Using a tailor-made microfluidics labelling system to supply 13CO2 at steady state, we investigated in vivo labelling kinetics in intermediates of the Calvin Benson cycle and sugar, starch, organic acid and amino acid synthesis pathways, and in protein and lipids, in Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii, which is the fastest growing green alga on record. We estimated flux patterns in these algae and compared them with published and new data from C3 and C4 plants. Our analyses identify distinct flux patterns supporting faster growth in photosynthetic cells, with some of the algae exhibiting faster ribulose 1,5-bisphosphate regeneration and increased fluxes through the lower glycolysis and anaplerotic pathways towards the tricarboxylic acid cycle, amino acid synthesis and lipid synthesis than in higher plants.Photosynthesis-related pathways are regarded as a promising avenue for crop improvement. Whilst empirical studies have shown that photosynthetic efficiency is higher in microalgae than in C 3 and C4 plants.Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C Modern agriculture has witnessed an ~160% increase in global production since the 1950s. This gain was achieved without expanding cropland, by extensive use of fertilizer and plant protection agents as well as improved crop varieties. Future gains face the challenges of shrinking farmland area, an increasingly unpredictable climate and the environmental imperative to use less fertilizer and agrochemicals, and will require new breeding strategies. Most breeding efforts during the Green Revolution addressed pathogen resistance and brought plant architecture and the harvest index close to their theoretical upper limit4. Indeed, yield is stagnating in most major crops8.An increase in agricultural yield of 70% or more is required by 2050 to meet the growing demand of the world population11. The theoretical limit, defined as the fraction of light energy that is captured in conversion of CO2 and water to glucose, is ~12% (ref. 3). Measured efficiencies reach ~3.5% and ~4.3% for C3 and C4 plants, respectively, and 5–7% for microalgae, emphasizing the potential of microalgae as a resource for yield improvement12. Many factors reduce photosynthetic efficiency, including photochemical limitations and the bifunctionality of ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO). In addition to carboxylation13, RuBisCO catalyses a side-reaction with O2 that leads to formation of 2-phosphoglycolic acid (2PG), which is recycled via glycine, serine and glycerate to regenerate 3-phosphoglyceric acid (3PGA), with concomitant loss of CO2 and ammonium14. Several mechanisms have evolved to increase CO2 concentration around RuBisCO and decrease the rates of the side-reaction with O2. Microalgae pump bicarbonate allowing them to accumulate CO2 in microstructures called pyrenoids in which RuBisCO is located16. C4 plants possess a biochemical pump in which bicarbonate is combined with phosphoenolpyruvate (PEP) in externally located mesophyll cells to form four-carbon metabolites that move into the bundle sheath cells and are decarboxylated to generate a high concentration of CO2 around RuBisCO18.Photosynthesis is a promising target to increase yield. Photosynthesis is relatively conserved among crops and its efficiency is well below the theoretical maxima19. However, the importance of photosynthesis for yield is underlined by consistent increases in yield in free air elevated [CO2] experiments9, by the increased yield of transgenic plants that overexpress various carbon (C) assimilation enzymes11 and by the finding that enclosed outdoor photobioreactor designs that allow faster photosynthesis often increase algal productivity20. Existing efforts to redesign photosynthesis focus on improving light harvesting, light energy conversion and C uptake and conversion, including suppression of photorespiration4. Despite its potential impact, little work has addressed photosynthesis-associated metabolism and downstream reactions (often-termed photosynthate investment) as a route to improve yield.A key question is whether optimizing photosynthetic efficiency will result in higher productivity. Earlier studies with panels of crop genotypes did not find a clear relationship between leaf photosynthetic rate and yield22. Unique capabilities are more likely to be found in organisms that cope with severe conditions. The green alga Chlorella ohadii was recently isolated from a desert biological soil crust, one of the harshest environments on Earth23. It exhibits unparalleled resistance to extreme illumination levels , high photosynthetic rates25 and the fastest growth rate ever reported for a photosynthetic organism26. C. ohadii may provide us with essential information on the photosynthetic machinery and what limits plant performance27. Physiological and ‘omics studies have uncovered several factors that contribute to its extraordinary resilience under EIL, including a remarkably robust photosystem II, rapid poising of redox status, rapid post-translational redox regulation of protein kinases and reactive oxygen stress (ROS) and heat-shock (HS) responses and thylakoid remodelling27.Exploring biodiversity in algae, especially algae from extreme environments, offers means to improve photosynthesis28. Due to the complex relationship between protein activity and substrate level, protein and metabolite levels do not provide reliable information about flux. Flux in photosynthesis can be investigated by performing 13CO2 pulse labelling and analysing the temporal labelling kinetics of metabolic intermediates using chromatography linked to mass spectrometry38. Here, we monitor the 13C-labelling kinetics of >40 intermediates in the Calvin Benson cycle (CBC), photorespiration and central C and nitrogen (N) metabolism, as well as starch, protein and lipids in C. ohadii and, for comparison, C. sorokiniana and Chlamydomonas reinhardtii . The experiments were designed to address two questions: first, are there changes in flux patterns between algal species that could explain the fast growth of C. ohadii? Second, do flux patterns in algae differ from those in higher plants? For the latter, we compared labelling kinetics in algae with published and new data from Arabidopsis thaliana and Zea mays (maize) as model plants for C3 and C4 photosynthesis, respectively.The key phenotype emerging from changes in gene expression and protein regulation is pathway fluxk-means clustering . Enrichment in cluster 1 rose faster in C. ohadii than in C. sorokiniana  or C. reinhardtii (P = 0.0032) and faster in C. ohadii under EIL than under low light  (P = 0.0299). Differing enrichment kinetics might reflect different fluxes and/or pool sizes. Most CBC intermediates and PEP had larger pool sizes in the Chlorella species than in C. reinhardtii, , sedoheptulose-7-phosphate (S7P) and (in EIL) dihydroxyacetone phosphate (DHAP) separated further from other CBC intermediates in C. ohadii than in the other two algae. Glucose-1-phosphate (G1P) showed similar labelling to CBC intermediates in C. ohadii but clustered with further intermediates of starch and sucrose synthesis in the other two algae. PEP grouped with CBC intermediates (cluster 1) in both Chlorella species but not in C. reinhardtii.The clusters were visualized by multidimensional scaling  Fig. . MetabolArabidopsis , SBP, ribulose 1,5-bisphosphate (RuBP); Supplementary Tables 31 or measured here) and faster in C. ohadii than in maize ; Supplementary Tables 32).Comparison of labelling kinetics between algae and plants was made possible thanks to the experimental design that guaranteed immediate and equivalent enrichment of all Ci species . Enrichm2 fixation product and immediately upstream from DHAP, because in algal samples 3PGA consistently exhibited lower apparent enrichment than did DHAP  than in C. sorokiniana  or in C. reinhardtii . In C. ohadii under EIL, all slopes were essentially identical to DHAP  pointing to especially rapid CBC flux in these conditions.To facilitate interspecies comparison, we regressed the enrichment of each intermediate against enrichment of DHAP and computed the correlation coefficient and slope  and ribulose-5-phosphate (Ru5P)), RuBP) or slightly lower (R5P and F6P) than in algae  rose more slowly than F6P, probably reflecting slow labelling of cytoplasmic pools as described in higher plants (Supplementary Enrichment in intermediates of starch and sucrose synthesis (mainly cluster 2) rose slightly more slowly than for CBC intermediates  which decreases the side-reaction of RuBisCO with O2 that leads to formation of 2PG (Chlorella species (0.78–0.90) than in C. reinhardtii (0.48), resembled Arabidopsis (0.76) and was higher than maize (0.40) . The 2PG levels in C. ohadii under EIL resembled those in maize.Microalgae possess a COn of 2PG  text; 2Pardtii 0.8, resembardtii 0.8, resembC. ohadii  and much lower in C. reinhardtii (0.79), Arabidopsis (0.76) and, especially, the C4 species maize (0.25) . Aspartate followed a similar pattern . The faster rise in enrichment in C. reinhardtii might be attributed to 5–25 smaller pool sizes than in the Chlorella