Chlamydomonas reinhardtii to a metabolic model of the same organism in order to develop the first transient metabolic model for diurnal growth of algae capable of predicting phenotype from genotype. We first transformed a set of discrete transcriptomic measurements  into continuous curves, producing a complete database of the cell’s transcriptome that can be interrogated at any time point. We also decoupled the standard biomass formation equation to allow different components of biomass to be synthesized at different times of the day. The resulting model was able to predict qualitative phenotypical outcomes of a starchless mutant. We also extended this approach to simulate all single-knockout mutants and identified potential targets for rational engineering efforts to increase productivity. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition, and intracellular fluxes for diurnal growth.Economical production of photosynthetic organisms requires the use of natural day/night cycles. These induce strong circadian rhythms that lead to transient changes in the cells, requiring complex modeling to capture. In this study, we coupled times series transcriptomic data from the model green alga IMPORTANCE We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii. The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals. The dawn of a new century has also led to an awakening about our energy use; there has been a concerted push to develop more sustainable sources of energy and feedstock chemicals. One of the major challenges in developing more sustainable fuels and chemical supply chains is that the current industry is large; significant capital has already been spent to develop the infrastructure for processing and distribution. To avoid an extensive and expensive redesign of infrastructure, renewables will have to be chemically similar to the fossil fuels we currently rely on. The fossil fuels we use today are derived from prehistoric biomass; therefore, it is a logical extension to engineer biomass to produce high concentrations and/or yields of equivalent chemical feedstocks and fuels that can be “dropped in” to current infrastructure. This approach has been successfully commercialized for a few chemicals, such as 1,3-propanediol , 1,4-but3–2; unfortunately, the development of photosynthetically derived fuels and chemicals has fallen far short of the touted potential because the unique demands of photosynthetic organisms have not been considered in the strain development process. For example, algae and cyanobacteria have extremely strong circadian rhythms  have proven extremely useful in minimizing the time it takes to develop heterotrophic production strains –32. One –thaliana , 34 and thaliana –38. ThesChlamydomonas reinhardtii. This model combines the easy-to-implement stoichiometric constraint-based model of C. reinhardtii , which is commonly used as a housekeeping gene . This way, the abundance of any transcript can be modeled at any time step. By fitting transcripts to best-fit functions, we can also easily compare the profile of expression across several genes . The tra35–ing gene  and whicLHCSR3.1  . This ramping supports that the instantaneous light switch likely has a somewhat minimal overall effect on the cell’s phenotype and growth relative to the gradual rise and fall in illumination the cell would experience under natural light. While some stress transcripts are produced at the moment of illumination, the transcript ramps associated with energy production still combine to produce light uptake bounds that rise and fall over the course of the day, reflecting the natural ramp of outdoor light. This consistency implies that the immediate light switch was not particularly impactful on energy availability; if it were, we would expect to see that light harvesting transcripts become immediately expressed at full force and persist for the length of the day. As light uptake is the limiting overall factor for autotrophic growth under non-nutrient-limited conditions, the rise and fall imply that the cell’s growth is not drastically affected by the sudden onset of light. However, it is possible that the internal constraints on the cell’s metabolism are shifted by the onset; that is, the cell still experiences roughly the same ramp up and down of light harvesting that it would under natural light onset, but—due to changes in the expression patterns of other transcripts associated with other subsets of metabolism—the cell allocates the energy captured from the light it receives differently. This change would result in the cell producing different components of biomass at different time points, but the overall effect on total biomass would likely be relatively minimal, as the ramp in light uptake still remains; the cell would just have a slightly different composition at different time points. We therefore remain confident that the sudden light onset does not invalidate overall predictions of this model. We also observed the cell shuttling large amounts of carbon between the chloroplast and the mitochondria, likely to make use of the more efficient mitochondria ATP synthase    in diurny et al. . This alsta6 mutant in both continuous light and diurnal light. In continuous light, both strains accumulate more lipids with decreasing nitrogen in the medium. We also saw the phenotype that has been reported in a number of papers on sta6  with nitrogen limitation. This feature of the model lets us evaluate how external or internal changes in the cell impact biomass composition.Our model can predict not only growth and metabolic fluxes but also changes in biomass composition. This feature is incredibly important, as most efforts to engineer algae for biofuels have focused on the accumulation of lipids or fatty acids and our model is capable of predicting the effect of environmental or genetic changes on biomass. We used the model to predict the effect of nitrogen limitation on the accumulation of lipids in the wild type and  on sta6 , 48–50, nic2, nic7, and nic13 are auxotrophic for nicotinamide (GYD1 and AGT2), thus requiring high CO2 for growth, and one strain is lacking a key glyoxylate shunt enzyme (MAS1), which is a known carbon-conserving mechanism. The other mutants predicted to have better growth have not yet been characterized experimentally for growth in diurnal light.The model was also used to predict phenotypes of other genetic knockouts . A key ptinamide , two strin silico modeling, it is possible to quickly assess bioengineering approaches for feasibility, as demonstrated by individual simulations of a broad swath of single knockout mutants. Additionally, this approach requires relatively few discrete types of data: the primary requirements are only a preexisting constraint-based model, an appropriate transcriptomic data set, and a time course of biomass. There is no other organism-specific information required. Because of this, our approach can be generalized to any other photosynthetic alga species with similar data availability.While steady-state metabolic modeling is an important tool for biological engineering, photoautotrophic species exhibit strong circadian rhythms that are evident in the cyclic changes in transcript abundance and biomass composition. These transient changes require a modeling approach that allows for the dynamic changes occurring in the cell. In this study, we leveraged the ease of implementation of dynamic FBA models with gene expression and biomass composition data to develop a model that is capable of predicting phenotype in diurnal light. This model simulates growth in both day and night continuously, accounting for catabolism, changing objective functions, and transcriptomic constraints. We have shown its ability to accurately predict phenotype from genotype, making it a useful tool for both physiological interrogation of biology and metabolic engineering efforts. When that predictive power is combined with the inherently parallelizable nature of In order to utilize a large data set of circadian transcriptomic expression patterns, we built a pipeline for mass fitting simple expression curves to every single individual transcript. These curves are simple enough to reduce overfitting, but they also have enough parameters to preserve critical cyclical information. Once these curves were fit, we used them in two ways. The first was a clustering approach that enabled us to assess how every single transcript’s expression pattern compared with every other transcript within the cell. This produced a heat map where several clear correlations could be identified, including the collection of genes with a strong response to sudden light onset. The second was the integration of these curves into a genome-scale metabolic model; by querying the transcriptomic curves at every time point, the boundaries of the metabolic model could be dynamically adjusted over the course of the day .Chlamydomonas reinhardtii, we used published transcriptomic data from cells grown in 12-h:12-h day-night cycles in a mixed photobioreactor with ambient air bubbling, replete nitrogen, and 200 μE of light . By calculating these measurements for every possible pair of transcriptomic interactions and clustering the results, we generate a heat map that summarizes the relationships between the circadian expressions of every gene. This map can be built for either method of comparison, either score or integral; the clustering in this study was performed with the integral method.O(2n).The score method is faster than the integral method at examining correlations between transcripts and provides a good “first look” at potential clusters. However, it is also much more abstracted than the integral method. By vectorizing the parameters of the curves associated with each transcript, we can compare two vectors with relatively few calculations. Minimizing calculations is critical, as the number of comparisons scales as i and the lowest AICc within the set of transcript expression models associated with the curve (here represented by R):In order to vectorize a given expression curve, we begin by calculating the weight of the transcript expression models associated with the curve. A model’s weight is calculated by comparing the AICc to the AICcs of all models summed together in the following fashion, where R shows that the weights of all model fits for a particular set of data must sum to 1  is loaded. As this file is generally around 6 GB, it is infeasible to