Viability and metabolic assays are commonly used as proxies to assess the overall metabolism of microorganisms. The variety of these assays combined with little information provided by some assay kits or online protocols often leads to mistakes or poor interpretation of the results. In addition, the use of some of these assays is restricted to simple systems (mostly pure cultures), and care must be taken in their application to environmental samples. In this review, the necessary data are compiled to understand the reactions or measurements performed in many of the assays commonly used in various aspects of microbiology. Also, their relationships to each other, as metabolism links many of these assays, resulting in correlations between measured values and parameters, are discussed. Finally, the limitations of these assays are discussed. Similarly, the consumption of carbon sources and production of by-products  are also valuable tools. As these assays all focus on metabolism, there are correlations between some of them, making comparison or validation possible.Metabolism can be defined as the sum of all reactions in a living organism aimed at maintenance, development, and reproduction (division for microbes). Consequently, measuring all reactions that contribute to metabolism in microbial cells is impossible with current tools even if metabolomics using NMR and mass spectrometry (MS) can provide very valuable snapshots of the metabolites present . In addiMetabolic assays have been used for a wide variety of applications. For example, in ecological studies, such assays can be used to evaluate the number of active bacteria in a sample . However, in pharmacological and biotechnological applications, these assays are less often focused on metabolic rates and are mostly used to assess viability to compare the effects of different products on microbial cultures. Similarly, yield and other important information can be gathered from some of these assays or their combinations.Nevertheless, depending on the intended application parameters, such as volume and sensitivity, the necessary equipment can be a significant factor in the choice of a metabolic assay. For example, metabolic assays with pure cultures can have very different requirements from those with environmental or food samples. Indeed, many of these assays have been devised for pure cultures and are applicable only to such cultures. Many have been modified for applicability to environmental studies, but the resulting limitations are often neglected. In addition, the cost and practicality can also vary a lot, thus influencing potential application. Therefore, it is important to take these parameters into account and review the different assays.Using all the techniques described below, it is crucial to discriminate between activities  and amounts . In many studies, this difference is neglected, which results in directly linking growth to metabolic activity. Many studies have measured metabolic activity using growth (often measured by a net increase in cell number) as a proxy. Although such approximation can be understood as high metabolic activity is required for growth, it has major flaws. For example, in batch liquid culture, when entering the stationary phase, the cell number still increases while the metabolic activity is often already decreasing. In this context, it is often worth coupling both measures and determining the activity per cell over the course of the culture. Ultimately, at the end of the experiment, usually at the late stationary phase, there is a very large number of cells, but the overall metabolic activity is very low. On the other extreme of the spectrum, it was recently shown that even with no net biomass increase, biofilm could have a very high metabolic activity as measured by calorimetry . This isHaloferax volcanii, Haloarcula marismortui, and Halobacterium sodomense have been shown to perform such reduction  will have minimal (if any) reduction activity. This can be used to determine the respective proportion of active and inactive cells in various samples by comparison to microscopy count, for example . Similarp-tolyl)tetrazolium chloride (CTC), but also iodonitrotetrazolium chloride (INT) and 2,3- bis(2-methoxy-4-nitro-5-sulfophenyl)-2h-tetrazolium-5-carboxanilide inner salt (XTT); In addition, there are other limitations to the use of tetrazolium-based dyes that must be taken into account, especially for environmental studies. Several tetrazolium salts . Again, it is important to compare rates with other matching rates, for example, formazan production rates with oxygen consumption rates (see example in +/NADH) and their phosphorylated derivatives (NADP+/NADPH) are important redox-active molecules used in many catabolic and anabolic reactions. Ratios of NAD+ to NADH and NADP+ to NADPH are therefore considered key indicators of the overall intracellular redox potential and metabolic state. Because tetrazolium salts are often used to assess the activity of NADH or NADPH utilizing enzymes  and electron mediator (redox intermediate)   (Kern etmediate) . The ass+ and NADPH, the assays rely on the conversion of glucose-6-phosphate (G6-P) to 6-phosphogluconolactone by glucose-6-phosphate dehydrogenase , where the simultaneous reduction of NADP+ to NADPH is coupled to the reduction of a tetrazolium salt  have to be accounted for (see below). When considering only ATP, the general assumption is that microorganisms generate ATP through catabolic reactions and subsequently use it for “housekeeping,” growth, and replication. As a result, ATP is believed to indicate the presence of viable microorganisms in samples. Alternatively, as the intracellular concentration of ATP per cell has been shown to vary with changing environmental and physiological conditions, it can be used as a proxy to indicate the metabolic activity of cells . Indeed, to 5 mM . For a w5 mM  to oxidize luciferin into oxyluciferin with concomitant use of ATP and production of AMP. The overall reaction  releases2O2). Finally, a peroxidase (EC: 1.11.1.7) catalyzes the redox-coupled reaction of H2O2 with 4-aminoantipyrine (4-AAP) and N-ethyl-N-(3-sulfopropyl)-m-anisidine (ESPA), leading to the formation of a purple dye that is measured at 540 or 570 nm  to produce glycerol-3-phosphate and ADP. The glycerol-3-phosphate is then further oxidized by glycerol phosphate oxidase (EC: 1.1.3.21), producing dihydroxyacetone phosphate (glycerone phosphate) and hydrogen peroxide  into a colored compound. In addition, for some applications (mostly with cultures), there is also no need to remove cell culture medium or wash cells before adding the reagent, which can be fully automated for high throughput. Still, serial measurements are needed to assess the flow of intracellular and extracellular ATP and get a complete image of metabolic processes over time. If sample processing needs to be performed at a later stage , it is recommended to snap-freeze the samples in liquid nitrogen or on dry ice. For this assay, ATP extraction remains a crucial step and different extraction methods were tested on microbial culture samples, showing strong variation in ATP recovery . Still, 2 consumption, although many studies have shown that the two are often correlated (E. coli) and has been reported to be prone to artifacts, including incomplete stain penetration or false staining of live cells as dead  hydrolysis is considered to be a simple and affordable method of estimating microbial activity in various samples, including soils, sludges, marine sediments, and cell cultures . In thisrrelated . Still, rrelated . Intracerrelated . Such mi as dead  in many  as dead . FACS an490 nm) lies between 67,000 and 79,000 cm–1⋅M–1  to the sample. After a determined incubation period, the reaction is stopped using rather large amounts of organic solvents [chloroform:methanol (2:1) or acetone]. In many environmental samples, the solvent mixture also serves as a fluorescein extractant by dissolving membranes . In cellcm–1⋅M–1 . Such a cm–1⋅M–1  and its 2/H2O, NO3–/NO2– and Fe3+/Fe2+, for example (+/NAD(P)H have been discussed previously in the text.Electron acceptors are key players in microbial metabolism, providing energy for many other processes. Indeed, many microorganisms obtain their energy from redox reactions. Among these redox reactions, aerobic respiration, anaerobic respiration, and oxidation of reduced inorganic compounds rely on redox couples such as O example . The conOxygen is among the most used proxies to assess the metabolic activity of various organisms. This is also true for microorganisms and microbial oxygen consumption rates, and their measurement could be the topic of a review by itself. As a result, many methods have been developed to assess oxygen consumption by heterotrophs or production by photosynthetic microbes  can be used to measure several gases in gas phase. TDLAS measures the absorption of laser light in the near- to mid-infrared wavelength. Detection of gases can be improved using techniques such as wavelength modulation spectroscopy (WMS) or frequency modulation spectroscopy (FMS) . It workin vials , 2018. Faled air  using ot3–) and nitrite (NO2–) can be measured by high-performance liquid chromatography (HPLC), ion chromatography (IC), or ion-specific electrodes, the Griess reaction ethylenediamine (NED or NEDA). Most variations of the Griess assay were reviewed in Like many assays, the Griess reaction has been modified, and the original sulfanilic acid and 1-naphthylamine were replaced by sulfanilamide and Finally, it must be noted that the low cost and simplicity of the Griess reaction have attracted the attention of a wide spectrum of microbiologists ranging from soil scientists to clinical microbiologists.