species  and maize  revealed that labelling of malate in algae was faster than in C3 photosynthesis but slower than in C4 photosynthesis21 , as expected because PEP carboxylase (PEPC) adds H13CO3− to PEP. However, the m + 2 isotopomer of aspartate and malate appeared by 40 s, in parallel with m + 1 becoming the dominant isotopomer of PEP , we used direct bubbling of running cultures. The main aim was to investigate flux into major products. We first analysed sugars, organic and amino acids using gas chromatography linked to mass spectrometry (GC–MS)k-means clustering based on Euclidian distance  and serine and several tricarboxylic acid (TCA) cycle intermediates . Enrichment of citrate, fumarate and succinate rose early (clusters 1 and 2) in all algae . These TCA intermediates were mostly in clusters 1 and 2 and were localized quite close to malate and aspartate in the MDS analysis. Intermediate 2-oxoglutarate (2OG) was not reliably detected in our analyses but probably had a similar pattern because enrichment rose rapidly in glutamate in all algae  than in the Chlorella species under LL (cluster 4) possibly due to the smaller pool in C. reinhardtii. Gly3P was among the most abundant metabolites in these algae (Supplementary Table C. reinhardtii42) with one to two orders higher levels than previously reported in Arabidopsis (Table 43).Enrichment of glycerol 3-phosphate (Gly3P) rose faster in Chlorella species (cluster 3) than in C. reinhardtii (cluster 4) under LL and rose even faster in C. ohadii under EIL  and a larger pool .Starch is often a major C reserve in chlorophyte algae13C accumulation was accompanied by a significant decrease in the amount of 12C in starch . PSR correlated positively with growth rate across the three algae in LL .We next examined 48, which have a much higher lipid content than plant leaves  gave estimates eightfold to 100-fold lower than algae  in C. reinhardtii, C. sorokiniana and C. ohadii under LL and even faster labelling in C. ohadii under EIL . Importantly, both flux to starch and combined flux to sugars were lower than flux to protein in algae, whereas both were higher than flux to protein in Arabidopsis.Flux to sucrose was undetectable in is Table . The estes Table . In theses Table , resulti13C in malate and aspartate was used to estimate a minimum flux at PEPC . In algae, minimum estimated flux at PEPC was of the same order as estimated flux to sucrose or starch   were 9.8 ± 0.8 and 32.5 ± 3 nmol C eq. g−1 DW s−1 in C. ohadii under LL and EIL, respectively  was most efficient in C. ohadii, followed closely by C. reinhardtii, with the lowest efficiency in C. sorokiniana  to estimate intracellular fluxes Fig. . CO2 uptsol Fig. . C fixatana Fig. .Fig. 4Fl13C incorporation in detected metabolites we estimated minimum C assimilation rates in the algae H for lipid synthesis was 3.5-fold to sevenfold higher under EIL than LL  three algal species with differing growth rates, (2) C. ohadii in low versus very high light and (3) algae and model C3 or C4 plants.Metabolic fluxes are emergent metabolic phenotypes that are generated by the levels of transcripts and the levels and properties of the encoded proteins and associated changes in metabolite levelsArabidopsis31 and maize32 . Compared to Arabidopsis, algae are characterized by high fluxes to PEP and metabolic pathways downstream of PEP, rapid protein synthesis rates . Rapid flux through PEP was also apparent in longer pulses that investigated labelling patterns of end-products in algae (next section). This agreement makes it unlikely that the slow initial labelling of PEP in leaves is due to a large unlabelled pool of PEP in non-photosynthetic cells  or via PEPC and the TCA cycle for the synthesis of many amino acids and specialized metabolites or through pyruvate and isoprenoid synthesis. Rapid lipid synthesis in algae . Anaplerotic flux is needed to supply C skeletons for amino acid synthesis to support the twofold to tenfold higher rate of protein synthesis in the Chlorella species compared with Arabidopsis  that exchanges triose phosphate, 3PGA and inorganic phosphate (Pi) between the chloroplast stroma and the cytosol69, increased conversion of triose phosphate to 3PGA in the cytosol, for example, by non-phosphorylating NADP-GAPDH (as recently suggested for C. ohadii in EIL27) or increased activity of cytosolic PGM and ENO. In addition, INST-MFA flux modelling and published gene expression and localization data indicate that a small part of the flux to PEP and pyruvate in algae might occur in the plastid  are largely confined to the cytosoltid Fig. . Increastid Fig. , with soC. ohadii under EIL) and Gly3P  rose slowly, their large pool sizes mean that flux of fixed C into these metabolites was substantial especially in C. ohadii . Whilst such differences are especially relevant under stress, they may impact on metabolic patterns in non-stress conditions. More generally, higher metabolite levels might support increased metabolic flexibility, including the ability of C. ohadii to cope with extreme stress26. Flexibility may result from more efficient enzymes but might also require higher substrate saturation, allowing higher fluxes, and partly compensating for the lower protein content in the Chlorella species 74. This comparison is limited by the availability of robust proteomic datasets for algae. Quantitative analysis of more algal proteomes combined with studies of the activities and kinetic properties of enzymes could improve our understanding of what limits growth rates in different algae.The lower protein content of the ies Fig.  might coC. reinhardtii .Biochemicals, enzymes and other special reagents were obtained from Sigma-Aldrich, Roche and Merck. All solutions were prepared with purified deionized water , as previously described26. The medium in the bioreactors was HP without acetate75 to grow the cells photoautotrophically. Unless specified otherwise, the experiments were initiated at low cell density corresponding to optical density OD735nm = 0.02 and the culture temperature was stabilized at 35.0 ± 0.3 °C for C. ohadii and C. sorokiniana or 25.0 ± 0.3 °C for C. reinhardtii. Irradiance levels included two regimes: 100 μmol photons m−2 s−1 (LL) and 3,000 μmol photons m−2 s−1 (EIL), as described in ref. 27. Air for bubbling was supplied using an air-pump at ∼1 l min−1. The pH and dissolved oxygen concentration were monitored in situ every 1 min. Optical densities OD680nm and OD735nm  were monitored every 1 min. All cultures were grown starting from the same inoculum density. Bioreactors were autoclaved with medium and electrodes, and axenic inoculum was added on a sterile bench, together with 0.22 µm filters on all air inlets/outlets of the system. The cultures were axenic, as validated with light microscopy  and Luria–Bertani plating/incubation. Independent biological replicates were collected from three separate bioreactor runs for each alga or condition. Ash-free dry weight per volume for each culture was determined as in ref. 76.Cultures of 2 evolution of algal suspension withdrawn from the bioreactor was measured for 2 min at temperature corresponding to the growth conditions, in a closed temperature-controlled 0.65 cm diameter cuvette under continuous stirring . Light was supplied from a custom-made ring-formed LED light system; the applied intensity was made equivalent to the current growth intensity by measuring the penetrating light inside a cuvette filled with culture using an integrating sphere . Measurements were made at ambient CO2.The O13C to algal liquid cultures (see Supplementary 13C pre-equilibrated medium bubbled with a synthetic gas mixture. For longer incubation times (15–300 min), inorganic 13C was introduced via direct bubbling of the cultures with the same gas mixture. In both, 78% N2, 21% O2 and 400 ppm 13CO2 were mixed using mass flow controllers (Brooks Instruments) as described in ref. 31.Two setups were used to provide inorganic 12CO2 levels , as confirmed by the rapid drop in 12CO2 signal within <1 min of bubbling with the labelled gas mixture  and connected to transparent tubing . Cultures and fresh media were pushed through a tailor-made transparent mixer designed on the basis of ref. 78  through the entire route from sampling at the bioreactor to the quenching tube, with intensities made equivalent to the penetrating light for each culture and avoiding manual shading. Pulse duration was implemented by fitting the length of the transparent tubing downstream to the mixer to spray the culture/13CO2 solution mixture into the quenching tube at desired timings. Cooled tubes were kept at –70 °C until centrifugation . Thereafter, supernatant was discarded and pelleted tubes were frozen immediately in liquid nitrogen. LC–MS/MS analysis of supernatants from concentrated (15×) samples of all algae demonstrated metabolite levels of up to ~1% that of equivalent pellets. Non-labelled samples for each pulse time-series replica were generated by mixing of the same algal culture with fresh HP medium bubbled with ambient air through a humidifier at the same temperature as the 13C-labelling. Frozen pellets were resuspended