generate the entire map at once. Instead, a set of linkages is created using scipy’s cluster.hierarchy.linkage with the ward map. The linkages are then used to produce a truncated dendrogram with a set number of leaves. Each leaf is then processed into a nonsquare heat map, where every pixel represents one pairwise comparison between transcripts. The maps are made iteratively, with each being cleared out of the memory before the next is added. Once all the maps are complete, the data frame is no longer required and can be cleared out as well, leaving space for each map to be loaded back in and stitched together into a final square map. For the weight sidebar, each model weight is included as a section of a stacked bar graph, which is saved separately and then added to the square map. Additional information can be added if required, such as the highlighting of specific genes.Simulating a cell with a constraints-based approach requires constraints, which ideally reflect actual physiological or environmental phenomena. Transcripts can be integrated into these models in order to inform these constraints, using approaches like the E-Flux2 methodology ; this meWe then extended this method by dynamically shifting the constraints of all reactions with transcriptomic linkages at every time point. At each time point, each curve was interrogated and normalized against the maximum for that curve over the day, and the bounds were lowered to that normalized fraction. When a reaction is catalyzed by a multimeric enzyme requiring the simultaneous expression of multiple genes, then the bound for said reaction is calculated by setting it to the minimum expression level of all the required genes, defined by the Boolean logic AND. Conversely, some reactions can be catalyzed by multiple enzymes; in this case, the reaction bound is calculated by proportionally setting it equal to the sum of transcript abundance of all associated gene products (defined by the Boolean OR). This is depicted mathematically below; a at time t, where a(t) represents the value of the best-fit curve for gene a at time t.a OR (b AND c); A at time t. This value is used for the upper bound of the reaction. If the reaction is reversible, the negative of it is used for the lower as well. Otherwise, the lower bound of the reaction is zero. The maximum reaction flux of the system was calculated such that the maximum rate of light uptake over the course of the day matched the light availability of the system. This approach is feasible, despite the discrete nature of the transcriptomic data set, because the process of fitting the curves to the point creates a continuous representation of the cell’s transcriptome that can be queried at any arbitrary time. It is worth noting that the confidence of this approach is a function of the collection resolution; faster and more transient events demand smaller intervals between data points, as implied by the Nyquist-Shannon sampling theorem.Finally, the flux fraction is multiplied by the maximum reaction flux of the system to calculate the bound for the reaction a, chlorophyll b, protein, lipid, lipid droplet, carbohydrate, nucleotide, redox, and starch; details of these are in A typical constraint-based model formulation uses a static biomass formation equation to represent metabolites being used for the creation of biomass. Such a fixed equation is inappropriate for a circadian model, both because the composition of the cell changes over the diel cycle and because the cell’s capability to make individual components of the broader equation may change over time. In order to accommodate this difference, we split the cell’s biomass equation into subgroups and laid a control system over the top of it, such that the model would always attempt to aim for a long-term stable solution that approached the known biomass composition at different times of the day. The decoupled biomass equation consists of 10 components: carotenoid, chlorophyll In order to properly treat lipid production, we separated the lipid components of biomass into both a general lipid component and a lipid droplet component. The lipid droplet contained all triacylglycerides in the original biomass equation that had tails measured in the work of Wang et al., while the general lipid class contained all other lipids . This seAfter the model is constrained to a time point, the production possibilities need to be evaluated. Critically, this means that the production space for biomass components must be defined. In this modeling approach, the biomass equation of the original FBA model—iCre1355  in this Once the time points of the daily cycle have been evaluated, the model begins to run. At each time point, the model is constrained according to the transcriptomic curves, and the production goals and consumption preferences are set by the error terms for the components. These error terms are calculated using a pair of proportional integral (PI) control systems—one for the production terms and one for the consumption terms—thereby abstracting the complex cellular regulatory networks that govern anabolic and catabolic processes into a simpler parameter space. After the production and consumption error terms for a given time point have been calculated, the model tries to produce the non-growth-associated maintenance ATP (NGAM) with the medium; if it cannot, it will then consume one or more components of biomass to stay alive.Component consumption is prioritized by the consumption error terms; those with the highest positive error  are consumed first. Once the cell has made NGAM, it evaluates production potential. If the medium can be successfully consumed , or if any components with positive degradation error remain after making ATP, the cell will attempt to produce components from either the medium or the positive-error components, respectively. The production possibilities are precalculated for each time point and possible energy source, either medium or specific components. In the event that metabolites in a component cannot be catabolized by the cell, they are assumed to be dumped out of the cell entirely, resulting in an overall mass and energy loss. The cell uses the production error terms and the production possibilities to calculate which components are produced at what ratios. These components and ratios form the initial objective function for the cell’s simulation step.During the production phase, the cell attempts to produce as much of the initial objective as possible. Once it reaches a limitation, the model fixes the production flux, then checks which component is limiting production, and attempts to make more of the nonlimiting desired ones. This iterative expansion of the production window continues until there are no more desired components or—more commonly—the constraints of the cell limit overall production. Once components have been consumed and produced, the rates of production and consumption are multiplied by the total cell mass to generate the rates at which each biomass component is changing. This rate represents how much of each component has accumulated or been consumed since the previous timestep. Multiplying it by the timestep yields the change in biomass components across the length of the timestep, thus linking the cell’s metabolism to its overall biomass. By repeating this entire process each and every timestep, including the readjustment of transcript bounds and the recalculation of new objective functions based on how the new biomass compositions differ from the existing setpoints, the model can effectively keep track of biomass amounts, accommodate dynamic constraints that make individual reactions infeasible at certain times and prioritize the production or degradation of biomass components in ways that recapitulate energetic and experimental influences. In the current approach, the model simulates growth in 6-min timesteps, so the timesteps occur 10 times every simulated hour. Due to the continuous nature of the transcriptomic fitting process, that timestep is arbitrarily adjustable if more or less resolution is required for different applications.In order to demonstrate the utility of this modeling approach, we conducted a survey of all gene knockouts within the model. Using high-performance computing (HPC), we were able to simultaneously evaluate large numbers of genes. To simulate each gene knockout, the transcriptomic fits for the gene in question were set to zero, thereby simulating a complete knockout. The knockout mutants were simulated for 168 h, a full week, at light and non-growth-associated maintenance ATP light levels that produced approximately one doubling in the wild-type cell over the length of the simulation. While these conditions differ from other simulations, they were selected for specific reasons: the long growing period gives the cells time to acclimate to the mutants and stabilize their own control loops, while the reduced available energy means that impacts of the mutations can more clearly be seen. For every run, the full biomass composition was recorded at the end of the simulation, and the impacts of each gene on the overall growth and biomass composition can be clearly seen.https://github.com/metcalex/Transcriptomic_Circadian_Modeling_Supplemental.Transcriptomic data were drawn from reference


