Chlorella sp. HS2, which previously showed excellent performance in phototrophic cultivation and has tolerance for wide ranges of salinity, pH, and temperature, was cultivated heterotrophically. However, this conventional medium has been newly optimized based on a composition analysis using elemental analysis and ICP-OES. In addition, in order to maintain a favorable dissolved oxygen level, stepwise elevation of revolutions per minute was adopted. These optimizations led to 40 and 13% increases in the biomass and lipid productivity, respectively (7.0 and 2.25 g l-1d-1 each). To increase the lipid content even further, 12 h heat shock at 50°C was applied and this enhanced the biomass and lipid productivity up to 4 and 17% respectively  relative to the optimized conditions above, and the values were 17 and 14% higher than ordinary lipid-accumulating N-limitation (6.2 and 2.31 g l-1d-1). On this basis, heat shock was successfully adopted in novel Chlorella sp. HS2 cultivation as a lipid inducer for the first time. Considering its fast and cost-effective characteristics, heat shock will enhance the overall microalgal biofuel production process. Recent microalgal biomass production has been limited to photoautotrophic cultivation because it is the only method to use photosynthesis; however, the scale of biomass production has shown limitations and inadequate feasibility , 2. In rAt the same time, most microalgae strains are accustomed to growing in phototrophic rather than heterotrophic conditions. Therefore, careful strain selection, adaptation, and serial optimization in heterotrophic cultivation are essential for massive biomass production. The ideal strain for heterotrophic cultivation should use various carbon and nutrient sources in order to utilize various low cost carbon sources or wastes/wastewaters , 6. In aWhile adaptation is not always necessary, it should be conducted for stable cultivation. Algae strains that normally grow in natural phototrophic cultivation seldom use glucose or other carbon sources. Although they sometimes have genes coding the enzymes that are part of a metabolism using organic carbon sources, the genes do not work effectively and the cells consequently show lower performance than they would have normally . TherefoThe optimization starts from setting a system for the target strain or product. The system is decided based on data of final productivity and a techno-economic assessment. In the case of a certain strain, it will show inhibited growth when cells are exposed to a high concentration of nutrients, and therefore a fed-batch cultivation system would be a better option . Fed-batChlorella sp. HS2 is a microalga having beneficial traits as a target strain for heterotrophic biodiesel production. It has been reported that Chlorella sp. HS2 can utilize various carbon sources and has higher cell density in both phototrophic (4.34 g l-1) and heterotrophic (7.71 g l-1) conditions [The recently isolated nditions . It has nditions . It thrinditions -19. The nditions . If micrChlorella sp. HS2 was selected as an ideal heterotrophic microalga and optimized in a fed-batch cultivating system. For the initial optimization of biomass productivity, the fed-batch fermentation was compared with batch fermentation and enhanced by modification of the concentration of the nutrients, which was calculated by elemental analysis and ICP-OES data of the cell biomass composition. Second, stepwise elevation of RPMs was adopted to satisfy increasing oxygen demand as cells grew. Lastly, heat shock was applied as a more cost-effective and stepwise lipid inducer compared with conventional N limitation.In this study, Chlorella sp. HS2, which is a green microalga, was generously provided by Dr. Hee-Sik Kim at the Korea Research Institute of Bioscience and Biotechnology (KRIBB). The culture was maintained in a modified BG-11 medium that was previously optimized in a batch condition: 72 g glucose, 8 g NaNO3, 0.22 g K2HPO4∙2H2O, 1 g MgSO4∙7H2O, 0.02 g Na2CO3, 0.006 g ferric ammonium citrate, and 1 ml of the A5 solution per 1 L DI water [3BO3, 1.81 g MnCl2∙4H2O, 0.22 g ZnSO4∙7H2O, 0.39 g NaMoO4∙2H2O, 0.079 g CuSO4∙5H2O, and 0.05 g CoCl2∙6H2O per 1 L DI water. The maintaining conditions were 37°C and 120 rpm in a heterotrophic incubator.DI water . The A5 -1. Culture conditions were 1 vvm air flow rate, 37°C, pH 7, and 300 rpm respectively. The initial concentration of the inoculum was 7.2 × 106 cells ml-1, and the total cultivation period was ten days. Batch cultivation was also conducted with the same conditions except for the feeding.The seed culture was prepared in a 500 ml flask with 300 ml of the modified BG-11 described above. Cultivation was conducted in a shaking incubator at 37 °C and 120 rpm in the dark. The initial pH was adjusted to 7.0 using 1 N HCl. Fermenter scale cultivation was conducted using a 5-L jar fermenter  containing 3 L of the modified BG-11. For the fed-batch cultivation, stock solutions of the modified BG-11 were used to maintain the concentration of glucose at around 20-40 g lFor the optimized fed-batch cultivation, optimized BG-11 medium and stepwise increase in rpm were applied. The optimized BG-11 medium was calculated using a stoichiometry equation (Eq. 1) based on the cell composition data, where the cell condition was an exponential phase, from Elemental Analysis (EA).3 and 2.2 g K2HPO4∙2H2O per 1 L DI water and the other components were the same as in the modified BG-11 medium. Feeding was conducted using only glucose stock to maintain the concentration of glucose at around 20 to 40 g l-1. In the case of rpm, in order to minimize the negative effect from shear stress [6 cells ml-1 and the total cultivation periods were ten days. All the cultivations were conducted twice.The optimized BG-11 medium contained 15 g NaNOr stress , stepwis-1 NaNO3 was used in a 5-L fermenter. Glucose feeding using only glucose stock was conducted to keep the concentration of glucose at around 20 to 40 g l-1. The temperature was kept at 37°C. In the case of heat shock, 3 L of optimized BG-11 medium containing normal 15 g l-1 sodium nitrate was used in a 5-L fermenter and only glucose feeding was adopted to maintain the concentration of glucose. The temperature was started at 37°C and increased to 50°C on day 7. For both nitrogen limitation and heat shock, culture was started with the same initial cell density (7.2 × 106 cells ml-1), air flow rate, and pH, and stepwise rpm elevation was applied . All the cultivations were conducted twice.In order to induce nitrogen limitation in the fermenter, 3 L of optimized BG-11 medium containing 7.5 g lFor the dry cell weight (DCW), a 1-ml sample was centrifuged at 13,000 rpm for 3 min. After washing twice, cells were dried in a 90°C oven for 24 h. To measure the total lipid amount, 20 ml of the biomass samples was centrifuged at 4,000 rpm for 10 min. The pellets were washed twice with distilled water and then stored at −70°C in a deep freezer for more than 24 h. Frozen cells were lyophilized in a freeze dryer for up to three days. Total lipid was determined using the modified Folch method , 22. TheWD: weight of the aluminum dish, WL: weight of aluminum dish with lipid, WS: weight of the sample, VC: volume of chloroform, VP: volume of chloroform in the aluminum dish4.The supernatant from the culture was filtered using a 0.2 µm syringe filter . A high-pressure liquid chromatograph  equipped with a refractive index detector (RI) was used to analyze the glucose concentration. The Aminex HPX-87H column  was maintained at 65°C with an eluent that contained 0.01 N, H2SO2CO3 and 1 mM NaHCO3, was supplied at a flow rate of 0.7 ml min-1 into the column for the analysis [The nitrate concentration was measured with an ion chromatograph  with an anion column (Metrosep A Supp 5). The eluent, which consisted of 3.2 mM Naanalysis .For the elemental analysis and ICP-OES, cells at the exponential phase were used. The contents of C, H, N, S, and O were analyzed using an elemental analyzer  in the KAIST Analysis Center for Research Advancement . VariousChlorella sp. HS2 showed superior dry cell weight (50.0 ± 0.7 g l-1) and lipid content (39.8%) compared to batch cultivation . Although fed-batch cultivation consumed a greater amount of glucose and nutrients, considering the better biomass and lipid productivity (41.6% increase in biomass productivity and 114% increase in lipid productivity), economic feasibility could be enhanced by short operation time, using organic waste and wastewater, and having more efficient harvest yield [The fed-batch process has been used as the general method of biomass and pharmaceutical product production in the industry because of the characteristics of easy scale-up and stable production , 13, 25.st yield . TherefoHowever, there are two factors to consider: an insufficient level of dissolved oxygen and incomplete consumption of nitrate. A lower level of dissolved oxygen is common in heterotrophic cultivation owing to the respiration caused by active cell metabolism. However, the level of dissolved oxygen should be controlled at a certain level because it can determine the glucose consumption and will decide the maximum achievable biomass yield.-1 day-1). Although decreased DO inhibited cell growth with an insufficient oxygen supply, interestingly, DO showed a slow increase from day 2 with sudden decreases at day 4, 6, and 8. The increase of DO continued until the end of the cultivation after the cells reached the optimum level of growth. It can be assumed that overall growth lost its activity due to the increase in the aged cell and insufficient oxygen supply. Whereas DO continued to increase and fluctuate after day 2, a certain range of DO is necessary for stable cell growth [Generally, dissolved oxygen (DO) is simply the measured concentration of dissolved oxygen in the media, and thus ΔDO is the most appropriate parameter to represent the oxygen utilization by the cells. For the initial two days, ΔDO showed negative values, which indicates that oxygen utilization exceeded the supply . For thel growth , 30. TheIn addition, as shown in Chlorella sp. HS2 biomass. Cell biomass was prepared in favorable conditions to determine the cell composition that shows the most robust growth. Cell biomass-based prediction could be a better method than prediction based on the consumption profile in the medium, because the concentration of the nutrients in the medium may not be accurate due to secreted chemicals from the cells and various interferences [Chlorella sp. HS2 reveals the exact weight-based ratio of C:N:H:S:O (51.19:4.17:7.67:0.35:36.63), as presented in Chlorella sp. HS2 biomass was calculated as CH1.88O0.54N0.07. Assuming that glucose, sodium nitrate, and oxygen were provided to make the biomass of Chlorella sp. HS2, the biomass can then be represented by the following mass balance equation as Eq.1.In order to optimize the amount of supplied nutrients including nitrate, elemental analysis and Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) were conducted on ferences , 32. The-1 biomass production was calculated as 76 g l-1 through Eq. 1. In the case of glucose, it could be used as an energy source as well as a carbon source, and therefore cells require a greater amount of glucose than the calculated amount based on Eq. 1. However, the supplementation of too much glucose could have a negative effect on cell growth, such as osmotic shock [-1 of glucose and add additional glucose to keep the glucose concentration at around 20 to 40 g l-1, which is the ideal range for cell growth [First, the required glucose for 60 g lic shock , and thul growth , 34.