in ice-cold methanol/chloroform . Following four cycles of freeze–thaw of resuspended cells, metabolites were extracted according to ref. 75.For rapid labelling , a fresh HP medium (pH 6.2) was bubbled for 30 min to ensure <1% residual 13CO2. This was done because, in the closed microfluidic system, longer incubation times would lead to Ci depletion. The gas mixture (bubbled ∼1 l min−1 through a humidifier) was switched from natural air to the same synthetic 13C-labelled air mixture as above. Measured residual 12CO2 in the cultures was below 2% within a few minutes of pulse  and pellets immediately frozen in liquid nitrogen. Centrifugation was necessary because, with time, some metabolites can accumulate in the medium, so labelling patterns in the complete suspension may not reflect that in the cells (ref. 75). Frozen pellets were resuspended in precooled (–20 °C) MTBE solution  to follow a two-phase extraction approach developed by ref. 42. Following four cycles of freeze–thaw of resuspended pellets, metabolites, protein and starch were extracted and analysed. The lower phase, containing the polar and semipolar metabolites, was dried in a SpeedVac concentrator and stored at –80 °C for metabolite profiling. The upper MTBE phase, containing lipids, was dried and analysed for lipid content and labelling (below). The solid pellets containing the precipitated protein and starch were stored at –20 °C before further analysis (below).For slow labelling, running algal cultures were bubbled with Arabidopsis, to confirm and deepen our previous study31, 13C-labelling was repeated with a total of 15 time points  and with three to 13 replicates per time point. 13CO2-labelling and data analysis were performed as described in ref. 80.For Arabidopsis Col-0 material was taken from ref. 43 for analyses of galactose and Gly3P. Briefly, plants were grown in a 1:1 mixture of soil (Stender) and vermiculite, in controlled-environment chambers with 16-h photoperiod and day/night temperatures of 22/18 °C and 150–160 µmol m−2 s−1 irradiance provided by fluorescent lights. Rosettes were harvested in the ambient growth conditions by rapid quenching in liquid nitrogen, ground and metabolites were extracted with methanol-chloroform81.82. Data for 3PGA are solely based on signal peak areas obtained in extracts, as chromatograms obtained with the standards could not support absolute quantification. The obtained peaks were integrated using the Thermo Finnigan processing software package LCQuan-2.5. Since mass spectrometry detects ionized compounds separated by their mass-to-charge ratio, the mass-to-charge ratio of 13C-labelled compounds increases by an amount that equals the number of incorporated 13C atoms. Therefore, by determining the ratio of intensity of the monoisotopic ion and its isotopic ions, the level in stable isotope labelling (enrichment) was quantified. In maize, SBP has an ‘active’ pool that is involved in the CBC and is rapidly labelled, which our analysis  with an Agilent 6890N24 gas chromatograph  with splitless injection onto a Factor Four VF-5ms capillary column, 30-m length, 0.25-mm inner diameter, 0.25-μm film thickness (Varian-Agilent Technologies), which was connected to a Pegasus III time-of-flight mass spectrometer . Retention indices were calibrated by addition of an n-alkane mixture  to each sample. GC–MS chromatograms were acquired, visually controlled, baseline corrected and exported in NetCDF file format using ChromaTOF software . TagFinder software v.1.0 (ref. 83) was used for GC–MS data processing into a standardized numerical data matrix and compound identification. Mass features were identified by mass spectral and retention time index matching to the reference collection of the Golm metabolome database 84. Requirements for the manually supervised analyte identification were the presence of at least three specific mass fragments per mass feature and a retention index deviation <1.0%. Metabolite isotopologues were evaluated for uniqueness by correlation analysis, using the sum of all detected isotopologues per mass fragment and comparing among fragments or molecular ions from the same analyte. The sum of isotopologues were used in both labelled and non-labelled conditions for quantification, while isotopologue distributions were used for stable isotope enrichment analysis. Absolute quantification was performed, using authenticated commercially available reference substances and co-analysed calibration graphs. Enrichment analysis was restricted to mass fragments with known atomar composition. Expected atomar composition and natural enrichment were checked by non-labelled samples, for example those used to obtain calibration curves. After correction for natural occurring isotopes, experimental enrichment of approximately zero was obtained. Laboratory and reagent contaminations were evaluated and removed by non-sample control experiments.For GC–MS, metabolites were methoxyaminated and trimethylsilylated manually as previously describedhttps://www.mpimp-golm.mpg.de/19405/Corrector_package_V1_91.zip). Metabolite quantification from both platforms was performed on the basis of calibration curves obtained with authentic standards whose matrices were also corrected. Relative isotopomer abundance (m + n) for each metabolite is calculated as in ref. 31. Total amounts of PEP were determined by using freshly prepared extracts in trichloroacetic acid. Levels were determined enzymatically with Shimadzu UV-2600i spectrophotometer (Shimadzu) based on ref. 85.Both LC–MS/MS and GC–MS matrices were subsequently processed using the Corrector software tool  (Biosolve) and measured using Waters Acquity ultra-performance liquid chromatography system  coupled with Fourier transform mass spectrometry (UPLC–FT-MS) in positive and negative ionization modes. Analysis and processing of mass spectrometry data was done with REFINER MS 10.0  and comprised peak detection, RT alignment and a chemical noise removal. Derived mass features characterized by specific peak ID, m/z values, retention time and intensity were further processed using custom R scripts. Before annotation of metabolic features using in-house lipid database, isotopic peaks were removed from the MS data. Annotated lipids were confirmed by manual investigation of the chromatograms using Xcalibur . Peak intensities were day-normalized and sample median-normalized and subsequently log2-transformed. For lipid labelling, we restricted our analysis to m0 decay of major labelled lipids between 0 and 300 min due to their high molar mass and the very complicated spectra with partly overlapping envelopes of 13C-labelled lipids.Measurement of lipids was performed as described by ref. 89). MDS was performed for the four algae and conditions with stats R package v.3.6.0 using Euclidean distance90. The input Euclidean distance matrix was calculated on a row-wise concatenated enrichment–time-series matrix of all algae and conditions. Clustering has been performed separately for each alga using k-means91 and the number of clusters has been estimated using GAP statistics for k between 2 and 20 in 100 bootstrap samples92 in Cluster R package v.2.0.8 (ref. 93). ANOVA were performed using the car R package v.3.0.9. Minimum estimates of fluxes to all organic and amino acid quantified by GC–MS analysis were performed by summing the slopes over time of level × enrichment products for pyruvate, citrate, glutamate, proline and glycine, provided in Supplementary Tables Data processing and statistical analysis were performed in R  and following exclusion of the MIDs of 2OG and glutamate and PEP at 40 s . The agreement between estimated and measured starch and sucrose synthesis rates (Table χ2 for all algae. In line with ref. 37, metabolite pools were treated as parameters to be fitted and dilution was allowed to model effects of unlabelled subpools. The 95% confidence intervals were computed for all estimated parameters by evaluating the sensitivity of the sum-of-squared residuals to parameter variations96. To obtain statistically acceptable fits, the minimum standard deviation of MIDs was set to 0.001 and the maximum standard deviation over all mass fractions for a given metabolite and time point were considered as error.Flux estimation by INST-MFA, statistical analysis, as well as confidence interval calculation were performed using implementations provided along the INCA software packageFurther information on research design is available in the Supplementary InformationSupplementary text, Tables 3, 6 and 7 and Figs. 1–11.Reporting SummarySupplementary TablesSupplementary Tables 1, 2, 4, 5 and 8.Supplementary Table 9INST- MFA parameters.Supplementary Data 1Enolase sequence alignment from algae and plants.Supplementary Data 2TPT sequence alignment from algae and plants.Supplementary Data 3PEPC1 sequence alignment from algae and plants.Supplementary Data 4PEPC4 sequence alignment from algae and plants.Supplementary Data 5PDH_E1 sequence alignment from algae and plants.Supplementary Data 6PDH kinase mitochondrial sequence alignment from algae and plants.Supplementary Data 7PDH kinase sequence alignment from algae and plants.


Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model  provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories.Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model  can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. Saccharomyces cerevisiae dominates the scene and many research efforts focus on engineering this species for particular applications  described species, and much less attention has been paid to nonconventional yeasts. These species remain a mostly untapped resource of alternative metabolic routes for substrate use and product formation as well as tolerances to specific stressors , 10. To S. cerevisiae strains in continuous (steady-state) fermentations  can contextualize high-throughput data and predict genotype-environment-phenotype relationships , 12. Whintations , their uIn batch operation, cell culture follows a growth curve with the following phases: lag phase, exponential growth, growth under nutrient limitation, stationary phase, and cellular decay. Available dynamic GEMs of yeast metabolism focus on the exponential phase and explain reasonably well the measured dynamics of biomass growth, carbon source uptake, and the production of relevant primary metabolites –17. The –S. cerevisiae  a novel metabolic reconstruction, based on an extension of the current consensus genome-scale model of  (Yeast8 ); (ii) mS. cerevisiae and Saccharomyces uvarum strains in rich medium fermentation. Recent studies revealed that S. uvarum strains show interesting physiological properties. S. uvarum is more cryotolerant than S. cerevisiae, produces more glycerol and less ethanol than S. cerevisiae wine strains, and has different aroma profiles  shunt as an alternative NADPH source and store reductive power—necessary to subdue oxidative stress under cold conditions—in lipids or other polymers. Additionally, our results are compatible with recent experimental observations showing that most carbon skeletons used to form higher alcohols  are synthesized S. cerevisiae S288C (v.8.3.1)   to incluv.8.3.1) . Further10.1128/mSystems.00260-21.4TABLE S1Table S1, XLSX file, 0.01 MB.Novel metabolic reconstruction additions. Table sheet 1 presents the list of reactions and metabolites added to the novel metabolic reconstruction. Among the metabolites added, 13 aroma compounds were included, namely, methionol, ethyl-hexanoate, ethyl-octanoate, 1-hexanol, 1-octanol, 4-tyrosol, hexanal, hexyl-acetate, octyl-acetate, benzyl-acetate, 4-hydroxyphenylacetaldehyde, and 3-methylsulfanylpropanal. Table sheet 2 presents the chemical family  and several aroma descriptors for each of the added compounds. Download Copyright © 2021 Henriques et al.2021Henriques et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. cerevisiae strains, including S288C.Among the metabolites added, 13 aroma compounds were included. Noticeably, we found that prior genome-scale reconstructions lacked methionol and tyrosol impeding simulated growth on methionine and tyrosine as sole nitrogen sources, which is known to be possible for several S. cerevisiae T73 and S. uvarum BMV58, and a strain, S. uvarum CECT12600, found in nonfermentation environments  per hour, Glxv and Fv are the fluxes of glucose and fructose, 2Ov is the flux of O2 present only at the beginning of the fermentation, v4NH is the flux of ammonium, viAA is the exchange rate of the amino acid i , and jPv are the constraints associated with the F, NH4, AAi, and jP correspond to the concentrations of glucose, fructose, ammonium, amino acids, and products, all expressed in millimoles per liter. The general formulation of the FBA problem reads as follows:10.1128/mSystems.00260-21.5TABLE S2Table S2, XLSX file, 0.08 MB.Detailed mathematical formulation (objective and constraints) of the multiphase multiobjective pFBA implementation. Download Copyright © 2021 Henriques et al.2021Henriques et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Gmaxv is the maximum uptake rate, Gk is the MM constant, EiKis the strength of ethanol inhibitory effect, and E is its concentration (mmol/liter). A similar expression, Fv, exists for fructose (F). Additionally, in our case studies, the medium was supplemented with sucrose; thus, we included a mass action-type expression, characterized by the kinetic constant khydro, describing its hydrolysis.The uptake of glucose and fructose was modeled using Michaelis-Menten (MM)-type kinetics with competitive ethanol inhibition :(1)vGlx2Ov and 2Ok are the oxygen uptake and transport rate constants and O2 is the concentration of oxygen in the medium.A certain amount of dissolved oxygen is present in the medium and consumed during the lag phase . Download Text S2, PDF file, 2.8 MB.Bootstrap parameter estimation of the model. The text presents further numerical results, such as the convergence curves of the bootstrap approach, the distribution of parameter values, and the plots of the model versus data for all measured states not included in the main figures , v4NHmax is the maximum uptake rate achieved, and k4NH is the MM constant. To avoid an excessive number of parameters, amino acid transport was modeled following mass action kinetics:iis the extracellular concentration of the amino acid (in millimoles per liter) and kiAAis the associated kinetic parameter.The uptake of ammonium was modeled byAX the active biomass and the flux, iPv, proportional to the amount of transported hexoses:iP refers to the excreted product iPkthe production rates.Production of alcohols and higher alcohols, carboxylic acids, and esters follows mass action kinetics:An exception to this was the formulation of the dynamics of acetate. This metabolite is produced during exponential growth and consumed during stationary phase following mass action kinetics.de novo synthesis pathways during stationary and decay phases to guarantee optimal adaptation to perturbations in nitrogen homeostasis.Since nitrogen sources are depleted before the stationary phase, we developed a new model of nitrogen homeostasis that considered protein turnover. The proposed model describes the combined use of the Ehrlich and iAAis associated with the stoichiometric coefficient of the amino acid i in the protein pseudoreaction. Mathematically, the dynamics of protein content reads as follows:content is the fraction of protein in the newly formed biomass, kdeath(per hour) is the rate of biomass degradation during decay phase, VX is the simulated viable biomass (grams per liter), AX is the simulated active biomass (grams per liter), vProtis the protein production rate (grams per gram [DW] per hour), and λis the protein turnover rate (per hour).To introduce protein turnover, we simulated the degradation of the existing protein fraction inside biomass (Prot), into a pool of amino acids that subsequently produce new proteins. During stationary and decay phases, the lower bounds on the amino acid uptake are set asThe degraded proteins (viAA are computed by maximizing protein production (vProt) and ATP (VATP) while solving the FBA problem:ϕ is estimated for each strain, S is the stoichiometric matrix, v is the vector of fluxes, viAA is the exchange rate of amino acid AAi, iPvare constraints associated with fermentation products, and vPro(r_1904) is the amount of excreted proline. The last amino acid accumulates in the extracellular medium throughout the fermentation  accounts for the ammonium and free amino acids present in the medium, excluding proline, which is not catabolized under anaerobic conditions.A static biomass equation was not able to explain nitrogen assimilation 2. Thereffittedis a species- or strain-dependent parameter estimated from data and the rest are polymerization costs of the different biomass precursors , μis the growth rate computed with pFBA, kdecayis the decay rate (only active during decay), vProt(r_4047) is the exchange flux for protein production, and λis the turnover rate (both only active during stationary and decay phases). The behavior of viable cell mass differed from that of active cell mass by a decline induced by ethanol (vX (grams per liter) is the growth rate obtained with the constraint based model, E is the ethanol concentration (millimoles per liter), and kEdeath, n, and k are the parameters controlling susceptibility to ethanol.In addition, we discriminated between active cells (able to ferment) and viable cells (able to divide and ferment) to capture the dynamics of CFU and biomass 2. The dy ethanol :(14)Xv˙The former mechanisms, coupled to parameter estimation, allowed us to predict nitrogen consumption, CFU, and biomass dynamics accurately 6.The final model consisted of 46 ordinary differential equations depending on 66 parameters which we estimated from time series data for all measured external metabolites and biomass. The mean standard deviation on the parameters ranges from 2.5%for SuCECT12600 and 12.6%for SuBMV58. The reasonably low distribution on the parameters resulted in a reasonably low uncertainty associated with the model simulations  for each measured variable and each strain-based fermentation. The medians of the R2values are above 0.94 for all strains.The model described the dynamics of our illustrative examples successfully. The best fit to the data and the associated uncertainty, as computed by the bootstrap, are shown in R2values are reported in Interested readers may find further details on the parameter estimation in 10.1128/mSystems.00260-21.6TABLE S3S. uvarum strains. In particular, the uptake rate for threonine is 740%higher for SuCECT12600 than for SuBMV58. Table sheet 2 presents the R2goodness-of-fit as computed for all measured compounds and biomass for the three individual strains. The vast majority of the coefficients were above 0.9. Some negative values appeared—10 out of 141—typically associated with low signal-to-noise ratio and high data dispersion observed in the measured variable, e.g., cysteine. Download Table S3, XLSX file, 0.02 MB.Optimal parameter values and goodness of fit of the model. Table sheet 1 presents the list of all parameters together with their optimal value as achieved by the bootstrap approach for the three individual strains. The comparison between the wine strains ScT73 and SuBMV58 reveals substantial differences—above 100%relative difference—in the growth-associated ATP maintenance, the rate of transport of specific amino acids and hexoses, and the rate of production of succinate. The lag phase is more than three times longer for the natural strain; in addition, the non-growth-associated maintenance is practically zero for wine strains, while it is around 0.8 for the SuCECT12600 strain. The uptake of various amino acids varies significantly between Copyright © 2021 Henriques et al.2021Henriques et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the At the extracellular level, the most striking differences between strains occur in the production of compounds associated with the central carbon metabolism and nitrogen metabolism, in particular, in the dynamics of acetate and the yields of succinate, 2,3-butanediol, and glycerol  and G anWe used the model to decipher the metabolic strategies used by the different strains that lead to such differences. 