, as one of the major pathways in photosynthesis, SFCR provides a suitable alternative to INST-MFA approaches (e.g. as implemented in INCA). Recent developments in omics technologies result in high coverage in terms of data for other pathways, including: the TCA cycle and amino acid biosynthesis, and parts of lipid metabolism, for which SFCR can be applied in the future. In addition, the formulation of SFCR could be used to compare the extent to which flux distributions obtained from different constrained-based approaches fit experimentally measured labeling pattern. However, our study also pointed at several issues with flux estimation, even in data rich scenarios, and indicated that future efforts must focus on careful comparison of multiple alternative model structures and provide validations and plausible explanations for the discrepancy between measured and fitted (compartmented) pool sizes. To this end modifications to the approach may need to be introduced, such as rendering it applicable to estimation of fluxes akin to local approaches .44.1We used two variants of a metabolic model for the CBC differing in their complexity. The first model version, denoted as ‘simple CBC model’, considers only CBC with a branch towards starch synthesis and no compartmentation of metabolite pools. The second model version, denoted as ‘compartmented CBC model’, includes reactions for the CBC and related processes, i.e. photorespiration, starch and sucrose synthesis. The model considers metabolite compartmentation into chloroplast and cytosol. Under the assumptions indicated in the main text, ODEs for the MIDs of RuBP, F6P, FBP, G6P, G1P, and ADPG could be written for the ‘simple CBC model’ and ODEs for the MIDs of RuBP, F6P, FBP, G1P, G6P, UDPG, and ADPG could be written using the ‘compartmented CBC model’. A full list of model reactions and ordinary differential equations can be found in 4.2https://github.com/ankueken/MFA_Reg).To assess the quality of the fit we used: (i) the 4.3C. ohadii under LL we did not consider the last time point (see main text). Data on metabolite pool size, MID level and starch/sucrose synthesis rates that entered in the program above can be found in -6Having measurements of absolute pool size concentrations and MIDs at five time points 0, 5, 10, 20 and 40s, 4.4To obtain CI and The fit between model prediction and data can be accepted if the obtained rix N) .4.513C labeling of the total pool are the summation of the labeling dynamics observed in the plastid and those observed in the cytosol.For a metabolite present in both the chloroplast Hence, in the QP above matrix 4.6The SFCR approach also allows to treat the pool size parameter https://github.com/ankueken/MFA_Reg.Publicly available datasets were analyzed in this study. This data can be found here: Conceptualization, ZN; Experimental data, HT; Implementation, AK; Formal Analysis, AK, ZN; Writing – Original Draft, AK, ZN; Writing – Review and Editing, AK, HT, and ZN. All authors contributed to the article and approved the submitted version.


Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput 13C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris.Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5.P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris’s central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate The online version contains supplementary material available at 10.1186/s12934-023-02123-0. Pichia pastoris (syn. Komagataella phaffii) has gained a lot of attention in the recent years due to its increased use in metabolic engineering applications -propionic acid sodium salt) were mixed. TSP was used as internal standard and quantified using a commercial succinate solution at 1 g L− 1 . 1D-1 H analyses were carried out on a Bruker Advance III 800 MHz spectrometer  equipped with a 5 mm CQPI cryoprobe. Samples were analysed at 280 K using a 30o pulse sequence with water suppression (zgpr30), with a relaxation delay of 7 s. TopSpin v3.6.4  was used for the analysis of the NMR spectra. Commercial succinate at 1 g L− 1  was used for the re-quantification of TSP. All data was corrected based on the quantification of succinate. The peak integration data from the NMR analyses can be found in Supplementary File Glycerol, 3-HP, and D-arabitol were quantified from the filtered supernatant samples using Nuclear Magnetic Resonance (NMR). 180 µL of sample and 20 µL of 10 mM TSP (3-(trimethylsilyl)-[2,2,3,3-Quenched samples were vortexed for 30 s and centrifuged at 5,000 g for 5 min in a swing-rotor centrifuge. Pellets were dried using a Rotavapor . The biomass was treated with 150 µL 6 N HCl at 110 °C for 16 h. The acid was evaporated in the rotavapor. Each sample was washed using 100 µL milliQ water and subsequently dried in the rotavapor. This step was repeated twice to eliminate all the acid traces. Finally, the pellets were resuspended on 200 µL milliQ water. The samples were centrifuged at 10,000 g for 5 min to eliminate the biomass debris. 40 µL of supernatant (containing the amino acids) were diluted onto 460 µL milliQ water.− 1, and the temperature of the sampler and the column were set to 4 and 30 °C, respectively. Solvent B set points were varied as follows: 0 min: 2%; 2 min: 2%; 10 min: 5%; 16 min: 35%; 20 min: 100%; 24 min: 100%. Finally, the set points of the initial conditions (2% solvent B) were set for 6 min before the injection of the next sample. The injection volume was 5 µL.The diluted amino acids were analyzed using a previously described HPLC-MS method . The UHPThe MS detector was set to detect the positive ions on FTMS mode. The resolution was set to 70,000 (at m/z = 400), the capillary temperature to 320 °C, and the source heater temperature to 300 °C. Sheath gas and auxiliary gas flow rates were set to 40 and 10 arbitrary units, respectively. The S-lens RF level was set to 40% and the source voltage to 5 kV. The MS was set to measure the exact mass of all carbon isotopologues of all the amino acids (Supplementary File https://github.com/MetaSys-LISBP/IsoCor) [The isotopologue distribution of the amino acids was corrected considering the isotopologue labelling of the inoculum and the natural abundance of all isotopes using IsoCor v2.2.0 (/IsoCor) . Raw and600 and the NMR supernatant analyses results were used to calculate the growth rate (µ) and the specific production or consumption rates (q-rates) for glycerol, 3-HP, and D-arabitol. To this end, PhysioFit v1.0.2 (https://github.com/MetaSys-LISBP/PhysioFit) was used [600 was correlated to the biomass concentration using a constant conversion factor for all the strains, the rationale being that all the strains used in this study shared the same conversion factor in the culture conditions used. This assumption is supported by experimental data from our previous studies [600) to the cell dry weight (CDW) was statistically identical for strains PpHP6, PpHP8, and PpHP18 in batch cultures using the same growth medium. The conversion factor value was derived from calculating the µ (which is independent from the conversion factor) of the triplicate cultures of the reference strain (X-33) at pH 5. Afterwards, the genome scale model iMT1026v3 was used to calculate the uptake rate of glycerol at such growth rate (0.21 h− 1) considering a non-growth associated ATP consumption of 2.51 mmol gCDW−1 h− 1 [Glyc gCDW−1 h− 1. This calculation was done in Matlab using the CobraToolbox v2.26.0 [CDW/OD600. The biomass yield for strain X-33 using this calculated conversion factor resulted in 0.73 gCDW gGlyc−1, which falls within the reported experimental values [The ODwas used . The abs studies , 5, wherW−1 h− 1 , which r v2.26.0 . Finallyl values .600 data in Supplementary File The biomass concentration data can be found together with the ODThe bioprocess parameters results calculated using PhysioFit can be found in the Supplementary File influx_si v5.3.0  and theAll fluxes were normalized to the rate of the substrate uptake rate. A sensitivity analysis was performed using the Monte-Carlo method with 100 independent simulations. Statistical analysis (chi-square test) was used to determine sufficient goodness of the fit (based on a 95% confidence level) for each experiment. The average fluxes and associated standard deviations of the mean were calculated from the biological replicates with sufficient goodness of the fit.13C-data have been followed [Throughout this study, the reference guidelines for the calculation of fluxes derived from followed . TherefoFlux distributions were visualized using Omix v2.0.7  . The figPpaCore_3HP.mat model. The experimental confidence intervals obtained from the Monte Carlo analyses were used as upper and lower bounds for each flux. Afterwards, FBA was performed using the maximization of the flux of the ATP of maintenance reaction (ATPM) as objective function. This is the objective function resulting in the best description of the intracellular fluxes of a cell culture growing on a batch culture with excess of substrate [Flux Balance Analysis (FBA)  was perfubstrate . FBA resP. pastoris strains[13C-fluxomics platform [P. pastoris strains are depicted in Fig. The series of previously available 3-HP-producing s strains, 5 were platform . The stomax) for each strain  and a cytosolic version of the mitochondrial NADH kinase from Saccharomyces cerevisiae (cPOS5) in strain PpHP6 led to a further increase in 3-HP yield by enhancing the conversion of acetyl-CoA into malonyl-CoA (3-HP’s metabolic precursor) and NADPH supply, respectively. In addition, the expression of a second copy of the gene encoding the C-terminal domain of MCR yielded the highest 3-HP-producing strain (PpHP8), which was also the slowest growing strain and the strain producing the largest amount of D-arabitol in batch cultures. As previously reported, the overexpression of the genes encoding for the cytosolic acetyl-CoA synthesis pathway products production rates (q-rates) and µain Fig. , which iactivity , 46, hensiae