Methylomicrobium album BG8 is an aerobic methanotrophic bacterium with promising features as a microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding of the biochemistry of M. album BG8. Flux balance analysis, constrained with time-series data derived from experiments with various levels of methane, oxygen, and biomass, was used to investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that M. album BG8 requires a ratio of ∼1.5:1 between the oxygen- and methane-specific uptake rates for optimal growth. Integrative modeling revealed that at ratios of  >2:1 oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds, while at ratios of <1.5:1 acetate accounted for a larger fraction of the total excreted flux. Our results showed a coupling between biomass production and the excretion of carbon dioxide that was linked to the ratio between the oxygen- and methane-specific uptake rates. In contrast, acetate excretion was experimentally detected during exponential growth only when the initial biomass concentration was increased. A relatively lower growth rate was also observed when acetate was produced in the exponential phase, suggesting a trade-off between biomass and acetate production.IMPORTANCE A genome-scale metabolic model (GEM) is an integrative platform that enables the incorporation of a wide range of experimental data. It is used to reveal system-level metabolism and, thus, clarify the link between the genotype and phenotype. The lack of a GEM for Methylomicrobium album BG8, an aerobic methane-oxidizing bacterium, has hindered its use in environmental and industrial biotechnology applications. The diverse metabolic states indicated by the GEM developed in this study demonstrate the versatility in the methane metabolic processes used by this strain. The integrative GEM presented here will aid the implementation of the design-build-test-learn paradigm in the metabolic engineering of M. album BG8. This advance will facilitate the development of a robust methane bioconversion platform and help to mitigate methane emissions from environmental systems. Anthropogenic activities have led to significant increases in atmospheric methane , which c3–8–11–Methylomicrobium album BG8  is an obligate aerobic, Gram-negative, gammaproteobacterial methanotroph that uses methane or methanol as its sole source of carbon and energy (Methylomicrobium agile ATCC 35068 (99.16%), Methylotuvimicrobium alcaliphilum 20Z (75.69%), and Methylotuvimicrobium buryatense 5G (76.64%)  lacus LW14  . Recent cus LW14 . M. albuediments , 20 and ediments –23. Thro factory , 25 for  factory .Gammaproteobacteria) utilize the ribulose monophosphate (RuMP) cycle to metabolize formaldehyde derived from methane oxidation, group II (Alphaproteobacteria) members direct the carbon flux resulting from the oxidation of methane to formate to a complete serine cycle, and group III (Verrucomicrobia) members possess a complete Calvin-Benson-Bassham cycle for carbon dioxide utilization. The metabolic engineering of methanotrophs has enabled advances in the biotechnology of methane conversion to produce succinate (Methylotuvimicrobium buryatense 5G(B1)  , Methyloilum 20Z , and Mettus Bath , 37] and hirsuta , Methylorain SC2 , Methylorain SB2 , Methylovus OBBP , Methylolvestris , and Metium OB3b ). AlthouM. album BG8 as a tool in environmental and industrial biotechnology, no GEM of this methanotroph has been developed; therefore, an integrative system-level understanding of methanotrophy in this strain remains lacking. In this study, a high-quality GEM of M. album BG8 was constructed by stringently following well-established systems biology protocols  to calculate the DCW from a measured OD600 value. The model with a y intercept at the origin produced the best fit (see −1 = 0.26 × OD600.A linear regression model was established between gram of dry cell weight (gDCW) liter10.1128/msystems.00073-22.1FIG S1Methylomicrobium album BG8 cultures at 600 nm (OD600) and the cell mass density expressed as grams of dry cell weight per liter (gDCW liter−1). The numbers inside the circles indicate the independent samples. Two different linear models (with or without passage through the origin) and their respective computed parameters are shown. Download FIG S1, PDF file, 0.2 MB.Linear regression between the optical density of Copyright © 2022 Villada et al.2022Villada et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. album BG8, requires an equimolar ratio of oxygen to methane. We first hypothesized that excess methane in a batch culture of M. album BG8 favors growth. To test this hypothesis, we characterized the effects of two initial methane (20% [0.41 mmol] or 45% [1.0 mmol]) and oxygen (20% [0.43 mmol]) headspace ratios on metabolite excretion and biomass production. The time-series profiles in t test, 5% threshold for P value)  not only the biomass yield but also the oxygen uptake and carbon dioxide and acetate excretion yields  . Contrarn yields . AlthougM. album BG8 under 20% methane. The time-series profile in P < 0.05; P < 0.05) except biomass concentration  on the metabolism and growth of ntration . Oxygen ntration .10.1128/msystems.00073-22.2FIG S24−1), excreted metabolites (as mmol of metabolite mmol-CH4−1), and oxygen uptake (as mmol mmol-CH4−1) in Methylomicrobium album BG8 cultures. The color gradient represents the different initial headspace percentages of oxygen (5% to 25%) in 5% increments together with 20% methane. P values were calculated using ANOVA. Data are presented as the mean values from biological triplicates, and the error bars represent the standard errors. Download FIG S2, PDF file, 0.2 MB.Yields of the biomass  of 0.11 h−1  and a half-saturation constant (mK) of 1.69 mM (CI95 = 0.78 to 2.60)  . The chato 2.60) , suggestto 2.60) . The speto 2.60) . In the to 2.60) . Interesto 2.60)  and a noto 2.60) .M. album BG8. To achieve a system-level understanding of M. album BG8 metabolism, we constructed a high-quality GEM that incorporated all of the central metabolic pathways contributing to methane oxidation   were observed in the methane oxidation pathway, the H4MPT pathway, the folate metabolism, the partial serine cycle, the RuMP cycle, the RuMP EDD variant, and the RuMP EMP variant  , with th < 0.05) . Signifi variant . Notable variant  activity variant , while t variant  was only variant .M. album BG8 cultures provided with different initial oxygen headspace percentages reveal that these cultures differ mainly in the regulation of formaldehyde oxidization and assimilation. At an initial oxygen-to-methane headspace ratio of 1.25:1, formaldehyde can be efficiently oxidized through the H4MPT pathway and assimilated mainly through the RuMP cycle and its EMP variant . Under both ratios, the initial biomass concentration had a significant (P < 0.05) and specific effect on the formate and acetate yields  mean absolute fluxes in the overall metabolic network  cycle (P < 0.05) on the mean absolute fluxes in the metabolic pathways of H4MPT and the EMP and EDD variants of the RuMP cycle  cycle . In the MP cycle .A detailed analysis of affected pathways and reactions in the acetate-excreting phenotype indicated that the BS variant of the RuMP cycle, the reactions of which are linked to fermentation-like reactions, exhibited a high level of flux . In factM. album BG8, we quantitatively investigated the associations of these factors with the oxygen-to-methane uptake flux ratio, an important parameter used to define phenotypes  is similar to other reported growth rates for M. album BG8 . HoweveM. album BG8, as it exerted strong effects on both biomass production and organic compound excretion. In our analysis, optimal growth could be sustained at a ratio of ∼1.5:1 between the specific uptake rates of oxygen and methane. In agreement with our results, a ratio of ∼1.5:1 between oxygen and methane uptake rates has also been found by FBA to be optimal for growth for the methanotroph Methylomicrobium buryatense 5GB1 after careful correction of pathways in the GEM  among a group of industrially relevant methanotrophs , M. album BG8 could be using the mentioned pathways to more efficiently oxidize methanol to gain biomass. A recent transcriptomics and metabolomics study of M. album BG8 grown on methanol . The GEM was parametrized  with an uptake rate bounded at 1.52 to 3.16 mmol hmethanol  yielded methanol . Taking methanol  together4−1) was achieved under culture conditions of 20% methane and 20% oxygen, and the highest formate yield (0.025 mmol mmol-CH4−1) was obtained under conditions of 20% methane and 25% oxygen. In contrast to a recent report H]. Based on these results, developers of metabolic engineering strategies that aim to optimize carbon dioxide and/or formate excretion by M. album BG8 during methane metabolism may consider directing carbon flux toward the H4MPT pathway. Nevertheless, future attempts to optimize formate production by M. album BG8 should consider the achievement of significantly larger formate yields by other methanotrophs grown on methanol instead of methane  was unexpectedly obtained under the conditions of 20% methane, 20% oxygen, and a relatively high initial biomass concentration  oxygen-to-methane uptake flux ratio  profiles of biomass production, oxygen and methane consumption, and