Monascus azaphilones pigments (MonAzPs) produced by microbial fermentation are widely used as food chemicals for coloring and supplying beneficial biological attributes. In this study, a fermentation perturbation strategy was implemented by separately adding different amino acids, and detecting the intracellular metabolome via UHPLC-Q-Orbitrap HRMS. With the aid of weighted gene co-expression network analysis, two metabolic intermediates, fumarate and malate, involved in the tricarboxylic acid cycle, were identified as the hub metabolites. Moreover, exogenous addition of fumarate or malate significantly promoted red pigment production, and reduced orange/yellow pigment production. The importance of the tricarboxylic acid cycle was further emphasized by detecting intracellular levels of ATP, NAD(P)H, and expression of oxidoreductase-coding genes located in the MonAzPs synthetic gene cluster, suggesting a considerable effect of the energy supply on MonAzPs synthesis. Collectively, metabolomics is a powerful approach to position the crucial metabolic regulatory factors, and facilitate the development of engineering strategies for targeted regulation, lower trial-and-error cost, and advance safe and controllable processes for fermented food chemistry industries. Monascus spp. are important edible food microbial resources in Asian countries , producing a variety of secondary metabolites, such as pigments, alcohols, antibiotics, antihypertensives, and taste compounds, that are extensively used to improve the flavor and quality of foods Monascus gulation , redox pgulation , light [gulation , and lowgulation , have coMonascus to adapt to different cultivation conditions for growth, but consequently leave Monascus fermentation to be hardly controlled for the specific MonAzPs components or their combinations. Similar cases are also involved in many other complex biosynthetic pathways, especially in edible fungi that often detail relatively simple carbon skeletons from polyketide biosynthesis to produce a large variety of secondary metabolites, which define the final characteristics of the products, such as nutritional quality and safety.Taken together, the existence of multifactor regulation leads to the fact that quite a number of exogenous and endogenous factors could give rise to the variation of food MonAzPs production. Such multi-dimensional metabolic regulations force Understanding the complexity of such a system like MonAzPs regulation, and identifying and associating biochemical changes with the desired product quality characteristic, largely depend on the experimental and analytical techniques feathered with a powerful ability related to systematic analysis . The metOmics analysis is highly dependent on diverse computational approaches, with network-focused strategies providing a more full view of biological responses than individual gene/protein/metabolite-focused strategies . WeighteM. purpureus with intracellular metabolic intermediates, and provide robust fermentation regulation strategies of such significant food chemicals with healthy physiological activities. To the best of our knowledge, this is the first report of WGCNA-based metabolomics analysis of MonAzPs fermentation by M. purpureus to position the crucial metabolic regulatory factors, and facilitate the development of engineering strategies for targeted regulation for MonAzPs fermentation.Therefore, we present a hypothesis in this study that UHPLC-Q-Orbitrap HRMS and WGCNA-based metabolomics could reveal the associations of phenotypical variation in food MonAzPs fermentation by M. purpureus M7 [Strain. ureus M7  was origM. purpureus M7 were harvested and stored in a 20% (v/v) glycerin aqueous solution at −80 °C until needed. Seed medium (g/L): rice flour 30.0, NaNO3 3.0, KH2PO4 2.5, MgSO4·7H2O 1.0, and pH 6.0. Fermentation media (g/L): glucose 50.0, (NH4)2SO4 5.0, KH2PO4 4.0, MgSO4·7H2O 0.5, MnSO4·H2O 0.03, ZnSO4·7H2O 0.01 and FeSO4·7H2O 0.01, and pH 6.8.Media. Potato dextrose agar (PDA) solid medium for producing spores (g/L): potato extract 4.0, dextrose 20.0, agar 15.0. Mature spores of Cultivation methods. Spore suspension (1 mL) was transferred to seed medium (40 mL) in a 250 mL Erlenmeyer flask incubated at 30 °C and 180 rpm, for 48 h in a shaking incubator . Seed culture (4 mL) was added to the fermentation medium (40 mL) in a 250 mL Erlenmeyer flask and cultured in the shaking incubator  for a 4-day fermentation.Fermentation perturbation experiments were conducted by adding 17 kinds of amino acids into the fermentation media, respectively, with a final concentration of 0.2 g/L, while the group without any addition was used as the control. These amino acids were glutamic acid, aspartic acid, lysine, arginine, histidine, leucine, isoleucine, alanine, phenylalanine, threonine, serine, proline, tyrosine, methionine, valine, glycine, and tryptophan. The fermentation experiment was repeated at least three times.g for 15 min . The obtained mycelium pellet was washed once with deionized water, then dried to a constant weight at 60 °C. DCW was recorded by electronic precision balance. At the same time, the supernatant was filtered through a 0.22 µm filter for determining the glucose concentration. The glucose detection system was UltiMate 3000 HPLC  equipped with a Bio-rad Aminex HPX-87H column and a refractive index detector. The mobile phase was 5 mM sulfuric acid aqueous solution with a flow rate of 0.6 mL/min at 60 °C.Fermentation broth (5 mL) was centrifuged at 2000× v/v), maintained at 80 °C for 1 h, then ultrasonicated  with a maximum power for 30 min. After centrifugation at 2000× g for 15 min, the supernatant was filtered through a 0.22 μm filter for MonAzPs determination by HPLC , equipped with a ZORBAX SB-C18 column (Agilent) and a DAD detector set to 410 nm. The mobile phase was 0.1% (v/v) formic acid–water solution and acetonitrile at a ratio of 65:35 (v/v) at 1.0 mL/min, 25 °C. The concentrations of monascin, ankaflavin, rubropunctatin, monascorubrin, rubropunctamine, and monascorubramine were calculated by standard curves of pigment standards.The fermentation medium (2 mL) was mixed with aqueous ethanol , Na2HPO4 (1.44 g/L), NaCl (8 g/L), KCl (0.2 g/L), dissolved in deionized water, pH 7.2). Wet mycelia (1.0 g) were treated with methanol/acetonitrile/H2O  at −20 °C for 60 min to extract intracellular metabolites. After freezing and thawing in liquid nitrogen at least five times, extract was centrifuged at 5000× g at 4 °C for 10 min and supernatant was retained to determine metabolite profiles.Sample preparation. The mycelia of 5 mL fermentation broth were collected and washed in precooled phosphate-buffered saline (PBS) (KHData acquisition and processing. Metabolite determination was carried out using ultra-high-performance liquid chromatography-Q exactive hybrid quadrupole orbitrap high-resolution accurate mass spectrometry (UHPLC-Q-Orbitrap HRMS), as described previously with negative ion mode  in Shangp-value < 0.05) within modules were assigned for the identification of hub metabolites that have high intramodular connectivity, high module membership (MM), and metabolite significance (MS).WGCNA was built based on the metabolomics data, followed by the online tutorial  with R 4To validate the effects of biomarkers proposed by WGCNA on the production of food MonAzPs, the three compounds were, respectively, added into the control media with a final concentration of 0.05 g/L, combined with the quantitative determination of the six pigments and mycelia biomass.M. purpureus YY-1 [Actin (C5.619) as the internal reference. qRT-PCR was carried out on a 7500 Real-Time PCR System , using Q-PCR was performed by using 2 × RealStar Green Fast Mixture , following the 2−ΔΔCt method [Total RNA was extracted using an RNA prep pure Cell/Bacteria kit , following the manufacturer’s instructions. cDNA was obtained by reverse transcription with total RNA as the template, using a PrimeScript™ RT reagent kit . Genome of eus YY-1  was usedt method . The PCR+/NADH, and NADP+/NADPH Assay Kits  according to manufacturers’ instructions. Three biological repeats were conducted for the above analysis.The levels of intracellular ATP, NADH, and NADPH were determined using ATP Assay Kit, NADIf not specified otherwise, Student’s t test for independent samples was used for statistical analysis by R 4.1.0.Monascus spp. incorporates different forms of nitrogen sources into the metabolic systems through hydrolysis, transport and transformation, and produces a series of target food products, accompanied by the adjustment of intracellular metabolism. Monitoring phenotypic and intracellular characteristics are the basis of understanding the adaptive metabolic regulation. Therefore, in this study, a physiological perturbation experiment was conducted by exogenously adding small amount of amino acids (0.2 g/L) into the fermentation media, combined with the determination of food MonAzPs production profiles, biomass, glucose consumption, as well as the intracellular metabolome.Growth perturbation is a common strategy to investigate cellular systems, in which employing the growth conditions, such as pH, temperature, nutrients, and gene manipulation, gives rise to the stimulation on microorganisms to generate metabolic variation and adaptive adjustment to reach a new steady state consequently, at the cost of phenotypic changes on growth rate, sugar consumption and the product yield. Due to its strong adaptability, 4)2SO4 as the basal nitrogen source (control group), phenotypic characteristics of Monascus fermentation at different phases were recorded. Based on profile of mycelial biomass, M. purpureus growth obviously fell into two phases, i.e., ascending phase (12~60 h) and declining phase (60~84 h). It was found that the production of R1, R2, and O2 reached the peaks in the middle period of fermentation, whereas O1 and Y1 increased continuously during the whole period. Interestingly, the production of Y2 reached the maximum at 36 h, decreased at 60 h, then slightly rebounded at 84 h.As shown in It could be seen that fermentation at 60 h appeared to be a key observation point to distinguish different MonAzPs production profiles, as well as the watershed between the ascending phase and declining phase. Hence, it was used as the sampling point for detecting the metabolic perturbations by adding different amino acids. Each fermentation broth sample was divided into two parts, one for the extraction and detection of metabolome, and the other for the detection of pigment production.Monascus.Violin plots of The UHPLC-Q-Orbitrap HRMS used in this study allows for complete scan in a precise and fast way, providing reliable and substantive details about molecular weight and structure. According to the metabolomics analytical procedure, a total of 68 metabolites, mainly including phosphate compounds, amino acids, and low molecular weight carboxylic acids  were ideMonascus metabolism, and it was suited for establishing the quantitative relationship between intracellular and extracellular properties using correlation analysis.According to the recommended procedure, metabolome samples were clustered and connected with the phenotypic data by WGCNA as shown in p-value < 0.05. Interestingly, the MEyellow module was positively correlated with R1 , and R2 , and was significantly negatively associated with Y1 , O1 , Y2 , and O2 .For the construction of WGCNA, the soft-threshold was set at 7 for the criterion of free-scale topology, and 68 metabolites were grouped into four modules (module MEgrey contained unrelated metabolites and was excluded in WGCNA) . Module–p-value of each