10.1128/mSystems.00260-21.7TABLE S4S. uvarum strains. Download Table S4, XLSX file, 0.2 MB.Dynamic flux ratios and transcriptomic data. Sheets 1 to 4 present the overall, growth, decay, and stationary dynamic flux ratios, respectively, as millimoles of produced compound per millimole of consumed hexose × 100 (mmol/mmolH). Only those reactions for which the maximum flux ratio value over the three species is above 0.01 mmol/mmolH are reported. Sheet 4 presents the unpublished data by M. López-Malo, A. Querol, and J. M. Guillamon showing substantial accumulation of GABA in Copyright © 2021 Henriques et al.2021Henriques et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the r_2057).Cells produced the most significant fraction of succinate during the stationary and decay phases, with a significantly higher dynamic flux ratio by SuBMV58 and SuCECT1600 (6.08 and 1.66) than ScT73  and the reductive branch of TCA . Remarkably, succinate production through the GABA shunt was between 3 and 4.6 times higher for S. uvarum species than for S. cerevisiae . This result suggested an important role for the GABA shunt in the maintenance of the cellular redox state. Incidentally, revisiting data from reference During the decay phase, most succinate was produced through the tricarboxylic acid (TCA) cycle reductive branch in the two species. However, during the stationary phase, succinate production was distributed between the GABA shunt  and until the entry into the stationary phase. Afterward, while the extracellular acetate concentration remained constant in ScT73, a decrease was observed in both S. uvarum fermentations, indicating acetate consumption. As shown in The three strains produced acetate during the growth phase , into mevalonate  also consuming NADPH. In addition, mevalonate is a reducing equivalent that can be further metabolized into, for example, ergosterol (r_0127in our reconstruction) possibly acting as storage of NADPH in cryotolerant species.According to the modeling constraints, the most parsimonious explanation for this observation would have been an operative glyoxylate cycle. However, based on the repression by glucose of the key enzymes of the glyoxylate cycle  and previous intracellular data , we deciS. uvarum strains also used the carnitine shuttle system to transport acetyl-CoA into the mitochondria . Inside the mitochondria, acetyl-CoA was used to form isopropylmalate  intermediates, rather than coming from the catabolism of precursor amino acids  , and isobutanol were in great part synthesized ctively) .S. uvarum strains produced more PEA than ScT73 , while the opposite occurred for isoamyl alcohol . We found that the production of PEA and isoamyl alcohol contributed substantially to the redox metabolism related to glycerol accumulation. Approximately 43%, 36%, and 27%of the glycerol produced by the ScT73, SuBMV58, and SuCECT12600 strains was attributable to NADH derived from isoamyl alcohol and PEA.S. uvarum strains occurs through the higher flux through the shikimate pathway (r_0996and r_0279). Interestingly, while ScT73 had a larger flux ratio through the oxidative pentose phosphate pathway (r_0091and r_0889) partly redirected toward glycolysis, S. uvarum simulations reflected the inverse pattern (r_0984), with glycolytic flux being shifted toward the nonoxidative PPP.The higher production of PEA observed in r_0961) and 2-acetyllactic acid (r_0097). Noticeably, S. uvarum strains also contributed to acetyl-CoA using the carnitine shuttle (r_0254). 2-Acetyllactic acid can further be converted to 3-methyl-2-oxobutanoate, consuming one NADPH (r_0096), which also showed two different fates: the production of 2-isopropylmalate  or valine .Pyruvate in the mitochondrion showed two different fates: acetyl-CoA  observed in ScT73 (1.646 against 0.917 and 0.727 in the S. uvarum strains) was associated with the need for shuttling NADPH into the mitochondria (r_2131), utilized in 3-methyl-2-oxobutanoate synthesis  resulted in excess NADP+. In the case of S. uvarum, given the reduced influence of oxidative PPP, NADP+ recycling was achieved mostly through the GABA shunt.Production of PEA and isoamyl alcohol also affected NADPS. cerevisiae and cryotolerant S. uvarum strains in a rich medium (grape must) fermentation.Genome-scale models have the potential to decipher how nonconventional yeast species use metabolism to produce industrially relevant products and tolerate specific stressors, such as cold temperatures. This study aimed to develop a dynamic genome-scale model to investigate the dynamics of yeasts primary and secondary metabolism in batch cultures. To generate biological hypotheses for modeling, we considered the description of the metabolism of The first question in this research was how to model all phases in the batch process: lag, exponential growth, limited nitrogen growth, stationary, and decay. Prior studies focused on the exponential growth phase and were based on available reconstructions with many missing reactions, particularly those related to secondary metabolism , 17, 30.As a first step, we needed to extend a yeast genome-scale reconstruction to account for the production of higher alcohols, carboxylic acids, or esters. We extended the Yeast8 consensus model incorporating missing reactions and metabolites. A similar curation process has been recently applied to the iMM904 reconstruction . The autA critical aspect for an improved accuracy was the multiphase multiobjective dynamic FBA scheme. Previous works focused on ATP consumption to explain the metabolism after depletion of the limiting nutrient , 33, 34;35–S. cerevisiae and S. uvarum, in rich medium (grape must) fermentation. Recently, it was hypothesized that S. cerevisiae and S. uvarum species might have different redox balance strategies  was poorly expressed when succinate was produced in S. cerevisiae and that the GABA shunt played a minor role in redox metabolism. In contrast, Coleman et al.  and that GHB was used to form the polymer polyhydroxybutyrate (PHB). Also noteworthy is that PHBs are synthesized by numerous bacteria as carbon and energy storage compounds  as reducing equivalents to withstand oxidative stress induced by low temperatures. The role of the GABA shunt and the production of reducing equivalents in the metabolism of cryotolerant species may be plausible routes worth exploring.The fact that López-Malo et al.  found hide novo rather than from the incorporation and catabolism of amino acids . This result agrees with the findings of Crépin et al.  were mainly synthesized n et al. , who expS. cerevisiae and S. uvarum.Noticeably, most of NADPH consumption was associated with higher-alcohol synthesis. The increased flux through cytosolic isocitrate dehydrogenase associated with isoamyl alcohol is compatible with reports associating high expression of IDP2 with nitrogen deficiency  and statde novo synthesis—are not in contradiction but two extremes of a common mechanism. Incidentally, Yuan et al.  accumulate in small amounts due to perturbations in the amino acid pool. These results confirm the hypothesis raised by Shopska et al. , who sugn et al.  showed tOur bootstrap-based identifiability analysis of the proposed model and the fact that model predictions are consistent with numerous previous findings lead us to conclude that the present model  can simulate yeast metabolism in batch culture in a general chemically characterized medium; the only requirement would be to update the metabolic reconstruction if required for the specific yeast species. The model can also be used to explore and engineer novel metabolic pathways toward specific bioproducts.S. cerevisiae and S. uvarum were used: the commercial strain T73 , originally isolated from wine in Alicante, Spain (S. cerevisiae (ScT73) representative; the commercial strain BMV58 , originally isolated from wine in Utiel-Requena (Spain), and the noncommercial CECT12600 strain, isolated from a nonfermentative environment , represented S. uvarum.In this study, three yeast strains belonging to e, Spain , was sel−1. All fermentations were performed in 3 independent biological replicates in 500-ml controlled bioreactors  filled with 470 ml of natural grape must. Each bioreactor was inoculated using an overnight starter culture cultivated in Erlenmeyer flasks containing 25 ml of YPD medium  at 25°C and 120 rpm in an agitated incubator . Strain inoculation was done at an optical density at 600 nm (OD600) of 0.100. The dynamics of the fermentation was registered using different probes and detectors to control and measure temperature, pH, dissolved oxygen , and effluent carbon dioxide level . Data were integrated into the BioExpert software tools . The fermentation was complete when a constant sugar content was reached as measured by high-performance liquid chromatography (HPLC).Fermentation assays were performed with grape must obtained from Merseguera white grapes, collected in the 2015 vintage in Titaguas (Spain) and stored in several small frozen volumes (4 liters at 20°C). Before its use, the must was clarified by sedimentation for 24 h at 4°C and sterilized by adding dimethyl dicarbonate at 1 ml liter−1; and oven temperature, 50°C. For sucrose determination, the same HPLC was equipped with a Hi-Plex Pb, 300- by 7.7-mm column , and the following analysis conditions were used: eluent, Milli-Q water; flux, 0.6 ml min−1; and oven temperature, 50°C. The retention times of the eluted peaks were compared to those of commercial analytical standards . Metabolite concentrations were quantified by the calibration graphs  of the previously obtained standards from a linear curve fit of the peak areas using 10 standard mixtures.Extracellular metabolites, including sugars, organic acids, main fermentative by-products, and yeast assimilable nitrogen (YAN), were determined at 10 sampling times during the fermentation. Residual sugars (glucose and fructose), organic acids , and the main fermentative by-products  were quantified using HPLC  coupled with refraction index and UV-visible (UV-Vis) (210-nm) detectors. Metabolites were separated through a HyperREZ XP Carbohydrate H+ 8-μm column coupled with a HyperREZ XP carbohydrate guard (Thermo Fisher Scientific). The analysis conditions were as follows: eluent, 1.5 mM; flux, 0.6 ml min18 100A column  and Accucore C18 10- by 4.6-mm, 2.6-μm Defender guards . For derivatization, 400 μl of the sample was mixed with 430 μl of borate buffer , 300 μl of absolute methanol, and 12 μl of diethyl ethoxymethylenemalonate (DEEMM) and ultrasonicated for 30 min at 20°C. The ultrasonicated sample was incubated up at 80°C for 2 h to allow the complete degradation of excess DEEMM. Once the derivatization finished, the sample was filtered with a 0.22-μm filter before injection. The target compounds in the sample were then identified and quantified according to the retention times, UV-Vis spectral characteristics, and calibration curves  of the derivatives of the corresponding standards. Amino acid standard (reference no. AAS18), asparagine, and glutamine purchased from Sigma-Aldrich were used for calibration.Determination of yeast assimilable nitrogen in the form of amino acids and ammonia was carried out following the same protocol as described previously . A volum−1). The oven temperature program was as follows: 5 min at 60°C, 5°C min−1 to 190°C, 20°C min−1 to 250°C, and 2 min at 250°C. The detector temperature was 280°C, and the injector temperature was 