acetate excretion for the experiment with 20% methane, 20% oxygen, and a relatively high initial biomass concentration (C20_O20_ODH). Download Copyright © 2022 Villada et al.2022Villada et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the M. album BG8 genome encodes homologs of enzymes found in the EDD and EMP RuMP cycle variants in M. alcaliphilum 20Z and M. buryatense 5G(B1) -dependent malic enzyme] required for fermentation-like metabolism in M. alcaliphilum 20Z (M. buryatense 5G(B1)  . Howeverilum 20Z  and M. be 5G(B1)  at low olsewhere , 70. FinM. album BG8. However, many other aspects of metabolism in this strain remain to be explored in detail. The newly developed GEM of M. album BG8 is a valuable tool that will aid future investigations of this important methanotroph in the context of methane conversion applications in biomanufacturing or methane emission mitigation in environments that experience dynamic fluxes of methane and oxygen.In summary, the experimental results and the FBA presented in this study elucidate some of the characteristics of M. album BG8 was purchased from the American Type Culture Collection . The genome of this strain has been sequenced  medium (2 (5 μM) was used as the copper source  medium  in a 160) medium , the inir source . The heaTo determine the headspace concentrations (mM) of hydrogen, oxygen, methane, and carbon dioxide, samples were collected using a gas-tight syringe  and analyzed on a gas chromatograph  as described previously . After wThe concentrations of cellular amino acids and total lipid fatty acids in bacterial cells were analyzed during the exponential growth phase. Seventeen amino acids were measured quantitatively using HPLC at the Proteomics and Metabolomics Facility, Center for Biotechnology, University of Nebraska-Lincoln (USA) by following standard protocols. The total levels of 15 lipid fatty acids were measured quantitatively using thin-layer chromatography, which was performed by a commercial laboratory (AminoAcids.com) by following standard protocols.600) and the dry cell weight (DCW), 13 cultures of M. album BG8 were grown under 20% methane and 20% oxygen to various cell densities (OD600 of 0.2 to 1.0), and the corresponding lyophilized cell masses were weighed. OD was measured using a UV-visible spectrophotometer , and DCW was determined using an MS-TS analytical balance .To determine the conversion factor between the observed optical density at 600 nm (OD4−1) and of specific metabolites (mmol mmol-CH4−1) were calculated by considering the changes in concentration per total methane consumed throughout the experiment. The growth rate (per hour) was calculated over the exponential phase as the log-linear regression between the biomass concentration and time. The specific uptake rate and excretion rate were considered equivalent to the uptake flux and excretion flux, respectively, when integrated as constraints into the GEM. To meet the steady-state assumption of the FBA, both rates or fluxes were calculated for each metabolite (in mmol h−1 gDCW−1) in each experiment by linear regression of only those data points that were collected during the exponential phase, as described in a previously published protocol  must be inputted into a GEM. All of the components in the experimental NMS medium   . Pyrrolohia coli  and othehia coli , 37, andvus OBBP . These Ain silico NMS medium as the nutrient source. All of the reactions added in the gap-filling step were manually checked to determine their essentiality in the GEM using a leave-one-out approach, wherein single reactions were excluded and an FBA was applied to maximize the biomass production reaction. Reactions that were not essential to in silico biomass production were discarded.After the biomass production reaction in the GEM was edited as described in the previous section, automatic gap filling of the draft GEM was performed using KBase and components of the After gap filling, the model was fine-tuned using an iterative refinement approach as described previously , 70. ThecheckBalance function in the COBRA Toolbox 2.0. The final version of the M. album BG8 GEM was named iJV803 according to standard convention  were identified using locus tags that differed from those in the NCBI (accession no. NZ_CM001475) and Joint Genome Institute (genome ID 2508501010) depositories. To reconcile these differences and facilitate the use of the GEM in future research applications, all of the genes present in the GEM were aligned against genes identified in different repositories using Blast  was applied to test the difference between more than two groups. The parametric tests were applied based on three criteria: (i) the Shapiro-Wilk test indicated a normal distribution, (ii) the variance of the samples was within the same order of magnitude, and (iii) the same number of independent samples was in each group. The majority approach was adopted when the criteria for parametric testing did not apply perfectly for some sample groups (as in the case for some of the data presented in The https://juanvillada.github.io/iJV803.The final version of the model can be downloaded in RData, SBML, JSON, and XLS formats from


In the kidney metabolic model, the best predictions were obtained with ammonia transport as an objective. Other objectives resulting in a fair correlation with the measured fluxes (correlation coefficient >0.5) were growth, protein uptake, urea excretion, and lysine and phenylalanine transport. These predictions were improved when specific gene expression data were considered in HD conditions, suggesting a role for the mitochondrial glycine pathway. Further studies are needed to determine if regulation through the mitochondrial glycine pathway and ammonia transporters can be modulated and how to use the kidney as a therapeutic target in hyperammonemia.The kidney plays a critical role in excreting ammonia during metabolic acidosis and liver failure. The mechanisms behind this process have been poorly explored. The present study combines results of in vivo experiments of increased total ammoniagenesis with systems biology modeling, in which eight rats were fed an amino acid-rich diet (HD group) and eight a normal chow diet (AL group). We developed a method based on elementary mode analysis to study changes in amino acid flux occurring across the kidney in increased ammoniagenesis. Elementary modes represent minimal feasible metabolic paths in steady state. The model was used to predict amino acid fluxes in healthy and pre-hyperammonemic conditions, which were compared to experimental fluxes in rats. First, we found that total renal ammoniagenesis increased from 264 ± 68 to 612 ± 87 nmol (100 g body weight) The kidney has a pivotal role in systemic hyperammonemia and has been shown able to switch from an organ of net ammonia production into an organ of net ammonia removal in an experimental model of liver failure4. Renal ammoniagenesis and urinary ammonia excretion are highly regulated processes as they are central in the regulation of the acid-base balance5. In the kidney, ammonia is produced by proximal tubular cells mainly by degradation of glutamine7. After production, ammonia is released in the tubule through passive diffusion and through the NHE-3 ammonia transporter8. About 40–80% of ammonia is reabsorbed in the thick ascending limb (TAL) of Henle’s loop9 predominantly via the co-transporter Na-K-2Cl (NKCC2)11. Ammonia is transported from the cytoplasma into the interstitium by the NHE4 exchanger located on the basolateral side of TAL12 cells. After accumulation in the TAL, ammonia is either passively diffused by transepithelial gradient or actively transported by the Rhesus transporters into the collecting tubules to be excreted in the urine13. Though transport of ammonia and its substrates has been extensively studied various conditions such as metabolic acidosis and altered potassium balance14, it has yet to be understood in hyperammonemic situations.Hyperammonemia remains the major cause of hepatic encephalopathy in cirrhotic patientsThis study was designed to enlighten renal ammonia handling related to metabolic pathways and transporters in an experimental rodent model of increased renal ammoniagenesis. The diet was aimed to induce increased renal ammoniagenesis in animals with normal liver function avoiding harmful effects associated with hyperammonemia, such as systemic alterations of pH.15 and structural flux analysis17. Here, we developed a method based on structural flux analysis and gene expression measurements to study changes in amino acid flux distributions in the kidney in an experimental model of increased renal ammoniagenesis. Like flux balance analysis, structural fluxes rely on the assumption that cells optimize certain objectives; living organisms do not behave randomly but are expected to have evolved to maximize the production of certain compounds that are essential for their survival and growth or for their function. In some cases, an objective other than growth is more relevant, e.g., ATP production to optimize energetic availability. In this paper we investigate the suitability of different objectives to model kidney metabolism and for the first time we compare these predictions with in vivo data.Systems biology has become