-1 was also calculated as 15 g l-1 based on Eq. 1. While this number was substantially larger than in the previous fed-batch test, optimization of nutrients and modification of the aeration method would enhance the consumption rate, and therefore the nitrate consumption profile was tested later, as described in the following section. The third major nutrient, the phosphorus source, was examined by using ICP-OES. The amount of dipotassium phosphate needed for 60 g l-1 of biomass was calculated to be 2.2 g l-1 given that the phosphorus composition in the cell biomass was about 6.7 mg/g biomass , 15 g l-1 sodium nitrate, and 2.2 g l-1 phosphate while the other components were the same as in the modified BG-11 medium.In addition, the amount of sodium nitrate required for the production of biomass of 60 g l biomass . For dip2 and modifying the baffle or bubble size have been suggested [In order to increase the DO level in the fermenter, numerous strategies such as supplying only Ouggested . Among tuggested . Howeveruggested , 39 and uggested . In addiuggested , 40. ThuChlorella sp. HS2, as presented in Chlorella sp. HS2 in the optimized conditions showed a maximum DCW of 57.6 ± 1.3 g l-1 and total lipid content of 36.6% at day 10 . During this period, there was no significant cell growth, but the lipid content kept increasing until reaching 36.6%. In view of the biomass production, the biomass productivity was 10.3 g l-1d-1 at day 5, and therefore the cultivation period could be shortened. However, considering the lipid production with the aim of biodiesel production, the eight days of cultivation would be optimal for any subsequent process.The enhanced performance was mainly the result of the application of stepwise rpm elevation, as shown in from 4.9  to 8.1  were tested under the same upgraded conditions noted above. As shown in -1 sodium nitrate was supplemented, 81% was consumed, and therefore 12 g l-1 of sodium nitrate was anticipated to be the optimum concentration for both biomass production and lipid accumulation. However, 12 g l-1 of sodium nitrate was not consumed completely during the whole cultivation period .The other method of increasing lipid content is heat shock. Modifying the temperature also has been used as an inducer of lipid accumulation . GeneralChlorella sp. HS2.Two lipid-accumulating processes are compared and heat shock provided better performance in terms of both biomass and lipid yield with shorter induction time. For the N limitation, there was limited biomass production caused by lower nitrate concentration, and for the lipid accumulation, the cells need more time to consume intercellular nitrate and turn on the lipid-accumulating metabolism. However, heat shock treatment, which could be applied with exact timing, showed the highest biomass productivity and the treatment takes only 12 h to induce lipid accumulation. Therefore, heat shock could be well utilized in this system. Thus, it is noteworthy that the powerful and fast lipid induction performance of heat shock showed strong potential for biofuel production from -1d-1) and total lipid content (26.3 to 39.8%), finally resulting in doubled total lipid productivity (0.93 to 1.99 g l-1d-1). In addition, improved nutrient supplementation and stepwise elevation of rpm were successfully adopted and increased biomass productivity (5.0 to 7.0 g l-1d-1). There was little decrease in the total lipid content (39.8 to 32.2%), but the increase in biomass productivity compensated for this and showed higher total lipid productivity (1.99 to 2.25 g l-1d-1). Finally, application of heat shock for lipid accumulation enhanced the biomass productivity (7.0 to 7.3 g l-1d-1) and increased the total lipid content (32.2 to 36%), and this resulted in 2.64 g l-1d-1 total lipid productivity. These values are substantially higher compared to the other Chlorella spp.  than the results obtained with Chlorella sp. HS2 in the present study. The biomass productivity of Chlorella sp. HS2 was slightly lower at 0.39 g l-1d-1, but accompanied fairly high levels of biomass accumulation and lipid content. Therefore, the lipid productivity of Chlorella sp. HS2 was high. Each lipid productivity (g l-1d-1) was 1.05, 0.93, and 2.06, based on the 3-L working volume. Thus, it is noteworthy that the performance of lipid productivity of Chlorella sp. HS2 was achieved by its beneficial traits for heterotrophic cultivation, adequate optimization, and newly applied heat shock, which have not been used before well.As a result, we applied optimization and additional lipid accumulation methods, as delineated in lla spp. . Chlorelresearch , 13, 33,Chlorella sp. HS2. Through an elemental analysis and ICP-OES on cell biomass, the theoretically required amounts of nutrients were calculated and stepwise rpm elevation was selected. These optimizations increased the total lipid productivity by enhancing the biomass productivity. This has been supported by a comparison between stepwise lipid-inducing methods, N limitation, and heat shock to provide more lipid accumulation. As a result, heat shock was successfully adopted in a lipid induction process in heterotrophic fed-batch of Chlorella sp. HS2 for the first time. Thus far, there have been fewer endeavors involving heterotrophic microalgae cultivation, because phototrophic cultivation is the mainstream method of algal cultivation. However, recent circumstances in the algal industry necessitate a more stable and feasible process to produce target products. In this situation, actual improvement of growth performance and application of heat shock, which is an easy and rapid inductor, will bring novel applications involving fermentation of microalgae through industrial-scale production.In order to increase the total lipid productivity for biodiesel production from microalgae, serial optimization and stepwise lipid induction were applied in heterotrophic fed-batch cultivation. First, efforts were made to improve the nutrient utilization and oxygen transfer in the fermenter, which resulted in increased biomass productivity of http://jmb.or.kr.Supplementary data for this paper are available on-line only at


Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate.Microbial production of propionate from diluted streams of ethanol  is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium.Our work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.The realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer The online version contains supplementary material available at 10.1186/s12934-022-01841-1. Propionibacterium - well-studied due to their involvement in cheese production [2 and H2 (syngas) by acetogenic bacteria. Syngas-to-ethanol fermentation technology has been deployed at large scale, and recent advances are expected to accelerate its development in the years to come [Propionic acid is a naturally-occurring carboxylic acid produced by propionigenic bacteria as end-product of their anaerobic metabolism. It is an important intermediate in anaerobic fermentative processes such as those occurring in the human gut, anaerobic digesters and cheese production. It is also an essential platform chemical in the manufacture of cellulose-derived plastics, cosmetics and pharmaceuticals and, due to its antimicrobial properties, it can be used as food preservative , 2. At poduction . Ethanoloduction , 5. More to come –8.Anaerotignum neopropionicum, formerly Clostridium neopropionicum [Anaerotignum propionicum [Clostridium propionicum [Desulfobulbus propionicus [Pelobacter propionicus [2 with concomitant production of acetate, according to the theoretical Eq. Acetobacterium wieringae was co-cultivated with A. neopropionicum; A. wieringae converted CO to ethanol, which was used by A. neopropionicum to produce propionate.pionicum , was thepionicum . The abipionicum  (formerlpionicum ), the supionicus , 13, andpionicus . In thesPropionibacterium and Cutibacterium, use the methylmalonyl-CoA pathway for growth. The acrylate pathway is mostly found within members of the phylum Firmicutes [D. propionicus and P. propionicus use the methylmalonyl-CoA pathway [A. neopropionicum and A. propionicum use the acrylate pathway [Two main pathways have been described for the fermentative production of propionic acid in bacteria: the methylmalonyl-CoA  and the acrylate pathway , 16. Mos pathway , 14, whe pathway .Clostridium ljungdahlii (iHN637) demonstrated the essential role of flavin-based electron bifurcation in energy conservation during autotrophic growth [Clostridium autoethanogenum (iCLAU786), helping to understand the effects of CO supplementation on CO2/H2-growing cultures [C. autoethanogenum and Clostridium kluyveri; the model provided valuable insight into the microbial interactions between the two microorganisms and predicted strategies for enhanced production of the end products butyrate and hexanoate [To fully exploit the potential of microorganisms for biotechnological applications, it is fundamental to understand their metabolism and cellular processes. Genome-scale metabolic models (GEMs) and their analysis with COnstraint-Based Reconstruction and Analysis (COBRA) methods  have becc growth . FBA enacultures . A multiexanoate .D. propionicus [P. propionicus [A. propionicum [A. neopropionicum [Propionibacterium freudenreichii species using pan-genome guided metabolic analysis [Propionibacterium subsp. shermanii and the pan-Propionibacterium GEMs to guide genetic engineering strategies for increased propionic acid production [P. propionicus and validated fermentative growth of this strain on ethanol [Many propionigenic bacteria have been sequenced to date –31, inclpionicus , P. proppionicus , A. proppionicum  and A. nanalysis . Navone oduction . Sun et. ethanol .A. neopropionicum and the first to model the acrylate pathway in a propionigenic microorganism. The model was reconstructed using automatic tools followed by an extensive manual curation, which led us to the identification of electron-transferring