220°C under splitless conditions. The internal standard was 2-heptanone (0.05% [wt/vol]). Volatile compounds were identified by the retention time for reference compounds. The quantification of the volatile compounds was determined using the calibration graphs of the corresponding standard volatile compounds.We also determined the concentrations of higher alcohols and esters for each sampling time. Volatile-compound extraction and gas chromatography (GC) were performed following the protocol described in reference 600, dry weight (DW), CFU, and average cell diameter (ACD), were determined at each sample time, provided that the cell sample was sufficient to perform the corresponding measure. DW determination was performed by centrifuging 2 ml of the fresh sample placed in a preweighed Eppendorf tube in a MiniSpin centrifuge  at maximum speed  for 3 min. After centrifugation, the supernatant was carefully removed, and the pellet was washed with 70% (vol/vol) ethanol and centrifuged under the same conditions. After washing, the aqueous supernatant was removed carefully and the tube placed in a 65°C oven for 72 h. DW was finally obtained by measuring the mass weight difference of the tube with a BP121S analytical balance . OD600 was measured at each sampling time using a diluted volume of sample and a BioPhotometer spectrophotometer . CFU were determined using 100 to 200 μl of a diluted volume of samples plated in YPD solid medium  and incubated for 2 days at 25°C. The resulting colonies were counted with a Comecta S.A. colony counter. Only plates with a CFU count between 30 and 300 were used to calculate the CFU of the original sample. For ACD determination, a volume of cell sample was diluted into a phosphate-buffered saline solution and cell diameter measured using a Scepter handheld automated cell counter equipped with a 40-μm sensor .Physiological and biomass parameters, including ODPRJNA471597; M. Morard, L. G. Macías, A. C. Adam, M. Lairón-Peris, R. Pérez-Torrado, C. Toft, and E. Barrio, unpublished data) were sequenced and assembled previously. Genome assemblies were annotated by homology and gene synteny using RATT (S. cerevisiae S288c annotation (Genomes of ScT73  and SuBMing RATT . This apSaccharomyces cerevisiae S288C (v.8.3.2) metabolites and reactions related to amino acid degradation and higher-alcohol and ester formation. This refined model was then used as a template for reconstructing strain-specific genome-scale models for SuBMV58, SuCECT12600, and ScT73. MetaDraft, AuReMe and the results from the orthology analysis were used to create the strain-specific models.We added to the consensus genome-scale reconstruction of S is stoichiometric matrix of (n metabolites by m reactions) and v is a vector of metabolic fluxes. The number of unknown fluxes is higher than the number of equations, and thus, the system is undetermined. Still, it is possible to find a unique solution under the assumption that cell metabolism evolves to pursue a predetermined goal which is defined as the maximization (or minimization) of a certain objective function (J):J include growth rate, ATP, and the negative of nutrient consumption, etc.Flux balance analysis (FBA) , 61 is aTypically, multiple optimal solutions exist for a given FBA problem. In parsimonious FBA (pFBA), the result is the most parsimonious of optimal solutions, i.e., the solution that achieves the specific objective with the minimal use of gene products and the minimization of the total flux load .nexp, nobs, and nstare, respectively, the number of experiments, observables (measured quantities), and sampling times, while jmyrepresents each of the measured quantities, mX and mC in our case, and X and C. Observation functions were included for CFU and OD600 in order to scale viable cell mass (vX) and active cell mass (AX), respectively.The aim of parameter estimation is to compute the unknown parameters—growth-related constants and kinetic parameters—that minimize some measure of the distance between the data and the model predictions. The maximum likelihood principle yields an appropriate measure of such distance :(20)JmcParameters are estimated by solving a nonlinear optimization problem where the aim is to find the unknown parameter values  about a given experimental scenario. The median of the simulated trajectories regards the model prediction, while the distribution of the individual solutions at a given sampling time provides a measure of the uncertainty of the model.To account for model uncertainty, we used an ensemble approach. To derive the ensemble, we apply the bootstrap smoothing technique, also known as bootstrap aggregation (the bagging method) , 66. Theootstrap . Each dai,GScorresponds to the score of the flux i during growth, i,SScorresponds to the score during the stationary phase, i,DS corresponds to the score during the decay phase, We selected the most relevant metabolic pathways using a flux ratio, which provides a measure of the net flux over time during growth and stationary phases. In particular, we computed the integral of each flux multiplied by the biomass (millimoles per hour) over time and normalized its value with the accumulated flux of consumed hexoses (glucose and fructose):Enhanced Scatter Search  was usehttps://sites.google.com/site/amigo2toolbox/examples).The ensemble model generation procedure is computationally intensive. However, since each parameter estimation instance in the ensemble is an entirely independent task, we were able to solve this problem in less than a day using 60 CPU cores on a Linux cluster. These tasks were automated with the help of bash scripts and the Open Grid Scheduler. All the scripts necessary to reproduce the results are distributed (


Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes. Flux Balance Analysis (FBA) is a constraint-based optimization method that is used to study metabolic networks in steady state ,2. In thHowever, Flux Balance Analyses often become much more complicated. For example, because the studied cell can use many different nutrients, in which case there may be many uptake rates with an upper bound, or the modeler may be interested in inferring the full metabolic behavior from the measurement of some reaction rates, which would lead to equality constraints that fix these experimentally measured rates. Alternatively, models may include a growth-independent maintenance parameter via a lower bound on the production of the energy-equivalent ATP. All these cases lead to Flux Balance Analyses with multiple constraints, and a small sample of published models indeed shows that multi-constraint models are abundant . When FBA comprehensive list of minimal reaction sets that can sustain a steady-state flux through the network is given by the Elementary Flux Modes (EFMs) . EFMs caAlthough it has been well-understood that FBA with one constraint selects the maximal-yield EFM, this logic underlying the solution cannot be readily extended to cases with multiple constraints. We will use small toy models to prove that the highest-yield pathways are not always selected in such cases. Instead, we will show that FBA selects Elementary Flux Modes based on a weighted combination of their product yields for the various constrained uptake rates.Elementary Conversion Modes (ECMs) [cost vector formalism [To visualize all possible product yields for a model, we use the enumeration of s (ECMs) ,23. The s (ECMs) . We willormalism  to obtaiEscherichia coli how much the FBA-solution changes when one gradually changes a constraint, as such providing insight in the robustness of the FBA-predictions . GenomeN is the stoichiometry matrix that indicates which metabolites (rows) are used or produced in each reaction of the metabolic network (columns). It is multiplied by the flux vector, K constrained uptake rates. Although in Equation  are selected, respectively, for their substrate-efficiency in making biomass and ATP. However, combining only these two EMs would leave some of the oxygen supply unexploited. Therefore, the seemingly in-efficient brown EM can take over some of the ATP and biomass production while using relatively more oxygen than the red EM.In larger models, the number of constraints often becomes larger than two, for example, because more nutrient uptakes are constrained. In addition, the number of secondary objectives may rise, for example, because uptake rates are fixed to their experimentally observed value, so that the uptake is forced. To facilitate the analysis in these more complicated cases, we introduce a simplified way of plotting the costs in E. coli model iJR904 [l iJR904 . Althoug2 is the most oxidized product, the electrons from glucose all go to oxygen . Again, the ATP yield drops, and more glucose is needed to produce biomass, but only 0.596 oxygen molecules are needed per glucose molecule. A further decrease in oxygen (to 0.066 per glucose) is possible by the production of the more reduced ethanol, and, finally, growth is possible under complete anaerobiosis by producing some succinate. Some CO2-production is uncircumventable, as the citric-acid cycle needs to remain active for synthesis of biomass components. For the same reason, ethanol production is increased, as it oxidizes the additional electron-carrier NADH that is produced in the citric-acid cycle. Note that, even though the number of products seems to increase incrementally, we do see large shifts in product formation, most notably the replacement of almost all CO2-production by formate-production. Incidentally, the production of formate requires the enzyme pyruvate formate lyase, which is unstable in the presence of oxygen. Therefore, this reaction can only be active in the last, anaerobic, EM in real E. coli cells, showing that what is stoichiometrically feasible, may not always be possible due to kinetic constraints.o oxygen . This EM2, acetate, formate, and relatively small fractions of glycolate and ethanol. However, when oxygen-limitation is less stringent, there is only a very small region in which formate is produced, but not glycolate. This predicts that it is very unlikely to observe formate production without glycolate production. These regions of qualitatively different behavior are equivalent to the phenotype phase planes that were introduced in [duced in . Our ECMOften, many constraints are used to model an organism’s metabolism, for example when the metabolic behavior of the organism is hard to predict from first principles. This could be because we have insufficient understanding of the constraints and the objective that determine this organism’s metabolic behavior. For example, not all organisms have been selected for maximal growth rates, and it is a much debated question which constraints shape metabolism the most . In suchLactococcus lactis [L. lactis limits its uptake of amino acids, even when these are available at unlimited concentrations. Moreover, at high growth rates, lactic acid is used as a fermentation product, while at lower growth rates, it uses mixed acid fermentation, which produces more ATP per consumed carbon molecule [As an example, we use a genome-scale model of s lactis ). This ls lactis ,35, and s lactis ,36. It imolecule ,37. It imolecule ).L. lactis in the L. lactis in doing so, or is this a solution that has just not been found by