a promising tool to understand metabolic diseases. Several methods exist to predict metabolic fluxes but they are difficult to calibrate and validate against in vivo data. Constraint-based methods such as Flux Balance Analysis have been widely applied and rely on a stoichiometric model of the metabolic network, a set of constraints on exchange fluxes, and the definition of a metabolic objective. The application of Flux Balance Analysis to animal/human cells and to whole organs is challenging due to the difficulty to precisely characterize exchange fluxes between cells and their environment, which is required a priori knowledge for accurate predictions. The lack of tissue-specific metabolite uptake, secretion, and growth rates urges the development of techniques to understand substrate usage of human cells and organs. Elementary Mode Analysis identifies all feasible paths inherent to a metabolic network. It has the advantage that it does not require parameters beyond stoichiometry, such as constraints on substrate uptake rates and that it also computes non-optimal paths. Alternative methods based on elementary modes (EMs) are based on a more precise computation of the efficiency of individual metabolic routes, such as control-effective fluxesThe experimental model characterizes the changes in fluxes across the kidney and gene expression associated with increased total renal ammoniagenesis.The reconstructed kidney metabolic model contains 106 reactions of central metabolism representing glycolysis, TCA cycle, pentose phosphate pathway, urea cycle, amino acid metabolism, growth, and protein breakdown; it contains 85 internal metabolites and 23 external metabolites. 6,625,064 elementary modes were obtained in the kidney metabolic model. In the following, we first present the results related to the healthy kidney, followed by the changes in rats fed with a pre-hyperammonemic diet.Of the 15 tested objectives, three of the predicted flux distributions gave significant correlations with the measured fluxes across the kidney: ammonia excretion, glucose production, and energy production (ATP maintenance reaction). These objectives reflect relevant biological renal processes. The best predictions in terms of Pearson correlation coefficient were obtained when ammonia transport was used as the objective with a correlation coefficient of 0.80 Fig. . Other oP = 0.08]. Changes in ammonia transporters were not due to altered systemic pH, as pH was normal and not significantly different between both groups . Renal flow  and urinary flow were not significantly altered [AL: 7.1 (5.0–10.0) vs. HD 9.0  vs HD 45 (30–72) grams, −1 min−1, P = 0.021). The HD diet resulted in a 2.6-fold increase in ammonia efflux to urine (201 ± 28 vs. 80.2 ± 19.6 nmol (100 g bw)−1 min−1) (P = 0.029) and no significant increase in ammonia efflux into the systemic circulation (49.2 ± 17.6 in HD vs. 44.8 ± 7.21 nmol (100 g bw)−1 min−1 in AL). Relatively, urinary ammonia excretion increased from 73% of total renal ammoniagenesis in the AL group to 90% in the HD group (P = 0.028)  Fig. .Fig. 2PrP < 0.2 and a combined P of 0.0025)  (blue), whereas the kidney released amino acids into the systemic circulation in a normal diet (red) (each with a 25) Fig. . Note th25) Fig. .P value >0.4).Gene expression profiles were analyzed for three animals in each group. RNA of 17,051 genes was measured, of which 11,512 were expressed in at least one sample (read count of at least 30). Hierarchical clustering of these expression levels revealed distinct clusters of the AL and HD groups Fig. . In totar = 0.92) or ATP citrate lyase (Acly) (r = 0.96) were considered for HD conditions. The highest correlation between measured and predicted fluxes is through increased activity of Acly and increased cellular turnover, and thus higher anabolic demand. Although this hypothesis is supported by the increased activity of many genes in fatty acid metabolism, it would need follow-up experiments for validation, in which also cellular turnover and fatty acid fluxes over the kidney are measured.Flux predictions with ammonia transport as the biological objective best represented the measured amino acid fluxes over the kidney in the AL group Fig. . Hence, In this work, measurements were focused on amino acid metabolism and we studied Shmt2 in more detail. Figure P < 0.96). The pre-hyperammonemic diet did not increase the expression of Rhesus transporters in the collecting ducts (P = 0.61).Although the expression levels of most genes support the predicted and measured flux changes, the downregulation of Gls is apparently contradictory. However, regulation of metabolic activity may take place at the post-transcriptional level. In fact, Gls is known to be regulated by ADP. NKCC2 , this approach of combining gene expression with the metabolic restrictions imposed by stoichiometry, shed light on the changes that might occur during a pre-hyperammonemic diet.In contrast to FBA, EMs analysis has the advantage that it does not require constraints substrate uptake rates, which are often lacking. It has the disadvantage that the enumeration of the complete set of elementary modes for large networks has been infeasible so far, even undesirable due to the hardly manageable number of modes that would be generated. We hence reconstructed a medium-scale model for the kidney using specific information about protein abundances contained from the Human Proteome Atlas and RNA abundances from our own dataset as the main sources of evidence to represent central metabolism of the kidney. We also assumed an artificial biomass reaction based on overall composition. Although we defined the model to our best possible knowledge and data of the kidney, choices on the presence/absence of specific reactions might influence the results of the simulations as in any tissue-specific or reduced model.18, this study is the first to systematically compare a range of biological objectives with experimental data. It is important to note that our modeling approach considers all possible pathways (elementary modes), whereas for instance in classical Flux Balance Analysis, using ATP production as an objective would lead to a single optimal pathway being highlighted and thus disregard cell renewal and other cellular fluxes. The present approach does predict growth for all objectives, since growth is included in most pathways. Hence, whereas growth must explicitly be included in the objective function for Flux Balance Analysis, a single objective is sufficient for structural fluxes. We found that using ammonia transport as the objective led to the best prediction for measured fluxes.Although a biological objective function had been suggested in metabolic flux analysis of kidney metabolism by Chang et al.20, the validity of genome-wide gene expression profiles for flux prediction has been questioned21, since an up- or downregulation of a specific gene might not relate with a (linear) change in the respective flux. It might be regulated at a translational level, by post-translational modifications, or depend on other enzymes in the network. As Machado et al.21 concluded in their review, it is known that in particular the central metabolic pathways are more heavily regulated at post-transcriptional levels, hence genome-wide transcript levels are in general not suitable for estimation of fluxes of the central carbon metabolism. Albeit the importance of post-transcriptional regulation, transcriptional regulation of specific genes undoubtedly plays an important role. Hence, rather than using all genome-wide gene expression levels, as for example done by Kurata et al.22, we like to argue that using only genes, known to be regulated at a transcriptional level, is more promising in the search for biologically meaningful flux predictions. This, however, is often unknown. Here, we investigated one-by-one which overexpressed gene contributes to enhanced flux predictions and found that in particular fluxes through the Shmt2 pathway and Acly might be activated at a transcriptional level in rat kidneys at hyperammonemic conditions. The activity of the mitochondrial glycine synthesis pathway has also been implicated in cancer cell lines by isotope labeling experiments24. Shmt2 is also a drug target in other diseases, for which approved drugs are available, such as the antimalarial agent Artenimol25.Though the use of gene expression profiles to construct tissue or cell line-specific metabolic models has been established26. However, pathophysiological changes can create a shift in the substrates used by the kidney to generate ammonia28. Several glutamine transporters were overexpressed at RNA level. Of particular interest are the transporters from the SLC family. The most acknowledged glutamine transporters belong to the SLC1, 6, 7, and 38 families29. They have been shown to be of crucial importance in drugs development30 and in cancer research32 and are known to be expressed in the proximal tubule, where glutamine transport occurs. The results of the present study show an upregulation of glutamine transporters at RNA levels.Metabolism of ammonia in the kidney is solely limited to the proximal tubules. Glutamine is considered the main substrate of renal ammoniagenesis33. It has been shown that NHE-3 expression