cPOS in straisiae cPOS in strai− 1 and 0.22 h− 1, respectively), but also the substrate uptake rate (2.10 and 3.37 mmol gCDW− 1 h− 1). In fact, the glycerol specific uptake rate (qS) of PpHP8 was significantly lower than the mean qS value observed for the rest of strains . A high activity of the malonyl-CoA to 3-HP pathway probably led to a reduced availability of acetyl-CoA for this strain. Acetyl-CoA plays a central role on the biosynthesis of precursors, and it plays a key role in physiological regulation processes, such as the acylation of histones [S. cerevisiae strains harbouring a high acetyl-CoA carboxylase activity, where depletion of acetyl-CoA was described as the most likely cause of hampered growth [acsSeL641P was overexpressed support this hypothesis.Results in Fig. histones . Growth d growth . MoreoveacsSeL641P , and PDC1 plus acsSeL641P (PpHP18) were lower than the biomass yield of strain PpHP8.Strikingly, the biomass yield for PpHP8-derived strains overexpressing ArDH) in strains PpHP15 and PpHP17 resulted in increased growth rates but reduced 3-HP production, compared to their corresponding parental strains .Notably, all strains showed production of D-arabitol under the tested conditions. This by-product is typically produced due to an imbalance on the NADPH regeneration , 5, resuThe bioprocess parameters of the series of strains obtained from batch cultivations and the amino acids isotopologue distributions derived from the corresponding labelling experiments were used to calculate the intracellular fluxes. The fluxome of each strain can be found in the Supplementary File max) were higher than the ones observed in chemostat cultures at lower growth rates (0.10 and 0.16 h− 1), coherent with the positive correlation between growth rate and the UG and PPP fluxes previously observed in glycerol chemostats  obtained using the HT robotic platform is comparable to the previously reported results for the same strain growing in glycerol chemostats on a similar medium . NoticeacPOS5Sc) was overexpressed (PpHP5), the observed fluxes of the UG and PPP reactions were 15∼30% lower compared to PpHP2. It is well known that the NADPH/NADP+ ratio controls the fluxes towards the oxidative branch of the PPP [cPOS5 in P. pastoris provides the cell with an additional source of NADPH, leading to a higher NADPH/NADP+ ratio [+ ratio.To facilitate comparison of metabolic flux distributions amongst strains, a heat map illustrating the fold-change between the relative fluxes  of each recombinant strain compared to the relative fluxes of the reference strain cultivated at pH 5 is shown in Fig.  the PPP . In addiP+ ratio . TherefoACC1Yl was overexpressed . For the strain PpHP8, which harboured an additional copy of the gene encoding for MCR-CCa, the highest fluxes towards 3-HP production were observed, while the UG and PPP fluxes were the lowest among all strains. Such observation agrees with the results for the strain PpHP5, as the increase in the NADPH requirements due to production of 3-HP followed independent trends with the fluxes of the oxidative branch of the PPP.No major changes were observed when acsSeL641P in strain PpHP8  led to a drastic switch in the strain’s fluxome. The relative fluxes through the UG and PPP increased remarkably in PpHP13, while showing a lower relative flux towards 3-HP production compared to PpHP8 or PpHP6 strains. Moreover, the biomass yield of PpHP13 was lower. Therefore, considering the NADPH requirements for biomass and 3-HP production, increased production of NADPH through the PPP seems unfounded. Deletion of the gene encoding for the NADPH-dependent arabitol dehydrogenase enzyme (ArDH) in strains PpHP8 and PpHP13  led to minor changes in the strains’ fluxomes under the tested growth conditions. Finally, overexpression of PDC1 in PpHP17 (resulting in PpHP18) led to the highest relative flux through the UG and PPP. However, neither the biomass yield nor the 3-HP yield were affected , absolute changes of these fluxes did not have an impact on the strain’s biomass and product yields.Changes in the fluxes through LG and the TCA cycle reactions followed the opposite trend to the UG and the PPP. Small differences in the fluxes through the glyoxylate cycle were also observed. However, as the absolute values of these fluxes were low  in the reference and the 3-HP-producing strains PpHP8 and PpHP18 Fig.  providedCDW−1 h− 1 and 0.12 mmol gCDW−1 h− 1, respectively). The overexpression of ACSSeL641P and Pdc1 in strain PpHP8 did not result in a higher flux towards cytosolic acetyl-CoA in strain PpHP18, as the absolute flux values for the two strains were identical (0.30 mmol gCDW−1 h− 1). Compared to the reference strain, overexpression of the cytosolic acetyl-CoA production pathway in strain PpHP18 did not increase the LG fluxes. All these findings agree with previous results in S. cerevisiae, where the overexpression of PDC1 led to a higher flux towards this pathway without increasing the glycolytic flux [S. cerevisiae, and the glycolytic flux cannot be increased by overexpressing individual enzymes [P. pastoris, increased glycolytic fluxes have only been described under low oxygen availability [First, overexpression of the 3-HP production pathway led to a higher pyruvate decarboxylase flux at the pyruvate node in PpHP8 compared to the reference strain , supporting that the NADH kinase reaction contributed to cover the cell’s NADPH requirements.The results from the previous section show that the fluxes through the PPP decreased when a heterologous cytosolic NADH kinase was expressed. Consistently, NADPH production rates calculated from the ata Fig. A indicatSeL641P), also produced more NADPH through the oxidative branch of the PPP than the actual cell requirements. The substantial increase in the fluxes through UG and PPP for such strains coincided with an increase in the specific glycerol uptake rate and the growth rate  was used as an objective function. This is the function best describing the intracellular fluxes of a cell culture growing on a batch culture with an excess of substrate [To corroborate the consistency of the observed metabolic fluxes, FBA was used to verify the redox and energy conservation of the ubstrate . ConsequA higher ATP of maintenance for strains PpHP13, PpHP17, and PpHP18 was observed, as depicted in Fig. Figure Altogether, these results show the correlation between acetyl-CoA availability, growth rate, ATP requirements, and flux distribution at the pyruvate node, and their impact on the 3-HP production yield. Acetyl-CoA depletion in strain PpHP8 hampered growth rate. When the cytosolic acetyl-CoA biosynthetic pathway was overexpressed , growth rate increased. Increase in the growth rate increased ATP requirements, which increased channelling of pyruvate into the TCA cycle, hampering the 3-HP product yield.a of 3-HP) was optimal for the further downstream processing of 3-HP by solvent extraction [It has been reported that a pH of 3.5 , which is 23% lower than the product yield of the same strain at pH 5. See Additional Figure The fold-change of the relative fluxes of each recombinant strain compared to the reference strain Fig. C showed Regarding the NADPH production and consumption fluxes for each strain, the same trends were also observed at pH 3.5 . c stress , 55. Thuntenance . Moreovet pH 3.5 . The obsStill, as metabolic flux profiles at pH 3.5 remained mostly unchanged compared to those at pH 5, it is likely that strain engineering strategies at both pH will have the same outcome.P. pastoris recombinant strains at two relevant process conditions (pH 5 and pH 3.5) using a HT approach that has allowed to save time and resources compared to conventional strain-by-strain sequential approach. Overall, we show, step-by-step, the setup of an optimized workflow for HT metabolic flux profiling of Pichia pastoris. It provides meaningful insights regarding the impact of each genetic perturbation on the metabolic flux distribution of the 3-HP producing strain, pointing to a competition for energy and carbon resources for either cell growth or 3-HP production as the major cause of the observed phenotypes, regardless of the pH of the culture. Thus, it is concluded that both acetyl-CoA and ATP limitations are the main bottlenecks hampering 3-HP production in P. pastoris. To overcome such bottlenecks, a strategy to increase the glycolytic fluxes  should be addressed. Overall, this study will contribute towards the improvement of P. pastoris strains and bioprocess engineering strategies to produce 3-HP and other acetyl-CoA-derived products. Importantly, this study showcases the potential of automated fluoxomics workflows for accelerated strain characterisation in the context of the so-called Design-Build-Test-Learn cycle for metabolic engineering of industrially relevant microorganisms, beyond model organisms. To this end, we also provide an end-to-end description of the fluxomics workflow, as well as sharing the raw and processed datasets following the guidelines of good practices in publishing 13C-metabolic flux analyses studies, with the aim of contributing spreading the use of fluxomics analyses in P. pastoris.This study describes the parallel characterization of a set of 3-HP-producing Below is the link to the electronic supplementary material.Additional Figure 1. Flux maps of the parental P. pastoris strain (X-33) growing on methanol. Comparison of the flux map obtained in this study (batch mini bioreactor) with the previously reported flux maps in chemostat cultures (https://doi.org/10.1016/j.nbt.2019.01.005). Additional Figure S2. Bioprocess parameters of the parental P. pastoris strain and nine 3-HP-producing strains cultivated in glycerol batch mini bioreactor cultures at pH 3.5. Additional Figure S3. Flux map with the relative fluxes for the PpHP8 strain grown at pH 5 and pH 3.5. Additional Figure S4. Production and consumption rates of NADPH and specific glycerol uptake rates for each strain at pH 3.5.