metabolite in the linear regression between metabolite abundance and the phenotypes, and MM is a measure of intra-modular connectivity. For the MEyellow module, a scatterplot of MS vs. MM is presented in Within WGCNA networks, highly connected metabolites, also called hub metabolites, play an important role in understanding the biological mechanism of response under stresses/conditions. Hub metabolites are usually featured with the greatest metabolite significance (MS), module membership (MM), and the connectivity with other node metabolites, in which MS is defined as a As a conclusion of WGCNA analysis, two metabolites, fumarate and malate, were identified as the hub metabolites that had extensive and close relationships with other metabolites within the MEyellow module. The previous analysis on module–trait associations B showed p < 0.05, not significant in oxoacetate-treated group with R2). The other MonAzPs’ productions, i.e., Y1, Y2, O1, and O2, collectively, were decreased when adding fumarate, malate, or oxaloacetate (p < 0.05 except in Y1 of fumarate group). These findings suggested the effectiveness of adding hub metabolites for regulating the MonAzPs’ production.To validate the physiological effects of fumarate, malate, and oxaloacetate on the production of MonAzPs, the three compounds were, respectively, added into the control media. As depicted in 2 and H2O, with the concomitant production of energy (ATP) from NADH and FADH2 [+ ratio, the so-called “reductive stress”, or a lower ratio, “oxidative stress”, is crucial for numerous fundamental cellular processes, including energy metabolism, calcium homeostasis, cell death, and proliferation [+ ratios compared to the control group after supplementing fumarate, malate, and oxaloacetate.The TCA cycle is the common oxidative pathway for carbohydrates, fats, and amino acids, which is the most important metabolic pathway for the energy supply to aerobic organisms. The cycle oxidizes pyruvate to COnd FADH2  via elecnd FADH2 . As an iferation . The cof+ ratios in all the treated groups were enhanced, particularly in malate and oxaloacetate groups along with the higher NADH: NAD+ ratios (no significant difference of NADH/NAD+ in fumarate group vs. control). These findings demonstrated the feasibility of supplementing these compounds on regulating the intracellular redox state through TCA cycle. As a consequence, the elevated NAD(P)H:NAD(P)+ ratio promoted the formation of “reductive stress”, thus driving NADH-mediated reduction reactions, especially for the conversion of orange MonAzPs (O1 and O2) into the yellow MonAzPs Y1 and Y2, which was supported by the increased ratio of yellow pigments to the orange pigments (Y/O) in the treated groups H availability, and the electron transfer chain activity , the intMpigC, E, F, H, and N located in the MonAzPs synthetic gene cluster that mediates redox reaction during pigment synthesis, were determined by qRT-PCR. Among them, three oxidoreductases, MpigC, MpigN, and MpigE, are involved with the biosynthesis of the MonAzPs’ chromophore core, whereas the other two oxidoreductases, MpigF and MpigH, are associated with the synthesis and differentiation of orange and yellow MonAzPs, respectively. As depicted in MpigC was down-regulated in fumarate and malate groups, but up-regulated in oxaloacetate group; (2) MpigN and MpigF were up-regulated in all three experimental groups; (3) the expression change of MpigE was not significant under all three conditions; and (4) MpigH was up-regulated in malate and oxaloacetate groups. Upregulation of MpigF and MpigH, was expected to be closely related with the production improvement of yellow or orange MonAzPs, but unexpectedly, it led to the opposite results. These findings suggested a distant association between the pigment production and the gene cluster expressions, and the gene cluster expression was not the limiting factors during the production of MonAzPs.In addition to the effect of intracellular energy metabolism, the expressions of Monascus, and their productions often suffer a major setback when adapting to environmental changes. In this study, adding single amino acid of a small amount generated extensive metabolic perturbations, resulting in the alterations of phenotypic characteristics of M. purpureus M7. However, the stability of production and product quality of MonAzPs is exclusively critical for Monascus fermentation in industry. Until now, numerous factors, including: pH; temperature; carbohydrates; light; and especially nitrogen sources, such as peptone, yeast powder, ammonium chloride, and ammonium nitrate; have been proved to lead to different effects on MonAzPs production [Monascus fermentation. Hence, a simplified and efficient model, such as metabolic perturbation, is necessary to dissect the metabolic mechanism of MonAzPs biosynthesis and regulation, and aid to establish a high-efficient fermentation regulation strategy.Natural edible food MonAzPs and other secondary nitrogenous metabolites appear insignificant to the growth of oduction , which uMonascus spp. naturally produces several extracellular proteases and peptidases mediating the hydrolysis of proteins into amino acids as the nutrition for the growth and development [elopment . Amino aelopment ,47. In telopment ,32, was Monascus synthesistic gene cluster changed greatly, but it made little contribution to explaining the production change, possibly because gene cluster expression was not the main limiting factor of pigment synthesis under the conditions used in this study, as described previously [Fumarate and malate, as important intermediates of the TCA cycle, play an important role in driving TCA metabolic flow and promoting TCA derived energy metabolism ,48. Baseeviously .Monasucs fermentation, their production is inextricably linked to primary metabolism. Previously, we discovered that the pentose phosphate pathway influenced the synthesis of yellow pigments [Monascus pigment diversity has been largely clarified by Chen et al. [Monascus pigment production, transcriptome, proteome, and metabolome investigations are necessary.Despite the fact that MonAzPs are biosynthesized as secondary metabolites in the middle and late stages of pigments , and intpigments , while on et al.  through n et al. , energy n et al. , expressMonascus fermentation could be put forward in the future. More importantly, targeted exogenous addition strategies reduce the trial-and-error cost, and are safe and controllable for fermented food industries. The result is expected to provide theoretical framework and support for the research of food MonAzPs synthesis and engineering strategies development for targeted regulation, and subsequent quality and safety supervision for fermented food chemistry industries.Collectively, adding amino acids generated a perturbation on MonAzPs’ production, confirming the variation of pigment biosynthesis when changing the medium components. Such a complicated situation will force us to redesign the culture conditions and medium components to maintain the pigment production level. Fortunately, in this study, UHPLC-Q-Orbitrap HRMS-based metabolomics analysis pointed out that different amino acids exerted the most significant effect in the TCA cycle via fumarate and malate. Based on this hypothesis, the supplementation of biomarkers, and the determination of intracellular levels of ATP, NAD(P)H, and crucial gene expressions confirmed that two metabolic intermediates, fumarate and malate, impacted the pigment biosynthesis. Subsequently, associations between TCA cycle and the pigment biosynthesis pathways were comprehensively investigated. Due to the close relationship of TCA cycle with dissolved oxygen and respiration chain, more regulation strategies of


Clostridium autoethanogenum is usually used as a robust and flexible chassis for gas fermentation. Here, we leveraged constraint-based stoichiometric modeling and kinetic ensemble modeling of the C. autoethanogenum metabolic network to provide a systematic in silico analysis of metabolic engineering interventions for 2,3-butanediol overproduction and low carbon substrate loss in dissipated CO2. Our analysis allowed us to identify and to assess comparatively the expected performances for a wide range of single, double, and triple interventions. Our analysis managed to individuate bottleneck reactions in relevant metabolic pathways when suggesting intervening strategies. Besides recapitulating intuitive and/or previously attempted genetic modifications, our analysis neatly outlined that interventions—at least partially—impinging on by-products branching from acetyl coenzyme A (acetyl-CoA) and pyruvate  offer valuable alternatives to the interventions focusing directly on the specific branch from pyruvate to 2,3-butanediol.Gas fermentation provides a promising platform to turn low-cost and readily available single-carbon waste gases into commodity chemicals, such as 2,3-butanediol. IMPORTANCE Envisioning value chains inspired by environmental sustainability and circularity in economic models is essential to counteract the alterations in the global natural carbon cycle induced by humans. Recycling carbon-based waste gas streams into chemicals by devising gas fermentation bioprocesses mediated by acetogens of the genus Clostridium is one component of the solution. Carbon monoxide originates from multiple biogenic and abiogenic sources and bears a significant environmental impact. This study aims at identifying metabolic engineering interventions for increasing 2,3-butanediol production and avoiding carbon loss in CO2 dissipation via C. autoethanogenum fermenting a substrate comprising CO and H2. 2,3-Butanediol is a valuable biochemical by-product since, due to its versatility, can be transformed quite easily into chemical compounds such as butadiene, diacetyl, acetoin, and methyl ethyl ketone. These compounds are usable as building blocks to manufacture a vast range of industrially produced chemicals. Acetoin can be finally reduced to 2,3-BDO by a butanol dehydrogenase (here BTDDx), which is encoded by CAETHG_0385 in C. autoethanogenum, or by a primary-secondary alcohol dehydrogenase (here BTDDy), which is encoded by CAETHG_0553 in C. autoethanogenum  is mediated by the pyruvate:ferredoxin oxidoreductase (PFOR), which is present in two copies in the genome of HG_3029) , 14, 15.ofactors . 