is upregulated in metabolic acidosis34. The present study also demonstrated an increase in gene expression pre-hyperammonemic suggesting that the activity of this transporter is closely linked to the amount of ammonia produced in the proximal tubule. Following excretion in the lumen, the accumulation of ammonia in the TAL is mainly regulated by the NKCC2 co-transporter. It has been shown that the expression of this transporter is upregulated during chronic metabolic acidosis11 mainly through stabilization of mRNA35. Our results show that the protein expression of NKCC2 is also upregulated in conditions where ammonia levels are high but the pH is still neutral. Interestingly, it has been shown that the expression of NKCC2 is increased in a rodent model of liver failure known to induce hyperammonemia39, again suggesting a role for NKCC2 in medullary ammonia accumulation in liver failure. In these studies, the authors did not report on ammonia levels but the used model (bile duct ligation for 28 days in rodents) has been well documented and has repetitively shown its value as a model of hepatic encephalopathy and hyperammonemia40. These results and ours suggest an increased accumulation of ammonia following the increase in renal ammoniagenesis. NHE4 (Slc9a4), an ammonium transporter in the thick ascending limb has been shown to be critical in the renal handling of ammonia in rodents41. After accumulation in the ascending loop, ammonia is either passively diffused into the collecting tubules or actively transported through the Rh glycoproteins transporters family. In the present study their added value could not be shown. It remains unclear whether this was due gene expression below the detection limit or the factual lack of effects of hyperammonemia on the Rhesus transporters.After production in the proximal tubular cells, ammonia is transported into the lumen through the NHE-3 (Slc9a3) transporter. The apical NHE-3 transporter is thought to be the major transporter of ammonia in the proximal tubuleA limitation of this study is the fact that amino acids in urine were not measured. This study suggests that the kidney can be used as a target therapy organ by altering renal ammonia handling through transporters (NKCC2 and the SLC family) and the mitochondrial glycine pathway (particularly Shmt2).Animal experiments were conducted according to Home Office guidelines under the UK Animals in Scientific Procedures Act 1986. Male-specified pathogen-free Sprague–Dawley rats were obtained from Charles Rivers . All rats were housed in the comparative biological unit at the Royal Free and University College Medical School, University College London at least a week before start of the experiments. They were given free access to standard rodent chow  and water ad libitum, with a light/dark cycle of 12 h, at a temperature of 19 °C to 23 °C and a humidity grade of approximately 50%. All procedures and experiments were approved by the Animal Ethical Committee of UCL under license number PPL 70/6615.n = 8) were fed a high-protein/ammoniagenic diet (mixture of amino acids mimicking the hemoglobin molecule39) for 7 consecutive days. The amino acids preparation was mixed with standard rodent chow powder. Similarly to previously conducted studies, animals were fed 3 g kg−1 day−1 of the amino acids mixture42. One rat deceased before the end-point. The control group (n = 8) was fed standard laboratory powder chow ad libitum. Rats were randomly assigned to the groups.Naive male Sprague–Dawley rats (−1 oxygen containing 5% isoflurane), anesthesia was maintained with 2% isoflurane in air, delivered at 0.5 L min−1 rate through a facemask. Rectal temperature was maintained at 36 ± 1 °C using a heating pad.The animals were randomized for pre-hyperammonemic diet (HD group) or ad libitum standard laboratory chow (AL group). After 7 days of diet, surgery was performed. Briefly, following induction anesthesia (1 L min−1 after a prime infusion of 0.032 ml (100 g)−1 of 50 mM PAH to reach a steady state. The final concentrations of PAH have no influence on renal ammonia excretion43. An equilibration period of 30 min was observed before blood and urine collection.Para-amino hippuric acid (PAH)  was used to determine renal blood flow. Micro-catheters were inserted in the left jugular vein and left ureter. A primed, constant 1 mM PAH infusion was infused in the jugular vein at a rate of 0.08 ml hg for 10 min at 4 °C). Plasma was processed with 5% trichloric acid (for ammonia measurements) or with 5% sulfuric acid (for amino acids measurements) before being stored at −80 °C till further analysis.After the equilibration period, urine was collected on ice for 10 (±5) min. Urine quantity was measured using a Hamilton syringe. Immediately after urine collection, renal venous and arterial blood were collected by direct puncture. Blood was immediately centrifuged (1500×44 (1/1000).The NKCC2 antibody was kindly provided by the group of Prof Wagner in Zurich45 and in Supplementary File Section 3.Formalin-fixed tissue sections (5 mm thick) were used for immunohistochemistry. Immunohistochemistry was performed using the streptavidin ABC duet kit  as described in ref. 46 and in Supplementary File Section 3. The geometric mean of the expression levels of two reference genes, cyclophilin A (PPIA) and β2-microglobulin (B2M) was calculated and used as a normalization factor.RNA was extracted from half kidney using TRI reagent according to the manufacturer’s protocol and further processed as described in ref. 47.Ammonia, hematocrit, and PAH concentrations were determined using an enzymatic method . Amino acids were determined using High-performance liquid chromatographyIllumina TruSeq RNA Sample Prep Kit v2  was used with 100–1000 ng of total RNA for the construction of sequencing libraries. In short, total RNA samples were poly(A)-selected and subsequently reverser transcribed into double-stranded cDNA. The following steps were cDNA fragmentation, end-repair, and polyadenylation before ligation of the TruSeq adapters containing the index for multiplexing. We checked the quality and quantity of the libraries with a Qubit (1.0) fluorometer and the Caliper LabChip® GX . The product had an average fragment size of ~260 bp. We normalized the libraries to 10 nm in 10 mm Tris-Cl, pH 8.5 with 0.1% Tween 20.The TruSeq SR Cluster kit v3-cBot-HS  was used for cluster generation. Sequencing was performed on the Illumina HiSeq 2000 single-end 100 bp using the TruSeq SBS kit v3-HS . Isoform expression was quantified using RSEM v1.1.15. As a reference, we used the gene definitions from the University of California Santa Cruz for genome build rn5. RSEM was run with default parameters. Raw and processed data are deposited at GEO accession number GSE106682.48. Genes with read counts below 30 in all samples were excluded from further analysis. For all subsequent analyses, we normalized the read counts using variant stabilization by the DESeq2 R-package49. We used size factor correction to account for differences in sequencing depth between the samples. DESeq2 was also applied to obtain dispersion estimates and to measure differential gene expression between different conditions using default parameters. Results are reported as log2 fold changes and associated adjusted P values (Benjamini Hochberg correction). Functional gene enrichment analysis was performed using METASCAPE50 with default settings. Dendrograms were obtained by hierarchical clustering using Ward’s method and 1 − Pearson’s correlation was used as the distance measure.Gene expression was analyzed in the R statistical computing environment (version 3.3.0). The reads were mapped to Entrez gene identifiers and official gene symbols using Biomart51 as described in detail before28 and in Supplementary File Section 3. Positive flux values represent substrate release and negative values reflect substrate uptake28. Renal plasma flow and substrate fluxes represent metabolism of two kidneys. Total ammoniagenesis was calculated as renal flux plus urinary ammonia excretion.Blood flow rates of the kidneys were calculated using a formula based on the method of indicator dilution and Fick’s principle17 and manually adapted it for the reactions present or absent in kidney cells using the Human Protein Atlas52. All reactions were checked against the human Recon2 model53 and RNA abundance data from our own dataset. Since the metabolic model was aimed to simulate renal metabolism in postabsorptive state, important modifications were the addition of breakdown of proteins and free fatty acids, reactions which are typically absent in stoichiometric models of metabolism. Elementary modes were calculated using efmtool54. The kidney metabolic model is provided in Supplementary Files Sections 1 and 2.The metabolic model was reconstructed to represent kidney metabolism in the postabsorptive state. As a starting point, we used a model for central carbon metabolism of cancer cells17 with slight modifications , normalization is needed to quantify yields with respect to different substrates. Here, we chose to use the molecular weight of each substrate:cik is the coefficient of reaction k in EM i, cs,i is the coefficient of the input reaction of substrate s in EM i and MWs,i the molecular weight of each