Confronted with thermodynamically adverse output processes, free-energy transducers may shift to lower gears, thereby reducing output per unit input. This option is well known for inanimate machines such as automobiles, but unappreciated in biology. The present study extends existing non-equilibrium thermodynamic principles to underpin biological gear shifting and identify possible mechanisms. It shows that gear shifting differs from altering the degree of coupling and that living systems may use it to optimize their performance: microbial growth is ultimately powered by the Gibbs energy of catabolism, which is partially transformed into Gibbs energy (‘output force’) in the ATP that is produced. If this output force is high, the cell may turn to a catabolic pathway with a lower ATP stoichiometry. Notwithstanding the reduced stoichiometry, the ATP synthesis flux may then actually increase as compared to that in a system without gear shift, in which growth might come to a halt. A ‘variomatic’ gear switching strategy should be optimal, explaining why organisms avail themselves of multiple catabolic pathways, as these enable them to shift gears when the growing gets tough. Thermodynamics is a branch of chemical physics that studies the relationship between heat, energy, and work and how these quantities affect the behavior of matter . ThermodClostridium ljungdahlii, for instance, contains alternatives in its genome of redox reactions that produce different ATP/acetate ratios and have been proposed to mediate gear shifting [Paracoccus denitrificans and Escherichia coli avail themselves of various terminal oxidases enabling gear shifting in their proton pumping activity [Saccharolobus solfataricus contains the non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN) [Non-equilibrium thermodynamics has many applications in biology, including in cellular metabolism ,11,12, cshifting . Paracocactivity ,24. Sacce (GAPN) ,26, whicIn this paper, we develop the theory behind gear shifting in cell growth. We employ both mosaic and phenomenological non-equilibrium thermodynamics. Specifically, we examine how gear shifting may benefit an organism when growth becomes hard thermodynamically.This section describes the computations leading to the figures in this paper in a technical manner. The underlying theory is given in the Results section.L .The Python script, the data used in these simulations, the visualization of the data, and all the figures are available on a publicly accessible Github n1 > 0 molecules of ATP. The remaining driving force n1) multiplied by the Gibbs energy of ATP synthesis. As is customary in linear non-equilibrium thermodynamics [L1 for the proportionality constant  and write for the dependence of its flux  relations in this description, which is only an approximation of reality ,31. Our  Van Dam .The expression shows that at high counteracting Gibbs energy of ATP synthesis  reaction at flux Here, we again use the linear flow–force relation of linear non-equilibrium thermodynamics . Both LpFor an example where the two stoichiometries This is also evident if Below, we shall see that the gear shifting itself can also introduce uncoupling.The pink line in hway see c. The tohway see a and conecreases b. Apparen and the efficiency ATP synthesis and catabolism are coupled to each other. This coupling has two aspects. First, with more intensive coupling, the ratio of the ATP synthesis flux (control’ . This isquations :(16)JcL·These two expressions do not describe reality precisely , but thWhen mapping these phenomenological Equations (16) and (17) onto the above more mechanistic Equations (8) and (9) describing our system of two parallel pathways making ATP, we find for the phenomenological stoichiometry Notably, unless q depends on the stoichiometries of the two pathways as well as on their relative activities, but in a different way:In addition, the degree of coupling Z equals the flux stoichiometry Even at constant stoichiometries and with specific uncoupling, i) the catalytic capacity of catabolism ii) the magnitude of the Gibbs energy of catabolism , (iii) the degree of coupling iv)  the phenomenological stoichiometry More generally, the equation for the rate of ATP synthesis (Z to zero (and even below) with increasing counteracting force ratio The above mosaic non-equilibrium thermodynamic equations show that at any given catabolic Gibbs energy, the fluxes depend on five parameters, i.e., ’) ratio . They dir η in q . He conschondria . It coulchondria ,33.Little attention has been paid to dependencies on the stoichiometry The green line in It should be better for the ATP synthesis if at the higher force ratios, the organism would switch between pathways, activating lower stoichiometry pathways whilst inactivating higher stoichiometry pathways. At fixed values of The purple line in In the above, we considered situations of different phenomenological stoichiometries at the same value of the coupling coefficient le point a, implyile point a. A coroAbove, we showed that if two pathways take over the role of a single pathway, this has the effect of introducing a variable stoichiometry The study of gear shifting in cell growth is of interest because it has implications for understanding how cells optimize their metabolic pathways for efficient growth and energy transduction. The use of both mosaic and phenomenological non-equilibrium thermodynamics provides a comprehensive approach to investigating the underlying thermodynamic principles governing this process. The investigation of how the variation of the force ratio induces gear shifting from a mosaic non-equilibrium thermodynamics perspective is particularly relevant, as this approach allows for a more detailed understanding of the dynamics of individual components and their interactions within the metabolic pathways. In this paper, we showed that ATP synthesis flux decreases with increasing force ratio and that the gear setting  and phenomenological stoichiometry is more complex. At the lower force ratios, the growth/ATP synthesis will increase with phenomenological stoichiometry; at the higher force ratios, the growth will decrease with it.The relationship between the phenomenological stoichiometry, gear shifting, and varied relative pathway capacities shows how they impact overall cellular metabolism. By studying the effects of these factors on ATP synthesis and growth, the study shows that ATP synthesis flux is high for high stoichiometry at low force ratio and for low stoichiometry at high force ratio.Z should become a weighted function of the activities and stoichiometries of the two pathways but would remain independent of the counteracting force ratio. Only the actual stoichiometry n between ATP synthesis by catabolism as a whole and the total catabolic flux became dependent on the counteracting force ratio. The more thermodynamically uphill growth would be, the lower n, corresponding to a shift to lower gear setting. Further analysis suggested that by regulating which of various parallel catabolic pathways they use, organisms may improve their growth rate both at low and at high counteracting thermodynamic force.In this study, we used two types of non-equilibrium thermodynamics. One enabled us to discuss the performance of cell growth as free-energy transducer with the least possible detail on mechanism. This enabled us to show the effects of variation of the degree of coupling and the phenomenological stoichiometry. As its name implies, dynamics , which eIn using the two types of non-equilibrium thermodynamics, rather than a complete and validated kinetic or flux balance constrained model , the preIt is our expectation, however, that this restriction does not invalidate the major conclusions of the present paper, i.e., that organisms may shift between catabolic pathways that have different ATP stoichiometries. First, linear non-equilibrium thermodynamics has been able to develop insights on a variety on important phenomena in biology, including self-organization , stabiliThe present paper may help improve on the omission of thermodynamics from many FBA and kinetic models in the future, as it shows how the ability to shift between parallel pathways with different ATP stoichiometries that can be identified by FBA   is an aOverall, the study of gear shifting presented in this paper may improve our understanding of the fundamental thermodynamic principles that govern cell metabolism.