enzymes involved in the acrylate pathway, cofactor regeneration and energy conservation. In addition, a physiological characterisation of A. neopropionicum in batch cultures was performed to validate and complement the reconstruction of the model. FBA was used to assess growth phenotypes on several carbon sources, and dFBA was applied to simulate batch growth of A. neopropionicum on ethanol, and ethanol plus acetate. The combination of in-depth modelling and experimentation has enabled us to examine in detail the metabolism of ethanol fermentation in this bacterium and to address pre-existing ambiguities.Here we describe iANEO_SB607, the first GEM of A. neopropionicum was reconstructed in four main steps. First, the genome sequence of A. neopropionicum DSM 3847T  reaction direction was adjusted using eQuilibrator [-1 at standard conditions for reactants/products, pH 7.3 and ionic strength 0.1 M. In cases where eQuilibrator did not retrieve information for a specific reaction, reaction direction was adjusted based on information from MetaCyc [https://www.ebi.ac.uk/biomodels/curation/fbc) were combined in an OMEX archive file [The genome-scale metabolic network of 71775.1)  was retr71775.1) . An addiodelSEED . For thilibrator . Reactio MetaCyc  and BIGG MetaCyc  database MetaCyc  and Meta MetaCyc . (iv) Th MetaCyc  were rep MetaCyc  and BREN MetaCyc  database MetaCyc  standardive file  depositeive file  and assiA. neopropionicum was adapted from the biomass reactions of Clostridium beijerinckii  and C. iCLAU786 ). The coiCLAU786 , protons2 uptake rates. We repeated this process 1000 times, so that we obtained 1000 different biomass synthesis reactions. The composition of the cell wall components, proteins and phospholipids was stored for each biomass synthesis reaction, together with the growth rate, and acetate and propionate production rates. The obtained growth rate, acetate and propionate production rates were normalised with respect the original values and were plotted against each biomass building block  and cell wall components, considering cell wall components as the sum of teichoic acid, peptidoglycans and carbohydrates composition. The rest of components—DNA, RNA and trace- were kept fixed, as together they only represent 10% of the biomass. The composition of proteins and plipids was randomly selected within ± 10% of their original value. In this way, the total cell wall components composition was calculated following equation  COBRApy  maintainDW-1 h-1 (see Quantitative assessment of iANEO_SB607 through dFBA). Based on this, the lower bound of the substrate uptake rate per time point was constrained to 30 mmol gDW-1 h-1 to assess growth on a single carbon source, and to 30 mmol gDW-1 h-1 in total to assess growth on more than one carbon source, unless specified otherwise. The biomass synthesis reaction was used as the objective function. Growth was considered when the growth rate was higher than 0.0001 h-1. To better explore the solution space, the fluxes compatible with the applied constraints were sampled using the sample function with the ’achr’ method in the flux_analysis submodule of COBRApy [The model was qualitatively validated by assessing growth capabilities and product profile on several carbon sources in steady-state. Model simulations were done using COBRApy, version 0.24.0 , and Pyt COBRApy . The lowA. neopropionicum on ethanol and ethanol plus acetate. Model simulations were done using COBRApy, version 0.24.0 [Si,max and Km,i:Si is the uptake rate of substrate i  for substrate i and Si is the concentration of substrate i (mM). Km,i was determined based on experimental data and model fitting ; -1); Si is the concentration of substrate i (mM); qSi is the uptake rate of substrate i (mmol gDWPj is the production rate of product j (mmol gDWj is the concentration of product j (mM). Equations i and Pj, respectively. Si is used as input to calculate the next state following Eq. The reconstructed GEM iANEO_SB607 was subjected to dFBA to simulate batch growth of n 0.24.0 , IBM ILOmentclass2pt{minimA. neopropionicum DSM 3847T was obtained from the German Collection of Microorganisms and Cell Cultures . Batch fermentations were done in 117 mL serum bottles containing 50 mL medium with the following composition (per litre): 0.9 g NH4Cl, 0.3 g NaCl, 0.8 g KCl, 0.2 g KH2PO4, 0.4 g K2HPO4, 0.2 MgSO4 x 7 H2O, 0.04 CaCl2 x 2 H2O, 3.36 g NaHCO3, 10 mL trace element solution from DSM medium 318, 1 mL vitamin solution, 0.5 g yeast extract, 0.3 g Na2S x x H2O (x=9-11) as reducing agent and 0.5 mg resazurin as redox indicator. The vitamin solution contained (per liter): 0.5 g pyridoxine, 0.2 g thiamine, 0.2 g nicotinic acid, 0.1 g p-aminobenzoate, 0.1 g riboflavin, 0.1 g pantothenic acid, 0.1 g cobalamin, 0.05 g folic acid, 0.05 g thioctic acid and 0.02 g biotin. The headspace of the bottles was filled with a gas mixture of N2/CO2 . To test growth in the presence of H2, the headspace of bottles was filled instead with a gas mixture of H2/CO2/N2 . Growth was assessed on the following substrates: ethanol, lactate, glucose and xylose, at an initial concentration of 25 mM. Where indicated, acetate (10 or 25 mM) was added to ethanol-fed cultures. The pH of the medium was 7.1 - 7.2. Cultures were incubated at 30oC statically.600). Biomass concentration (mgCDW L-1) was estimated from OD600 measurements using the correlation: mgCDW L-1 = (OD600 - 0.016)/0.0032, which was experimentally determined from A. neopropionicum cultures grown on ethanol. Concentrations of soluble compounds in the supernatant of liquid samples were determined using high-pressure liquid chromatography (HPLC) . The HPLC was equipped with a Shodex SH1821 column operated at 65oC. A solution of 0.1 N H2SO4 was used as mobile phase, at a flowrate of 1 mL/min. Detection was done via a refractive index detector. Concentrations below 0.2 mM could not be accurately quantified and are considered traces. Concentrations of gases in headspace samples were determined via gas chromatography (GC) . To analyse H2, a Molsieve 5A column operated at 140oC coupled to a Carboxen 1010 column was used. CO2 was analysed in a RT-Q-BOND column at 60oC.Liquid and headspace samples were taken periodically over the course of batch fermentations and analysed for biomass, substrate and product concentrations. Biomass growth was measured by optical density at 600 nm . The drA. neopropionicum. The remaining 20 % of reactions are not associated with genes. Half of these reactions are mostly exchange reactions and diffusion transport reactions. The other half are spontaneous reactions or gap-filled reactions describing, in a summarised manner, the biosynthesis of biomass building blocks .Two compartments are recognised in the model: the intracellular compartment (id: ’c0’) and the extracellular compartment (id: ’e0’). Metabolites are assigned to either one of the compartments. Reactions are classified as metabolic reactions, transport reactions and exchange reactions. Metabolic reactions describe the biochemical conversion of metabolites within the intracellular compartment. Transport reactions describe the transport of metabolites across the intracellular and extracellular compartments. Exchange reactions simulate the excretion of metabolites outside the cell or the uptake of metabolites into the cell. Reactions are distributed within cell subsystems Fig. , except DW, as in the GEM of C. acetobutylicum [DW-1 h-1, an estimation based on the models of C. beijerinckii [C. autoethanogenum [The constructed biomass synthesis reaction (BIOMASS_Aneopro_w_GAM) accounts for the production of DNA, RNA, proteins, peptidoglycans, phospholipids, teichoic acids and trace, and it is normalised to 1 gram per mmol. It also includes the growth-associated ATP maintenance (GAM) as an hydrolysis reaction, and the non-growth associated ATP maintenance (NGAM) as a reaction of ATP phosphohydrolase (rxn00062_c0). GAM was assumed to be 40 mmol ATP/gutylicum . The lowerinckii  and C. aanogenum .A. neopropionicum was developed based on these two other species, we performed a sensitivity analysis to test its robustness. The analysis showed the effect of modifying the proportion of the main biomass components from the biomass synthesis reaction on model predictions . In all scenarios tested, growth and production rates remained virtually unaffected . The growth rate was slightly more affected when GAM was changed. The largest deviation was ± 0.00175 h-1, which corresponds to 10.8 % difference compared to the original growth rate. The biomass synthesis reaction was therefore considered a reliable representation of the biomass composition of A. neopropionicum.Since the biomass synthesis reaction of The quality of the iANEO_SB607 model was evaluated using the SBML validator  and the A. neopropionicum on several carbon sources and contrasting the results with experimental data. Model predictions matched most of the growth phenotypes observed in cultivation experiments from this and previous studies  as well as TCA-derived amino acids (lysine and proline), with exception of threonine, are not utilised.The model predicts growth of  glucose . Accordi-lactate . In our Further model validation was performed by assessing the product profile on a number of substrates from which sufficient experimental data was available, specifically: ethanol, lactate, glucose, xylose, L-threonine, L-serine, L-alanine, ethanol plus acetate, ethanol plus L-serine and ethanol plus L-alanine. For all the substrates tested, the model predicted mixed secretion of propionate and acetate, in accordance with experimental evidence , lactate, glucose, xylose). In addition, H2 was not utilised nor affected the growth or the product profile of A. neopropionicum cultures growing on ethanol ; yet, given the collected evidence, this reaction was blocked in the model.HA. neopropionicum was evaluated quantitatively by simulating the dynamics of batch fermentation using dFBA. Three conditions were considered, with regard to the substrates present: 25 mM ethanol, 25 mM ethanol plus 10 mM acetate, and 25 mM ethanol plus 25 mM acetate. To constrain the model, we used empirical data of ethanol consumption, product formation and cell growth from cultivation experiments. The fermentation profiles obtained by dFBA were contrasted with the experimental data of batch incubations. Across cultivations, carbon balance was 85 - 96 %, not completely closed likely due to the difficulty to accurately quantify CO2 and to slight evaporation of ethanol in the bottles, as reported by others [The iANEO_SB607 model of y others .2) as substrate, the time-course data obtained through dFBA accurately reproduced the fermentation profile, with only small deviations  at an empirical maximum consumption rate  of 36.2 ± 5.5 mmol ethanol gDW-1 