substrate s for EM i.Whereas the experimental model describes the fluxes across the kidney, the kidney metabolic model predicts also the fluxes in central and ammonia metabolism giving rise to these exchange fluxes by using structural fluxes. In summary, required inputs to the model are: the stoichiometrix matrix, the definition of a biological objective function, and for the HD simulation: gene expression data. Outputs are: all fluxes including exchange and intracellular fluxes. Structural fluxes estimate the efficiency of direct metabolic routes, or elementary modes (EM), toward a cellular objective. The contributions of all EMs are then weighed accordingly to predict the flux distribution, under the assumption that more efficient routes are favored. Structural fluxes are computed according to ref. ions Eq. ) and arek is obtained by weighting each EM containing the reaction by its efficiency and summing up all these contributions:The structural flux of reaction aSF values are not comparable across networks55. Appropriate normalization is necessary if one aims to compare structural fluxes across metabolic networks of different sizes, for example, when integrating with gene expression measurements:18 compiled an objective function consisting of the metabolic functions of the organ in relation to blood pressure. The objective function consisted of exchange fluxes, which are all equally weighted, such as absorption of glucose and amino acids, and secretion of urea. Note that ammonia removal was not in this list and that the predicted fluxes were not compared with experimental data. Here, we tested 15 objectives to account for a range of biological processes in the kidney, amongst others ammonia excretion, growth, and uptake of amino acids. Maximum growth is frequently used to simulate microorganisms and cancer cells and might be relevant for an organ with high cellular turnover. In addition, we tested maximum ATP production, which is commonly used to simulate a “normal” human cell (as opposed to a cancer cell). Other investigated objectives in the present study are related to the postabsorptive state of the kidney: lactate uptake, free fatty acid uptake, and protein breakdown.Although the assumption of optimality toward maximum growth of microorganisms is justifiable, the choice of the most relevant biological objective for human renal metabolism has not been widely studied. Chang et al.In healthy conditions, we assumed that all genes, and consequently reactions, in the kidney metabolic model might be active and contribute to the fluxes according to their efficiency and flexibility in the pathways Eqs. –4)). Di). Di4)).P of 0.05 or less was considered statistically significant. Combined P values were computed using Stouffer’s test. The performance of the computational models was evaluated by the Pearson correlation coefficient between predicted and measured fluxes.Statistical analyses were performed using R (version 3.3.0). Results are expressed as median and range unless otherwise specified. Nonparametric statistics were used. A Supplementary materials


Penicillium chrysogenum was investigated. Within a wide range of DO concentrations, obtained under chemostat conditions, we observed different responses from P. chrysogenum: (i) no influence on growth or penicillin production (>0.025 mmol L−1); (ii) reduced penicillin production, but no growth limitation (0.013–0.025 mmol L−1); and (iii) growth and penicillin production limitations (<0.013 mmol L−1). In addition, scale down experiments were performed by oscillating the DO concentration in the bioreactor. We found that during DO oscillation, the penicillin production rate decreased below the value observed when a constant DO equal to the average oscillating DO value was used. To understand and predict the influence of oxygen levels on primary metabolism and penicillin production, we developed a black box model that was linked to a detailed kinetic model of the penicillin pathway. The model simulations represented the experimental data during the step experiments; however, during the oscillation experiments the predictions deviated, indicating the involvement of the central metabolism in penicillin production.In large‐scale bioreactors, there is often insufficient mixing and as a consequence, cells experience uneven substrate and oxygen levels that influence product formation. In this study, the influence of dissolved oxygen (DO) gradients on the primary and secondary metabolism of a high producing industrial strain of  DOdissolved oxygen (concentration)CFDcomputational fluid dynamicsMFCmass flow controlleriqbiomass specific production/consumption rates of compound i iCconcentration of compount iμgrowth rateACVδ‐(L‐α‐aminoadipoyl)‐L‐cysteinyl‐D‐valinebisACVbis‐δ‐(L‐α‐aminoadipoyl)‐L‐cysteinyl‐D‐valineIPNisopenicillin NPAAphenylacetic acidoOHPAAo‐hydroxyphenylacetic acid6APA6‐aminopenicillanicacid8HPA8‐hydroxypenicillic acidOPC6‐oxopiperidine‐2‐carboxylic acidPhAc‐CoAphenylacetyl‐CoAPIOpenicilloic acidPenGpenicillin‐GAAAL‐α‐amino adipateValvalineCyscysteineACVSδ‐(L‐α‐aminoadipoyl)‐L‐cysteinyl‐D‐valine syntetaseIPNSisopenicillin‐N synthetaseAATacyl‐CoA: 6APA acyltransferaseIATisopenicillin‐N acyltransferaseIAHisopenicillin‐N amido‐hydrolasePAHphenylacetate hydroxylase1In large‐scale aerobic fermentation processes, sufficient oxygen supply IS often a challenge, because the mixing and the mass transfer is poor, especially when high biomass densities are reached and/or when the broth is highly viscous. To supply sufficient oxygen, intense mixing required could harm the cells. In industrial scale bioreactors, dissolved oxygen (DO) concentration wide range variations can occur, both in axial and radial directions in the reactor space , 2, 3, 4A scale‐down approach is usually used to study the effects of environmental variations present in large‐scale bioreactors , 7. UsinPenicillium chrysogenum response to limiting dissolved oxygen (DO) conditions is essential for optimized large‐scale penicillin production, where there is limited oxygen transfer that contributes to heterogeneous oxygen concentrations. Revealing the metabolic responses of the cells to low and fluctuating DO conditions provides two strategies for optimizing penicillin production. Firstly, metabolic engineering could be applied: in this study we showed that penicillin production was limited by the isopenicillin‐N synthetase (IPNS) enzyme under steady low DO conditions, while in oscillating DO conditions, the central metabolism was limited and increased ATP/precursor availability might promote production. Secondly, the design of a large‐scale penicillin production reactor could be improved to limit oxygen gradients, for example, by introducing additional sparger points. Finally, the developed model can be coupled to a computational fluid dynamics (CFD) model to predict the cellular growth and penicillin production on a seconds scale in the bioreactor under different operational conditions.The biological understanding of the Penicillium chrysogenum cells for oxygen for growth and maintenance, molecular oxygen is also required in the product biosynthesis pathway. The second enzyme of penicillin production pathway, isopenicillin‐N synthetase (IPNS), requires O2 for the conversion of δ‐(L‐α‐aminoadipoyl)‐L‐cysteinyl‐D‐valine (ACV) to isopenicillin N (IPN) [P. chrysogenum cells. However, the oxygen concentration that limits the oxygen uptake rate of cells is different from the oxygen concentration that limits penicillin production [−1 DO, while penicillin production sharply decreases at 0.082 mmol L−1 DO [−1 DO, cellular respiration and growth was unaffected, while the penicillin production decreased to zero [The DO concentration is a crucial parameter in the penicillin fermentation process. In addition to the demand of  N (IPN) , which ioduction . Vardar l L−1 DO . Similar to zero . These s to zero , 15. Nev to zero , 15. The to zero , 17. ExpP. chrysogenum that used periodic oxygen limitations have been conducted in one compartment vessels with intermittent feeding regimes [−1, compared to the penicillin production rate at a steady DO level of the fluctuation average (0.082 mmol L−1). In contrast, during oscillation cycles between 0.027 and 0.041 mmol L−1, the penicillin production rate was higher than during a steady DO value of 0.034 mmol L−1. According to our knowledge, no studies have investigated the metabolite concentrations of the penicillin pathway and the connected primary metabolism under scaled down conditions with oscillating DO levels, which could provide information that could lead to deeper understanding of these observations.Scale‐down studies of  regimes  or multi regimes . The stu regimes  focused  regimes . Vardar  regimes  reportedUnderstanding the cellular responses to a fluctuating environment and describing those by kinetic models can lead to the identification of metabolic engineering targets for improved productivity . When th−1 DO. Modeling aspects on penicillin production are currently lacking solid evidence for the effect of DO levels below 0.068 mmol L−1, and the assumed linearity might not be valid in a broader DO ranges. A detailed understanding of the influence of DO concentrations on the penicillin pathway is required to predict the