The vast majority of Parkinson’s disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson’s disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes. Transcriptomics and metabolomics data integration coupled with systems biology reveal signature differences in idiopathic Parkinson’s disease vs healthy controls, with pyruvate, amino acid, lipid and NAD metabolism as most dysregulated pathways. Investigation of idiopathic PD (IPD) cases is challenging due to unclear etiology and their multifactorial nature2. One of the explanations of how the interaction between multiple factors contributes to PD development is proposed by the “multiple hit” theory4. It suggests that the risk to develop PD is already determined at the early neurodevelopmental stage, where the ‘first hit’ in the form of genetic mutation, exhausts compensatory mechanisms, modulating cell resistance to perturbations of cellular homeostasis. The “second hit” as environmental or lifestyle factors later leads to the disease onset and determines its progression.Parkinson’s disease (PD) is a neurodegenerative disorder with increased prevalence among elderly people. The main cellular hallmarks of PD, such as selective loss of dopaminergic neurons and the presence of Lewy bodies in the PD patient’s brain, have been well described; however, disease molecular mechanisms are not yet clear. Moreover, approximately 90% of all PD cases are classified as sporadic or idiopathic9. Moreover, diminished mitochondrial energy production capacity is among the most discussed pathogenic mechanisms implicated in both familial and idiopathic forms of PD development13. In addition to the deficient energy metabolism, dysregulation in lipid metabolism has also been linked to PD development18. Furthermore, other studies describe impaired cofactor metabolism contribution to neurodegeneration and amino acid concentration changes, the latter also having been proposed as biomarkers of PD progression22.Changed cellular metabolism as a response to the ‘first hit’ might eventually lead to increased dopaminergic neuron vulnerability and susceptibility to PD-related neurodegeneration. Lately, the role of metabolic dysregulation has been highlighted in the context of neurodegenerative diseases27. We analyzed NESCs from three IPD patients and three healthy individuals at the transcriptomics and metabolomics levels to obtain an overview of the IPD-associated metabolic changes at the very early neurodevelopment stage. In addition, we used the XomicsToModel pipeline29 to extract context-specific models from the most comprehensive genome-scale human metabolic network Recon 3D30. The flux distribution was further addressed using entropic flux balance analysis. Additionally, predicted metabolic alterations we compared to PD-associated metabolic biomarkers reported in clinical studies.In this study, we were interested in the initial metabolic changes occurring even before neuronal differentiation, to understand the basis of dopaminergic neuron vulnerability. Investigation of early metabolic changes might provide a deeper understanding of IPD pathogenesis and provide clues to disease-modifying strategies. We used iPSCs-derived neuroepithelial stem cells (NESCs), which are pre-patterned to midbrain/hindbrain identity and can give rise to neurons, including midbrain dopaminergic neurons, as well as to oligodendrocytes and astrocytesWe here show that IPD NESCs have changed pyruvate, lipid and amino acid metabolism. In addition, we demonstrate that IPD NESCs have decreased mitochondrial respiratory capacity and a NAD pool deficiency. Moreover, using NAD precursors, we show rescue of decreased levels of ATP in IPD NESCs, highlighting role of NAD metabolism in energy generation sustainability, and as a preventative PD therapeutic target. Finally, using data integration analysis, we confirm the link between energy generation, lipid metabolism and NAD regeneration, which leads to the identification of glycerol-3-phosphate as the main intermediate of metabolic pathways altered in IPD NESCs.p < 0.05), showing a strong transcriptomic difference between IPD and control NESCs , being nearly two-fold downregulated in IPD NESCs  are known to be involved in neurodevelopmental processes. Overall, a gene ontology enrichment analysis showed that DEGs are involved in the regulation of cell development, cell cycle, synapse membrane potential, as well as metabolic processes particularly related to lipid metabolism  and cofactor metabolic processes  analysis of the polar and non-polar-phase intracellular metabolites. A principal component analysis (PCA) showed a strong separation between IPD and control samples for non-polar metabolites further suggesting alterations in lipid metabolism in IPD NESCs Fig. . In contLDHA in IPD NESCs suggest a dysregulation of ATP-producing pathways. Therefore, we wanted to investigate the mitochondrial capacity to metabolize various metabolic substrates and the ability to generate ATP. First, we assessed mitochondrial functionality using MitoPlateS1, which is a microplate pre-coated with various NADH and FADH2-producing metabolic substrates33. We observed that the variety of substrates metabolized by IPD NESCs is reduced , palmitoyl DL-carnitine chloride and L-leucine the maximal metabolic rate was between 1.5 and 8 times higher for control NESCs compared to IPD NESCs.The negative regulation of several metabolic processes and the downregulation of Next, we looked more specifically at mitochondrial respiration efficiency using the Seahorse Extracellular Flux Analyzer. IPD NESCs demonstrated a significant decrease in all parameters of the mitochondrial stress test Fig. . AlreadySince we observed impairment in mitochondrial metabolism but not in glycolysis, we examined the activity of pyruvate dehydrogenase (PDH), which is at a transition step between glycolysis and the TCA cycle. By, using NAD+ reduction to NADH as a readout, we indeed found that the activity of PDH was significantly downregulated in IPD NESCs Fig. . However28. Using Recon 3D as a reference of human metabolism, we extracted a unique context-specific metabolic model for each IPD and control cell line and further constrained models using bibliomic constraints regarding the dopaminergic neuron metabolism and cell culture media composition36. First, we performed a topological composition analysis of the generated models by correlating genes, reactions and metabolites present in each model  = 0.5; ρAv (reactions) = 0.4; ρAv (metabolites) = 0.45) of IPD models nor control models, suggesting that each model represents a distinct, disease independent metabolic network.In addition to the dysregulation of lipid, pyruvate and several amino acid metabolisms as suggested by RNA sequencing and untargeted metabolomics, we found that the NAD amount is reduced in IPD NESCs. To further investigate the overall metabolic differences between IPD and control NESCs, and to confirm the most dysregulated pathways, we complemented our analysis with a constraint-based modeling approach. Metabolic models were generated using XomicToModel pipelineA mathematical modeling approach maximizing the entropy of forward and reverse fluxes was employed for predicting reaction fluxes within each generated model. The approach uses entropic flux balance analysis (eFBA) to avoid thermodynamically infeasible cycles in the metabolic network, being in accordance with the first law of thermodynamics. The top 50 reactions with the highest absolute FC for all models relative to the mean flux of control models, separated IPD and control models using unsupervised clustering Fig. . ControlNext, we wanted to confirm altered NAD metabolism in IPD NESCs. We analyzed the metabolic flux of reactions that belong to the NAD metabolism subsystem in Recon 3D Fig.  and 5. WWe applied a supervised data integration approach to transcriptomics, metabolomics and Seahorse mitochondrial assay data to investigate a potential link between dysregulated metabolic pathways. The top 10 polar and non-polar metabolites, the top 50 gene transcripts and all six mitochondrial respiration features measured by the Seahorse assay were integrated. We applied a Partial Least Squares discriminant analysis (PLS-DA) to these data features to assess the separability between disease states of NESCs—IPD or healthy control. All four datasets showed a separation between sample conditions along the first component  showed the same trend of qualitative change with respect to matched controls in more than one studyels Fig. , 28 metaels Fig. . The numIn the present study, we investigated primary metabolic changes in IPD patient-derived neural precursor cells. The transcriptomic data analysis confirmed significant metabolic alterations in IPD