2,3-BDOanogenum , 17. It anogenum . Whereasanogenum .Clostridium. A few key process parameters have been identified as able to influence 2,3-BDO production. It has been shown that increasing the hydrogen composition of the gaseous substrate and the specific rate of hydrogen consumption by the microbial culture negatively impacts 2,3-BDO productivity and that increasing the age of the cells in culture favors 2,3-BDO productivity . In the first step, we looked for any internal flux after blocking all the exchange reactions. In the second step, we looked at flux through ATP hydrolysis reaction in the absence of any exchange fluxes. iCLAU786 failed both tests, while Metaclau succeeded in both cases. There was no flux-carrying reaction in the absence of exchange fluxes in Metaclau. However, there were many reactions with non-zero flux in iCLAU786, including the ATP hydrolysis reaction, which shows infeasible energy-generating cycles. Finally, we assessed the accuracy of both genome-scale metabolic reconstructions by constraining each of them with experimental substrate uptake rates and by evaluating the ability of the models to predict experimentally observed biomass and by-products’ production rates. We retrieved the experimental data sets for validation purposes from research articles that explore gas fermentations using C. autoethanogenum grown on CO-rich gas substrates and which report quantitative data on gas uptake and production rates  have been previously developed for iCLAU786 , 34, andiCLAU786 . Prior ton rates . We conson rates , we used2 . However, the usage of CO as an electron donor is associated with the release of CO2 –38. For 2 production, we explored feed gas substrates combining CO with H2. Indeed, if H2 is present in the gas, additional reducing equivalents are made available by hydrogen oxidation (H2→2 H+ + 2e−) and less CO has to be dissipated into CO2 via the biological water gas shift catalyzed by CODH. Furthermore, CO2 can be advantageously fixed in the Wood-Ljungdahl pathway since the direct use of H2 in the CO2 reduction to formate by the formate-hydrogen lyase activity . Each intervention can foresee the increase, decrease, or elimination of the flux value corresponding to each of the involved reactions. The OptForce tool suggests genetic manipulations at the metabolic flux level. However, the lack of a quantitative mapping between flux and gene expression levels does not allow translation of the suggested sets of reaction flux changes into executable genetic modifications, especially when attempting combinatorial changes. Furthermore, the OptForce tool does not account for kinetic and thermodynamics features governing the metabolic behavior of C. autoethanogenum . Download Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 C. autoethanogenum was instrumental to elucidate the change in 2,3-BDO production rate as a function of changing the expression levels of the enzymes catalyzing the reactions, which took part in the interventions suggested by OptForce. In particular, we simulated multiple scenarios where the fold changes of the enzymes catalyzing each reaction of the single, double or triple interventions were set to low (±20%), medium (±40%), or high (±60%). Each intervention is associated with a distribution of 2,3-BDO production rates that resulted from simulating all of the 18 sets of kinetic parameters of the ensemble model  fold changes were simulated. The low, medium, and high fold changes correspond to 3-, 5-, and 10-fold for upregulation and 0.3-, 0.2-, and 0.1-fold for downregulation. Overlaid are the mean and standard deviation of the 2,3-BDO production rates corresponding to each intervention at each simulated change in gene expression. The figure only refers to selected single interventions among the interventions reported in Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 10.1128/mSystems.01111-21.5FIG S4FIG S4, TIF file, 0.3 MB.Plots showing the distribution of 2,3-BDO production rates across 18 kinetic parametric sets under the wild-type scenario and under the double-modification scenario. For each gene involved in a double intervention, three  fold changes were simulated. The low, medium, and high fold changes correspond to 3-, 5-, and 10-fold for upregulation and 0.3-, 0.2-, and 0.1-fold for downregulation. Overlaid are the mean and standard deviation of the 2,3-BDO production rates corresponding to each intervention at each simulated change in gene expression. The figure only refers to selected double interventions among the interventions reported in Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 10.1128/mSystems.01111-21.6FIG S5FIG S5, TIF file, 0.3 MB.Plots showing the distribution of 2,3-BDO production rates across 18 kinetic parametric sets under the wild-type scenario and under the triple-modification scenario. For each gene involved in a triple intervention, three  fold changes were simulated. The low, medium, and high fold changes correspond to 3-, 5-, and 10-fold for upregulation and 0.3-, 0.2-, and 0.1-fold for downregulation. Overlaid are the mean and standard deviation of the 2,3-BDO production rates corresponding to each intervention at each simulated change in gene expression. The figure only refers to selected triple interventions among the interventions reported in Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 10.1128/mSystems.01111-21.7TABLE S1C. autoethanogenum core metabolism. Download Table S1, XLSX file, 0.02 MB.The table reports the reactions and metabolites included in the network representation of Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 10.1128/mSystems.01111-21.8TABLE S2C. autoethanogenum. Shown are the single interventions that result in a statistically significant higher 2,3-BDO production rate compared to the wild-type condition . The low, medium, and high fold changes correspond to 3-, 5-, and 10-fold for upregulation and 0.3-, 0.2-, and 0.1-fold for downregulation. Download Table S2, XLSX file, 0.01 MB.Summary of first-order interventions validated by the kinetic model of Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 10.1128/mSystems.01111-21.9TABLE S3C. autoethanogenum. Shown are the double interventions that result in a statistically significant higher 2,3-BDO production rate compared to the WT . The low, medium, and high fold changes correspond to 3-, 5-, and 10-fold for upregulation and 0.3-, 0.2-, and 0.1-fold for downregulation. Download Table S3, XLSX file, 0.01 MB.Summary of second-order interventions validated by the kinetic model of Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 10.1128/mSystems.01111-21.10TABLE S4C. autoethanogenum. Shown are the triple interventions that result in a statistically significant higher 2,3-BDO production rate compared to the WT . The low, medium, and high fold changes correspond to 3-, 5-, and 10-fold for upregulation and 0.3-, 0.2-, and 0.1-fold for downregulation. Download Table S4, XLSX file, 0.01 MB.Summary of third-order interventions validated by the kinetic model of Copyright © 2022 Ghadermazi et al.2022Ghadermazi 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 C. autoethanogenum  condition, primarily concern the upregulation of enzymes involved in the 2,3-BDO biosynthetic pathway. As shown in anogenum . Thereinoduction . This inAs shown in cetogens . This alThe benefit induced by the knockout of ALCDx, which catalyzes the NAD-dependent conversion of acetaldehyde into ethanol, is probably attributable to the negation of a competitive reaction for the electrons required for 2,3-BDO in the form of NADH. Indeed, the flux through the NAD-dependent butanediol dehydrogenase increases 29%. Furthermore, the ALCDx knockout induces a 2.78-fold-higher flux through the NADP-dependent acetaldehyde dehydrogenase (ACALDy), which operating in the opposite direction relative to ethanol formation, increases the acetyl-CoA pool for PFOR whose flux increases 14%.Enhancing the expression of multiple enzymes involved in the 2,3-BDO biosynthetic pathway appears as a relevant choice in double interventions . Many ofC. autoethanogenum using a CO-containing gas substrate. However, according to the outcomes of our in silico analysis, the double intervention did not lead to a tangible increase in the 2,3-BDO production rate compared to overexpressing exclusively the acetolactate synthase, whereas, according to reference Among the double interventions that do not encompass PFOR, our analysis suggested, first, the combined overexpression of ACLS and BTDDx, and second, the overexpression of ACLS and ACLDC, which ensure an around 2-fold-higher 2,3-BDO production rate compared to the wild-type condition  Company , wherebyl-glutamate and 2-oxoglutarate C. autoethanogenum contains a strictly NADPH-dependent primary-secondary alcohol dehydrogenase, which could reduce acetoin to 2,3-BDO , which catalyzes the NADPH-dependent interconversion between  2,3-BDO . TherefoA slightly less effective intervention foresaw the ACLS upregulation combined with the ethanol:NAD oxidoreductase (ALCDx) knockout Another double intervention featured the combination of ACLS upregulation with formate dehydrogenase (FDH) knockout Also, the double intervention consisting of ACLS upregulation and ACAFDOR downregulation is related to the provision of reduced ferredoxin since ACAFDOR downregulation allows limiting of the consumption of reduced ferredoxin d-fructose-1,6-bisphosphate (fdp) into d-fructose-6-phosphate (f6p) and which was found to afford an almost 2-fold increase compared to the WT case d-fructose-1-phosphate (f1p) through the action of fructose-1-phosphate kinase (FRUK). The advantage conferred to 2,3-BDO production by this intervention could derive first from the generation of ATP associated with the conversion of fdp into f1p and, second, from an increased pyruvate pool made available for 2,3-BDO through d-fructose transport via the phosphoenolpyruvate:pyruvate phosphotransferases FRUpts and FRUpts2.An alternative intervention foresaw the simultaneous upregulation of ACLS and the knockout of the rate-limiting step in gluconeogenesis catalyzed by fructose-bisphosphatase (FBP), which converts When we explored the effectiveness of triple interventions, we interestingly noted that the upregulation of the entire branch from pyruvate to 2,3-BDO alone  worsens the 2,3-BDO production rate compared to overexpression of pyruvate:ferredoxin oxidoreductase alone l strain .C. autoethanogenum in reference Additionally, our analysis pointed out triple interventions that involved ACLDC and ACLS upregulation but which, overall, were