–3 g⋅L–1 − Ksp = 1.0842⋅10–10 (25°C)], which is measured turbidimetrically  ion-sensitive electrodes are very practical and are often used because they are available in macro and micro format. With microelectrodes that are used in situ (or ex situ), measurements are taken directly and possibly on-site. They have been extensively used in sediment, microbial mat, and biofilm studies , or nanoparticle of pyrite (FeS2)] that might be present on cell surfaces or attached to biofilms/extracellular polymeric substance (EPS) matrix  matrix . In huma) matrix .Many metal ions can be biologically reduced (and less frequently oxidized), thus releasing sufficient amounts of energy to sustain growth processes. Among those metals, Fe, Mn, V, Cr, Cu, Mo, As, Hg, Se, Au, U, and Tc have been reported to be reduced by microorganisms . Among t2+ and Fe3+) can be assessed easily using assays relying on the reaction of a chromogene with iron II   to convert Fe3+ into Fe2+. Many commercial kits do not provide the composition or the type of assay present in the kit. However, it is possible to decipher them according to the measurement wavelength of the assay  . Many ofiologist . UsuallyCarbon sources are the key to microbial metabolism, providing carbon for biosynthetic processes but also for energy metabolism in many cases. Current technologies (such as Biolog EcoPlate) using well-plate readers allow rapid screening of several important carbon sources for their utilization and eventually link such data with metagenomics data obtained separately . HoweverThe variety of carbon sources that can be used by microbes is indeed matched by a high number of assays to assess them. Especially, there are many enzymatic assays matching the substrates to be assessed. There are also plenty of chemical assays that are useful for assessing the uses of various carbon sources.o-toluidine, anthrone, carbazole) that allows the development of a color measurable by spectrophotomeric means  are hydrolyzed using a strong acid and heat. The reaction mixture contains a developing reagent  to form a colored compound in the reaction solution. Finally, oxidized o-dianisidine reacts with sulfuric acid to form a more stable colored product (Sigma Aldrich). The other enzymatic assay used for glucose determination relies on hexokinase (EC: 2.7.1.1). Glucose is phosphorylated by hexokinase, and the resulting glucose-6-phosphate is then oxidized to 6-phospho-gluconolactone by glucose-6-phosphate dehydrogenase (EC: 1.1.1.49). The NADH produced results in increased absorbance at 340 nm. Of note, glucose-6-phosphate dehydrogenase usually uses NADP+ as a cofactor, but this enzyme is rather unspecific with regard to the cofactor (NADP+ or NAD+). Thus, in many commercial assays, NAD+ is used because of its lower cost and better stability (2O2)] to measure glucose. The approach is cheap, although calibration is required as those meters have been optimized for blood tests and might have an offset in other media or solutions . The incorporation rate of the selected compounds is assumed to be proportional to the metabolic activity and the viability of anabolically active microbes. As a result, many incorporation assays relying on different detection methodologies have evolved. In addition, many of these techniques are compatible with further use of microscopy and single-cell analysis as well as (meta)genomics and (meta)proteomics tools . Thymidine is incorporated into microbial DNA and thus provides a measure of DNA synthesis. Similarly, leucine is incorporated into proteins and acts as a proxy for protein synthesis. The combination of both allows the measurements of protein synthesis and DNA synthesis in a single experiment. This approach assumes that thymidine and/or leucine is taken up into the cells; however, some microbes, especially strict autotrophs and oligotrophs, lack the necessary transporters for the uptake of many organic molecules, including amino acids and nucleotides . PracticStable isotope probing (SIP) refers to the use of stable isotope-labeled substrates to probe the microbial utilization of these specific substrates to build their macromolecules . As a reAs an alternative to MS, Raman microspectroscopy can be used in combination with optical tweezers or single-cell ejection to sort and separate labeled from unlabeled cells with high accuracy. The incorporation of heavy isotopes in the cell macromolecules results in a shift of the Raman peaks toward lower wavenumbers compared with unlabeled cell spectra . That deSubstrate analog probing is a term introduced by Hatzenpichler  to diffeSimilarly to radiolabeled thymidine incorporation, 5-bromo-2′-deoxyuridine (BrdU) has been used in the same way. Indeed, BrdU is an analog of the DNA precursor thymidine. When it is added to a sample containing growing cells , the cells incorporate BrdU into their DNA instead of thymidine. The BrdU-containing DNA is then detected using an anti-BrdU antibody. Then, a secondary antibody specific for the anti-BrdU primary antibody is used to capture the DNA (with the help of paramagnetic beads), thus providing a new sample of reduced complexity that corresponds to cells effectively dividing , which cin situ hybridization (FISH), nanoscale secondary ion mass spectrometry (nanoSIMS), (meta)genomics, and (meta)proteomics  has garnered much interest because of its compatibility with many other approaches such as FACS, fluorescence oteomics . In BONCoteomics . Indeed,oteomics . After aoteomics  using thoteomics .Similarly, biotin tags allowing quantification, affinity purification, and further identification of protein using MS have been used as well . AlthougIn living organisms, the result of metabolic activity leading to maintenance, development, and reproduction also includes some heat release , 2013. Trxn) can be easily estimated  and measuring the additional enthalpy caused by the hydration of CO2 into HCO3– (–108.5 kJ⋅mol–1) .In addition, isothermal calorimetry is a quantitative tool, and for some types of metabolism and media, the enthalpy of the overall metabolic reaction (ΔHJ⋅mol–1) , thus he3–, for example) or fermentation (sensu stricto). Similarly, calorespirometric ratios (heat per O2 and heat per CO2) can give valuable information. Heat per O2 can help detect the presence of anaerobic processes, and heat per CO2 can provide hints on the type of carbon source used  , the consequence is that many of them provide data that can be correlated or combined . Indeed, lipids) . For heamol–1 e- . Therefotabolism . Still, tabolism . In addiThe following conservation equations can now be written:d, e, and f are negative) allows for determining the other ones. Moreover, calorimetry can provide an additional equation to solve this system. Indeed, using the enthalpy of reaction provides an additional equation that can further help solve the equation system. The enthalpy of reaction (ΔHrxn) measured by the calorimeter can also be calculated using enthalpy of formation (ΔHf) or of combustion (ΔHc); using enthalpy of combustion simplifies the equation even more  (from ΔHH4) from The enthalpies of formation and combustion of organic matter, and especially organic matter from microbial biomass, can be found in several review papers , thus maHowever, such an approach requires that the metabolism of the organism (or group of organisms) is already known and that the organisms have been characterized up to the point where the growth equation can be written in an accurate manner.Many of these assays have been used for a long time and have been improved and optimized using new chemicals and instruments. Still, as mentioned for glucose, many studies continue to improve or adapt (not to say “hack” or “exploit”) these methods and approaches. Among the possible promising approaches for many of these assays, the lab on a chip is very appealing. Microfluidic systems, piezoelectric pumps, and sub-millimeter size sensors make it possible to build an array of sensors that can monitor different compounds over time and thus provide real-time monitoring of metabolic rates. It is likely that, in the future, many of these assays (or replacements for some of them) will be combined on single disposable chips. Alternatively, for now, 3D printing and rapid prototyping methods also allow rapid development of microfluidic devices that can incorporate such new sensors. Finally, it must be noted that such chips are very close to becoming reality, as several authors have already presented multiple parameters using separate microprobes or microfluidic chips simultaneously measuring pH, OD, and