h-1. Modeled ethanol consumption fitted the experimental data with a small margin of error. Propionate and acetate were produced simultaneously during the exponential phase, at empirical maximum production rates  of 12.0 ± 0.1 mmol propionate gDW-1 h-1 and 8.6 ± 0.5 mmol acetate gDW-1 h-1, respectively. The production profile of propionate was well predicted by dFBA, estimating a final propionate concentration (10.9 mM) close to the experimental value (9.5 mM). However, dFBA predicted a final concentration of acetate (11.5 mM) moderately higher than experimentally observed (8.6 mM). The empirical maximum specific growth rate of A. neopropionicum (max) was 0.082 ± 0.006 h-1 (duplication time = 8.4 h), which was used to constrain the model. In incubations, the biomass concentration peaked (44.7 ± 1.3 mgDW L-1) at DW L-1) matched the empirical value. Propanol (1.3 mM) and butyrate (1 mM) were detected as minor products in batch incubations; the evolution of both products was predicted correctly by the dFBA simulations. Traces of isobutyrate and isovalerate were also detected and predicted by dFBA (not shown).For the condition with only ethanol  and acetate (10 and 25 mM) as susbtrates, and dFBA was used to simulate the dynamics of these fermentations. dFBA reproduced with high accuracy the fermentation profile of incubations containing ethanol plus 10 mM acetate ; 19 % higher than in the incubations without acetate. However, less biomass was formed in comparison; the maximum biomass concentration was 41.1 ± 0.8 mgDW L-1 . Here, again, the simulation predicted a similar propionate concentration to the observed value (12.2 mM), and a higher final acetate concentration (18.3 mM) than observed (16.7 mM). The incubations containing 25 mM acetate at the start followed a different trend than the incubations with 10 mM acetate (fermentation profile not shown). In batch bottles, the biomass concentration reached a similar value to that obtained in the condition with 10 mM acetate, but the max, qP,max and qA,max were similar to the condition without acetate (data not shown). The final propionate concentration was 12.5 mM, the highest of the three conditions tested.To further evaluate the ability of propanol  or ethanpropanol  as electate Fig. . In thisA. neopropionicum, which is reflected in the fermentation yields. The biomass yield (YX/S) was slightly lower in the presence of both 10 and 25 mM acetate (1.4 gDW mol ethanol-1 vs. 1.6 gDW mol ethanol-1 when no acetate was present). With acetate present at the start of incubations, more ethanol was invested in propionate production, as indicated by the propionate yields , which were 0.33, 0.38 and 0.42 for the conditions with no acetate, 10 mM acetate and 25 mM acetate, respectively. The production of acetate followed the inverse trend; acetate yields  were 0.29, 0.18 and 0.06 for the conditions with no acetate, 10 mM acetate and 25 mM acetate, respectively. Similarly, lower yields were obtained for propanol and butyrate when acetate was present (data now shown).The presence of acetate had an effect on the utilisation of ethanol by A. neopropionicum  that can catalyse this two-step conversion. According to our model, two other alcohol dehydrogenases, encoded by adh (CLNEO_16910) and adhB (CLNEO_00480), could also drive the oxidation of ethanol to acetaldehyde. Initially, the model also predicted this reaction to be catalysed by NAD(P)H-dependent butanol dehydrogenase (BdhA), encoded by bdhA . However, the well-characterised BdhA of C. acetobutylicum, which shares 60.7 % identity with that of A. neopropionicum, is known to contribute primarily to butanol production and it is the alcohol dehydrogenase least involved in ethanol metabolism [A. neopropionicum and excluded this reaction from model simulations.Ethanol is oxidised to acetyl-CoA via acetaldehyde through alcohol and acetaldehyde dehydrogenases. The genome of tabolism . Thus, w2-fixating reaction catalysed by pyruvate:ferredoxin oxidoreductase . This conversion requires reduced ferredoxin (Fd2-) as electron carrier. Our hypothesis, supported by model predictions, is that Fd2- is produced in the Na+-translocating ferredoxin:NAD+ oxidoreductase (Rnf) complex. The Rnf complex is a membrane-bound respiratory enzyme involved in energy conservation in anaerobic microorganisms [+ with electrons from Fd2- coupled to the translocation of two cations (H+ or Na+) across the membrane. The electrochemical potential established by the Rnf complex can then be used by a membrane-bound ATP synthase for energy generation. The Rnf complex can also operate in the reverse direction to produce Fd2- at the expense of ATP [A. neopropionicum harbours a complete rnf cluster, composed of the genes rnfA (CLNEO_01390), rnfB (CLNEO_01400), rnfC (CLNEO_01350), rnfD (CLNEO_01360), rnfE (CLNEO_01380) and rnfG (CLNEO_01370). With ethanol as substrate, our assumption is that the Rnf complex of A. neopropionicum operates in reverse, generating Fd2-. The endergonic reduction of ferredoxin (Eo’= - 500 to - 420 mV) with NADH (Eo’= - 320 mV) is driven by reverse electron transport across the membrane which, in turn, is an energy-driven process. A membrane-bound V-type ATPase is present in the genome of A. neopropionicum, encoded by the genes atpA/ntpA (CLNEO_280), atpB/ntpB (CLNEO_290), ntpC, (CLNEO_260), atpD/ntpD (CLNEO_23400), atpE (CLNEO_250), ntpG (CLNEO_270), ntpK (CLNEO_240) and ntpI (CLNEO_23330). We theorise that ATP is hydrolysed in the ATPase to create a proton- or sodium-motive-force that is used by the Rnf complex to catalyse the reduction of ferredoxin. The production of Fd2- is an energy costly process, the implications of which are addressed later in this section.Acetyl-CoA is partly used in the reductive reactions of the metabolism and partly invested in the formation of acetate, an energy-generating step. Acetate is synthesised via phosphate acetyltransferase  and acetate kinase , yielding ATP via substrate-level phosphorylation (SLP). In the reductive path, acetyl-CoA is converted to pyruvate through the COrganisms . During e of ATP . The gen+/NADH [A. neopropionicum did not include Pct. However, an acetate CoA-transferase was present, encoded by the gene ydiF (CLNEO_17700), that shared 96 % identity with the purified and well-characterised Pct of A. propionicum [ydiF encodes for Pct in A. neopropionicum and included this reaction in the model. Lactoyl-CoA dehydratase (CLNEO_17710 and CLNEO_17720) catalyses the dehydration of lactoyl-CoA to acryloyl-CoA, which is subsequently reduced to propionyl-CoA by acryloyl-CoA reductase. Our genome annotation revealed that the acryloyl reductase of A. neopropionicum forms an enzymatic complex with an electron-transferring flavoprotein (EtfAB). The complex, hereafter named acryloyl-CoA reductase-EtfAB (Acr-EtfAB), is also present and has been well characterised in A. propionicum [acrC) or EtfAB  were found in the genome: (i) CLNEO_21740 (acrC), CLNEO_21750 (acrB_1) and CLNEO_21760 (acrA); (ii) CLNEO_26130 (acdA_1) and CLNEO_26120 (acrB_2); and (iii) CLNEO_29850 (acdA_2) and CLNEO_29840 (acrB_3). The acdA_1 and acdA_2 genes encode for acyl-CoA dehydrogenases that share low identity  with the acryloyl-CoA reductase encoded by acrC; thus, we assumed that the former two enzymes are not responsible for acryloyl-CoA reductase activity. The first cluster is the only complete one, composed of acryloyl-CoA reductase (acrC) and the A (acrA) and B (acrB_1) subunits of EtfAB. The proteins encoded by these three genes share an identity of 92.9 %, 89.7 % and 89.1 %, respectively, with their homologues from the Acr-EtfAB complex of A. propionicum. The Acr-EtfAB of A. propionicum is a non-bifurcating soluble enzyme that catalyses the irreversible reduction of acryloyl-CoA to propionyl-CoA with NADH via electron transfer to a flavin moiety and appears not to be involved in energy conservation [A. neopropionicum. To our knowledge, this is the first time that the Acr-EtfAB complex is identified in this microorganism.Pyruvate produced by the PFOR is subsequently reduced to lactate with NADH via D-lactate dehydrogenase (CLNEO_28010). We assumed NADPH is not used as electron carrier in this reaction, since lactate dehydrogenases have a strict specificity for NAD+/NADH , 66. Lac+/NADH , 68. Ourpionicum . Three gervation , 70. GivA. neopropionicum; rather, ethanol fermentation resulted in a A. neopropionicum due to energetic constraints of the cell, specifically, due to the requirement of Fd2-. Model simulations were performed to confirm this. The oxidation of three moles of ethanol generates six moles of NADH and three moles of acetyl-CoA. To fit the theoretical 2:1 propionate to acetate ratio, two moles of acetyl-CoA would have to be used in the reductive part of the metabolism, and one mole of acetyl-CoA should be invested in acetate, with the concomitant production of one mole of ATP via SLP. The synthesis of two moles of pyruvate from acetyl-CoA would require two moles of Fd2-, which is produced at the Rnf complex at the expense of ATP. However, the hydrolysis of one mole of ATP  depends on how much Fd2- can be produced per hydrolysed ATP, which in turn depends not only on the Gibbs free energies of ATP hydrolysis and ferredoxin reduction with NADH under physiological conditions but also on the coupling ratio of the ATPase (number of cations translocated per ATP hydrolised). While the Rnf complex can be assumed to translocate two cations per ferredoxin reduced/oxidised, the coupling ratio of the ATPase remains unknown for A. neopropionicum. Our model fitted with a coupling ratio of the ATPase of 3 to 3.5 H+ or Na+ translocated per ATP.According to the theoretical stoichiometry, the fermentation of ethanol yields propionate and acetate in a 2:1 ratio  could dJ mol-1; ). MoreovA. neopropionicum produces propanol and butyrate as minor products of the fermentation of several substrates  and adhB (CLNEO_00480).tes Fig. . Propanotes Fig. . ReductiA. neopropionicum takes place via the acetyl-CoA pathway  by NADH to the endergonic reduction of Fd by NADH [A. neopropionicum, reduction of crotonyl-CoA could be catalysed by the Acr-EtfAB complex or by either of the two acyl-CoA dehydrogenases that cluster with subunits of the EtfAB complex (acdA_1-acrB_2 and acdA_2-acrB_3). Among the three, the acyl-CoA dehydrogenase encoded by acdA_2 showed the highest identity with the butyryl-CoA dehydrogenases (Bcd) of C. acetobutylicum and of C. kluyveri . It remains a question whether, in A. neopropionicum, the latter two complexes could be involved in the reduction of ferredoxin.A key enzyme of this pathway is the butyryl-CoA dehydrogenase/electron-transferring flavoprotein complex (Bcd-EtfAB). Bcd-EtfAB is an electron-bifurcating enzyme that couples the reduction of crotonyl-CoA to butyryl-CoA  and acetate kinase (Ack). The Ack of A. neopropionicum is significantly similar to the well-characterised Buk (71 % identity) of C. acetobutylicum. We have considered this similarity to arise from the fact that the two enzymes belong to the same family, yet it has been established that they do not have the same function, since differences in the substrate binding site ultimately determine substrate specificity [A. neopropionicum. Instead, we hypothesise that butyrate production in A. neopropionicum takes place via butyryl-CoA:acetate CoA-transferase activity. Our model predicts that the propionate-CoA:lactoyl-CoA transferase (Pct) encoded by the gene ydiF catalyses this reaction. The Pct of A. propionicum exhibits broad substrate specificity for monocarboxylic acids, including butyrate, supporting the model prediction [Two distinct routes have been described for the last step of the pathway, the conversion of butyryl-CoA to butyrate. The first route, identified in utylicum , involveutylicum . The gencificity –77. Thusediction .