penicillin production rate under dynamic conditions and develop dynamic models that can be coupled to CFD simulations.Most modeling studies that describe the effect of DO on the penicillin pathway , 22 relyIn this study we aim to (i) understand correlations between DO levels and primary and secondary metabolic processes and (ii) model the influence of low and oscillating DO conditions on penicillin fermentation. We expand our existing knowledge on the influence of DO concentration on penicillin production and central metabolism using oxygen levels in the growth‐limiting range, which have not been studied in detail. For this, stimulus‐response experiments and scale‐down experiments are conducted, which provide powerful insight into the in vivo kinetics of this system . Additio22.1Penicillium chrysogenum strain (DS17690) donated by Centrient Pharmaceuticals  as spores grown on rice grains was used in all experiments. Two different reactor configurations with total volumes of 2 and 7 L and corresponding working volumes of 1.25 and 4 L were inoculated with 3.1 or 10.0 g of rice, respectively. The spores were separated from the rice grains by stirring for at least 1 h in 50 and 100 mL sterile demineralized water. These solutions were filtered through a mesh to remove the rice and the resulting spore suspension was aseptically transferred to the 2 and 7 L reactors, respectively.A high‐yielding, penicillin producing 2.2P. chrysogenum cultivation medium was prepared as previously described [−1 (NH4)2SO4, 1 g L−1 KH2PO4, 0.5 g L−1 MgSO4⋅7H2O, 16.5 g L−1 C6H12O6⋅H2O, 0.68 g L−1 phenylacetic acid (PAA) and 2 mL L−1 trace element solution. The trace element solution contained 75 g L−1 Na2EDTA⋅2H2O, 10 g L−1 ZnSO4⋅7H2O, 10 g L−1 MnSO4⋅1H2O, 20 g L−1 FeSO4⋅7H2O, 2.5 g L−1 CaCl2⋅2H2O, 2.5 g L−1 CuSO4⋅5H2O. The cultivation medium was used for the batch phase, except the PAA concentration was lowered to 0.41 g L−1. The PAA concentration in the batch medium (3 mM) was lower than in the chemostat medium (5 mM) in order to keep the residual PAA level within a minimal range of variation [The escribed . The culariation . However4⋅H2O and FeSO4⋅7H2O separated from the rest), which were then mixed and the pH was adjusted to 6.0 using NaOH. The solution was stirred overnight and stored at 4°C until needed.The trace elements were dissolved in demineralized water as two separate stocks  and added to the autoclaved PAA solution.The salts, glucose, and trace elements were dissolved in demineralized water, and the pH was adjusted to 5.5 with KOH. The PAA was dissolved separately in demineralized water with a PAA:KOH molar ratio of 1:1.2, and then the pH was adjusted to 5.5 with KOH or H2.3All experiments were conducted in stirred tank bioreactors , which were stirred with magnetically driven six‐bladed Rushton impellers. The details of the experimental setups and fermentation conditions are presented in Table The pH, temperature (T), and DO were continuously measured and logged during the fermentations. The pH was measured (Mettler Toledo 405‐DPAS‐SC‐K8S) and maintained at a value of 6.50 ± 0.05 with an automatic pH control system, that supplied a base solution (KOH or NaOH) by a peristaltic pump. The applied base type is not expected to have any impact on the fermentations. The temperature was maintained at 25°C by a thermo‐circulator that controlled the water temperature based on the measured temperature of the reactor content.During the DO step down experiments, the DO was controlled based on measurement by a conventional Clark electrode (Applikon Z01002325) or an optical DO probe  to keep the DO at the desired level. During the reference experiments, air was supplied to the reactor and the DO was not controlled.2 as inlet gas to the reactor. These MFCs were controlled based on the measurements from a DO probe, and the switches between the MFCs and thus, between the inlet gases took place at given DO levels  provided either air or Ns Figure . During e Figure . In osci−1 by setting the medium inflow rate. While the inflow was supplied continuously by a peristaltic pump, the effluent was removed periodically by an automatically controlled pneumatic valve on the bottom of the reactor and a peristaltic pump to regulate reactor weight. In this way, the broth volume was controlled at 4000 ± 20 g. The effluent from the 2 L reactor was removed every 30 min via an overflow tube. The feed medium vessels were continuously stirred. The effluent vessel was placed on a load cell that continuously recorded its weight to determine the exact dilution rate. The weight of the base solution and antifoam was also recorded. The overpressure of the reactor headspace was set by a reducing valve in the gas outlet. The reactors were aerated by a sparger underneath the impeller. The inlet gas flow was controlled by mass flow controllers . Before the gas entered the reactor, it was passed through a sterile 0.2 μm membrane filter (Millipore). The offgas left the reactor via a condenser at 4°C, through a sterile cotton wool filter. The O2 and CO2 concentrations in the offgas were quantified with a gas analyzer . An approximate drop of antifoam  was added to the system by switching on the antifoam pump for a few seconds at given time intervals .All fermentation experiments were started with a batch phase that lasted approximately 60 h, after which continuous operation was initiated. The pH control and antifoam addition began after the spores had germinated, which took approximately one day. Thereafter, to avoid splashing or depositing spores on the lid and glass wall above the liquid, which would result in wall growth, the airflow and stirrer speed were increased stepwise until they reached their final values. The chemostat phase was initiated after the glucose of the batch medium was exhausted, which was apparent from a sharp increase of DO, a sharp decrease of respiratory activity, and an increase of a pH above 6.5. In each experiment, air was supplied to the reactor and the DO was not controlled for approximately 5 residence times (100 h). During this phase, the DO remained at values between 0.136 and 0.190 mmol L−1 after correction for probe delay and an average cycle time of 112 s and 120 s, respectively. The two experiments differed in the amplitude of the oscillations, whereby the DO in oscillation I varied in a smaller range (0 – 0.127 mmol L−1) compared to oscillation II (0 – 0.178 mmol L−1). Furthermore, in oscillation I, the DO fell below 0.003 mmol L−1 for 11 s, while in oscillation II, it was below 0.003 mmol L−1 for 23 s. Therefore, in oscillation II, the cells were exposed to higher DO variations and longer periods of near‐zero DO levels. The DO during a cycle in each oscillation experiment is presented in the During the oscillation experiments I and II, the average DO in a cycle and the cycle time remained similar for both experiments, with an average DO of 0.062 and 0.058 mmol L−1 steps and in oscillation experiment I by increasing the DO after 5 residence times back to non‐limiting conditions that were above 0.136 mmol L−1.The recovery of the cells and the penicillin production rates were investigated after the 0.009 and 0.013 mmol L2.52.5.1The biomass concentration was determined with dry weight measurements of culture samples withdrawn from the reactor. Samples were taken with a 60 mL sampling bottle attached to the reactor, where the broth entered due to a pressure difference, while in some cases a syringe was used to help creating vacuum in the bottle. Aliquots of 5 mL culture broth were collected in triplicate and filtered through dried and pre‐weighed glass fiber filters . These filters were dried for at least 12 h at 70°C and then cooled in a desiccator for 1 h before pre‐weighing. Filtration was done with a vacuum pump and the filtrate was stored at ‒80°C until it was used for HPLC and total organic carbon (TOC) analysis. After collecting the filtrate, the mycelium on the filter was washed twice with 10 mL demineralized water, dried at 70°C for at least 24 h and then cooled in a desiccator for 1 h before weighing.2.5.2g for 15 min . The supernatant was collected and stored at ‒80°C until analysis.Rapid sampling and quenching was performed with a dedicated rapid sampling device . Right b2.5.313C labeled yeast extract stored on ice for metabolite quantification with isotope dilution mass spectrometry (IDMS). The sample mixed with the labelled cell extract and the remaining 900 μL of sample were immediately frozen in liquid nitrogen and stored at ‒80°C until analysis. A 100 μL aliquot was removed from the thawed 900 μL sample and transferred to a tube containing 20 μL 13C labelled P. chrysogenum cell extract. Extracellular glucose and trehalose concentrations were determined from the samples containing yeast extract, while the extracellular metabolites of the penicillin pathway were analyzed from the samples mixed with P. chrysogenum extract.By using the rapid sampling device, approximately 1 mL broth was withdrawn and immediately quenched by cooling to approximately 1°C within a precooled syringe at ‒20°C that contained stainless steel beads  . Immedia2.5.42PO4 in 28 % acetonitrile dissolved in water, pH 2.5 or 3 with 85 % phosphoric acid; the flowrate was 1 mL min−1. The defrosted samples were stored in the autosampler of the HPLC at 4°C . The quantification was performed with a photodiode array detector at 214 nm .Penicillin G and phenylacetic acid concentrations in the culture filtrate were quantified with high‐performance liquid chromatography (HPLC) using a Zorbax SB‐C18, 4.6 × 12.5 mm, 5 μm, guard column and a Zorbax SB‐C18, 4.6 × 75 mm, 3.5 μm analytical column  kept at 25°C. The eluent contained 5 mM KH13C‐labeled Penicillium chrysogenum cell extract was used as an internal standard. The intracellular amino acids were analysed by GC‐MS as described by de Jonge et al. [Both the intracellular penicillin metabolites and amino acids were analyzed from the samples taken from the rapid sampling device and processed by cold methanol quenching, while the extracellular metabolites of the penicillin pathway were analyzed from samples obtained from the rapid sampling device and quenched by the cold beads, as explained in Sections e et al. .