dissolved oxygen, for example . Still, OB and MA-F drafted and wrote the manuscript. TW and GB critically reviewed the manuscript and improved it. All authors read and approved the submitted version.GB was employed by the company Alta-Uro AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The loglinear pattern of respiratory scaling has been studied for over a century, while an increasing number of non-loglinear patterns have been found in the plant kingdom. Several previous studies had attempted to reconcile conflicting patterns from the aspects of statistical approaches and developmental stages of the organisms. However, the underlying enzymatic mechanism was largely ignored. Here, we propose an enzyme-driven law of photosynthetic scaling and test it in typical crop seedlings under different water conditions. The results showed that the key enzyme activity, the relative photosynthetic assimilation and the relative growth rate were all constrained by the available water, and the relationship between these biological traits and the available water supported our predictions. The enzyme-driven law appears to be more suitable to explain the curvature of photosynthetic scaling than the well-established power law, since it provides insight into the biochemical origin of photosynthetic assimilation. The scaling model of the power law has been widely employed to describe net primary production scaling5 in log–log space. Some researchers have applied log-transformed power law to dark respiration rates in trees8 and shrubs9. A few authors have used photosynthetic rates as metabolic indicators in ocean and plants11, but the subjects are marine systems and saplings, respectively. However, the relationship between photosynthetic rate and biomass in crop plants has not been studied under the light of power law.Photosynthesis is the fundamental metabolism for most of world’s ecosystems13, and towards its resolution, a series of arguments about the power law and the ‘true’ value of scaling exponents have been suggested17. It has theoretically been shown that the scaling exponents should range between 0.31 and 1.00 in aquatic invertebrates and algae18. The models based on polynomial equation have been used to describe and quantify the double-log linear deviation in basic metabolic scaling20. However, there are still contentious in the origin of photosynthetic assimilation and the methods used to estimate parameters21.Photosynthetic assimilation and respiration are both important metabolic processes, and they reverse each other. However, metabolic scaling of basic respiration is a longstanding problem22. Transformations of logarithms are referred to as patterns of “non-loglinear allometry”23 or patterns of “nonlinear allometry”24, which can be represented by adding other terms based on power laws27 or energy dynamics28. Models can explain the variation in scaling exponents in terms of resource allocation (Dynamic Energy Budget Model)29, metabolic level30 and thermodynamics processes31. A sigmoidal model predicted by the plant adaptive strategies hypothesis in NPP scaling1. However, although photosynthesis and respiration are a series of biochemical reactions, chemical kinetics is rarely used to explain the challenges of metabolic scaling. We have developed an enzyme-driven law (EDL), which was successfully applied to quantify the curvature and exponent variations of respiration scaling in microbes32. For terrestrial plants, photosynthesis differs significantly from respiration in microbes. Thus, a modified law is required to describe the photosynthetic scaling by incorporating biochemical mechanisms.Many log–log relationships of metabolic scaling have been shown to be nonlinear and cannot be represented by a simple power function34) to explain the biochemical origin of curvature in photosynthetic scaling.In this study, a new enzyme-driven law for photosynthetic scaling was derived from the hypothesis and both the relative rates of photosynthetic assimilation and growth were constrained by the key enzymatic activities. The law was tested by the data of photosynthesis and growth under different water conditions in represented crop seedlings (rice and maize3) and maize (C4) with different parameters  and C4 (maize) plants under the experiment conditions. Key enzyme activities varied with available water in accordance with the Michaelis–Menten equation  were bigger than that in maize , which meant maize was more sensitive to available water; The water potential in rice was lower than that in maize when the key enzyme activity was zero  and growth (lnM)  Fig. . Meanwhi Km Eqs. , Fig. 3. Km Eqs. . The AIC Km Eqs. . While t11, in which the effect of body size on the metabolism had not been considered. However, the remarkable36 effects of body size on the metabolism35 could not be ignored. It is widely acceptable that both the metabolism and growth are a series of enzymatic process, which are constrained by the key enzymatic activities37, so it is reasonable to deduce the general law of metabolic scaling. The enzyme-driven law may be the origin of various metabolic scaling.The enzyme-driven law had been tested gradually from enzymatic Eq. , Fig. 1,27 or the energy dynamics28. The constraint factor of limited resource(s) have been considered in metabolic level boundary hypothesis, which was used to explain the variations of scaling exponent against metabolic level30. Moreover, the similar nonlinear pattern of metabolic scaling had also been found in teleost fish38. Meanwhile, a model of growth scaling was used to describe the growth of many diverse species proposed with parameter-less curve39. In addition, the polynomial equation of energy dynamics had been used to describe the nonlinear curve of active metabolic scaling during ontogeny in birds and mammals28. Instead, our equation  and rice (Oryza sativa L. Nipponbare) are representative species of the C4/C3 photosynthetic pathways34 and therefore were used in this study. Seedlings of two leaves were cultivated in black plastic buckets filled with Hoagland solution (pH 6.0) under a series of water potentials, which were adjusted to using polyethyleneglycol 6,000 (PEG-6000). Before the stage of two leaves, photosynthetic photon flux density (PPFD) and temperature were controlled in a stepwise diurnal sequence, with daily PPFD of 300 μmol m−2 s−1, and a photoperiod of 16 h. Temperature varied between 25 ℃/daily and 23 ℃/might.Maize  was used for the measurement of photosynthetic rate. Sample CO2, block temperature and photosynthetic active radiation (PAR) were set at 400 μmol mol−1, 36 ℃ and 1,200 μmol m−2 s−1 respectively. Dry weights of plants were obtained by oven drying at 105 °C for 30 min and then at 65 °C for 48 h.A portable open system , equipped with a CO42. 0.2 g of leaf was put into a precooled mortar, then 100 mmol/ L of precooled Tris–HCl buffer ; This sample was then centrifuged 4 ℃ for 20 min at 15,000 r/m. Then, we used supernatant fluid to measure the activity of RuBPCase and PEPCase with spectrophotometry43.The fluid enzymes were extracted by the modified li-ren’s method37, we hypothesized that the relative change in photosynthetic assimilation (dQ/Q) was constrained by the change in key enzymatic activity (dvq) at individual level, including the effects of body size40. Hence, the relationship between photosynthetic assimilation rate (Q) and the key enzymatic activities (v) was obtained:According to metabolism is a series of enzymatic processesv and substrate concentration S was assumed to abide by the Michaelis–Menten equation, therefore the relationship between Q and S can be described as follows:Qm is the maximum metabolic rate when S approaches saturation, Kq is a half-saturation constant , R represent resource. S = R − R0, lnQ0 = lnQ when S equals zero.The relationship between dvg)41, which included the effects of body size on the growth40. The substrate-dependent equation of logarithmic body mass can be obtained by the integration of Mm is the maximum body mass when S approaches saturation. lnM = lnM0 when S = 0, Km is the half-saturation constant.We could hypothesize that the relative change in growth