less effective than the double intervention to increase the 2,3-BDO production rate . One sucirection , 43, 47.irection  validateirection . FurtherCombining the upregulation of ACLDC and ACLS with the upregulation of either phosphate acetyltransferase (PTA), ferredoxin:NAD oxidoreductase (RNF), or acetate kinase (ACK) afforded only lower 2,3-BDO production rate compared to the double intervention encompassing the combined upregulation of ACLDC and ACLS 2 , whose activity has been shown to be favored by NADH  offer valuable alternatives to the interventions focusing directly on the specific branch from pyruvate to 2,3-BDO. Among the alternative options, the ACK downregulation, the double interventions combining ACLS upregulation with glutamate dehydrogenase or ethanol:NAD oxidoreductase knockout, and the triple intervention consisting of the ALCDC upregulation with the knockouts of glutamate dehydrogenase and electron-bifurcating, NADP- and ferredoxin-dependent [FeFe]-hydrogenase are worthy of attention and further exploration.C. autoethanogenum. The C. autoethanogenum GEM iCLAU786 was realized in reference C. autoethanogenum GEM Metaclau was made available in reference in silico design of strains overproducing 2,3-BDO, we validated each GEM by running three tests. We assessed (i) the flux through the network in the absence of flux through the exchange reactions, and (ii) the flux through the non-growth-associated ATP hydrolysis reaction in the absence of flux through the uptake reactions. (iii) Finally, we benchmarked condition-specific models against several experimental data sets. Step-by-step benchmarking of both models is provided in Validation.m MATLAB script in the GitHub repository .A couple of genome-scale metabolic models (GEMs) are publicly available for Exchange reactions were found in iCLAU786 and Metaclau, fluxes through exchange reactions were set to zero, and the sum of absolute flux through the whole network was maximized.Uptake reactions were found in iCLAU786 and Metaclau, fluxes through exchange reactions were set to zero, and the ATP hydrolysis flux was maximized .in silico design of strains with potentially superior characteristics, it is advisable to assess the accuracy of the model by constraining the GEM with publicly available experimental data. More precisely, we constrained the GEM with substrate uptake rates, maximized biomass yield in FVA calculations, and compared model predictions with published experimental data concerning specific growth rate and by-products’ production rates. We surveyed the literature stored at Web of Science Core Collection, PubMed, and individual publisher websites for research articles focused on CO-based gas fermentations with C. autoethanogenum and reporting quantitative data on gas exchange rates, as well as biomass and by-products’ formation rates (https://www.gurobi.com/products/gurobi-optimizer/) as the optimization solver. The GEM was constrained with experimentally quantitated gas uptake rates. The objective used in the simulations was maximizing the biomass formation rate. Finally, the experimentally reported values for acetate, ethanol, 2,3-BDO, and the biomass formation rate were contrasted with the range of allowed values resulting from FVA. Furthermore, flux balance analysis (FBA) was used to predict acetate, ethanol, 2,3-BDO, and the biomass formation rate corresponding to each data set included in https://github.com/chan-csu/OptForce_Bdoh/tree/master/Report/Model_Validation).The flux variability analysis (FVA) technique uses two linear optimization problems for each reaction of a genomic-scale metabolic reconstruction to evaluate the minimum and maximum values of each reaction rate satisfying the constraints, which occur as inequalities imposing bounds on the system and as mass balance equations imposing mass conservation at steady state. Results obtained through the application of FVA provide insight into the capabilities of a metabolic network to investigate the steady-state behavior of a microorganism. Before applying the model for on rates . FVA as on rates  and Guro2 uptake reactions stepwise in the range from 0 mmol/gCDW/h to 50 mmol/gCDW/h, and the optimal objective value was calculated as a function of those fluxes. We investigated the effects of various gaseous substrates’ uptake rates on 2,3-BDO production rate and CO2 production rate.We varied the fluxes through CO and H2 uptake rates set to 20 and 33 mmol/gCDW/h, which we retrieved from the gas fermentation study described in reference in silico design and screening of strains with gene modifications which, subject to the model stoichiometry and boundary conditions, could lead to the production of higher yields of the 2,3-BDO compound. The design and screening of mutant strains were carried out using the computational tool OptForce . The perturbations suggested by OptForce to meet the overproduction target can include the increase or decrease in the flux values of particular reactions or the elimination of particular reaction fluxes, which can be achievable through genes’ overexpression, downregulation, or knockout, respectively. In particular, the top 500 theoretical interventions , which were found consistent with the imposed 2,3-BDO overproduction target according to the OtpForce strain design procedure, were retained for further independent validation.The genome-scale metabolic reconstruction Metaclau was constrained with CO and HOptForce  as it isC. autoethanogenum core metabolism previously developed with the ensemble modeling framework (C. autoethanogenum in reference https://github.com/chan-csu/OptForce_Bdoh/blob/59a42acaa7be018702cd748a441a6076391306c6/Results/Supplementary_Table_1.xlsx). The kinetic ensemble model of C. autoethanogenum was employed to calculate the change in 2,3-BDO production rate as a function of changing enzymes’ expression levels according to the combinations of single, double, and triple interventions on reactions’ fluxes, which were suggested by OptForce. We set three fold change (FC) values in enzymes’ expression: FC = 1.1, 1.3, and 1.5 for upregulation and FC = 0.9, 0.7 and 0.5 for downregulation. The kinetic representation of the core metabolism was constrained by a single reference state where the CO and H2 uptake rates were set to 20 and 33 mmol/gCDW/h, respectively. By calculating the forward finite difference slopes for each intervention across each of the final 18 kinetic parameter sets, we obtained a distribution of 2,3-BDO production rates in correspondence to each intervention. In order to identify the interventions that reflect a statistically significant higher production of 2,3-BDO compared to the wild-type strain, we applied the pairwise Wilcoxon’s test. Raw P values were adjusted for multiple testing by the Bonferroni approach. Statistical analysis was performed within the R software environment for statistical computing.Interventions were further analyzed through a kinetic model of the ramework . The kin


Modelling higher plant growth is of strategic interest for modern agriculture as well as for the development of bioregenerative life support systems for space applications, where crop growth is expected to play an essential role. The capability of constraint-based metabolic models to cope the diel dynamics of plants growth is integrated into a multilevel modelling approach including mass and energy transfer and enzyme kinetics. Lactuca sativa is used as an exemplary crop to validate, with experimental data, the approach presented as well as to design a novel model-based predictive control strategy embedding metabolic information. The proposed modelling strategy predicts with high accuracy the dynamics of gas exchange and the distribution of fluxes in the metabolic network whereas the control architecture presented can be useful to manage higher plants chambers and open new ways of merging metabolome and control algorithms. Modelling crop growth has been a topic of research since the mid-twentieth century given the relevance that agronomic-related activities have in the global economy, but the focus on plant modelling research has evolved in the last decades moving towards new applications . A lot o2 and CO2), and the nutrient uptake by the roots, most of the phenomena involved in plant growth are not addressed given their complexity and lack of knowledge. This complexity is associated with several factors; higher plants are multicellular, compartmentalized organisms undergoing strong metabolic changes associated with the cyclic switch between light and dark phases of the day. They are also characterised by having a complex substrate partitioning strategy with different parts being coordinated to uptake and distribute specific compounds. This has contributed to the preference of empirical at the expense of mechanistic models due to the usually satisfactory information provided by the former, especially in nominal conditions , whereas the lower the level the smaller the characteristic length of the modelled phenomena . Information follows a top-bottom flow, and it is eventually used to calculate the metabolic flux distribution by applying a flux balance analysis (FBA). This multilevel modelling approach is firstly validated with experimental data and secondly integrated in a model-based predictive control, representing, to the best of the authors, the first attempt to incorporate cell metabolism in an advanced control strategy.In this study, the modelling of higher plants is approached through the design of a multilevel organization of the mechanistic processes that take place during crop growth. To present the results, Overall, the modelling and control methodology presented in this study may pave the way for a more efficient and sustainable agriculture either for intensive cultivation systems or as a part of BLSSs in space exploration.The model developed here is organized following a multilevel approach, considering the different levels of the plant, from canopy to metabolic level, and uses the output of the higher levels as the input to the lower levels. In this section, the models used in the different levels are explained. A graphical description of the model organization is presented in LAI) as an indicative parameter of the leaf area density over ground surface. The photon flux density inside the canopy I declines along the canopy exponentially and is a function of the leaf area index:When dealing with whole-leaf or canopy modelling, it is necessary to