−1 and the run time was 30 min. The autosampler  and analytical ion‐exchange column  were maintained at 60°C; a UV detector  and a refraction index detector  were also used for the analysis of samples. For glucose concentrations below 200 μM, glucose and trehalose were quantified using GC‐MS as previously described [When the extracellular glucose concentration was higher than 200 μM, HPLC analysis was used to quantify the glucose concentration of the broth. The eluent  had a flow rate of 0.6 mL minescribed . For botThe total organic carbon concentration was quantified by subtracting the measured amount of inorganic carbon from the measured total amount of carbon present in the sample using a TOC analyzer . Microscopic analysis  was conducted using 15× diluted biomass samples at 10× and 100× magnification.2.62.6.1−1 by applying the Henry constant of 1.3 × 10−5 mol m−3 Pa−1 [−1. During the oscillation experiments, the time delay of the DO probe response [2 gas into a stirred vessel saturated with air. The probe delay was determined from the exponential curve fitted to the measured DO response. With the measured probe time constant, τprobe, the ‘‘real’’ DO CO2,L was than calculated using Equation O2,LM).The DO values reported in this study were converted from the DO probe measurements and expressed as % air saturation at atmospheric pressure to a concentration in mmol Lm−3 Pa−1 , resultiresponse  was esperesponse , 42. The−1 DO.After the corrections, the curve was smoothed by taking a 9‐s moving average and negative DO values were corrected to 0 mmol L2.6.2O2 and qCO2 rates were calculated assuming quasi steady state, as the DO stabilised rapidly and the gas and liquid phase accumulation terms were considered to be negligible compared to the uptake rates. In this way, from the measured biomass, residual sugar, and penicillin concentrations and the offgas composition, the biomass specific growth, penicillin production, and oxygen uptake/carbon dioxide emission rates were calculated using the corresponding material balances.To obtain the biomass specific rates under non‐steady state conditions, a polynomial curve was fitted to the experimental concentration data and from the derivative of the fitted polynomial, the infinitesimal changes in time were estimated. The q2.6.3A system of differential equations describing the kinetics of the enzymatic conversion steps and the changes in extra‐ and intracellular metabolite concentrations based on mass balances was set up and solved in Matlab 2018b. The details of the solver and experimental data processing are presented in the Appendix.33.1O2) and carbon dioxide production (qCO2) rates in response to low DO concentrations, a glucose limited chemostat experiment at a dilution rate of 0.05 h−1 was performed. This experiment aimed to reveal the DO values that affected qO2, and was further used in model developed to estimate the half‐saturation constant for the cell oxygen uptake rates. After a steady state was reached, the DO was reduced in several steps to values ranging between 0.246 and 0.005 mmol L−1. Each step had a duration of 2 h, after which the DO was increased back to air saturation level for the subsequent 3 h. During cultivation, degeneration took place and therefore, penicillin production slowly declined over time [p under sugar limitation resulted in an increased Cx and decreased biomass specific sugar and oxygen uptake rates. To account for this gradual loss of productivity and change in qO2 in these chemostat experiments, a relative oxygen uptake rate was measured, where the oxygen uptake rate during the step down was normalized to the oxygen uptake rate at non‐limiting DO levels just before the step. The relative rates were multiplied by the qO2 before degeneration started to obtain the corrected qO2. These results showed that below a DO of 0.025 mmol L−1, a sharp reduction in the qO2 occurred , and concentrations of biomass (Cx), sugar (Cs), and penicillin (Cp) relative to the values during the initial chemostat phase and at the end of the low DO step .The concentration of by‐products in the culture was quantified as the difference between the measured total organic carbon concentration (TOC) in the culture supernatant and the sum of the residual sugar, penicillin, and PAA concentrations. The by‐product formation rate was only affected by a 0.009 mmol L2 consumption, CO2 production, and penicillin production rates decreased during all long‐term DO step down experiments, and these rates have restored to their original values after the DO was restored to a non‐limiting level. Lower DO values (<0.025 mmol L−1) resulted in a more progressive decrease of these rates  resulted in a lower qp even though the cycle mean was the same in both experiments. After the DO was restored to its original values, the qp increased to that observed under the initial steady state condition in experiment I.When analyzing the penicillin production rates, we found that the periodically recurring DO values of zero mmol Ln Figure  and fromn Figure . The dif−1. While the cysteine concentrations remained steady, the AAA concentrations slightly decreased and the valine concentration showed a quick increase and a slow decrease to the original value during the oscillation phase.The metabolite level measurements are presented and described in 3.4x, Cp and Cs.In order to obtain the biomass specific rates of respiration, sugar uptake, growth, and penicillin production, and better understand the metabolite concentration profiles obtained under different DO levels, the metabolism of the cells was modeled. Initially, a black box model was created to link the DO level to respiration rates and then linked to a penicillin pathway kinetic model  to serveWith the black box model, we aimed to link the primary and secondary metabolism of the cells to low DO conditions. The model predicted the growth, sugar uptake, oxygen uptake, carbon dioxide emission, and penicillin production rate as a function of DO. The oxygen‐related model parameters were then estimated based on the experimental data.oqo) was introduced into the hyperbolic kinetic model, which represented the DO value at which the oxygen uptake rate decreased to half of its maximum value; thus, determining the change in the biomass specific oxygen uptake rate at different DO values. in this model, Koqo was estimated from the experimental data, including both the short DO perturbation (oxygen uptake rates) and step experiments .In the model, the distribution of oxygen and carbon between growth, maintenance, penicillin production, and by‐product formation were evaluated according to Herbert‐Pirts law and hyperbolic kinetics was used for both sugar and oxygen consumption. Both the effects of low oxygen concentration on the central metabolism, as expressed by limitation of the respiration rate, and the influence on penicillin production rate via IPNS were included. Additionally, to predict the penicillin productivity of a reactor, the effects on the glucose and biomass concentrations were also taken into account, since the penicillin gene cluster is inhibited by glucose , and the2 concentration within the range of 0.07 to 0.18 mmol L−1 [2 concentrations (<0.07 mmol L−1), the effect of DO on the rate of the IPNS enzyme has not yet been investigated. In our study, we reported that the oxygen dependency of IPNS enzyme followed hyperbolic saturation kinetics at DO levels in the 0.009‐0.025 mmol L−1 range.The activity of the second enzyme in the penicillin pathway, IPNS, is influenced directly by oxygen concentration because this step consumes molecular oxygen. Bainbridge et al. reported a linear relationship between IPNS activity and the Ommol L−1 , which wmmol L−1 , 22, 45.oIPNS that was estimated based on our data, and the gene cluster that was inhibited by high glucose concentrations. To predict the sugar and biomass concentrations, a black box approach was used, thereby coupling the two models. KoIPNS was estimated from the measured penicillin concentration patterns during the DO step experiments. The model equations and estimated values of the parameters are presented in the Appendix.The penicillin pathway model was based on Deshmukh et al. , to whic3.53.5.1x, Cp, and Cs are presented in Figure s and Cx at growth limiting and non‐growth limiting DO were reproduced by the