Pseudomonas aeruginosa is an opportunistic human pathogen that is well known for causing infections in the airways of people with cystic fibrosis. Although it is clear that P. aeruginosa is metabolically well adapted to life in the CF lung, little is currently known about how the organism metabolizes the nutrients available in the airways. In this work, we used a combination of gene expression and isotope tracer (“fluxomic”) analyses to find out exactly where the input carbon goes during growth on two CF-relevant carbon sources, acetate and glycerol (derived from the breakdown of lung surfactant). We found that carbon is routed (“fluxed”) through very different pathways during growth on these substrates and that this is accompanied by an unexpected remodeling of the cell’s electron transfer pathways. Having access to this “blueprint” is important because the metabolism of P. aeruginosa is increasingly being recognized as a target for the development of much-needed antimicrobial agents. Pseudomonas aeruginosa is an opportunistic human pathogen, particularly noted for causing infections in the lungs of people with cystic fibrosis (CF). Previous studies have shown that the gene expression profile of P. aeruginosa appears to converge toward a common metabolic program as the organism adapts to the CF airway environment. However, we still have only a limited understanding of how these transcriptional changes impact metabolic flux at the systems level. To address this, we analyzed the transcriptome, proteome, and fluxome of P. aeruginosa grown on glycerol or acetate. These carbon sources were chosen because they are the primary breakdown products of an airway surfactant, phosphatidylcholine, which is known to be a major carbon source for P. aeruginosa in CF airways. We show that the fluxes of carbon throughout central metabolism are radically different among carbon sources. For example, the newly recognized “EDEMP cycle”  plays an important role in supplying NADPH during growth on glycerol. In contrast, the EDEMP cycle is attenuated during growth on acetate, and instead, NADPH is primarily supplied by the reaction catalyzed by isocitrate dehydrogenase(s). Perhaps more importantly, our proteomic and transcriptomic analyses revealed a global remodeling of gene expression during growth on the different carbon sources, with unanticipated impacts on aerobic denitrification, electron transport chain architecture, and the redox economy of the cell. Collectively, these data highlight the remarkable metabolic plasticity of P. aeruginosa; that plasticity allows the organism to seamlessly segue between different carbon sources, maximizing the energetic yield from each. Pseudomonas aeruginosa is an opportunistic pathogen. This cosmopolitan microbe has become one of the most frequently detected causative agents of nosocomial infection  such as palmitate. The liberated glycerol is then metabolized through the action of enzymes encoded by the glp operon, whereas the FAs are iteratively degraded by β-oxidation to yield acetyl CoA (acetyl-coenzyme A). The acetyl moiety is then shuttled into the tricarboxylic acid (TCA) cycle and the glyoxylate shunt to generate energy and gluconeogenic precursors for biomass production, respectively. Other sources of short-chain fatty acids in CF airways have also been recently identified , coupled with reverse genetics, to systematically investigate the pathway(s) of carbon assimilation during growth on acetate and glycerol. Surprisingly, the presence of these different carbon sources not only leads to a “rewiring” of central metabolism, it also gives rise to profound changes in the levels of expression of pathogenicity-associated functions, including denitrification, redox balance mechanisms, and the electron transport chain (ETC). These data underline the striking metabolic flexibility of P. aeruginosa, which allows this organism to carry out efficient, real-time free energy conservation, a trait which is likely to aid its ability to proliferate under diverse environmental conditions.Despite the obvious importance of these substrates for pathophysiology, we still do not know how glycerol and acetate impact the metabolism, redox balance, and gene expression profile of P. aeruginosa during cultivation on acetate or glycerol as a single carbon source. This revealed that P. aeruginosa grew more slowly in glycerol (μmax growth rate = 0.37 ± 0.01 h−1) than in acetate (μmax = 0.80 ± 0.01 h−1), glucose (μmax = 0.88 ± 0.05 h−1), or succinate (μmax = 0.87 ± 0.05 h−1)  in baffled shake flasks containing MOPS (morpholinepropanesulfonic acid)-buffered media. This allowed us to elucidate specific impact of carbon source utilization on P. aeruginosa metabolism and physiology without the confounding factors of nutrient and oxygen limitation that accompany entry into the stationary phase.We first examined the growth characteristics of P. aeruginosa central carbon metabolism and also are thought to have distinct effects on redox metabolism  (see Transcriptome sequencing (RNA-Seq) analyses of quadruplicate biological replicates yielded quantification of the mRNA levels from 5,578 genes. After normalization and statistical analysis  to C, we −2) see  15)..P value 10.1128/mBio.02684-19.1FIG S1P. aeruginosa. (A) Principal-component analysis plot (PCAplot) of RNA-Seq data. (B) Scatterplot matrix of gene-level and isoform-level expression (csScatterMatrix). (C) Scatterplot comparing the FPKM values determined under the two conditions (csScatter). (D) Principal-component analysis of proteomic data. (E) Heat map and hierarchical clustering of proteomic data. (F) Pairwise comparisons of proteomic data. Download FIG S1, TIF file, 2.4 MB.Transcriptomic analysis (CummeRbund) and proteomic analysis (LIMMA Package) of MOPS-acetate-grown versus MOPS-glycerol-grown grown Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02684-19.8DATA SET S1Data Set S1, XLSX file, 1 MB.Summary of the electron transport chain-associated omic changes described in the main text. Full transcriptomic and proteomic data are provided in Table B and Table C, respectively. Download Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. aeruginosa to date. Following normalization and statistical analysis   transcriptome and proteome analysis carried out on analysis , we idenl to −2) . There wl to −2) .P value ≤ 0.01; log2 fold change greater than or equal to 2 or less than or equal to −2) as Voronoi tree maps. This provided a global overview of the proteomic consequences of growth in each carbon source. As shown in To enable a more detailed analysis of the ‘omic alterations between these conditions, we used the proteomaps Web service  to illusPseudomonas species are scarce. In addition, all P. aeruginosa metabolic flux analysis (MFA) studies reported to date have been carried out using glucose as a sole carbon source  using three separate tracers per carbon source   . The cal10.1128/mBio.02684-19.9DATA SET S213C fluxomics data of MOPS-acetate-grown and MOPS-glycerol-grown P. aeruginosa, including calculations for anabolic demand (A), OpenFLUX SimVector files (B), reaction network (C), goodness of fit (D), and metabolic fluxes (E). Download Data Set S2, XLSX file, 0.1 MB.Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the During growth on glycerol, there was strong induction of the glycerol uptake system at both the proteomic and transcriptomic levels . Glycero27–bis-phosphate , then to fructose 6-phosphate (F6P), and finally to glucose 6-phosphate (G6P) through the action of Fda, Fbp, and Pgi, respectively. This route is a necessary prerequisite for the generation of hexose sugars for cell wall synthesis and biomass production. The alternative fate of DHAP is catabolic. Here, G3P is converted to 1,3-bis-phosphoglycerate for glycolytic energy production  of the flux proceeding through the ED pathway-catalyzed reactions as follows: glucose → gluconate → 6-phosphogluconate → 2-keto-3-deoxy-6-phosphogluconate → G3P/pyruvate  through the action of triose phosphate isomerase (TpiA). DHAP and G3P are subsequently converted into fructose 1,6-oduction . Howevereactions . This “Epyruvate . Intrigupyruvate .P. aeruginosa was grown on glycerol, the fluxomics indicated that about 15% of the triose phosphates (G3P + DHAP) were diverted into the EDEMP cycle to form hexose phosphates (P. aeruginosa lacks phosphogluconate dehydrogenase activity (a major source of NADPH in many other species), so this apparently futile cycling reaction may be of major importance for growth. Indeed, more than two-thirds of the carbon in the malate pool was shuttled back to pyruvate through the action of malic enzyme, with less than one-third being converted to oxaloacetate via the malate dehydrogenase reaction. Overall, glycerol metabolism seems to be characterized by operation of the EDEMP cycle, net catabolism of G3P to pyruvate, and cycling of the latter through the pyruvate shunt.Consistent with the proteomic/transcriptomic data, when osphates . Hexose osphates . HoweverP. aeruginosa 3234 [PA3234]) , which are known to be essential for growth on acetate , 34, andPA3234]) . The TCAPA3234]) . Unlike one pool .in vivo. This presumably relieves the limitation on oxaloacetate usage in anabolism. At the steady state, our flux data show that around two-thirds of the carbon flowing through the branch point is fluxed through the isocitrate dehydrogenases.Carbon fluxes in acetate-grown cultures were very different from those observed in glycerol-grown cells. First, the net flux through the lower reactions of the ED pathway was in the gluconeogenic direction. Second, the flow of carbon through the EDEMP cycle was much lower than that observed in glycerol, and the flux terminated at the gluconeogenic endpoint, G6P. The absence of flux through the G6P dehydrogenase (Zwf) reaction following this point is significant, since this reaction is often thought of as a major source of NADPH for anabolism. Third, and compounding this, the extent of carbon cycling through the NADPH-producing pyruvate shunt was low. Fourth, most of the carbon for anabolism was derived from oxaloacetate rather than malate. Presumably, this may reflect the increased rate of conversion of malate to oxaloacetate by the acetate-induced malate:quinone oxidoreductase (MqoB). However, perhaps the most notable difference was that during steady-state growth on acetate, around one-third of the carbon reaching the TCA cycle-glyoxylate shunt branch point was redirected into the glyoxylate shunt.  