+ oxidoreductase (Nfn). Nfn is an iron-sulfur flavoprotein complex with electron-confurcating/bifurcating activity that reversibly catalyses the endergonic reduction of NADP+ by NADH coupled with the exergonic reduction of NADP+ by Fd2- [A. neopropionicum under the locus tags CLNEO_00270 and CLNEO_00280, respectively. In the initial automatic annotation, these two genes were assigned to ferredoxin:NADP+ oxidoreductase and glutamate synthase, respectively. It has been reported that NfnA/B share sequence similarities with these two enzymes [C. kluyveri [C. autoethanogenum [During the genome re-annotation and manual curation process, we identified the enzyme NADH-dependent reduced ferredoxin:NADP by Fd2- . Nfn is  enzymes . Upon makluyveri  and of CA. neopropionicum during growth on ethanol. The model shows that the Nfn generates NADPH from NADH and Fd2- for NADPH-dependent reactions of the cell. For instance, NADPH is required during butyrate production in the reduction of acetoacetyl-CoA to 3-hydroxybutyryl-CoA, a reaction catalysed by a NADPH-dependent 3-oxoacyl reductase. NADPH is also required in the biosynthesis of amino acids and biomass precursors. In our model, the Nfn complex does not function in the reverse direction, the production of Fd2-, during growth on ethanol; this would require NADPH, and ethanol oxidation is assumed to occur only via NAD+-dependent reactions.We used modelling to look into the role of the Nfn complex in the metabolism of A. neopropionicum can grow on lactate, sugars and some pyruvate-derived amino acids  was expected, since there were almost no available annotations of the genome of A. neopropionicum in public databases recognised by Memote. A limitation of the GEM is the lack of an organism-specific biomass composition and GAM/NGAM measurements. Our sensitivity analysis showed a maximum deviation of 10.8 % of the growth rate when varying the composition of biomass components or the GAM. NGAM has a more limited impact on growth rate predictions, given that it does not directly relate to the biomass synthesis reaction, still dedicated measurements of these parameters could furthe improve the predictive power of the model. Here, our focus has been on gaining insight into the metabolism of ethanol fermentation to propionate, which in this bacterium occurs via the acrylate pathway.In this study, we have presented iANEO_SB607, the first GEM of the propionate-producer A. neopropionicum. During growth on ethanol, Fd2- is required to reduce acetyl-CoA to pyruvate. In the earliest description of the metabolism of A. neopropionicum, authors suggested that the oxidation of acetaldehyde proceeded with ferredoxin as electron carrier, thus fulfilling this demand [o’= - 500 to - 420 mV) with NADH (Eo’= - 320 mV) via electron bifurcation, given the high reduction potential of the acryloyl-CoA/propionyl-CoA pair (Eo’= + 70 mV). Yet, this complex appears not to be involved in the reduction of ferredoxin [2- is produced in the Rnf complex, as previously reported for other anaerobes during growth on low-energy substrates [A. propionicum [rnfA-E, rnfG) and via modelling we verified its involvement in the metabolism of the cell.We have also addressed an important issue regarding the energetic metabolism of s demand . Howevers demand , which irredoxin , most lirredoxin , 83. Insbstrates . The Rnfpionicum . Here, tA. neopropionicum revealed the presence of another key enzyme of the metabolism of anaerobes: the Nfn complex. Our model showed that the Nfn generates NADPH for NADPH-dependent reactions of the metabolism, which is essential during growth. Further investigation is needed to define the instances in which the Nfn operates in the reverse direction, bifurcating electrons from NADPH to produce Fd2- and NADH. The directionality and role of Nfn will depend on the cofactor requirements of the cell.Our annotation of the genome of A. neopropionicum as minor products during the fermentation of several substrates . This is an advantageous trait when considering this strain for its application in syngas-fermenting co-cultures, since syngas contains H2. Interestingly, H2 tolerance is manifested differently in functionally-related strains. While P. propionicus and D. propionicus both use the methylmalonyl pathway to metabolise ethanol, the first is not affected by the presence of H2 while the second is strongly inhibited by it [Another aspect of the metabolism that we aimed to clarify was the ability of reported . Our resed by it .A. neopropionicum can utilize these sugars . For glucose and xylose, model predictions agree with our batch incubations that observed . Yet, thwer rate .A. neopropionicum in batch cultivation  fermentation by ion Fig. . With etion Fig.  was not A. neopropionicum during growth on ethanol. However, despite higher rates, final biomass concentrations in batch cultivations were slightly lower in the presence of acetate (10 or 25 mM). Our model showed increase flux through acetate:CoA ligase  in the presence of acetate (10 mM). This reaction assimilates acetate consuming ATP, which would explain the lower biomass concentrations observed. Model predictions showed that, in this scenario, more acetyl-CoA is converted to pyruvate through PFOR, which is another energy-consuming step. We also observed a higher flux through butanoyl-CoA:acetate CoA-transferase . Batch cultivation experiments did not show a noticeable increase in butyrate concentration when acetate was present, rather lower. Therefore, we hypothesise that, in vivo, most acetate consumed is assimilated via acetate:CoA ligase, as our model predicts, or via reverse direction of PTAr and ACKr, instead of Pct. This deviation to the model is likely due to the fact that biomass synthesis was set as maximization objective in dFBA which would be achieved by a higher flux of acetate towards butyrate instead of assimilating it, saving ATP.Interestingly, we observed that a low acetate concentration (< 25 mM) or low acetate:ethanol ratio (< 1) at the start boosted the growth rate and propionate production rate of A. neopropionicum. As a result, this study means a step forward to further exploit this species as a cell factory for propionate production in mono-culture or in co-cultivation from sustainable feedstocks, e.g., syngas, as recently stated by Moreira et al. [A. neopropionicum can act as an intermediate species to extend the range of products from propionate to longer odd-chain carboxylic acids.Overall, this work shows the advantages of using a model-driven approach to gain insight into the metabolism of microorganisms. The new findings fill in knowledge gaps and unravel key metabolic features of a et al. . AdditioA. neopropionicum. Combining experimental data with a manual curation of the annotated genome and a comprehensive network reconstruction, we have gained insight into the central carbon and energetic metabolism of this microorganism. The model predicted the metabolic capabilities of A. neopropionicum with high accuracy, which allowed us to investigate with detail the enzymatic routes involved in the fermentation of ethanol to propionate. Our analysis showed that A. neopropionicum produces propionate via propionate-CoA:lactoyl-CoA transferase, the characteristic enzyme of the acrylate pathway. Our in silico analysis revealed, for the first time in this microorganism, the presence of the electron-bifurcating Nfn complex. This model provides the basis to explore the capabilities of A. neopropionicum as microbial platform for the production of propionate from dilute ethanol as substrate. While beyond the scope of this study, the construction of this model signifies a step closer towards the development of multi-species models that describe syngas-fermenting co-cultures comprised of acetogens with ethanol-consuming propionigenic bacteria. Follow-up studies that integrate, e.g., omics analyses with data from steady-state fermentations should help improve this GEM.In this study, we have constructed iANEO_SB607, the first GEM of Additional file 1: Figure S1. Sensitivity analysis of the biomass reaction. Effect of varying the composition of main biomass building blocks on the growth rate and product formation. Growth rate and product formation are represented as the difference between the values obtained when the new biomass reaction is defined as objective function and the values obtained when the original biomass synthesis reaction is defined as objective function. Figure S2. Effect of varying the growth-associated maintenance (GAM) of the biomass composition on the growth rate. The effect is represented as the difference between the values obtained when the new biomass reaction is defined as objective function and the values obtained when the original biomass synthesis reaction is defined as objective function. Table S1. Parameters used to simulate batch fermentations through dFBA. Column ‘Source’ indicates whether the parameter was constrained based on the experimental value (considering standard deviation error) or by model fitting. Figure S3. Effect of H2 on ethanol-growing cultures of Anaerotignum neopropionicum. A) Cell growth profiles, determined by optical density at 600 nm (OD600). B) End products and ethanol consumed at the end of batch fermentations. Error bars indicate the standard deviation of biological triplicates. Table S2. Fermentation balance of batch cultures of A. neopropionicum cultivated on different substrates. Note that CO2, present in the headspace of bottles, is consumed but not included in this table. iBut: isobutyrate; iVal: isovalerate. Traces are concentrations < 0.2 mM. The hyphen symbol indicates undetected products. ND indicates not determined.Additional file 2. Genes and homologos of the acrylate and butyrate pathways found in other Clostridium species.