consider the effect of shading among leaves, which is not accounted in single-leaf models . The comuI represents the photon flux density at the top of the canopy, and k represents the extinction coefficient. Extending (1), leaves can receive direct photon flux density (sI) or diffuse photon flux density (dI) as stated by -2 leaf s-1 by using the extinction coefficient k:Parameter LAI term should be differentiated between the fractions exposed to direct and diffuse light sources (sLAI and dLAI respectively), as suggested by Notice in (2) that the parts of the canopy under direct irradiance also receive diffuse irradiance. The sLAI and dLAI should be obtained and used to integrate sI and dI to calculate the overall irradiance received by the canopy:The derivative of hsE) and diffuse (hdE) terms, which are expressed as follows:The way light irradiates exposed and shadowed leaves strongly affects the energy balance in the leaf surface, with the shadowed leaves irradiated by diffused light mainly of a wavelength of 600 nm corresponding to the green-colour spectrum of transmitted light. Energy received by irradiance contains the direct  and (11). The radiation energy emitted by the plants (rE), the energy lost by convection (convE), and the energy lost by transpiration (trE) are determined by the following equations:ϵ and σ represent the leaf emissivity and the Stefan–Boltzmann constant, respectively. In (13), pC and tk represent the molar specific heat capacity at constant pressure and 298.15 K and the heat transfer coefficient, respectively, the latter being a function of the diffusion coefficient and the boundary layer thickness, as follows :In (12), molλ is the water latent heat of vaporization and ExH2O the transpiration rate defined in the following section. Finally, leafT is the leaf surface temperature, bT is the bulk temperature, and R is the ideal gas constant.In (14), ExCO2 , ExO2 , and ExH2O ). The approach to modelling gas exchanges between the leaves and the atmosphere follows Fick’s law, with the concentration gradient being the driver of the molecular transport:This level is dedicated to calculating the uptake and release rates between the atmosphere and the leaves concerning the exchange gases  and the internal  partial pressure (LA) are calculated according to In (16)–(18), the atmospheric partial pressure is calculated assuming gases behave following the general gas equation, whereas the conductance for the different gases (GgP) is calculated using the Farquhar model , substrate of the ribulose-1,5-biphosphate carboxylase (RuBisCo), or the RuBisCO carboxylation rate itself (cV).The gross photosynthesis rate  and its capacity to generate reducing power. Thus, it is necessary to define an expression for LETCF, which can be approximated by a quadratic equation . As previously demonstrated, it is necessary to consider the direct and diffuse irradiances when considering the whole canopy. Therefore, combining with (4) and (5):In (19), the maximum Considering that two electrons are necessary per molecule of NADPH generated, the light electron transport chain rate resulting from (21) is divided by 2. This is necessary to provide information to the FBA with consistent units considering the stoichiometry matrix used (see S2 for a full list of reactions and stoichiometry matrix).LETCF defined in (21) to a flow of ribulose 1,5-biphosphate (Ru5P) regeneration (J), through the following expression derived from the stoichiometry of the light electron transport chain and the Calvin cycle  approach, it is necessary to convert in cycle :c(V) kinetics is of Michaelis–Menten type and is a function of the leaf internal oxygen (iO) and carbon dioxide (iC) concentrations:The RuBisCo carboxylation cmaxV represents the maximum carboxylation velocity of RuBisCo, cK and oK are the Michaelis–Menten half-saturation constants for the carboxylation and oxygenation activities of RuBisCo, respectively, and Г represents the carbon dioxide compensation point. Details about the calculation of the internal carbon and oxygen concentrations are found in cV can be directly used to feed the FBA as an upper bound (see In (23), gP) is determined by finding the minimum between the Ru5P regeneration rate (J), the RuBisCo carboxylation rate (cV), and the gas exchange rate (ExCO2 ) and the net photosynthesis rate (nP) is determined by retrieving the RuBisCo oxygenation (oV) to gP as follows:Finally, the gross photosynthesis rate  to RuBP regeneration (J) unnecessary, because this information is already included in the stoichiometric matrix. Similarly, the discontinuity introduced by Notice that Equation (24) can be formalized due to the conversion applied in Equation (22) considering that L. sativa is described. The stoichiometry matrix is based on the work of L. sativa, such as the reduced starch content to store carbon and the definition of the elemental composition that makes up the macromolecules of the biomass. This model though has been extended to include relevant reactions like the pentose phosphate pathway or the photorespiration cycle, originally missing. In the model used, the cellular organelles are described as different compartments. The model also describes dark and light phases of the day by duplicating each one of the reactions. Hence, a diel model is achieved where both light and dark phases of the day account for separate pools of metabolites and organelle compartments. Here, only those metabolites that have been reported to be accumulated in one phase and consumed in the other are connected by exchange reactions that simulate the transference of nutrients between phases. For example, sugars that are synthesised in the light phase can be used in the dark phase due to the addition of exchange reactions among day phases. In In this level, a simplified network model of the photosynthetic leaves’ metabolism of The diel model is composed by different organelles including chloroplasts, mitochondria, peroxisomes, and cytoplasm. The proposed model also features plants’ simultaneity of metabolic pathways  as well as the aforementioned coordination between day phases. A summary of the metabolic model is found in The metabolic network model is mathematically formalized as a constraint-based model, and the fluxes are calculated by applying a flux balance analysis :Arabidopsis thaliana that biomass growth also takes place during the dark phases of the day  are fixed only for those fluxes indicated in C contains information regarding the flux ratios specified in F represents the resulting flux. The inequality constraints represented in maintenance and NAD(P)Hmaintenance includes the reactions that contribute to the consumption of ATP and the reducing agent for respiration purposes. As suggested in In the FBA formulation in (27), the objective function refers only to the daily biomass production. It is well known for other crop species like  the day . HoweverPn, considering that all carbon molecules captured by the plant are fixed into structural biomass:The evolution of the different states of interest through time for either dry biomass, leaf temperature, or gas compositions can be obtained integrating their rates of generation or consumption over time. For dry biomass, this can be done straightforward from For the evolution of leaf temperature, it is necessary to solve an energy balance between the leaf temperature and the environment and to convert energy units to temperature degrees:Finally, the oxygen concentration can be obtained by solving a mass balance within the growing crop chamber:u, oxygen concentration in the input flow by V.In (30), gas flow is represented by Once having defined the modelling strategy for higher crops, the second scope of this study is to integrate the use of metabolic models into an advanced control strategy. In different complex systems ranging from microgrids , life suvExO2 ) as the generation rate in a steady-state mass balance:The SSTO solves the following system of equations using the output of FBA, considering oxygen exchange rate (s.t.sx) and the external gas flow (su) both at steady state. The internal model at steady state is defined in (3), with the generation term (vExO2 ) being the output of the FBA, sx. A disturbance (d) is incorporated in (32) to take into account any possible plant-model mismatch or a measured perturbation and must be taken into account for the new reference generation. Hence, the final prediction value (sy) at steady state considering the presence of any given disturbance will match the process measurement guaranteeing offset-free control  from the concentration (sx) and the gas flow (su) references generated in the SSTO. A third penalization term to the rate of change of the manipulated variable is also included in (36) aimed to adjust the speed of the controller response. All three penalization terms are normalized using the range of possible maximum and minimum values for both controlled and manipulated variables (defined by Δx and Δu) and are subject to a scaling factor (λ). Prediction and control horizons are represented by pN and cN, respectively. Constraints in (37) reflect the dynamics of the growing crop chamber with sT as the sample time of the MPC. The internal model is initialized with the current process measurement (py). In (39), the disturbance is integrated to the internal model prediction as similarly done in the SSTO in (32). This disturbance is estimated at each sampling time and is defined as the difference between the process measurement (py) and the first step prediction of the previous MPC execution (U) is expressed in (41), and only the control action for the first step of the control horizon is sent to the control actuators until the next SSTO and MPC execution. This control strategy is implemented in Matlab® 2021 using the Optimization Toolbox for solving non-linear programming problems and Cobra Toolbox 2022 for the FBA resolution (The cost function in (36) includes penalization terms to the deviation of the internal prediction  are presented. The parameters used for the simulation are those provided in The day flux distribution is presented in As previously stated, higher plants store carbon during the light phase of the day, to be used during night respiration and fuel maintenance processes. For non-starchy crops, even though they can still generate starch, most of the carbon fixed during the light phase is stored as soluble sugars or organic acids. In this study, the sugar molecules