The glyoxylate shunt serves to supply the cell with malate and thence  also the gluconeogenic precursor, oxaloacetate. In an elegant feedback loop, high levels of oxaloacetate (such as would accumulate if the NADPH supply for anabolism was limiting) stimulate the activity of one of the isocitrate dehydrogenase isozymes, IDH, thereby restoring flux through the TCA cycle . One outP. aeruginosa by providing sufficient reducing power (with respect to NADPH) to counteract host-mediated oxidative stress , the HQNO (N-oxo-2-heptyl-4-hydroxyquinoline)-resistant proton pump Nqr (encoded by nqrA to nqrF [PA2994 to PA2999]), and a single-subunit flavoenzyme which does not participate in ion translocation   . All thrPA4538]) .P. aeruginosa respiratory chain is its use of high-affinity terminal oxidases, even during aerobic growth. This unusual wiring of the P. aeruginosa ETC is thought to drive the formation of a microaerobic environment, a trait that may give the pathogen a fitness advantage during infection  pool or by responding to electron flux through the terminal oxidases. UQ acts as the electron donor for complex III, the quinol oxidases (Cyo), and the cyanide-insensitive terminal oxidase, CIO. Complex III transfers electrons to a c-type cytochrome, which then acts as the electron donor for the terminal cytochrome oxidases Cox, Cco1, and Cco2. Cco1 is constitutively expressed at high levels, whereas Cco2 is thought to support growth under conditions of low-oxygen tension (∼2% O2), as it is regulated by the anaerobic sensor Anr ..P. aerug+ or NADP+ by NADPH or NADH, respectively. The primary physiological role of Sth is thought to be in the NAD+-dependent reoxidation of NADPH  were increased ca. 3-fold during growth on acetate. Pyridine nucleotide transhydrogenases catalyze the reversible reduction of either NADof NADPH . Reoxidaof NADPH . Interesnditions .P. aeruginosa cultured in glycerol has a significantly lower growth rate than cells grown in acetate, glucose, or succinate (P. aeruginosa cells grown in MOPS (morpholinepropanesulfonic acid) media with various carbon sources. To do this, we introduced an enhanced green fluorescent protein (eGFP)-expressing plasmid (pMF230) to PAO1 and cultured the strain in MOPS medium containing different carbon sources  is dependent on growth rate.As noted earlier, uccinate . This louccinate , suggestuccinate . To inve sources . When ce10.1128/mBio.02684-19.2FIG S2+ ratios alongside CFU data from cells grown in MOPS. (A) Acetate. (B) Glycerol. (C) Glucose. (D) Succinate. NADPH/NADP+ ratios alongside CFU data from cells grown in MOPS. (E) Acetate. (F) Glycerol. (G) Glucose. (H) Succinate. Values represent total NADP(H) concentrations detected over multiple time points from cells grown in MOPS. (I) Acetate. (J) Glycerol. (K) Glucose. (L) Succinate. Values represent total NAD(H) concentrations detected over multiple time points from cells grown in MOPS. (M) Acetate. (N) Glycerol. (O) Glucose. (P) Succinate. Three biological replicates were analyzed per time point. Download FIG S2, TIF file, 1.9 MB.NADH/NADCopyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02684-19.3FIG S3P. aeruginosa PAO1 carrying chromosomal promoter. Data represent lux fusions for the promoter regions indicated above for cells grown on various carbon sources, including MOPS-acetate, MOPS-glucose, MOPS-glycerol, and MOPS-succinate. (A) aceA. (B) glcB. (C) cco1. (D) cco2. (E) cox. (F) cyo. (G) glpD. (H) nir. Values were normalized to OD600 (RLU/OD600). Three biological replicates were analyzed per sample. Data were analyzed in GraphPad Prism (V 6.01) using t test statistical analysis . Download FIG S3, TIF file, 1.8 MB.Luciferase detection in wild-type Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.02684-19.10DATA SET S3Data Set S3, XLSX file, 0.1 MB.Correlation plot calculations to illustrate the log2 fold change differences in the transcriptomic, proteomic, and fluxomic data (A to C) and cell size data (D). Download Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 is limiting, P. aeruginosa can use nitrate as an alternative electron acceptor. This is made possible by the presence of nitrate reductase (NAR), which can accept electrons from the UQ pool, and nitrite reductase (NIR), which receives electrons via complex III and cytochrome c  and those encoding nitrous oxide (N2O) utilization (nos) are unlinked, whereas the genes encoding nitrite respiration (nir) and nitric oxide respiration (nor) are adjacent. In addition to the enzymes named above, the denitrification operons harbor genes for ancillary functions such as cofactor synthesis, transport, and protein maturation  sensor and contains a ferrous heme. Dnr protein expression was upregulated 2.8-fold during growth on acetate compared with glycerol  and energy (ATP) balances during growth on each carbon source . This re10.1128/mBio.02684-19.4FIG S43. Three biological replicates were analyzed per time point. (D) CFUs. (E) NAD(H) concentrations. (F) NADP(H) concentrations extracted from PAO1 wild-type (WT) and Δdnr cells grown in MOPS-acetate with or without 20 mM KNO3. Three biological replicates were analyzed per time point. Download FIG S4, TIF file, 2.7 MB.(A) CFUs. (B) NAD(H) concentrations. (C) NADP(H) concentrations. Data represent concentrations extracted from wild-type PAO1 grown in MOPS-acetate with or without 20 mM KNOCopyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the + ratio toward a more reduced state . To determine if aerobic denitrification might be used by P. aeruginosa to correct the reduced status of its redox pool, we therefore examined the impact of nitrate addition on the NADH/NAD+ ratio. As shown in + ratio during exponential growth. Absolute quantitation of the NADH and NAD+ levels  expression was abolished in the dnr mutant and also in an anr mutant (Anr controls the expression of dnr)  . Crucialfication , the dnrnd NADPH . We conc10.1128/mBio.02684-19.5FIG S5P. aeruginosa in MOPS-glucose, MOPS-succinate, or MOPS-acetate. NirS was not expressed in a Δanr or Δdnr mutant. Isocitrate dehydrogenases ICD and IDH were used as loading controls. (B) Expression of NirS during exponential growth of P. aeruginosa in MOPS-acetate or in MOPS-acetate plus sodium nitrate (20 mM). Sodium nitrate (20 mM) was capable of inducing NirS expression in the WT strain and in a ΔroxSR mutant but not in a Δanr or Δdnr mutant. Three biological replicates were analyzed per sample. The Δanr, Δdnr, and ΔroxR lanes are representative of the triplicate analyses. Download FIG S5, TIF file, 2.1 MB.Western blotting. (A) Expression of the Anr/Dnr-regulated denitrification enzyme NirS during exponential growth of Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, Basfia succiniciproducens, and P. putida in single carbon sources  during growth on acetate and increased cox expression during growth on glycerol. This suggests that different terminal oxidases are employed under different growth conditions, allowing metabolism to be optimally adjusted for energy generation  in the cell  strain defective in denitrification was unable to maintain optimal redox homeostasis. However, growth was not affected in the Δdnr mutant, perhaps suggesting that the organism has additional (possibly compensatory) mechanisms to deal with redox dysregulation . Anr dimerization is thought to be dependent on the formation of an oxygen-liable [4Fe-4S]2+ reaction, which is destabilized and disassociates in the presence of oxygen, abolishing Anr activity. However, there is accruing evidence indicating that Anr may be active even in the presence of oxygen. For example, Jackson et al. have shown that choline catabolism leads to aerobic expression of the Anr regulon, suggesting that Anr activity can be sustained in the presence of oxygen. Also, Anr overexpression in well-aerated cultures was previously shown to increase the levels of dnr transcript 29-fold  and cell length . A previously determined conversion factor of 0.42 g cell dry weight (CDW) per OD unit was used to calculate biomass-specific rates and yields from the obtained OD600 values  Oligonucleotide primers used in this study. (B) Bacterial strains and plasmids used in this study , 83, 103Copyright © 2020 Dolan et al.2020Dolan et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 600 of 0.5  and added to an equal volume of RNAlater. Samples were then sedimented in an Eppendorf 5810R centrifuge at 3,220 × g for 15 min (4°C), and the pellets were stored at –80°C. Total RNA was isolated as described previously (PAO1 was grown in 40 ml MOPS with acetate or glycerol as the sole carbon source (in quadruplicate) in baffled flasks (500 ml) at 37°C with shaking (250 rpm). An aliquot (5 ml) of culture was harvested from each sample at an ODeviously , followehttp://www.pseudomonas.com/) using TopHat v.2.0.3  ( v.2.0.3  and Bowt v.2.0.3  with an  v.2.0.3  with def v.2.0.3 . Gene exP. aeruginosa cells (OD600 = 0.5) (30 ml) were harvested under growth conditions identical to those reported in the transcriptomics section above. Pellets were resuspended in lysis buffer  with cOmplete Mini protease inhibitor cocktail (Roche). Following three rounds of sonication (3 × 10 s) on ice, supernatants were clarified by sedimentation  in an Eppendorf 5424R centrifuge. Aliquots (100 μg) of each sample were reduced with TCEP, alkylated with iodoacetamide, and labeled with tandem mass tags (TMTs). TMT labeling was performed according to the protocol specified by the manufacturer (Thermo Fisher).18 precolumn  from an Ultimate 3000 autosampler with 0.1% (vol/vol) formic acid for 3 min at a flow rate of 10 μl/min. Separation of peptides was performed by C18 reverse-phase chromatography at a flow rate of 300 nl/min using a Thermo Scientific reverse-phase nano EASY-spray column . Solvent A was water–0.1% formic acid, and solvent B was 80% (vol/vol) acetonitrile–20% water–0.1% formic acid. The linear gradient employed was 2% to 40% solvent B over 93 min .Dried fractions from the high-pH reverse-phase separations were resuspended in 30 μl of 0.1% (vol/vol) formic acid (14 combined fractions). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments were performed using a Dionex Ultimate 3000 rapid separation LC (RSLC) nano-ultraperformance liquid chromatography (nanoUPLC) system  and a Lumos Orbitrap mass spectrometer . Peptides were loaded onto a Thermo Scientific PepMap 100 Cm/z values representing eluting peptide ions were measured in an Orbitrap mass analyzer  and were scanned at between m/z 380 and 1,500 Da. Data-dependent MS/MS scans (top speed) were employed to automatically isolate and fragment precursor ions by collision-induced dissociation  analyzed in the linear ion trap. Singly charged ions and ions with unassigned charge states were excluded from selection for MS/MS, and a dynamic exclusion window of 70 s was employed. The top 10 most abundant fragment ions from each MS/MS event were then selected for a further stage of fragmentation by synchronous precursor selection (SPS) and MS/MS/MS (MS3) . The m/z values and relative abundances of each reporter ion and of all fragments  in each MS3 step were measured in the Orbitrap analyzer, which was set at a resolution of 60,000. This was performed in cycles of 10 MS3 events, after which the Lumos instrument reverted to scanning the m/z ratios of the intact peptide ions and the cycle continued.The eluted peptides were sprayed into the mass spectrometer by means of an EASY-Spray source (Thermo Fisher Scientific Inc.). All MS (MS3)  in the hPseudomonas aeruginosa data (common repository of adventitious proteins [cRAP] v1.0) .Proteome Discoverer v2.1 (Thermo Fisher Scientific) and Mascot (Matrix Science) v2.6 were used to process raw data files. Data were aligned with UniProt t test with variances moderated by the use of Limma’s empirical Bayes method. P values were adjusted for multiple testing by the Benjamini Hochberg method  in 96-well microplates (Greiner Bio-One) , covered with gas-permeable imaging seals . Luciferase expression was assessed every 30 min  for up to 24 h. Growth was assessed by taking OD readings at 600 nm simultaneously with the luminescence readings. Luciferase readings were expressed as relative luminometer units (RLU) normalized to OD600 in order to control for growth rate differences across the selected carbon sources.Luciferase and OD13C flux experiments, naturally labeled acetate and glycerol were replaced with three separate tracers per carbon source to maximize data set resolution and to accurately determine substrate uptake. Naturally labeled glycerol was replaced with glycerol (99%), [2-13C]glycerol (99%), and an equimolar mixture of [U-13C3] glycerol (99%)  and naturally labeled glycerol. Naturally labeled acetate was replaced with 99% [1-13C]sodium acetate or [2-13C]sodium acetate or a molar 1:1 mixture of [U-13C2] sodium acetate  and natural sodium acetate.Starter cultures were grown In LB medium. For the second and main cultures, PAO1 was grown in MOPS minimal media with 60 mM acetate or 40 mM glycerol as the sole carbon source (120 mM carbon). For P. aeruginosa PAO1  of the final sampled cell concentration to exclude potential interference of nonlabeled inoculum with subsequent calculations of flux  in an orbital shaker . These growth conditions were selected to ensure sufficient aeration during cultivation. The oxygen level was maintained above 80% of saturation under growth conditions identical to those used previously for osa PAO1 . In cult of flux .Mass isotopomer labeling analysis of proteinogenic amino acids, mass isotopomer labeling analysis of cell sugar monomers (glucose and glucosamine), and metabolic reaction network and flux calculations were carried out as described previously .−1) equipped with refractive index (RI) and UV detectors (210 nm) with 50 mM H2SO4 as the eluent , were quantified in filtered culture supernatants  using isocratic high-performance liquid chromatography   at 37°C with shaking at 250 rpm, using a culture volume of 150 ml in a 2-liter baffled Erlenmeyer flask. For each NAD(P)(H) extraction, 1.8 ml of culture was removed and immediately added to 7.5 ml ice-cold 100% methanol followed by centrifugation at 3,220 × g for 14 min at 4°C to obtain a cell pellet. The pellet was resuspended in 0.2 M HCl for NAD(P)+ or 0.2 M NaOH for NAD(P)H extraction, after which incubation was performed at 52.5°C for 10 min followed by incubation on ice for 5 min. HCl or NaOH was then neutralized by the dropwise addition of 0.1 M NaOH or 0.1 M HCl, respectively, while vortex mixing was performed at low speed. The mixture was then centrifuged for 5 min at 15,800 × g, and 135 μl of the supernatant was removed for immediate NAD(P)(H) measurement or storage at —80°C. Samples were stored for a maximum of 1 week before measurement.2O) was prepared. The reagent mix was incubated at 30°C and primed to injectors in a BMG Labtech FLUOstar Omega microplate reader. Aliquots (15 μl) of NAD(P)(H) extracts were added to individual wells of a 96-well microtiter plate, which was then incubated in the microplate reader at 30°C. Reagent master mix (80 μl) was added via a microplate reader injector (300 μl s−1) and vigorously mixed  followed by static incubation for 10 min. Immediately before measurement, a solution of alcohol dehydrogenase (1 mg ml−1 in 0.1 M bicine) was prepared for NAD(H) measurement or glucose 6-phosphate dehydrogenase (0.1 mg ml−1 in 0.1 M bicine) for NAD(P)(H) measurement and primed to a second injector. To start the reaction, each well was injected (300 μl s−1) with 5 μl of enzyme solution, followed by vigorous mixing . The absorbance at 570 nm was then recorded every 30 to 60 s for 20 min, with vigorous shaking  before each read. Slopes from plots representing absorbance over time were calculated for NAD(P)H and NAD(P)+ and were then used to calculate ratios.NAD(P)(H) concentrations were measured using an enzyme cycling assay in a 96-well microtiter plate  as described previously . ReagentAlongside each NAD(P)(H) extraction, an aliquot from the same culture was removed, serially diluted, and plated onto LB agar using a previously described single-plate serial dilution spotting method , and col600 of 0.5 in MOPS minimal medium supplemented with the indicated carbon sources. The samples were centrifuged at 3,220 × g for 10 min. Equal amounts of protein were resolved on a 12% SDS-polyacrylamide gel. The proteins were blotted onto a nitrocellulose membrane, which was blocked with 5% (wt/vol) dried skimmed milk in Tris-buffered saline (TBS) buffer. The membranes were probed with mouse anti-NirS  with a 100× 1.49-numerical-aperture (NA) oil lens objective (Olympus UAPON100XOTIRF) and a 488-nm laser  (Coherent Inc.). Samples were imaged using epi-illumination microscopy, and the images were relayed onto an Andor iXon Ultra 897 camera by a ×1.3-magnification Cairn Twincam image splitter (the second port of the image splitter was not used during these experiments). The resulting pixel width on the sample was measured to be 118 nm. For each field of interest, 100 frames were captured at 100-ms exposure time in a 256-pixelThe micrographs captured for this study exhibited typically one or two bacteria in a 30-mm-by-30-mm area. The raw data were segmented and filtered for analysis using a MATLAB script . The data were first segmented as follows. The MATLAB function adaptthresh was used to binarize the data using an adaptive threshold. This was deemed good enough for use as a segmentation step, since the bacteria were sparsely distributed over a dark background in a single frame. The MATLAB regionprops function was used to extract data representing the area, major axis length (length of the major axis of an ellipse fitted to the detected blob shape), eccentricity, and centroid of each segmented blob. A filtering step was implemented to include only those shapes detected with major axis lengths of between 1.7 and 4.5 μm (14 and 38 pixels), corresponding to the possible size ranges for the bacteria.E-MTAB-8374). The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE (PXD015615.The sequencing data were deposited at ArrayExpress (accession number he PRIDE  partner