NESCs, specifically, in lipid, amino acid and pyruvate metabolism. Untargeted metabolomics suggested a particularly strong difference between IPD and control NESC lipid profiles. Along with metabolic pathways, many important biological processes were also significantly altered at the transcriptomics level, including cell cycle, RNA binding, regulation of membrane potential and neurodevelopment, emphasizing the tight crosstalk between metabolism, cellular functionality and developmental state.LDHA. Downregulation of LDHA in IPD NESCs might indicate the regulation of a metabolic shift, which is forcing pyruvate to enter the oxidative metabolism, and not be converted to lactate in order to continue anaerobic glycolysis. Additionally, LDHA has been described as a regulator of the cell cycle and proliferation state of cells50. Unrestricted neural stem cells initially rely on glycolysis; however, it has been shown that fate-restricted, mature progenitors, such as NESCs, undergo a metabolic shift, becoming dependent on oxidative metabolism53. However, we found that PDH activity was significantly decreased, suggesting an impairment of pyruvate involvement in TCA. Moreover, Seahorse extracellular flux analysis revealed that IPD NESCs have decreased respiratory capacity, mitochondrial ATP levels, and the ability to metabolize a variety of mitochondrial substrates. Further, we demonstrated significantly decreased NAD levels in IPD NESCs, which potentially is the primary cause of mitochondrial enzyme inactivity, since most of the reactions require NAD as a cofactor, including the PDH complex for pyruvate conversion to Acetyl-CoA. NAD has an essential role in cellular bioenergetics, mitochondrial health and cell survival54. Furthermore, NAD depletion has been observed in aging and neurodegenerative diseases, including PD56. Our results suggest that IPD neural progenitors might not be able to undergo the metabolic shift from glycolysis to oxidative metabolism due to the cofactor imbalance.We found that pyruvate metabolism is negatively regulated by the decreased expression of 28. Since in this study, we used NESCs that are precursors of midbrain dopaminergic neurons, bibliomic data specific for dopaminergic neuron metabolism was a great advantage and allowed us to investigate metabolic alterations relevant to dopaminergic neurons. For flux distribution prediction we used maximization of flux and concentration entropy, which was the best performing objective regards of flux prediction accuracy as reported by Preciat et al.28. Entropy is a measure of energy distribution in the system. Maximizing for entropy, the system tends toward a state at which the usage of energy is minimal. We observed that IPD metabolic models compared to control models showed mostly increased flux for the analyzed reactions. This might suggest that control models are already in an energetically favorable state, while IPD models present less energetically stable metabolic networks, and they have to increase fluxes through many metabolic pathways to reach an energetically optimal state. Metabolic shift accompanied by changes in entropy has been observed in many diseases and aging58. Furthermore, modeling confirmed a changed NAD metabolism in cytosolic reactions in IPD models. This was consistent with increased levels of G3P in IPD cells as G3P biosynthesis replenishes the cytosolic NAD pool to ensure the activity of metabolic pathways in the cytosol and allows energy generation via glycolysis. Since NAD cannot be transported between cytosol and mitochondria, both NAD pools are considered relatively independent but linked with the electron shuttles that allows regeneration of NAD60. We observed a decreased activity of mitochondrial respiration and impaired pyruvate oxidation by PDH, suggesting that particularly the mitochondrial NAD pool might be affected in IPD NESCs. Accordingly, treatment of NAD precursors increased the ATP generation capability of cells, allowing IPD NESCs to reach the ATP levels observed in control NESCs. Following this observation, we suggest that restoring NAD levels as early as possible might be an efficient preventative strategy for PD.We complemented our analysis with metabolic modeling and data integration to have a deeper overview of genotype-phenotype relationships. We generated context-specific models applying media and bibliomic constraints using the XomicToModel pipelineUsing a data integration approach, we sought to identify a molecule that connects all altered metabolic pathways in IPD NESCs. All of the top discriminant gene transcripts showed a high degree of correlation with metabolites and Seahorse data, suggesting that the genetic signature plays an important role in disease development. However, in this analysis, we focused on metabolites since they might show higher specificity toward correlation with other features. We identified G3P as one of the highly correlating non-polar metabolites with genes and mitochondrial respiration features and the top significantly differentially abundant metabolite between IPD and control NESCs. Since we show that most dysregulated pathways in IPD NESCs include pyruvate metabolism, oxidative phosphorylation, NAD metabolism and lipid metabolism, G3P might have  a biomarker potential since it is at the crossroads of these pathways and therefore, could indicate their changed activity.37. This suggests that predicted metabolic changes using metabolic modeling have a high degree of accuracy and are comparable to the clinical data. Additionally, we identify G3P as a metabolite connecting observed IPD-associated metabolic changes and NAD as a potential early disease-modifying agent. Overall, we show that experimental and computational methods can be combined to better elucidate molecular causes and associations of observed phenotypes. It was particularly interesting to see that it is possible to distinguish IPD samples from healthy control samples by using complex signatures.In summary, our results show that IPD NESCs undergo specific metabolic changes at early neurodevelopment and their metabolism seems to be less optimal in terms of energy distribution. Moreover, we confirm that a large part of metabolites involved in the predicted dysregulated metabolic reactions using the modeling approach was consistent with the metabolites reported with a concentration change in PD patient biological samplesThere are some limitations of this study that have to be considered. This study was performed with a relatively small sample size (3 IPD vs 3 CTRL). Most of the experiments, particularly, untargeted metabolomics would benefit from a larger sample size, allowing better discrimination between the IPD and CTRL groups and an increase of statistical power. Furthermore, although we show decreased levels of NAD, we were not able to experimentally distinguish between cytosolic and mitochondrial NAD pools. However, we were able to separate mitochondrial and cytosolic metabolic NAD reactions in silico to estimate flux differences between the IPD and control NESCs. Nevertheless, modeling has rather a predictive value and for a definite result, all findings should be further validated experimentally. Non-polar or lipid metabolites were analyzed in an untargeted manner to achieve high coverage of metabolites and have a general overview of cellular metabolic profiles. In this case, most of these metabolites were not identified due to the limitations of the available library. Metabolites with no clear identity were not excluded from the study, however, they limit the interpretation of our data regarding specific lipid contributions to the disease phenotypes.The work with iPSCs has been approved by the Ethics Review Panel (ERP) of the University of Luxembourg and the national Luxembourgish Research Ethics Committee . CNER No. 201901/01; ivPD.27 from three female IPD patients and three female healthy controls , PAX6 1:300 , Nestin 1:200   was performed in R (version 4.0.2) using the Human Molecular Signatures Database (version 6.2.)64. Genes annotated to ‘Metabolic pathways’ were further assigned to specific pathways using the KEGG database31. The average log2FC was determined among all genes annotated in each pathway to determine the most dysregulated pathways.Total RNA was prepared using the Allprep DNA/RNA mini kit (QIAGEN #80204) following the supplier’s instructions. RNA-seq library was prepared from 1 μg of total RNA, using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (E7420L). Libraries were quantified with the KAPA Library Quantification Kit . Samples were multiplexed and adjusted to 2 nM concentration, and 75-bp paired-end sequencing was performed by Nextseq 2000. Reads were mapped with Bowtie2 using hg19 as the reference genome. Gene