evolution? As this example shows, the Elementary Modes-based analysis facilitates the exploration of the metabolic strategies that give rise to the optimal solutions, which could lead to further computational and experimental investigations.A cost-benefit analysis of all active EMs in the FBA-optimum is shown in Lactobacillus plantarum. This analysis was done in a less systematic way, and could only be done for a model of central metabolism where ATP-production was optimized, because it was impossible to enumerate all EFMs for the genome-scale metabolic model. It is therefore interesting to see if we still find the same conclusions when we use our approach with a full-scale model and optimizing the biomass-production. In the original study, L. plantarum was grown on glycerol, which initially allowed only for very slow growth. After laboratory evolution, the growth rates and measured uptake fluxes turned out to be in agreement with the FBA predictions. It was surprising, however, that both in the experiments and in the FBA prediction, on glycerol, lactate was still produced rather than oxidized to acetate. EFM analysis showed that this can be rationalized by an oxygen uptake constraint: Then it is optimal to ferment glycerol to lactate instead of acetate, because this can be done without using the limiting oxygen.EFM analysis of FBA solutions has been used before to understand the growth strategy of Here, we redo this EM-based analysis for the full genome-scale network  with theWe have investigated the logic that underlies the optimality of FBA-solutions. It was already known that there is a simple rationale explaining the solutions of FBAs when only a single nutrient uptake rate is an active (limiting) constraint: FBA selects the Elementary Mode with the maximal yield on the growth-limiting nutrient. However, even though the modeling of many organisms requires multiple constraints, this rationale could not simply be extended to multi-constraint models . We founWe believe that this work forms a useful contribution to the field of genome-scale stoichiometric modeling. User-friendly toolboxes ,41,42,43Escherichia coli. This result extends a similar result by Carlson and Srienc to genome-scale [E. coli under glucose- and oxygen-limitation can be fully grasped by understanding 10 different minimal metabolic strategies. Additionally, the visual overview enables us to test how much a certain solution would change if the glucose- or oxygen-limitation would be relaxed or tightened. This may aid in examining which parts of the FBA-solution are robust predictions, and which parts are coincidental results of the specific flux bounds that were used.In me-scale ,49. In tL. lactis, for example, we investigated the amino acid usage by the various EMs. Comparing experimental observations with FBA-predictions showed that some amino acids are taken up at a much higher rate than needed. Our analysis gives an explanation: amino acids like arginine and serine can be used for ATP generation to meet the ATP maintenance demand. With L. plantarum we confirmed with a genome-scale model that it is optimal to anaerobically ferment glycerol and citrate to lactate to produce ATP, probably to preserve the limited oxygen supply for biomass formation. Studying the cost vectors of the same EMs for different pairs of constraints thus provides a glimpse into the strengths and weaknesses of the different EMs. This can be used to shape hypotheses answering why a certain metabolic strategy is selected, which can then be further analyzed. Such hypotheses may be of guidance to experimental perturbations that enhance or weaken the use of this strategy. Unfortunately, in this multi-constraint case, we can no longer use the visual optimization procedure that makes it possible to immediately see which EMs are used in the optimal solution under two constraints. This is simply due to our inability to make high-dimensional plots, and can thus not be overcome.In Although in our examples we have considered only prokaryotes, there is no reason that our analysis of FBA-solutions cannot be generalized to more complex cells or to cases where, for example, proteome constraints are considered instead of flux constraints. However, there may be cases in which no clear objective can be defined, for example, in the constituent cells of multi-cellular organisms, in which case our approach clearly does not work. We therefore only claim that our analysis works as long as we have a metabolic network reconstruction, a linear objective function, and linear constraints.Our analysis of multi-constraint FBA solutions in terms of Elementary Modes was largely facilitated by two simplifying steps. First, Elementary Conversion Modes were enumerated instead of Elementary Flux Modes, which facilitates the enumeration of Elementary Modes on far larger networks ,23. ThisL. lactis and L. plantarum, we only computed the Elementary Conversion Modes for the subnetwork that was active in the FBA solution. For an FBA with only few constraints, this would have left us with a very small subnetwork, since the FBA solution would have used only a few EFMs [E. coli comprised only two EFMs. However, since we used the models of L. lactis and L. plantarum with around 20 constraints, there were also around 20 EFMs that thus gave rise to a much more versatile active network. Surprisingly, EFM enumeration was not even possible on the resulting active subnetwork. The enumeration of ECMs while using the hide-method to ignore all unconstrained external metabolites still returned 14,792 ECMs for the L. lactis model and 5541 ECMs for the L. plantarum model. This once again illustrates the almost impermeable complexity of genome-scale models. Again, there seems to be a disadvantage to this simplification: when the active subnetwork is selected, we can no longer get the complete overview of why the selected metabolic state was better than all the alternatives. This is because there may be alternatives that are missing because they use a reaction that was not part of the active subnetwork. This is a challenge that we cannot solve at this moment, since the number of alternatives in genome-scale models with many constraints is just too large even for ECM enumeration.Second, in the models of few EFMs . Indeed,The cost vectors that we used here were previously introduced for kinetic resource-allocation models. Although their translation to FBA-type problems was relatively straightforward, it still led to new insights that we did not obtain from their original application. This motivates their generalization to more types of models. We suspect that they can be used for any constraint-based model as long as Elementary Modes can be defined, and the costs of a certain mode scale proportionally to the usage of the mode. In the current case, this demand was satisfied: if the flux through an EFM increases by a factor, then the uptake of all limiting nutrients increases by the same factor. However, we have also tried to use cost vectors for Elementary Growth Modes , but thewhy a certain set of EFMs was selected. There have also been various approaches where the yield values of all EFMs were plotted in a so-called yield space [In the past decades, there have been several efforts to use an Elementary Modes-based perspective at FBA solutions. Poolman et al.  proposedld space ,56,57 toThe usage of the cost vectors for FBA with only two constraints reproduces results that could have been obtained through Phenotypic Phase Plane (PhPP) analysis . In thisN that correspond to internal metabolites. This cone thus comprises all steady-state flux vectors. The EFMs are defined as the elementary vectors of the flux cone, meaning that the set of EFMs is the minimal set such that each flux vector in In this paper, we have consistently used ECMs in computations, even though we discuss EFMs during the interpretation. To understand why this is acceptable, we should quickly recapitulate the definitions of the two Elementary Modes. To facilitate readability, we will not go into details here, but an elaborate explanation of the difference between ECMs and EFMs is given in . We starerred to .elementary vectors of the conversion cone:Analogously, the Elementary Conversion Modes are defined as the There are two facts about EFMs and ECMs that are important for this work. First, each ECM, The above two facts enable us to first use the ECMs to find out which overall conversions are optimal, and then reconstruct which EFMs lead to these conversions. It is true that many EFMs may lead to this one ECM, but this just reflects that there are often many optimal FBA-solutions ,27.As a final note, some EFMs do not map to a single ECM, but for example to a combinations of two ECMs. However, the yield-properties of such EFMs are interpolations of the two EFMs that lead to the ECMs, and therefore this intermediate EFM is never essential in constituting the FBA solutions. This is according to the intuition that everything that this EFM can do can also be done by at least one of the two other EFMs.In all ECMs on genome-scale networks is not yet feasible. The largest model for which all ECMs were enumerated until now is the iiT341-model of Helicobacter pylori comprising 485 metabolites and 554 reactions [For small enough networks, we can use ecmtool to enumerate all minimal overall conversions from nutrients to products and biomass. For example, this gave 689 ECMs for the e_coli_core-model. Unfortunately, even though the number of ECMs scales better with network size than the number of EFMs, the enumeration of eactions .To facilitate our Elementary Mode based-analysis on genome-scale networks, we can use the hide-method that was implemented in ecmtool. This method gives all stoichiometric relations between a user-defined set of external metabolites, while the information about the other metabolites is not shown  we can choose to consider only the active network. This means that all reactions that are zero in the FBA-solution are deleted from the model. (2) We can choose to compute the ECMs only between the external metabolites related to a constraint. If we choose this option, we here find all ECMtool-indices of the metabolites that are to be hidden.ECM enumeration. For this step, we used ecmtool as a Python library (as opposed to running it via the command line), of which the code can be found at https://github.com/SystemsBioinformatics/ecmtool accessed on 14 April 2021. Our computations were done with a version available at 14 April 2021. It can also be installed via the Python package manager pip.Compute costs for all ECMs with respect to each constraint normalized to each objective. If we have Calculation of the activities of the ECMs in the FBA solution. We first compute the overall conversion . This repository contains example scripts that were used to generate the figures in this paper; these can be used to build upon.The full Python-code that was used to make all figures is freely available on Github at We originally started this investigation with the question: “Does FBA maximize yield or rate?”. One of the first things that we have realized is that this is a badly-stated question for two reasons. First, we cannot speak of yield-maximization when we have more than one limiting nutrient: our toy networks  showed t