model. The model predicted well the penicillin concentration and also the penicillin pathway metabolite levels. In particular, the IPN was recused and ACV and bisACV accumulated in low DO conditions . The simulation of the pathway metabolites are presented in the The simulations of Cs Figure . Accordi44.1p was the decreased rate of IPNS enzyme due to oxygen limitation [−1 [−1, the IPN levels decrease to values that approach or fall below the Km values of the IAT and IAH enzymes, which reduce penicillin production.The short and long DO step experiments showed that with decreasing DO, penicillin production was limited, possibly through the oxygen limitation of IPNS. The penicillin concentrations showed the most significant reduction in the penicillin production rate at the lowest oxygen concentration. The expected mechanism behind the decrease in qmitation , which wtion [−1 ; howevertion [−1 , 21. Ourp at low oxygen concentrations (0.013–0.025 mmol L−1) resulted in an increase of the biomass yield. This result can be explained by the reduced penicillin production rate under low oxygen concentrations, which increased sugar availability for cell growth. According to the model predictions, the biomass‐specific sugar uptake decreased because of the reduction in the biomass specific penicillin production rate. However, with the elevated biomass concentration, the overall sugar uptake increased, resulting in a decreased residual sugar concentration that corresponded to a reduced specific glucose uptake rate, which was described by saturation kinetics. Similarly, reduced penicillin production rates under sugar limitation could have reduced the biomass specific oxygen requirements, as penicillin production requires O2. The experimentally observed gradual decrease of the penicillin pathway flux might have been caused by changes in the enzyme levels of the penicillin pathway over time because enzyme expression is a slower process than enzyme limitation.The reduced q−1 affected the primary metabolism of the cells as well. This was observed as a significant decrease of the respiration rate (qO2 and qCO2), indicating that oxidative phosphorylation was limited by oxygen availability. Between a DO of 0.013 and 0.009 mmol L−1, a shift from glucose‐ to oxygen‐limited growth occurred and resulted in an increased residual glucose concentration at 0.009 mmol L−1 DO. Consequently, the growth rate declined to a value below the dilution rate and resulted in wash out of the cells, as indicated by the decreased biomass concentration.Further decrease of the DO below 0.013 mmol L−1, the excretion of by‐products increased compared with the excretion at non‐limiting DO values. At 0.009 mmol L−1 DO, the biomass concentration declined, suggesting the observed organic carbon excretion might have been a result of cell death and autolysis, a phenomenon that has been reported at low DO concentrations in the filamentous fungi genus, Aspergillus awamori [Penicillium chrysogenum increased at low DO [At a DO of 0.009 mmol L awamori . Additio awamori  and simit low DO .−1 DO step experiment, the effect of sugar repression on the penicillin gene cluster and the reduced IPNS activity could have played a significant role in reducing qp. The IPN levels decreased to values close to the Km of the IAT enzyme for IPN; therefore, a decrease in the direct flux towards Penicillin‐G was expected. Additionally, the IPN concentration was reduced to a value 200 times below the Km for IAH, suggesting that almost no 6APA formation took place, further reducing flux towards penicillin production.The increased residual glucose concentration at growth limiting DO levels could have resulted in the repression of penicillin producing enzymes. In the 0.009 mmol LO2 and qCO2 results resulted in a good fit with the experimental data. The observed qO2 reduction with decreased DO in the long‐term step experiments were similar to those observed in the short perturbation experiments. Additionally, these results were similar to those reported by Vardar and Lilly [O2 was more pronounced compared to those observed by Henriksen et al. [−1, which did not show a reduction in qO2 [P. chrysogenum fungal strains used in the two studies.In contrast to previous results , the obsnd Lilly , as indin et al. , since tn in qO2 . The difx, Cs and Cp values well during the step experiments with a saturation constant for respiration Koqo of 0.014 mmol L−1, which was estimated from the data from the short‐term DO perturbation experiments. The obtained oxygen saturation constant was slightly higher compared to the reported value of 0.003 mmol L−1 for Aspergillus niger [The developed model described the experimentally obtained Cus niger . Howeverus niger .P. chrysogenum after exposure to limiting oxygen concentrations was previously reported [p after oxygen limitation recovery is consistent with previous observations, where instantaneous recovery was observed after the DO was increased to 0.08 mmol L−1 from a 0.019 mmol L−1 [p at DO values below 0.027 mmol L−1 [When the DO was restored after the low DO steps, the penicillin, biomass and residual sugar concentrations, and the oxygen uptake and carbon dioxide emission rates were restored to the original values that corresponded to the initial steady state conditions in all step experiments. The recovery of respiration in reported , 17, 18.mmol L−1 . Howevermmol L−1 . Similar4.2p compared to a steady DO of the average of the cycles. At this average DO (0.06 ± 0.002 mmol L−1) under steady conditions, neither qp nor qo was expected to be notably influenced according to both model predictions and experimental data, both of which indicated that during the DO step experiment at 0.054 mmol L−1 the penicillin production was not affected  significantly affected the oxygen uptake rate in an irreversible manner [−1 DO) was only achieved for short time periods of less than a minute.Similar to the long‐term step experiments, the Cmmol L−1 . Additio 1–2 min . Howevere manner , which wp. The dependence of qp on the central metabolism can be affected by the energy, precursors, and cofactors required for the penicillin production pathway [−1 DO step experiment, the intracellular AAA concentration decreased only slightly during oscillation experiment I. Therefore, the possibility that the central metabolism limited the AAA biosynthesis and ACVS rate cannot be excluded. The lack of ACV accumulation could correlate to the energy supply for the enzymatic conversion step catalyzed by ACVS; the ACVS enzyme requires ATP [The effect of oscillations on penicillin pathway metabolites differed from the step experiments. The main difference was that during the oscillation experiments, no accumulation of intracellular ACV was observed. In our model simulations, the IPNS flux decreased below the pathway flux during the oscillations and therefore, ACV accumulation was expected. However, the complex interactions between the primary and secondary metabolism could have affected the metabolite levels and explain the obtained q pathway . Therefo pathway ; howeverires ATP  and the 5P. chrysogenum to periodic oxygen limitations, and can be used to model the fermentation process under heterogeneous oxygen conditions as observed in large‐scale reactors. Fermentations were monitored during step‐wise changes in DO and during oscillating DO conditions. The metabolite levels of the penicillin pathway were also evaluated. The experiments showed that at a DO of 0.009 mmol L−1, the central metabolism was limited, while between 0.009 and 0.25 mmol L−1, only penicillin production was reduced. During oscillating DO conditions that are experienced by P. chrysogenum cells under large‐scale reactor conditions, the penicillin production rate decreased below the values expected in stable DO levels equal to the average cycling DO. A decrease in penicillin production, sugar and oxygen uptake, and growth rates due to low DO were reversible when the DO levels were restored to non‐limiting values (>0.136 mmol L−1). In order to predict the cellular metabolic response to varying DO levels, the experimental results were modeled with a coupled black box model and detailed kinetic model of the penicillin pathway. During a quasi‐steady state at low DO, the reduced penicillin production rate and altered metabolite levels were well described by the decreased IPNS rate and the inhibition effect of the accumulated extracellular sugar on the penicillin gene cluster. During oscillating DO conditions, the observed lack of ACV accumulation could be explained by the limited availability of precursors or energy supply from the central metabolism. The model predicted the observed respiration, growth, sugar uptake, and penicillin production rates during step‐wise DO changes, as well as during scale‐down conditions. Thus, the developed model can be applied to predict cellular behavior in large‐scale bioreactors where insufficient mixing takes place and the substrate levels show fluctuations in space and time.This study provides detailed insight into the metabolic response of This project is sponsored by Centrient Pharmaceuticals, a producer of lactam antibiotics, and DSM a global company active in Nutrition, Health & Sustainable Living.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.