differential expression analysis was performed in R (version 4.0.2) using the Biocundoctur package EdgeR with default parametersMetabolite derivatization was performed by a multi-purpose sample preparation robot (Gerstel). Dried cell polar extracts were dissolved in 20 µl pyridine, containing 20 mg/ml methoxyamine hydrochloride (Sigma-Aldrich), for 90 min at 45 °C under shaking. After adding 20 µl N-methyl-N-trimethylsilyl-trifluoroacetamide (Macherey-Nagel), samples were incubated for 30 min at 45 °C under continuous shaking. Dried non-polar extracts were only dissolved in 30 µl N-methyl-N-trimethylsilyl-trifluoroacetamide and incubated for 60 min at 45 °C under continuous shaking.GC-MS analysis was performed using an Agilent 7890B GC coupled to an Agilent 5977A inert XL Mass Selective Detector (Agilent Technologies). A sample volume of 1 µl was injected into a Split/Splitless inlet, operating in splitless mode at 270 °C. The gas chromatograph was equipped with a 30 m  ZB-35ms capillary column (Phenomenex) with 5 m guard column in front of the analytical column. Helium was used as carrier gas with a constant flow rate of 1.2 ml/min. The GC oven temperature was held at 90 °C for 1 min and increased to 270 °C at 9 °C/min. Then, the temperature was increased to 320 °C at 25 °C/min and held for 7 min. The total run time was 30 min. The transfer line temperature was set to 280 °C. The MSD was operating under electron ionization at 70 eV. The MS source was held at 230 °C and the quadrupole at 150 °C. Mass spectra were acquired in full scan mode (m/z 70 to 700).65. Compounds were annotated by retention index and mass spectrum using an in-house mass spectral library. Retention index calibration was based on a C10–C40 even n-alkane mixture (Sigma-Aldrich). The internal standards, U-13C–Ribitol , Pentanedioic-d6 acid (C/D/N Isotopes) and Tridecanoic-d25 acid (C/D/N Isotopes) were added at the same concentration to every sample to correct for uncontrolled sample losses and analyte degradation during metabolite extraction. The dataset was normalized using the response ratio of the integrated peak area analyte and the integrated peak area internal standard. Data were analyzed using RStudio (R version 4.0.2). Results were visualized using tidyverse package and factoextra package for the multivariate analysis.All GC-MS chromatograms were processed using MetaboliteDetector, v3.220190704Mitochondria substrate preferences were assessed using MitoPlate S-1 (Biolog) following the manufacturer’s instructions. The MitoPlate S-1 assay is a colorimetric assay and measurement is based on the electron flow rate through the mitochondrial respiratory chain. Tetrazolium redox dye MC acts as the final electron acceptor changing a color proportional to the rate of substrate metabolization, which is recorded as Omnilog units. 96-well microplate MitoPlate S-1 is pre-coated with 31 different NADH and FADH2- producing metabolic substrates. Plate layout allows measurements in triplicates, therefore each IPD and control cell line was seeded in 32 wells of the plate (31 substrates + blank). Before cell seeding, substrates were incubated for 1 h at 37 °C with the assay mix containing assay buffer, redox dye, water and permeabilization reagent saponin (100 µg/ml per well). Then, 20,000 cells were seeded per well and directly loaded in OmnilogLog (Biolog) for kinetic reading every 5 min for 10 h. After measurement, plates were centrifuged and cell density was measured with CyQUANT Cell Proliferation Assay (Invitrogen). CyQUANT GR solution was prepared following the kit protocol. Fluorescence was measured at 520 nm on a cell imaging system Cytation5M. Kinetic data was analyzed with Data Analysis software (version 1.7.). The maximal metabolic rate for each substrate between the initial and 8 h measurements was calculated by background subtraction and normalization to cell density in the respective well. Negative values were replaced with 0. A median of the normalized maximal rate between three IPD and three control samples for each substrate was visualized as a scatter plot using RStudio (R version 4.0.2).A day before the experiment, the XFe96 sensor cartridge (Agilent) was filled with Seahorse XF Calibrant Solution (Agilent) (200 µl per well) and kept at 37 °C overnight. Before the experiment, the Seahorse XFe96 Spheroid Microplate (Agilent) was coated with Cell-Tak (Corning) coating diluted in 0.2 M Sodium bicarbonate solution (1:50) (coating 30 µl per well). After 1 h of incubation at 37 °C, the plate was washed twice with filtered MiliQ. 100 000 were cells seeded per well; each cell line in 8 replicates (wells). To ensure the complete attachment of cells, the plate was incubated at 37 °C for 3–4 h. After the incubation time, cells were washed twice with Seahorse media . All wells were filled with 180 µl of Seahorse media and incubated for 1 h at 37 °C. For Mito Stress analysis XFe96 sensor cartridge was filled with drugs as follows: Port A: Oligomycin 1 µM, Port B: FCCP 1 µM, and Port C: Antimycin 1 µM + Rotenone 1 µM (1:1). For the Glycolysis stress test XFe96 sensor cartridge was filled with drugs as follows: Port A: Glucose 10 mM, Port B: Oligomycin 1 µM, Port C: 2-deoxyglucose (2DG) 50 mM. Analysis was performed using the XFe96 Cell Metabolism Analyzer. The obtained data was normalized to the cell density measured with CyQUANT Cell Proliferation Assay (Invitrogen). CyQUANT GR solution was prepared following the kit protocol. Then, 200 µl of the solution was added to each well of the Seahorse microplate (including empty wells). After 2 min of incubation, the content of the microplate was transferred to the imaging microplate (Perkin Elmer) compatible with the available cell imaging system Cytation5M. Fluorescence was measured at 520 nm.Both assays were performed three times using three different cell passages. Each well of the assay was represented as a separate data point. Wells with negative values or aberrant OCR or ECAR patterns were excluded from each of the individual experiments. Data was analyzed with GraphPad Prism version 9.Cells were collected at 80% confluency and lysed using RIPA buffer (Abcam). Protein was quantified using BCA assay (ThermoFisher). Then, 20 µg of protein was loaded per sample in 4–12% NuPAGE Bis-Tris polyacrylamide gel (ThermoFisher). Gel was run using MES SDS running buffer (ThermoFisher), following dry transfer on polyvinylidene difluoride membrane using iBlot (ThermoFisher). The membrane was blocked for 1 h in 5% BSA in PBS + 0.02% Tween solution following overnight incubation with primary antibody for PDHA1 1:500 . The membrane was washed 3 times for 5 min with PBS + 0.02% Tween. After washing membrane was incubated for 1 h with a secondary anti-rabbit, HRP-linked antibody (VWR). After 3 washing steps for 10 min, a signal was developed using a chemiluminescent substrate (Life Technologies) and imaged with STELLA 8300 imaging system (Raytest). After the reveal of the PDHA1 protein signal, the membrane was stripped for 15 min with a stripping buffer (ThemoFisher) to remove the signal. The membrane was washed 3 times for 10 min following blocking and overnight incubation with β-actin primary antibody 1:10,000 . β-actin protein was analyzed following the same procedure as described for PDHA1 protein. The experiment was performed three times for different NESC passages. Band intensity was quantified using ImageJ and PDHA1 relative abundance was estimated by normalizing the PDHA1 protein signal to the β-actin signal. Original blot images of one of the experiments are shown in Supplementary Fig. Intracellular ATP was measured using CellTiter-Glo® Luminescent Cell Viability Assay (Promega). Cells from three different passages were seeded in a Geltrex (ThermoFisher) pre-coated 96-well imaging plate (Perkin Elmer). The next day after seeding, cells were treated with 20 nM quinolinic acid (Sigma #P63204), 5 mM nicotinic acid (Sigma #N0761) or ultra-pure water as vehicle. After 24 h, the media was replaced by 50 µl of ATP assay reagent. After 10 min of incubation in the dark, luminescence was read with the cell imaging system Cytation5M. After luminescence recording, the ATP assay reagent was removed and the cell count in each well was determined using CyQUANT Cell Proliferation Assay (Invitrogen). The luminescence signal of ATP was normalized to the cell count.g for 5 min. Then, 10 µl of supernatant in duplicates were collected in a 96-well flat bottom plate (Corning) for each sample and mixed by shaking with the PDH assay substrate and developer. Samples were then incubated at 37 °C. Absorbance at 450 nm was measured after 3–5 min of incubation, following further incubation at 37 °C. The absorbance reading was repeated until the highest sample values reached the