In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement. This shifting paradigm created considerable challenges for the interpretation of large datasets and the generation of meaningful outcomes impacting process and product attributes. Among the numerous computational methods developed in this context, metabolic modeling tools allowed the coherent organization of large datasets into biological networks providing nonintuitive insights on biological systems that in vivo experiments alone cannot provide2. These model-based approaches have been proven to be invaluable at the level of preclinical research. For example, these approaches have been used for the design of new drugs by informing target selection and for the engineering of cells by rewiring metabolism towards the production of a product of interest4.The emergence of high-throughput technologies is elevating the biotechnology field to the era of big data8. However, model-based methods can be used for much more than this. For instance, these methods could also be applied at the industrial level in the field of process design, monitoring and control to lower the experimental effort and increase the process robustness. Indeed, model-based methods would allow a more rational design of bioprocesses but also the objective identification of the variables to monitor and control. Therefore, the routine implementation of these methods can be expected to greatly facilitate the implementation of regulatory requirements related to the Quality by Design paradigm  and Process Analytical Technologies initiative . Numerous modeling methods exist to describe and quantify the reactions occurring in a cell under specific environments, from deterministic kinetic models to stochastic and statistical models. While kinetic modeling has been widely used for small-scale metabolic models, these mechanistic models are for the moment not scalable and suitable for genome-wide approaches due to challenges in acquiring the required kinetic parameters  and computational problems associated with such complex parametric systems 10. On the other hand, while statistical modeling is very useful for the manipulation of large complex datasets, these black-box approaches have limited capacity to generate biologically relevant information for bioprocess improvement. Therefore, the lack of clear workflows to set up specific process models based on genome-wide information have limited the potential scope of computational biology applications in industry.Pharmaceutical companies are already investing substantially in computational approaches to guide drug discovery and cell design. While these important applications are out of the scope of this paper, we refer the interested readers to detailed reviews on the usage of metabolic engineering and synthetic biology in industrial context13, their practical implementation in biopharmaceutical research and development facilities still seems in its infancy. Actually, bioprocess improvement performance is still mainly achieved by semi-empirical media and bioprocess optimization 14. In this paper, gaps in current knowledge and technical limitations restraining the widespread usage of metabolic modeling tools for process development  in biopharmaceutical industry are highlighted. Specifically, research topics in three fields  are identified as key drivers for evolving the current state of some systems biology tools in industrial process engineering applications. Advances in these fields will be invaluable to address existing challenges in bioprocess development and improvement.However, the systems biology field is in continuous development and genome-scale metabolic networks are now available for most of the industrially relevant organisms. Therefore, we should be able to systematically predict cell metabolic behavior under different specific complex media compositions and process conditions. These types of predictions should be used to objectively determine the optimal operating conditions with respect to the desired cell phenotype and related production criteria. Unfortunately, while numerous studies have successfully used genome-scale models to rationally guide the culture process design16.Real-time monitoring is a key issue for effective bioprocess operation. Indeed, one needs first to recognize and analyze cellular physiological state in function of environmental conditions to be further able to adequately manipulate operating conditions. Therefore, real-time monitoring technologies development is a critical aspect of the bioprocess automatization. Previously, only a small number of so-called “process variables” were commonly measured online in bioreactors . The scope of available real-time probes has now been significantly expanded thanks to the recent advances in analytical technologies and the adoption of Process Analytical Technologies initiative17. Unfortunately, some of these experimental measurements cannot currently be achieved online in situ and, doing so, limit our capacity to have a live snapshot of the cell metabolism during the culture process. However, continuous improvements in analytical technologies and the emergence of the single-cell experiments open new perspectives for gaining insights into cell metabolism.In the context of bioprocesses, real-time measurements of the metabolites and cell concentrations in the cell culture medium are key elements to enable the prediction of cell phenotype with respect to operating conditions 19. However, the capacity to systematically extract accurate quantitative data is, for the moment, limited to a handful number of metabolites . We refer the reader to the publications of Li et al.20 and Ryder21 for detailed comparison of the performance and limitations of existing spectroscopic methods. In this context, chemometric modeling has been a state-of-art technique to effectively extract the maximum of information from spectroscopic spectra. It is worth to highlight that spectroscopic sensors can in this sense be embedded in the concept of soft sensor22 as they are used to compensate the lack of specific measurement by reconstructing the missing signals based on available measurements using data analysis models23. This type of hybrid approach will therefore continue to greatly benefit from the flourishing evolution of artificial intelligence tools and certainly allows to overcome the current limitations of spectroscopic monitoring methods24.Spectroscopic methods  have attracted an increasing interest for their potential capacity of simultaneous in situ measurement of nutrients, metabolites and cell concentrations. These techniques have been successfully integrated into real-time pharmaceutical manufacturing to provide a “molecular fingerprint” of samples enabling in-process correction needed to ensure the acquisition of a specific quality product25. These “single-cell approaches” in combination with adequate analytical methods could help unravel the impact of operating conditions on the cellular physiology. Specifically, the new generation of high-throughput-omics technologies allow to envision in a close future a near real-time measurements of cell transcriptome and proteome28. Interestingly, very few examples of interfacing flow cytometry with a bioreactor and its use for effective bioreactor control can be found in the literature29.Parallelly, the rise of online single‐cell probing opens the door for the specific investigation of cell metabolism and bioenergetics at a definition level never captured before12, the complexity of these large networks also hinders their utility in various practical applications. Actually, metabolic networks involving, most of the time, more reactions than metabolites, the associated system presents, generally, a multitude of solutions . Therefore, metabolic network structures are also too complex to be handled for the development of utilitarian tools .A genome-scale metabolic model is a network connecting all metabolic reactions that can occur in a specific organism with their associated metabolites, proteins and genes. While these models have been proven to be powerful tools for in silico simulation of cell metabolism30 or Ataman et al.31 allow an easy-to-implement systematic reduction of genome-scale networks into core models. However, these methods rely on the definition by the user of the part of the model that should be “protected” during the reduction  which is far from easy task. On the other hand, numerous algorithms have been developed to tailor genome-scale metabolic networks by integrating diverse types of -omics data, including transcriptomics, proteomics and metabolomics35. Actually, genome-scale models are used to link genes, enzymes, and metabolism through the use of gene-protein-reactions (GPR). Therefore, the knowledge of GPR is used by researchers to explain the metabolic state of a cell, based on the expression of metabolic genes and/or specific proteins37. The most common “integration algorithms” use gene expression data to recapitulate the metabolism of an