stored and mobilized between light and dark periods of the day have not been restricted and, as One of the critical phenomena of plant photosynthetic cell metabolism is the coordination of photosynthesis and respiration, which essentially determines how and where energy carrier molecules (ATP and NAD(P)H) are produced. Most of the ATP and NAD(P)H used for catabolic reactions are produced in the light electron transport chain in chloroplasts for amino acid and lipid production. Part of the ATP synthesized in chloroplasts is exported to the cytosol through the 3PG-G3P shuttle, satisfying the ATP demand together with ATP exported from mitochondria . The res2 during the day phase over produced CO2 due to night respiration (ExCO2[d]:ExCO2[n]) which sets the night carbon conversion.During the dark phase of the day in FBA is a powerful tool but strongly affected by its mathematical formalization and particularly by the flux boundary choice . A sensii index refers to the discrete steps in which the range of flux ratios has been divided  whereas index j and z refer to the reaction and the flux ratio, respectively. The parameter Δzr is the increment in the flux ratio expressed as a fraction and equivalent to 0.1 for all cases. The denominator Δj,zv is used to normalize the summation of slopes, and it represents the range of values for a given reaction j. The sensitivity analysis has been carried for light and night metabolism and is represented in 2 of 1,000 ppm in order not to limit by carbon substrate and a nominal light intensity of 400 µmol/m2/s.In (42), the Considering the light metabolism sensitivity analysis . The lacu) is increased, night mitochondrial activity (MP1_n and MP2_n) is also increased to respond to the maintenance reaction demands (2 is increased (ExCO2[n]:ExCO2[d]) with the availability of citrate and the usage of other TCA intermediates in light photoperiods reduced given the night metabolic increased activities as suggested by the citrate import flux. Therefore, an increased night CO2 release is corresponded by an increased glycolytic activity represented by GAPDC_n, also impacting the TCA intermediates and soluble sugar accumulation at night.Regarding night metabolism :• At 10 h of simulation time, a perturbation in the process output has been introduced by adding a sudden decrease in the oxygen concentration from the measurement to 20.8%.• At 16 h of simulation time, a change in the oxygen setpoint from 21% to 21.2% has been included.2 measurement in relation to the reference  can be found in the article/CC is the main writer of the manuscript as well as the one responsible for script development, simulation, and main analysis and interpretation of the results. IM and IMdM are the main contributors to the constraint-based metabolic modelling. IMdM contributed to the design, validation, and interpretation of the results. JM is the main contributor and is responsible for the design of a control architecture adapted to metabolic networks. CG and FG have contributed to the design of the mechanistic multilevel model, specially concerning the physical and enzymatic equations, and they have supervised the overall text. CG is the responsible of the original lettuce metabolic model development. All authors contributed to the article and approved the submitted version.MELiSSA is an international consortium of 15 partners led by the European Space Agency. Its activities are governed by a Memorandum of Understanding (ESA 4000100293/10/NL/PA). The MELiSSA Pilot Plant is funded from ESA contributions from Spain (main contributor), Belgium, France, Italy, and Norway, under Frame Contract C4000109802/13/NL/CP. Cofunding from Ministerio de Ciencia, Innovación y Universidades, Generalitat de Catalunya, and Universitat Autònoma de Barcelona is also acknowledged. Carles Ciurans is a PhD fellow from the POMP Program (MELiSSA Foundation) of ESA. This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920): Center for Research on Microgrids (CROM). Igor Marin de Mas and Ivan Martínez-Mongue would like to thank The Novo Nordisk Foundation (NNF Grant numbers: NNF10CC1016517 and NNF14OC0009473).The authors wish to thank the partners of the MELiSSA Consortium for carefully reviewing this publication.Author IMdM and IM are employed by Novo Nordisk Foundation Center for Sustainability.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Clostridium strains is also quite limited, demonstrating the physiological complexity of solventogenic clostridia. In fact, the strain performance is greatly impacted by process control. developing efficient process control strategies could be a feasible solution to this problem.Lignocellulosic biomass is recognized as an effective potential substrate for biobutanol production. Though many pretreatment and detoxification methods have been set up, the fermentability of detoxicated lignocellulosic substrate is still far lower than that of starchy feedstocks. On the other hand, the number of recent efforts on rational metabolic engineering approaches to increase butanol production in +, and NADH/NAD+ were increased by 25.1%, 81.8%, and 62.5%. ORP controlling also resulted in a 2.1-fold increase in butyraldehyde dehydrogenase, a 1.2-fold increase in butanol dehydrogenase and 29% increase in the cell integrity.In this study, oxidoreduction potential (ORP) controlling was applied to increase the fermentability of enzymatically hydrolyzed steam-exploded corn stover (SECS) for butanol production. When ORP of detoxicated SECS was controlled at − 350 mV, the period of fermentation was shortened by 6 h with an increase of 27.5% in the total solvent (to 18.1 g/L) and 34.2% in butanol (to 10.2 g/L) respectively. Silico modeling revealed that the fluxes of NADPH, NADH and ATP strongly differed between the different scenarios. Quantitative analysis showed that intracellular concentrations of ATP, NADPH/NADPClostridium cell growth and butanol production. The working mechanism can be summarized into three aspects: First, Glycolysis and TCA circulation pathways were strengthened through key nodes such as pyruvate carboxylase [EC: 6.4.1.1], which provided sufficient NADH and NADPH for the cell. Second, sufficient ATP was provided to avoid “acid crash”. Third, the key enzymes activities regulating butanol biosynthesis and cell membrane integrity were improved.ORP control strategy effectively changed the intracellular metabolic spectrum and significantly improved The online version contains supplementary material available at 10.1186/s12934-022-01824-2. Biobutanol may play a pivotal role in the overall success of the biofuels industry and is considered as a promising next-generation liquid fuel because of its superior characteristics over ethanol . HoweverClostridium  and aspartate kinase [EC:2.7.2.4] respectively. They are the key enzymes in aspartate metabolic pathway, which controls the biosynthesis of lysine, methionine, threonine, and isoleucine. TCA1 is the reaction catalyzed by pyruvate carboxylase [EC:6.4.1.1]. As the key enzyme of oxaloacetate replenishment pathway in bacteria, it serves as the gate of carbon flow into TCA cycle. In other words, folate, amino acid, and TCA cycle of cells were greatly improved under the controlling of oxidoreduction potential. At 60 h the fluxes of four reactions in UCG were at a higher level. it showed 3.23 times higher of PUR17 (CA_C3112/EC: 2.7.4.3), 1.67 times higher of PANCOA4 (CA_C3200/[EC:2.7.1.33]), 1.66 times higher of BUTAN10 (CA_C3075/[EC:2.7.2.7]) and 1.53 times higher of BUTAN4 (CA_C1743/[EC:2.7.2.1]) of that in OCG. PUR17 [EC: 2.7.4.3] represents the conversion reaction of ATP to ADP, indicating that UCG has a higher ADP generation rate. PANCOA4 [EC:2.7.1.33] is a key enzyme that catalyzes COA synthesis. BUTAN4[EC:2.7.2.1] and BUTAN10[EC:2.7.2.7] are the key enzymes that catalyze butyryl phosphate to butyric acid and butanoyl-COA to butyryl phosphate, respectively. These four key enzymes formed a reaction circuit resulting in acid accumulation. The illustration of the metabolic circuit is given in Fig. “acid crash” by adding acid to the culture medium. For the first time, we found a new metabolism circuit that may cause butyric acid accumulation by metabolic pathway analysis method.Additional file + and NADPH/NADP+, within C. acetobutylicum ATCC 824 from the three groups during the solvent-producing phase. As shown in Fig. + ratio in SG is 1.4-fold of that in OCG and 2.3-fold of that in UCG correspondingly. High energy and reduced power availability form one of the main reasons for high butanol production in SG and OCG. Meanwhile, we also detected the activities of key enzymes in the butanol biosynthesis and the result was shown in Fig. Metabolic flux reflects the instantaneous change in cell metabolism. In order to further confirm the real state of cells, we measured the key metabolites, including ATP concentration, NADH/NADA major drawback of solvent production by microorganisms is the toxic effect of the alcohols, especially n-butanol, on the cells themselves . The accC. acetobutylicum ATCC 824 under different culture conditions were observed using scanning electron microscopy (SEM). The experiment method and result were given in Additional file The changes of the cell wall of fluorescin” by esterase in the living cell, which can be detected by measuring the fluorescence or absorbance of the sample , which provides sufficient NADH and NADPH for the cell. Second, sufficient ATP is provided to avoid “Additional file 1.  Gene–Protein–Reaction realationship, metabolite abbreviation, and whole reaction set used in the model.Additional file 2. Low molecular weight compounds released from lignin due to degradation.Additional file 3.  The fermentation profiles of C. acetobutylicum ATCC 824 under different ORP levels which started at 0 h.Additional file 4. The gas production profiles of C. acetobutylicum ATCC 824 under different ORP levels.Additional file 5. Calculation process of the specific growth rates (μ), specific substrate consumption rates, and specific product secretion rates.Additional file 6. Details of compounds in the model.Additional file 7. The flux of each reaction at different times under different conditions.Additional file 8. Simulation of metabolic flux of the ATP generation/consumption reaction of C. acetobutylicum ATCC 824 strain under the different conditions.Additional file 9. The changes of cell wall of C. acetobutylicum ATCC 824 under different culture conditions.