linear range of the standard curve. PDH activity was calculated as NADH amount in nmol generated between the initial and final absorbance reading. The assay was repeated three times for three different NESC passages. The results of each run were normalized to the mean of control samples of the respective run. Data were visualized using GraphPad Prism version 9.Pyruvate dehydrogenase activity was detected using the PDH activity assay kit (Sigma MAK183) following the manufacturer’s instructions. Kit measures generated NADH amount which is proportional to enzyme activity. One million cells were pelleted and then resuspended in an ice-cold PDH assay buffer. After 10 min of incubation on ice, samples were centrifuged at 10,000×The NAD+/NADH ratio was measured with a Sigma kit (MAK037) following the manufacturer’s instructions. One million cells were pelleted by centrifugation. After the removal of the media, the pellet was washed with cold PBS. Total NAD+ and NADH were extracted by cell incubation with extraction buffer during two freezing–thawing cycles of sample incubation in dry ice for 20 min following incubation at room temperature for 10 min. Samples were then vortexed and centrifuged to remove insoluble material. For detection of NADH only, samples were further incubated at 60 °C for 3 min and cooled on ice afterwards. The concentration of the total NAD and NADH were calculated using the NADH standard curve. The NAD+/NADH ratio was estimated by subtracting NADH concentration from the total NAD and dividing by NADH. Each experiment was normalized to the median of the control sample results. The assay was repeated three times for three different NESC passages.28 within the COBRA Toolbox v3.466 on MATLAB version 2021b.For each IPD patient-derived and each control NESC line, a separate metabolic model  was generated. Model extraction was performed using the XomicsToModel pipelineThe thermodynamic, stoichiometrically and flux consistent subset of Recon 3D as a generic model, a genome-scale model for human metabolism.67 and protein concentration in each NESC line was determined using BCA kit (ThermoFisher).Context-specific data from transcriptomic and cell culture data. Normalized counts to Transcripts Per Kilobase Million (TPM) were transformed to log2 scale. The genes above a selected threshold were considered active and included in the model. Additionally, cell culture data provided the uptakes for each of the models based on Eq. 29 for setting reaction rates, and identifying active and inactive reactions, metabolites and genes. The literature curation used in ref. 29 was modified by excluding genes whose expression in the brain was referred only to animal models.Literature curation on dopaminergic neuron metabolism including dopamine metabolism, mitochondrial and central carbon metabolism, biomass precursors and media metabolites, presented in ref. 29 including thermoKernel as the tissue-specific solver; at least one active reaction per active gene as the method includes reactions corresponding to active genes, two TPM mapped reads as the transcriptomic threshold, below which genes were designated as inactive, closing the ion exchange and prioritize experimental data over literature curation.Same XomicsToModel parameters were used for the generation of the iDopaNeuro modelContext-specific models were extracted using:Flux distribution was determined by eFBA simultaneously maximizing the entropy of forward and reverse flux with the objective shown in Eq.  using thWhere d on Eq. 3\documen68 within the R software (version 4.0.2.). DIABLO aims at using combined metabolomic, transcriptomic and phenotypic data to separate healthy from IPD NESCs. The separation between sample groups was assessed using the correlation of the top 50 genes with the top 10 metabolites per dataset and 6 mitochondrial phenotypes from the first component of PLS-DA68. Heatmap visualization was used to demonstrate the top feature  expression profile responsible for cluster separation between conditions. Pearson correlations (abs(r) = 0.9) between features were further visualized in a circus plot. Features with the highest correlation degree (abs(r) = 0.95) are represented as a network using Cytoscape (version 3.8.2.).Experimental datasets of TPM normalized RNA expression, polar-phase and non-polar-phase metabolites from untargeted GC-MC analysis and mitochondrial phenotypes from Seahorse mitochondrial metabolism assay were integrated using Data Integration Analysis for Biomarker discovery using latent components (DIABLO)t-test if not stated differently. Significance asterisks represent *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars represent mean + SD.All experiments were performed with all three cell lines per condition representing biologically independent samples. All experiments were independently repeated three times using different cell passages. Except for RNA sequencing and metabolomics, which were repeated once. For statistical analysis, GraphPad Prism version 9 was used. Data were tested for outliers with the ROUT method (Q = 1%) and for normality with the Shapiro-Wilk test. For data of non-normal distribution statistical significance was determined by a two-tailed non-parametric Mann-Whitney test. For data of normal distribution, statistical significance was determined by unpaired two-tailed Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Supplementary data 5Reporting Summary


Temperature affects seed germination and seedling growth, which is a critical and complex stage in plant life cycle. However, comprehensive metabolic basis on temperature implicating seed germination and seedling growth remains less known. Here, we applied the high-throughput untargeted metabolomic and advanced shotgun lipidomic approaches to profile the Arabidopsis 182 metabolites and 149 lipids under moderate  and extreme high  temperatures. Our results showed that a typical feature of the metabolism related to organic acids/derivates and amines was obviously enriched at the moderate temperature, which was implicated in many cellular responses towards tricarboxylic acid cycle (TCA), carbohydrates and amino acids metabolism, peptide biosynthesis, phenylpropanoid biosynthesis and indole 3-acetate (IAA) biosynthetic pathway. Whereas, under extreme high temperatures, there was no seed germination, but 148 out of total 182 metabolites were highly enriched, involving in the galactose metabolism, fatty acid degradation, tryptophan/phenylalanine metabolism, and shikimic acid-mediated pathways especially including alkaloids metabolism and glucosinolate/flavone/flavonol biosynthesis. Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) also exhibited the gradually increased tendency from moderate temperatures to extreme high temperatures; whereas phosphatidylserine (PS), phosphatidic acid (PA), phosphatidylglycerol (PG), monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG) and sulfoquinovosyldiacylglycerol (SQDG) were contrary to decrease. Another typical feature of the distinguished metabolites between 22°C and 28°C, the TCA, disaccharides, nucleotides, polypeptides, SQDG and the biosynthesis of fatty acids and glucobrassicin-mediated IAA were obviously decreased at 28°C, while amino acids, trisaccharides, PE, PC, PA, PS, MGDG, DGDG and diacylglycerol (DAG) preferred to enrich at 28°C, which characterized the alteration of metabolites and lipids during fast seedling growth. Taking together, our results provided the comprehensive metabolites phenotyping, revealed the characteristics of metabolites necessary for seed germination and/or seedling growth under different temperatures, and provided insights into the different metabolic regulation of metabolites and lipid homeostasis for seed germination and seedling growth. As the Lipids constitute membrane structure and also mediate multiple cellular signaling transduction. In seed germination and seedling growth, PA showed the lowest levels in imbibition and followed an increasing trend during seedling growth, while PE molecules showed the highest levels in imbibition and a general decreasing trend at later stages . Heat stMetabolomic and lipidomic analysis have become a powerful technology to comprehensively clarify plant dynamic development , plant rIn this study, we investigated the comprehensive metabolic and lipidomic profiles during seed germination and seedling growth through the systematic biology approaches of both high-throughput untargeted metabolomics and advanced ESI-based lipidomics. We demonstrated the characteristics of metabolites and lipids in response to moderate (22°C and 28°C) and extreme high (34°C and 40°C) temperatures during the both stages of seedling establishment, and the distinguished characteristics between 