E. coli, and so on to produce high-yielding pyruvate strains has become a hot spot. The strategy and the understanding of the central metabolism directly related to pyruvate production could provide valuable information for improvements in fermentation products. One of the goals of this review was to collect information regarding metabolically engineered strains and the microbial fermentation processes used to produce pyruvate in high yield and productivity.Pyruvic acid has numerous applications in the food, chemical, and pharmaceutical industries. The high costs of chemical synthesis have prevented the extensive use of pyruvate for many applications. Metabolic engineering and traditional strategies for mutation and selection have been applied to microorganisms to enhance their ability to produce pyruvate. In the past decades, different microbial strains were generated to enhance their pyruvate production capability. In addition to the development of genetic engineering and metabolic engineering in recent years, the metabolic transformation of wild-type yeast,  Pyruvic acid or Pyruvate (PA) is the final product of the glycolysis pathway and is an important intermediate molecule in protein, fat and carbohydrate metabolism . PA is aE. coli and yeast. In E. coli, lactate dehydrogenase (ldhA), which is responsible for converting pyruvate into lactic acid, is inactivated, and the highest pyruvate production is 110.0 g/L with a mass yield of 0.87 g/g. In yeast, Pyruvate decarboxylase-negative saccharomyces cerevisiae, through directed evolution, obtained a high-yield pyruvate strain that was C2-independent and glucose-tolerant, with a maximum yield of 135 g/L [Torulopsis glabrataRS23 was screened out through continuous culture with pH control. The yield of pyruvate reached 94.3 g/L, and the mass yield was 0.635 g/g. Most of the above-mentioned strains with high pyruvate production use glucose as a substrate, and achieve high production of pyruvate by reducing the consumption of pyruvate. However, pyruvate production mainly uses a chemical synthesis method, which increases its cost and greatly limits its application. In past decades, different microbial strains were generated to enhance their pyruvate production capability. This review summarizes the methods and tools for the production of pyruvate, by the engineering of different microorganisms, conducted in recent years.At the industrial level, pyruvate is mainly used for the production of L-tyrosine, L-tryptophan, N-acetylneuraminic acid , 3-Dihydroxyphenyl (DOPA), levodopa drug class, etc. ,10. Curr2, during the catalytic oxidation of lactic acid, the research is mainly carried out in the comparison and selection of catalysts. With the Pb/Pd/C system as the catalyst [At present, the synthesis methods of pyruvic acid are mainly chemical methods, such as the tartaric acid method , the laccatalyst , the yieCompared to the chemical approach for pyruvate production, biotechnological methods, including enzymatic, resting cell and fermentative processes, offer a promising alternative for cost-efficient process development. The basic process is shown in Acetobacter sp. has been used to produce about 20 g/L pyruvate by oxidizing D-lactate at a high conversion rate. However, this process is difficult to commercialize, as D-lactate costs more than L-lactate. Another process for pyruvate production involves oxidation of L-lactate to pyruvate by using glycolate oxidase (obtained from Hansenula polymorpha. EC:1.1.3.15) [This process involves a single step carried out by either purified or raw enzymes . .1.3.15) .Acinetobacter sp. and Debaryomyces cerevisiae have been used to produce pyruvate by the resting cell method. This method consumes less time than the fermentation one; however, it involves different steps like cell cultivation, separation from the medium and washing. However, few resting cell methods are performed without separating cells from the medium. By controlling pH or nitrogen source, cell growth can be inhibited. The major issues associated with the extended resting cell processes, includes strain stability and contamination.Resting cell processes involve a series of enzymatic steps in non-growing microbial cells provided with a substrate, such as glucose. Y. lipolytica and E. coli, have been developed to accumulate pyruvate from different carbon sources. For example, as an oil producing yeast, Y. lipolytica has a relatively complete metabolic network in its body, so it can use gene editing technology to flow more metabolic flux into the production of pyruvate. These strains are discussed in detail in the section below.Direct fermentation processes have also been used to produce pyruvate. Many recombinant microorganisms, including yeast such as E. coli on glucose, pyruvate is generated mainly from Phosphoenolpyruvate (PEP) by pyruvate kinase (EC:2.7.1.40) and by the phosphotransferase system (PTS) of glucose uptake [Pyruvate is a key metabolite synthesized via glycolysis, and it occupies an important position in the central pathways . The bioe uptake . AerobicThe phosphotransferase system (PTS) is the major pathway in which a large number of sugars are phosphorylated and transported into the cell . This sy2+ [The PTS is composed of two soluble proteins; enzyme I and histidine phosphate carrier protein (HPr), and sugar specific protein(s), enzyme II . Enzyme 2+ . HPr  and PYKA (encoded by pykA) [After glucose is converted into glucose-6-phosphate and transported into the cell, it enters glycolysis, the stepwise degradation of glucose into pyruvate. There are 9 reactions and 9 enzymes involved in glycolysis, starting from glucose-6-phosphate. The final enzyme involved in glycolysis is pyruvate kinase (PYK). Pyruvate kinase catalyzes the conversion of PEP and ADP to pyruvate and ATP . E. coliby pykA) . The twoby pykA) . PYKF coby pykA) . PYKF isby pykA) .M. robertsii through pyruvate kinase upregulation. Similarly, downregulation of genes, like MAA_02871 and MAA_08787, enhanced pyruvate production [MAA_02871 encodes a pyruvate transporter, while MAA_08787 encodes the β-subunit of the pyruvate dehydrogenase E1 component. a new E. coli mutant. Mutations in phosphoenolpyruvate carboxylase (PPC), pyruvate dehydrogenase (PYD), and alcohol dehydrogenase (ADH) enhance pyruvate production [It was found that pyruvate production increased as a result of heat treatment in oduction . MAA_028oduction . Furtheroduction . The aux2 and binds to thiamine pyrophosphate (TPP), forming hydroxyethyl TPP (HETPP). The pyruvate dehydrogenase (E1) is a hetero tetramer (α2β2) with a total molecular weight of 154 kDa, and is encoded by the gene aceE. The second step is catalyzed by dihydrolipoyl transacetylase (E2), which involves acetyl group transfer to CoA. E2 is encoded by the aceF gene. Finally, the reduced dihydrolipoamide is oxidized again by NAD+ to active lipoic acid and NADH. This step is catalyzed by dihydrolipoyl dehydrogenase (E3) encoded by lpd. Several coenzymes are also involved in the PDH reaction, including NAD+, coenzyme A, thiamine pyrophosphate, lipoic acid and FAD.The conversion of pyruvate to acetyl-CoA is the connecting link between glycolysis and the TCA cycle. This reaction is catalyzed by pyruvate dehydrogenase complex, a sixty submit complex involving three enzymes: pyruvate dehydrogenase, dihydrolipoyl transacetylase and dihydrolipoyl dehydrogenase. These enzymes are associated noncovalently. The first step of the reaction is performed by pyruvate dehydrogenase (E1): pyruvate releases COThe complex is highly regulated by acetyl-CoA and NADH. High levels of either product allosterically inhibit the activity of PDH. PDH is also sensitive to the energy status of the cell. PDH is activated by AMP and inhibited by GTP.pflB catalyzes the nonoxidative conversion from pyruvate and coenzyme A (CoA) to acetyl-CoA and formate under anaerobic conditions [PFL encoded by nditions :PyruvateE. coli and other prokaryotes. PFL displays half-of-sites activity so that only one of the subunits is active at a time [This reaction plays an important role in anaerobic carbon metabolism in t a time . PFL is t a time . The expt a time .2, as shown below:POXB is a flavoprotein that catalyzes the conversion of pyruvate to acetate and COPOXB was reported to perform a significant role during aerobic growth in glucose minimal medium . GeneralpoxB gene. Each subunit contains a tightly, but non-covalently, bound FAD and loosely bound TPP through Mg2+ [This enzyme is a homo-tetramer and is encoded by the ugh Mg2+ . Phosphough Mg2+ .E. coli which convert pyruvate to lactate [ldhA) is a soluble enzyme which converts pyruvate to D-lactic acid [There are three lactate dehydrogenases in  lactate . Two of  lactate . The fertic acid , coupledE. coli is induced under anaerobic conditions at acidic pH and is allosterically activated by pyruvate [The fermentative LDH of pyruvate .PPS) is responsible for converting pyruvate to PEP [Phosphoenolpyruvate synthase . This enzyme is important to maintain adequate levels of acetyl-CoA, especially when organisms grow on acetate, or become unable to generate acetyl-CoA directly from pyruvate [Acetyl-CoA Synthetase (ACS) and Acetyl-CoA (acetyl-CoA) are important metabolites in a variety of important physiological process that link anabolism with catabolism during growth on glucose. Another pathway which converts acetate to acetyl-CoA is catalyzed by acetyl-CoA synthetase  Hrylation . This enrylation .+-ATP synthase can hydrolyze ATP in vitro independently of the F0 subunit. The E. coli pAGD gene encoding α, β and γ chains of the F1 subunit of (F1F0) H+-ATP synthase was reported to exert the strongest ATPase activity [E. coli strain BOE270 was 25% lower than at the highest ATPase (EC:3.6.1.8) expression, and the concentration of ADP increased by 65%. The flux of the glycolytic pathway increased gradually with an increase of the ATPase expression and reached 170% of the wild-type flux at the highest expression level.The soluble F1 subunit of (F1F0) Hactivity . The conE. coli TC36  was previously constructed for acetate production. To reduce growth during oxidative metabolism, mutations in (F1F0) H+-ATP synthase were introduced, which disrupted oxidative phosphorylation. Deletion of subunits b and c of F0 (encoded by atpFH) resulted in the separation of the F1 sector from the membrane. Therefore, the ATP synthesis function of (F1F0) H+-ATP synthase was disrupted, although the hydrolytic activity of F1-ATPase in the cytoplasm was preserved. The mutations resulted in a small reduction in cell yield and growth rate, compared with the wild-type and parent strains. A two-fold increase in glycolytic flux was observed, compared with W3110 (wild type). This increasing flux was achieved due to increase in the ADP available for glycolysis and a decrease of allosteric inhibition of oxidative phosphorylation by ATP.Pyruvic acid is the key hub of the metabolic process in an organism, participating in many subsequent metabolic reactions and generating acetic acid, ethanol, lactic acid and other by-products. The production of these by-products not only consumes a large amount of pyruvic acid, but is also not conducive to the recovery and purification of the later products. Therefore, it is necessary to use metabolic engineering to reduce or prevent the generation of by-products.pdc1 and pdc5 encoding pyruvate decarboxylase in S. cerevisiae Y2, and optimized the fermentation medium. After 96 h in a shake flask of S. cerevisiae Y2-15, the yield of pyruvic acid increased 16.86 times, and the accumulated pyruvic acid increased to 24.65 g/L [PDC in T. glabra, the accumulation of pyruvate in shake flask fermentation reached 20 g/L. It was 2.5 times higher than the original strain, while the production of the by-product, ethanol, was only 4.6 g/L. After 52 h fermentation in a fermentation tank, the yield of pyruvic acid was 82.2 g/L [ace E, pqo and ldhA genes, acetyl hydroxy acid synthetase with reduced activity was introduced (ΔC-TIlv N). Constructed engineering of bacteria C glutamicum ΔaceE Δpqo ΔldhA ΔC-tilv N produced 190 mmol pyruvic acid under shaking flask conditions with a yield of 1.36 mol pyruvic acid/mol glucose, but still secreted a large amount of L-alanine. The mutant alaT and avtA genes, encoding alanine