Industrial manufacturing endures as an essential human activity yielding a variety of useful products; it plays a significant role in the global economy with huge impacts in everyday life. However, the manufacturing process requires consumption of various raw materials , generates a variety of harmful waste products, causes pollution, and is energetically inefficient. Biological manufacturing from sustainable, affordable, and scalable feedstocks potentially enables the displacement of the entire portfolio of currently available products produced by industrial processes, enabling the manufacturing of renewable and eco-friendly products . Thus, sAmer and Baidoo discussed the importance of using multi-omic analytical approaches to monitor and improve the biomanufacturing process. These approaches include genomics, transcriptomics, proteomics, metabolomics and fluxomics  cycle in biomanufacturing process by collecting, visualizing, and utilizing large multi-omics datasets from various biological systems and emphasized their importance in the following metabolic engineering processes with machine learning approaches.In this research topic, luxomics . The mulGao et al. compared microflow and nanoflow liquid chromatography-selected reaction monitoring (LC-SRM) methods for analysis of hundreds of targeted peptides associated with 132 proteins in major pathways of Pseudomonas putida, a versatile bacterial host for production of bioproducts and biofuels via metabolic engineering. The increased throughput and accuracy of protein measurement will not only reduce the DBTL cycle time in future applications, but is, in addition, easily applied to other biomanufacturing host organisms.Fletcher and Baetz reviewed the toxicity of phenolic compounds which are produced from pretreatment or hydrolysis of natural lignocellulosic biomass based on functional genomics and transcriptomics approaches, especially to the important model organism and industrial bioproduction host strain, Saccharomyces cerevisiae. Information regarding physiological tolerances of toxic phenolic compounds may be applied and evaluated in other host strains for future improvement. In that regard, Garcia et al. developed the genome-scale metabolic model of Clostridium thermocellum for efficient conversion of lignocellulosic biomass which has unique preference for its anaerobic and thermophilic growth attributes. This model will provide a useful tool to understand physiological and metabolic parameters associated with potential future biomanufacturing process.Pinheiro et al. studied a xylose metabolism by Rhodosporidium toruloides, an oleaginous yeast with significant emerging potential in industrial applications, using a detailed physiological characterization interpreted with absolute proteomics and genome scale metabolic models. Kim et al. performed a multi-omics analysis on R. toruloides and the transcriptomics, proteomics, metabolomics and RB-TDNA sequencing data improved the current genome-scale model to make it a more exhaustive and accurate metabolic network model.Pomraning et al. integrated high-throughput proteomics and metabolomics data as part of a DBTL cycle focused on improving production efficiency of 3-hydroxypropionic acid (3HP) in engineered Aspergillus pseudoterreus strains. This was the first report of 3HP production in a filamentous fungus amenable to industry-level biomanufacturing of organic acids at high titer and low pH. Chroumpi et al. studied another filamentous fungus Aspergillus niger for better understanding of pentose catabolic pathways by deletion of the key genes. The high-throughput multi-omics data  generated on the mutant strains revealed that these genes are critical for metabolic pathways but not as critical for growth of A. niger on more complex biomass substrates, which raises fundamental questions on nutrient acquisition during growth on various carbon sources.Wu et al. investigated the metabolic potential of Zymomonas mobilis for conversion of glucose and xylose to 2,3-butanediol. This study used calculated thermodynamic and kinetic parameters to generate insights of Z. mobilis metabolism. They also performed pathway and dynamic flux balance analysis to understand metabolic potential and production efficiency for future industrial applications. Nitta et al. acquired metabolomics and transcriptomics data on antibiotic producing strain, Streptomyces coelicolor to understand the functional connections between the production of antibiotic, actinorhodin and the level of cAMP. They found that higher levels of cAMP improved cell growth and production of actinorhodin, which was confirmed by the metabolomic and transcriptomic data.via an environmental-friendly manner.We conclude by emphasizing that high-throughput multi-omics data play a critical role to unravel the complexities of metabolic engineering to improve production efficiency and product titer produced by a variety of industrial microbes. In addition, generation of multi-omics datasets accelerates the adoption and subsequent application of artificial intelligence approaches such as machine learning to design of improved microbial bioproduction host systems . In term


Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in  E. coli’s response to metabolic limitations by directly and indirectly regulating catabolic and anabolic enzyme expression. This regulatory program is adjusted locally through passive changes in metabolite concentrations.The transcription factor Crp coordinates  To sustain growth when facing such environmental variations, microbes need to coordinate their metabolic supply of biomass precursors, energy, and redox factors  and limitation in the ability to synthesize all amino acids while carbon is in excess (“anabolic limitation”). Previously, we constructed strains with titratable control of glucose uptake to implement catabolic limitation and strains with titratable control of glutamate synthesis  lacking endogenous cAMP production, such that Crp activity can be controlled externally through supplementation of cAMP (Kuhlman cyaA) and phosphodiesterase (encoded by cpdA) employed in the cAMP titration strain , only considering those reactions for which all substrates were quantified. In both limitations, the vast majority of ρS were positive, meaning that flux and substrate concentrations changed in the same direction  for the coordination of E. coli’s coordinated response to different metabolic limitations. From the large‐scale quantification of metabolic fluxes, metabolites, and proteins, in genetically implemented catabolic and anabolic limitations, we identify two mechanisms that facilitate the global as well as local coordination of metabolic activity , termed “limiting transcriptional capacity”, is based on the premise that protein expression is limited by different promoters in the cell competing for the same pool of free RNA polymerase molecules for transcription initiation. In this case, activation of Crp, which exerts its activating effect by directly recruiting RNA polymerase to catabolic genes , with a potential cost to the steady‐state growth rate , found an inverse relationship between catabolic and anabolic proteome sectors that is consistent with our findings  regulation of protein expression is widespread in bacteria  at 0.01 and 3 mM cAMP. The samples were processed as described previously . Then, protein samples of the 15N replicate cultures were mixed (50 μg per replicate) with 100 μg of an unlabeled wild‐type protein sample from a matched condition (without the addition of cyclic AMP) and processed as described previously  at −20°C  to remove residual particles, diluted 1:10 in water, and transferred to V‐bottom 96‐well plates (Thermo Fisher Scientific). Samples were measured by flow‐injection time‐of‐flight mass spectrometry with an Agilent 6550 QToF instrument operated in negative ionization mode at 4 GHz high resolution in a range of 50–1,000 m/z as described before  as described before   to remove residual particles, and transferred to V‐bottom 96‐well plates (Thermo Fisher Scientific). Measurements, data acquisition, peak integration, and quantification of absolute metabolite concentrations were performed as described previously  and transferred to V‐bottom 96‐well plates (Thermo Fisher Scientific). Glucose and acetate concentrations in supernatants were determined by colorimetric enzymatic assays (Megazyme). All other secreted metabolites were quantified by flow‐injection time‐of‐flight mass spectrometry as described above. Briefly, supernatants were diluted 1:10 in water and measured with an Agilent 6550 QToF instrument operated in negative ionization mode at 4 GHz high resolution in a range of 50–1,000 m/z. Sample processing and ion annotation was performed based on accurate mass within 0.001 Da using the KEGG E. coli database  as described before . Supernatants were separated from cells by centrifugation , or as a mixture of 20% (wt/wt) [U‐13C]  and 80% [12C] isotopes. Labeling samples (sampling volume 1 ml) were taken during mid‐exponential phase (OD600 0.5–0.7), cells were harvested by centrifugation , and cell pellets were washed once in cold 0.9% NaCl and stored dry at −20°C. 13C flux ratios were determined as described previously  using tl et al,. Two fluet al,et al,E. coli genome‐scale metabolic model iJO1366  The reaction boundaries of two additional reactions (“FBA3” and “F6PA”), which emerged empirically as glycolytic bypass reactions that were not supported by the 13C labeling data, were also set to zero. (iv) For those conditions in which the strain NQ393 was used , the reaction boundaries of the deleted enzyme glutamate dehydrogenase (“GLUDy”) were set to zero. (v) All measured major  and eight minor  exchange rates , as well as the measured growth rate and the oxygen consumption rate as inferred by 13C flux analysis (see above), were used as constraints   was determined by flux variability analysis using the Cobra toolbox command fluxVariability (using the “fastSNP” option to prevent loops). Due to numerical issues, both the optimality percentage and the lower bound of the ATP production rate constraint had to be relaxed very slightly (from 100 to 99.99%).To infer metabolic fluxes beyond central carbon metabolism, flux balance analysis (FBA) was performed with the Cobra toolbox v3.0  were excluded from the analysis. Kinetic orders were estimated independently for each flux–enzyme pair (considering both limitations) by least square optimization using the lsqlin function of MATLAB. Only reactions for which all substrates had been quantified were considered. As above, for reactions that are connected to more than one isoenzyme, the final regulation coefficients were calculated as the average regulation coefficients across all measured proteins.Enzyme saturation regulation coefficients were determined as follows. To account for potential non‐linearity of the relationship between substrate concentration and flux , approximate kinetic orders (α) for substrates were estimated using the following equation across all conditions as described previously (Chubukov Conceived and designed the study: KK, TH. Performed experiments and analyses: KK, HO, VP, TH. Supervised the study: JW, US, TH.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Review Process FileClick here for additional data file.