22°C and 28°C during seedling growth. Our findings provided a molecular characteristic phenotype for seedling establishment under heat stress, revealed the metabolic pathways of TCA, carbohydrates metabolism, peptide biosynthesis, and lipid homeostasis were crucially implicated in seed germination and seedling growth under heat stress, providing cues to further study functional mechanism of these cellular metabolic pathways.22.1Arabidopsis thaliana ecotype Columbia (Col) were surface-sterilized and kept in the dark at 4°C for 3 days. The seeds were then sown onto filter papers with pre-soaked sterile water, and placed at 22 °C under light for 3 hours before transferring to the dark chamber for another 21 hours at 22°C, 28°C, 34°C and 40°C, respectively.Seed germination was carried out according to the previous study . Briefly2.2Total metabolites and lipids from 50 mg samples with whole tissue were extracted with methyl tert-butyl ether (MTBE) and methanol (MeOH). Briefly, 50 mg plant materials were mixed with 225 μL of ice-cold MeOH and homogenized for three times. The homogenate was then sonicated in an ice bath for 10 min. Added 750 μL pre-cooled MTBE and vortexed the mixture for 30 s and then kept on ice for 1 hour. The samples were vortexed for 30 s after adding 188 μL deionized water, and centrifuged at 14000 g for 10 min at 4°C. All experimental steps were performed on ice. The upper organic phase (800 μL) was carefully transferred to new tubes. The lower phase (200 μL) was transferred to additional new tubes. Repeat the above extraction steps twice. Finally, the three extractions were combined and dried with nitrogen gas.The upper phase dried extract was dissolved in chloroform: methanol: 300 mmol/L ammonium acetate  for lipids analysis. The lower phase dried extract was dissolved in methanol: water (1:1) for metabolites analysis.2.34AC) and 0.02% formic acid, and phase B was acetonitrile: water  with 5 mM NH4AC and 0.02% formic acid. The flow rate was 0.3 mL/min. The ion source parameters were set as follows: scan range, 50-1200 Da; source temperature, 300°C; curtain gas, 35; collision gas, medium; two ion source gases, 60; ion spray voltage, 5500/-4500V; DP, 60; CE, 35 ± 15.The Shimadzu Nexera UHPLC system equipped with the 6600 QTOF mass spectrometer (AB Sciex) was applied for untargeted metabolomics analysis. 1 uL aliquots of sample was injected into a Waters Acquity UPLC T3 column  maintained at 45°C by gradient elution. Mobile phase A was deionized water with 5 mM ammonium acetate , identified according to the database, and some of them were confirmed by ZenoTOF 7600 (AB Sciex). The relative amount was comparatively quantified according to the peak intensity. The data of shotgun lipidomics was processed by LipidView Software (AB Sciex) for identification and quantification.The raw data of metabolomics was preprocessed by MetDNA2 , three subclasses of galactosyldiacylglycerides  and 2 ceramides  were identified  was applied to analyze the metabolites which drove the group separation. PC1 and PC2 captured 73.6% and 10.9% of the total variance in the metabolites of FW-normalized dataset, respectively  and ethylene. All the precursor and intermediates for auxin biosynthesis were detected, which showed that the precursor (tryptophan) and intermediates of the two bypasses including tryptamine, indole-3-acetonitrile, indole-3-pyruvate and indole-3-acetyl-beta-1-D-glucoside to form IAA were enriched at 34°C and 40°C . In our 3.5Phospholipids function not only as membrane structure but also as messengers to play vital roles for plant development. The volcano plot was used to analyze the significant differences between 22~28°C and 34~40°C, and 104 lipid species were obviously altered among 149 lipid species identified  and PGs species , and all the species of PSs  and PAs  tended to have high levels at moderate temperatures); whereas, other species of PEs and most PCs species tended to have high levels at extreme high temperature 4A, 5AThe degree of lipid unsaturation was closely related to membrane lipid fluidity, and might affect the physiological efficiency of membrane. The acyl fatty acid (FA) chain length and unsaturation of phospholipids were analyzed in response to moderate and extreme high temperatures, respectively. All the PC species with long FA chain and more unsaturated degree were accumulated at high temperatures; whereas, the high saturated PC species with short FA chain length (PC 32:0 and PC 32:1) tended to be abundant at extreme high temperature 5B. Thes3.6DAG plays important roles in lipid metabolism, which could generate TAG and were also as substrate to biosynthesize both the phospholipids intermediates PA and galactolipid MDGD. DAG was enriched at 28°C, compared with other temperatures, but DAG did not show significant difference between moderate and extreme high temperatures . In thisPG is essential component of the chloroplast membranes and also as substrate to biosynthesize galactolipids, which showed the similar altered tendency to galactolipids .3.7Based on the distinguished profiles of metabolome and lipidome during seed germination and seedling growth at moderate and extreme high temperatures, we further analyzed the metabolomic and lipidomic differences between the moderate temperatures of 22°C and 28°C. A total of 59 metabolites and 50 lipids were significantly altered between 22°C and 28°C, which included 23 amino acids, 10 sugars/derives, 9 organic acids, 5 CoA, 2 purine/derives, 2 nicotine/derives, 1 indole, 1 phenol, 13 PEs, 10 PCs, 7 PAs, 5 MGDGs, 4 DGDGs, 4 DAGs, 3 PGs, 2 PSs, 1 SQDG and 1 ceramide , TCA , phosphoenolpyruvate pool , FAs metabolism , nucleotide metabolism , bioactive cyclin dipeptides kyotorphin (l-tyrosyl-l-arginine) and tripeptides  , was reported to completely improve seed germination . EthylenOur study demonstrated the metabolic alterations of seed germination under heat stress, including sugars, amino acids, plant hormones and lipids Figure 7Bangia fuscopurpurea) sublethal exposure to high-temperature stress altered membrane fluidity via changes in fatty acid composition, which resulted in the acquisition of heat stress memory and better survival  metabolites and specific lipids were highly enriched at extreme high temperatures , which involved in the galactose metabolism, fatty acid degradation, tryptophan/phenylalanine metabolism, and shikimic acid-mediated pathways  and phospholipids metabolism . Whereas, TCA, carbohydrates and amino acids metabolism, peptide biosynthesis, phenylpropanoid biosynthesis, IAA biosynthetic pathway and lipids  were featured with high levels at 22°C and 28°C, especially the metabolites of amino acids, trisaccharides, PE, PC, PA, PS, MGDG, DGDG and DAG preferred to enrich at 28°C. In addition, the TCA molecules, disaccharides, nucleotides, polypeptides, biosynthesis of fatty acids, glucobrassicin-mediated IAA and SQDG were obviously high at 22°C. Our results characterized the distinguished metabolites and related regulatory metabolic pathways and the tendency of cellular metabolism pathways in response to different temperature conditions from 22°C to 34°C during seed germination and seedling growth. Our comprehensive metabolome and lipidome especially indicated that 1) phytohormones, such as the bypass of auxin biosynthesis might be activated under different temperatures, and SA might be implicated in the heat stress response; 2) most lipids tended to accumulate under moderate temperatures (22°C and 28°C), whereas the severe heat (34°C and 40°C) contrarily resulted in the decrease of most lipids; 3) most metabolites were induced to be highly enriched for resistance against severe heat stress (34°C and 40°C); 4) organic acids/derivates and amines implicating in cellular TCA, carbohydrates and amino acids metabolism, peptide biosynthesis, phenylpropanoid biosynthesis and IAA biosynthetic pathway were obviously enriched at the moderate temperature (22°C and 28°C). Heat stress is an increasing risk for agricultural crops due to global warming. Our results are crucial to study the comprehensive metabolites and lipids phenotyping, providing the cues to functional innovation with heat tolerance.The original contributions presented in the study are included in the article/WQ and HY carried out the assays and data analysis of metabolites and lipids. YZ and HY performed the preparation of plants materials. QC and TL extracted peaks and confirmed the structural data. SW and LC preformed the standard rectification of UPLC-MS/MS. HT guided for the data analysis. HY, WQ, YZ and HT wrote the manuscript. All the authors reviewed the manuscript. HY and HT were responsible for contact and communication. All authors contributed to the article and approved the submitted version.