aminotransferase, were further knocked out. Under the condition of fed-batch high-density fermentation in a fermentation tank, the by-product L-valine reduced by 90%, and the yield of pyruvic acid was 0.97 mol pyruvic acid/mol glucose [Wang et al. knocked out the genes 4.65 g/L . In anot82.2 g/L . By dele glucose .E. coli and yeast are still preferred to produce pyruvate due to the simple nutritional requirements and rapid growth.Although several microorganisms produce large quantities of pyruvate by using glucose and other carbon sources as substrates , E. coliE. coli [E. coli is not a commercially relevant producer of pyruvate, many strategies have been applied to modify wild-type E. coli metabolically to enhance its ability to accumulate the compounds. The strategies aim to enhance the flux flowing into pyruvate , and/or to repress or knock-out pathways which consume this compound . To increase pyruvate production, a number of E. coli strains have been studied (E. coli strain named W1485lip2 (lipA2) was earlier used for pyruvate production under aerobic conditions, using glucose as the substrate in a medium lacking lipoic acid [pdh activity in lipoic acid deficient conditions impaired conversion from pyruvate to acetyl coenzyme A. Under optimal culture conditions, by using 50 g/L glucose as the substrate, a total of 25.5 g/L pyruvate was obtained in 32–40 h at pH 6.0.Use of genetic engineering strategies improved pyruvate accumulation in E. coli . Althoug studied . An E. coic acid . DecreaslipA2 bgl+ atpA401 mutant (TBLA-1) was constructed to produce pyruvate aerobically from glucose [atpA401, into strain W1485lip2. TBLA-1 produced more than 30 g/L of pyruvic acid from 50 g/L glucose in 24 h [TBLA-1 strain enhanced the glycolytic pathway flow and, thereby, pyruvate production. TBLA-1 consumed more oxygen and possessed higher b-type cytochromes content than its parent strain (W1485lip2). The activities of the PTS for glucose uptake were higher in TBLA-1 than in W1485lip2. During exponential growth, a higher level of pyruvate kinase I and phosphoglycerate kinase was reported in TBLA-1 compared to W1485lip2.A  glucose . This F1 in 24 h . A decreaceF mutant (CGSC6162) and an aceFppc mutant (CGSC7916) were studied in complex media by using glucose and acetate as carbon sources. More than 30 g/L pyruvate was obtained with both CGSC6162 and CGSC7916 [CGSC6162 (0.72) and CGSC7916 (0.78). In CGSC6162 and CGSC7916, the volumetric productivities were 1.5 g/(L·h) and 1.2 g/(L·h), respectively. The higher pyruvate production rate occurred early in fermentation. The studies of the effect of pH and temperature on production of pyruvate showed that at 32 °C and pH 7.0, the pyruvate production was higher.An CGSC7916 . High maE. coli strain YYC202 ldhA::Kan (Hfrzbi::Tn10 poxB1 ΔaceEF) rpsL pps-4 pfl-1 ldhA) was used for the production of pyruvate in fed-batch fermentation [2 transfer rate and acetate consumption rate. In a subsequent study, using intergraded electrodialysis, a volumetric productivity of 4.5 g/(L·h) was achieved [An entation . This auachieved . HoweverTC44, was used to produce pyruvate in a mineral salt medium with glucose as the carbon source [pfl, ldh, and poxB encoding alcohol dehydrogenase, acetate kinase, and fumarate reductase, respectively, were knocked out to prevent pyruvate from converting into other fermentation products. In this strain, the glycolytic flux was 50% more than that of the W3110 strain . This increase in flux was due to the increase in ADP availability for glycolysis and the decrease in ATP accumulation by introducing the ATPase mutation (ΔatpFH). The oxidative phosphorylation was disrupted in this strain, while the hydrolytic activity of F1-ATPase was preserved. A principal difference between this strain and other pyruvate production strains was the presence of PDH. The TCA cycle was interrupted by repressing sucAB encoding 2-ketoglutarate dehydrogenase (EC:1.2.4.2) instead of PDH knock out to prevent pyruvate from being completely oxidized to CO2. This approach achieved 67.4 g/L with a yield of 0.75 g/g and a productivity of 1.2 g/(L·h) in a glucose defined medium. A strain, n source . The pflCandida lipolytic AJ 14353 accumulated 43.6 g/L pyruvate at a yield of 0.44 g/g in 72 h and S. cerevisiae accumulated 36.9 g/L pyruvate at a yield of 0.37 g/g in 48 h under conditions of thiamine limitation. Van Mari et al. bred a strain of S. cerevisiae TAM which was independent of C2 compound and glucose tolerant and lacked pyruvate decarboxylase. When the initial glucose concentration of TAM was 100 g/L, the pH was controlled at 5.0. After 60 h of inoculation, the pyruvate content reached 100 g/L, and the final pyruvate concentration reached 135 g/L after 100 h [Yeast is the preferred microbe for pyruvate production, using different types of substrates as carbon sources. For example, er 100 h .Torulopsis glabrata accumulated about 68 g/L in 63 h at a yield of 0.494 g/g. Thiamine, nicotinic acid, biotin and pyridoxine were required in the medium to grow this strain. These vitamins are co-factors of PDH, pyruvate decarboxylase, pyruvate carboxylase, and transaminase, respectively. Accumulation of pyruvate by these strains was caused by the reduced activity of these enzymes under conditions of deficiency of these vitamins. We recently reported that glycerol was a promising substrate for Y. lipolytica to produce pyruvate [Y. lipolytica [These strains are thiamine auxotrophic. Thiamine acts as a cofactor for pyruvate decarboxylase and PDH complex. A multi-vitamin auxotroph of pyruvate . Furtherpolytica .Blastobotrys adeninivorans VKM Y-2677 produced 43.2 g/L PA from glucose with a product yield (YPA) of 0.77 g PA/g glucose. The proportion of PA to byproducts was 18:1 for KGA and 8:1 for citric acid [S. cerevisiae., namely the dihydroxyacetone pathway  [GUT1 and GUT2) [GCY1 and DAK1 in S. cerevisiae enhanced glycerol decomposition in [GUT1 and GUT2 was reported to enable effective glycerol assimilation toward the synthesis of desired products in Y. lipolytica. Earlier, we observed that glycerol was the better carbon source for producing pyruvate in Y. lipolytica, compared to glucose and other carbon sucrose [Kamzolova, S et al. constructed a thiamine deficient yeast by disturbing thiamine-dependent pyruvate dehydrogenase. Finally, during cultivation in a fermenter, the strain ric acid . There a1, DAK2)  and glycnd GUT2) ,56. Overition in . Similar sucrose . GlucoseT. glabrata IF0005 . The production of pyruvate reached 480 mml/L after 135 h of fermentation at the initial glucose concentration of 100 g/L, 60% higher than that of the original strain [Miyata found that some nutrient deficient yeasts of vitamin battalion are beneficial to the accumulation of pyruvate, such as l strain .KGA was excreted from yeast cells as a byproduct of PA over-synthesis. Furthermore, the increased level of KGA in the yeast cells inhibited NAD-dependent isocitrate dehydrogenase in the TCA cycle and enhanced the production and excretion of citric acid, another byproduct of PA over-synthesis. The proportion of PA to by-products approximate was 18:1 for KGA and 8:1 for citric acid .S. cerevisiae under low glucose concentration (aerobic and anaerobic conditions) through BDF 13C labeling NMR and pointed out that the pentose phosphate pathway in cells only provided biosynthesis and did not participate in the energy supply system. In the aerobic system, 75% of mitochondrial pyruvate came from the pyruvate recycling pathway. The TCA cycle was mainly used for biosynthesis [S. cerevisiae and Pichia stipitis through BDF 13C labeling NMR, and quantified the proportion of the metabolic flow direction of S. cerevisiae overflow [The overall metabolic direction of cells can be predicted by analyzing metabolism, but the metabolism of microorganisms in vivo is complex, and the specific metabolism needs to be precisely located. Stable isotope labeling is a feasible method. Maaheimo H et al. analyzed the carbon source metabolism of ynthesis . Fiaux Joverflow . TherefoOACLP gene, encoding the oxaloacetate transporter, preventing pyruvate from entering the mitochondria and being consumed by the TCA cycle in Y. lipolytica. Since high concentrations of pyruvate affect pyruvate synthesis, ATP consumption was constructed by heterologous expression of the Pyc2 encoding pyruvate decarboxylase and PCK1 encoding phosphoenolpyruvate carboxylase from S. cerevisiae in Y. lipolytica. Finally, the pyruvic acid production in the 5-L fermenter reached 96.9 g/L, which was 41.6% higher than the starting strain constant.Recently, we used CRISPR/Cas9 technology to knock out the The fundamental aim of this review was to understand aspects of the regulation of microbial central metabolism and use metabolic engineering tools and fermentation process development to maximize the production of pyruvate. Wild-type microorganisms normally are not able to accumulate a single compound at a high rate and a high concentration if metabolism is not tightly regulated. The development of recombinant DNA technology provides a new way for the directed improvement of product yield and productivity. For example, by deleting pathways leading to by-products, the flow of the substrate can be redirected toward products. Fermentation process development is also essential for the improvement of product formation, and altering pathways can affect the process conditions, such as pH, temperature, oxygenation, and ion strength. By further understanding the inhibition mechanism of high osmotic stress and ion strength on cell growth and pyruvate production, additional genetic modifications might be able to increase tolerance of a strain in high ion strength.Y. lipolytica, have high homologous recombination efficiency. They are relatively easy to perform gene editing operations on so as to facilitate heterologous expression of related genes. In this way, some microorganisms can better combine the natural metabolic pathway of pyruvate. In becoming a natural strain for pyruvic acid production, this is more conducive to improving the production efficiency of pyruvic acid. In addition, with the cross development of genetic engineering, molecular biology, systems biology and other disciplines, rational research on microbial metabolic pathways and gene expression regulation networks has been promoted. Metabolic engineering is also gradually turning towards the development of dynamic regulation, such as cell self-induced dynamic regulation, through metabolite response elements or quorum sensing elements, or artificial control of physical or chemical signals to stimulate promoters and other elements to regulate the expression of downstream genes, providing more reliable theoretical support and technical means for dynamic regulation of microbial fermentation to produce pyruvate.As an intermediate in the synthesis of many drugs and pesticides, pyruvic acid has a very broad market prospect. However, there is a big gap in the domestic and foreign markets for pyruvic acid, especially for high-quality pyruvic acid. The traditional tartaric acid synthesis has its natural disadvantages. In recent years, there has been rapid development in the field of bioengineering, especially the emergence of gene editing technology, represented by CRISPR/Cas9, which has given more options to modifying microorganisms at the genetic level. Some yeasts, such as It is undeniable that, although the production of pyruvic acid by microorganisms is a major trend, there are still many deficiencies in the biological production of pyruvic acid. First, the number of food grade microorganisms that can be used to produce pyruvic acid is far from enough. Secondly, although biotechnology has made some progress in recent years, when using this technology to transform microorganisms, these technologies still cannot achieve the expected effects, which greatly limits the transformation of specific strains. In addition, microbial fermentation is affected by many factors, which affect the final pyruvic acid production. Therefore, it is hoped that more safe microorganisms can be found in the future, and more technologies for microbial transformation can be developed, so that the microbial production of pyruvic acid can be commercialized on a large scale at an early date.


Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution ‘patient-centric’ omics data sets, advanced  Chronic kidney disease (CKD) is an umbrella term for a variety of renal diseases with different etiologies, usually diagnosed on the basis of clinical and/or histopathological features, but commonly associated with longstanding diabetes and hypertension . CKD is Drug discovery in CKD has proved challenging. The global burden of CKD is increasing worldwide with a country-by-country prevalence ranging from 5% to 14%; CKD is ranked the fourth in a list of growing mortalities in 2020 . Moreoveex vivo is unfeasible at present. Therefore, efforts so far have focused on building particular capabilities that allow us to model different aspects of renal pathophysiology and anatomy in disease , have shown that drugs with targets supported by human genetics have an above-average chance of clinical success . In CKD,Beyond the foundation of genomics, other patient-derived large data sets that include transcriptomics, proteomics, and metabolomics, and which contain more temporal and tissue-spatial information, are critical to deciphering the downstream complexity of disease defined by the interplay between genetics and the environment. Various molecular data sets can be generated from clinical trial participants to build an understanding from gene to protein, and the metabolomic signature in patients with CKD .2), in which data, resources, and expertise in molecular target identification are shared across academia and the pharmaceutical industry with the aim of accelerating novel drug development for CKD through a systems biology approach  and controls  with the aim of making comparisons. In such a setting, differential abundance/expression analyses are used to uncover molecular features that are modulated in CKD in vitro and in vivo , which can be ranked by, for example, statistical significance or magnitude of effect, but cannot easily be tested experimentally or have unknown biological meaning. Therefore, a substantial part of data analytics is devoted to the prioritization of candidate features to shortlist the most promising potential drug targets for subsequent validation  in vivo . The priin vitro, kidney enrichment of intra-renal targets can inform pharmacokinetic aspects and safety issues, and the presence of orthologous genes, pathway conservation, and consistent directionality modulation in disease models can support human-to-animal translatability and selection of animal models in vivo. Prioritized candidate targets can then enter the pipeline of preclinical testing for further target validation.For selected candidate targets, pathway analyses and network biology approaches are used to provide additional biological context for dysregulated cellular mechanisms and to infer potential mechanisms of action . Furtherin vitro and tools for preclinical target validation can build confidence in novel CKD targets. Preclinical target validation can include building an understanding of the mechanism of action of a particular target and an understanding of its molecular network. Confidence in a candidate target identified in silico can be gained by demonstrating pathway activity in human cell-based renal disease models in vitro.The appropriate use of translatable models in vitro, both in cell culture systems and in more advanced cellular models. In this way, we can identify the appropriate cell type, model system, and the right renal stressor for the target pathway, in the same way as big data and bioinformatic analyses can identify these elements in patients with CKD. CRISPR screening in renal cells can help to handle the increasing number of targets arising from big data, making it easier to triage multiple targets and select the most promising candidate for further validation.As mentioned earlier, the complexity of kidney disease, which may involve the dysfunction of several different cell types in the glomerular, tubulo-interstitial, and vascular compartments, makes it challenging to identify translatable models in which to define the mechanism of action of potential targets in CKD. To facilitate selection of the best validation system, human omic data sources may be analyzed in combination with transcriptomic, proteomic, and metabolomic data in vitro renal systems, such as the generation of podocytes derived from human induced pluripotent stem cells (iPSCs) that have gene expression signatures resembling adult human podocytes more closely than available podocyte cell lines  creation of validated screening libraries and technologies to alter gene and protein function, and (iii) establishment of ‘end-to-end’ computational pipelines that can facilitate the quantitative analysis of cellular phenotypes resulting from genetic or other perturbations.The discovery of CRISPR technology and its applications to large-scale genetic loss-of-function screens, also known as functional genomics, holds great promise for the identification of novel validated drug targets, including in CKD. Functional genomics screens allow the systematic perturbation of large numbers of genes or proteins, revealing cellular phenotypes that allow for an inference of gene function. The success of functional genomic platforms in driving the identification of the most relevant targets in renal disease will be determined by advances based on three strategic pillars that are the foundation of functional genomics: (i) development of more advanced in vitro system will be critical for the identification of translatable targets in CKD. The value generated by functional genomic efforts will be directly proportional to the translatability of the cellular models employed in screening campaigns in which the targets were originally discovered. Consequently, there is a strong drive among researchers in the field to use cellular systems in vitro that can closely recapitulate disease-relevant phenotypes, rather than use easier to screen 2D cell lines with limited their physiological relevance. A successful example is the use of CRISPR methodology with iPSC-derived podocytes and primary renal cells, such as implementation of the ObLiGaRe doxycycline inducible (ODIn) Cas9 system in iPSCs , and th pathway .in vivo, yet provide some of the complexity of the kidney. Challenges remain in reconstituting the full gamut of renal physiology in vitro  allow the study of renal tissue in all its complexity while still in a defined culture environment, establishing a bridge between  studies . Historitruction  or slicetruction . An advatruction . It is win vivo. This can eliminate compounds are expected to show some efficacy in this model, but do not, and to determine an effect size of a compound and calculate group sizes for the experiments in vivo. The use of PCKS as a screening tool can reduce the number of animals needed to validate a test compound. As with brain slices, PCKS are also suitable for some imaging studies and can be superfused, for example, to examine tubule segment-specific changes in mitochondrial function  of the regulated target in the defined renal cell type results in a CKD-like phenotype5. Confirm that tool compounds or genetic modification can produce a treatment effect in a relevant translatable cell model6. Confirm whether genetic modification or tool compounds that agonize/antagonize a target in advanced models have an effect on clinical measures of renal function7. Continue target validation in advanced When designing a preclinical study, the aim is to use a mechanistic model in which target engagement can be easily captured, followed by a relevant proof-of-mechanism disease model in which an efficacy readout can be captured. The experiment in the mechanistic model is usually short and confirms that test molecule engages its target by measuring biomarkers directly linked to the target’s biology  in the plasma, urine, or in the kidney itself. During these studies, identification of additional markers of target engagement could influence the design of future clinical trials. For example, a viable target engagement model measuring the impact of a mineralocorticoid antagonist would measure plasma aldosterone and/or urinary electrolytes . This moin vivo to human disease is a key aspect of target validation in vivo (in vivo have been developed (Translatability of models  in vivo . Howevereveloped , represein vivo tool for ‘on/off’ target effects of candidate drugs. However, for several rodent preclinical models, inter-strain variability needs to be taken into account when selecting the disease model. For example, C57/Bl6 mice, one of the most commonly used background strains for generating knockout mice, are known to be less susceptible to renal injury than many other mouse strains (Another approach to evaluating the relevance of a target is to use genetically modified animals for target validation and as safety tools to understand a super-physiological antagonization or agonization of gene function, as well as  strains . In addiIn vivo target validation together with the in silico and in vitro methodologies discussed earlier, is crucial to build a sound scientific understanding of the disease mechanisms in play.CKD has steadily climbed the ranking of leading causes of death in recent years as a result of an aging population and increasing prevalence of risk factors such as diabetes and hypertension . Until nin vitro, ex vivo, and in vivo systems for target validation and aiming to maximize translatability to patient disease can be guided by cutting-edge in silico and omics-based analyses.A new patient-centric approach is key for future target identification and validation. Starting the target discovery based on patient data and a patient-centric approach using translatable models is essential to the identification and validation of targets that can result in successful CKD drug discovery programs and increase our understanding of mechanistic disease drivers. Scientific expertise in multiple areas of renal biology, pathophysiology and clinical aspects of renal disease is crucial, together with computational biology, systems-wide integration of omics with clinical data sets, and functional validation. Selection of the most appropriate in vitro models such as micro-physiological systems which facilitate studies of genes and pathways not possible in simple 2D cell culture systems. These advanced systems enable understanding of the complexity of the kidney by capturing the effect of 3D structure and cell-cell interactions. Despite the progress that has been made with advanced systems, there is still a need to capture adult renal cell biology, rather than the fetal signature that organoids present with today. In addition, more advanced models including multi-organ systems are needed to capture the full gamut of systemic responses and improve the validation tools for the future.The technological revolution continues to deliver patient omics data sets and AI systems for analysis of big data which allow researchers to use the patient as the starting point. Increased access to big data makes it impossible for one researcher to oversee all data. Therefore, the development of tools like knowledge graphs transform the way researchers work with big data. In recent years, much progress has also been made generating omics data from advanced in vivo research community continue to build on the understanding of disease mechanisms and translatability by using metabolomics, proteomics, bulk and single cell NGS data in animal models. The omics data sets are key for understanding the translatability of animal models to patients. Owing to the complexity of chronic kidney disease with cellular crosstalk and multiorgan involvement, we still need to secure reliable in vivo models to support pharmacology read-out.In parallel, the renal The increasing number of clinical trials for renal disease during the last decade have resulted in a better understanding of relevant endpoints, biomarkers and disease drivers. Clinical backtranslation beyond patient omics data will be a key factor and continue to improve therapeutic target identification and validation.in vivo for systemic pharmacological responses. We foresee that with the current progress in humanized in vitro models with increased complicity, the need for animal testing will decrease in the future. Using micro-physiological systems is expected to increase translatability to patients and reduce the number of animals used in pharmacological testing, a key ethical parameter in therapeutic target validation.The toolbox of state-of-the-art models now available to us to simulate the complexity of CKD can facilitate obtaining readouts that are translatable from preclinical to clinical studies, which until now has been a major challenge for drug discovery in renal medicine. This toolbox needs to be continuously enriched by infusion of novel raw data acquired by researchers across the disciplines, as well as by development of powerful tools to mine and interpret those data. The recent years technological success will continue to deliver improved tools in the future. Despite the increased use of complex micro-physiological systems, there is still a need for testing in silico analysis of human data, together with extensive in vitro complex humanized models and in vivo validation in CKD research improves the probability to identify disease drivers that could be successful as potential drug targets (In conclusion, the novel patient-centric approach building on the combination of  targets .