organism under a specific condition  by only extracting the subset of active reactions from the genome-scale model. These methods have been proven to be valuable for enhancing the accuracy of model-predicted growth rates and gene essentiality37. However, since currently no quantitative description of the GPR relationship exists in genome-scale metabolic models, the integration of gene expression data requires the use of strong assumptions to link the GPR expression and the metabolic reaction activity, which could lead to an oversimplification of the complex relation existing between fluxes, enzymes and genes38.Different approaches have been proposed for tailoring these metabolic networks based on a priori knowledge and/or available experimental data. For example, algorithms like the one presented in Erdrich et al.39) will always be required to achieve an instantaneous picture of the flux distributions in the cell. A recent research in this field presented a new approach using biochemical thermodynamic constraints  to shape the solution space of potential flux distributions40. The promising results obtained in this study pave a new way towards the understanding of the mechanisms that governs the definition of metabolic pathway usage across organisms and conditions. Further development of these computational methods will be crucial to obtain a holistic and integrated picture of the cell metabolism in function of specific environmental conditions while considering uncertainty associated with experiments, measurements and modeling.Finally, it is important to highlight that these “network tailoring” approaches generally do not completely leverage the problem of system underdetermination related to network complexity. Therefore, the choice of adequate strategy to solve the system  with data-driven methods to describe the unknown parts. This type of models provides an attractive method for modeling biochemical processes as much of the underlying complexity can be lumped in the data-driven part. Therefore, these approaches can be used to effectively establish the link between metabolism and operating conditions in a way that can be used to implement control and optimization strategies.46, the first application of hybrid modeling in systems biology only arose in 201047 and few have been reported ever since53. One reason might be the absence of dedicated software that would allow for a straightforward development of these type of models. Another reason might be the broad interdisciplinarity of competences required to implement these approaches . Finally, there is still several major open questions that need to be investigated to allow a better combined usage of hybrid modeling and metabolic modeling tools54. From the effective generation of sufficiently informative experimental data to the joint parameter identification of the mechanistic and data-driven parts of the model, the assessment of these gaps will be critical for the development of a systematic workflow for integrating knowledge from systems biology into model structures enabling a next-generation technology for the life-sciences and biotechnology sectors, besides others .Process intensification through continuous processing has been adopted for decades in industries such as chemical and petrochemical. In the context of biopharmaceutical production, the move from batch to continuous or semi-continuous mode of production has been slowed down mainly due to the more conservative approach of this industry resulting from the stringent quality/regulatory requirements. Therefore, innovation in biotechnology has become more product- than process-centered57.The proper implementation of continuous manufacturing requires a thorough understanding of the process to achieve and maintain steady-state conditions . Furthermore, the in-process quality should be evaluated and ensured by the real-time analysis of process data. To this end, in-line process monitoring system linked to robust model-based control will be crucial technologies to support this real-time release testing14.Despite their advantages, the potential of metabolic modeling tools is still not fully exploited to address these challenges in bioprocess development and improvement. This is mainly due to the upfront time and effort investment required to implement these approaches and gain in process understanding as critical gaps remain to set these methods up in clear workflows. Empirical process development techniques will continue to play an important role in addressing these challenges. However, systems biology-driven approaches are likely to be more impactful to provide insights into the cellular responses to process conditions changes59. In this context, computational tools will be invaluable to systematically identify system limitations and areas of improvement for production efficiency, as demonstrated in the work of Vilkhovoy et al.60.Finally, the research topics, highlighted in this review, in the field of monitoring, network science and hybrid modeling, will also be cutting-edge technologies to improve new emergent biopharmaceutical production platforms such as cell-free systems. Actually, cell-free protein synthesis has been emerging as a flexible and powerful platform to address challenges in biomanufacturing by overcoming inherent limitations related to the use of living cells. Cell-free systems offer the ability to design metabolic pathways towards the production of desired products but also to synthesize complex proteins with unnatural amino acids and to buildup artificial cells. Despite these promising features, challenges remain such as the proper control of post-translational modifications and the expansion of the genetic code for unnatural amino acids incorporation. To address these challenges, targeted gene editing and addition of adequate exogenous substances to control reaction conditions will be needed to optimally regulate the transcription and translation4. Taken together, these technologies could facilitate the switch to continuous processing in biopharmaceutical industry but also foresee needs for improvements in emergent biomanufacturing platforms such as cell-free systems.We highlighted gaps in our knowledge and technical limitations restraining the effective application of metabolic modeling tools throughout the lifecycle of a biopharmaceutical process. Specifically, advances in real-time monitoring of bioprocesses, biological network modeling and their combination with data-driven approaches will be the key drivers to lower the time and cost associated with the development of new drugsReporting Sum


GSE138379), metabolomics and proteomics (available via ProteomeXchange with identifier PXD015518) data, which we obtained from healthy controls and RD patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanate and displays fibroblast‐specific metabolic functions. Using this model, we investigated the metabolic phenotype of RD at the genome scale, and we studied the effect of phytanate on cell metabolism. We identified 53 metabolites that were predicted to discriminate between healthy and RD patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.Refsum disease (RD) is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. The disorder presents with late‐onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanate in plasma and tissues of patients. To date, no cure exists for RD, but phytanate levels in patients can be reduced by plasmapheresis and a strict diet. In this study, we reconstructed a fibroblast‐specific genome‐scale model based on the recently published, FAD‐curated model, based on Recon3D reconstruction. We used transcriptomics  is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. In this study, we used various omics obtained from healthy controls and RD patient fibroblasts to reconstruct a fibroblast‐specific genome‐scale model. Using this model, we identified 53 metabolites that discriminated between healthy and RD patients, several of which with a link to amino acid metabolism. They perform both anabolic and catabolic processes, including the α‐ and β‐oxidation of very‐long‐chain fatty acids, dicarboxylic acids and methyl‐branched‐chain fatty acids . FurtherRefsum disease (RD) is a rare inborn error of peroxisomal metabolism with an unknown incidence. It probably remains highly unrecognised since the awareness of inborn errors of metabolism is low among ophthalmologists. RD is caused by biallelic mutations in the gene encoding phytanoyl‐CoA 2‐hydroxylase (PHYH), resulting in defective α‐oxidation of the branched‐chain fatty acid phytanate  . The endRecently, computational models have become valuable tools to study the complex behaviour of metabolic networks. One type of computational models is genome‐scale models of metabolism, which contain all currently known stoichiometric information of metabolic reactions, together with enzyme and pathway annotation . These mIn the last decade, a paradigm shift occurred in the field of inborn errors of metabolism. Today, they are no longer viewed according to the ‘one gene, one disease’ paradigm as proposed more than 100 years ago, but recognised to be complex diseases . Howeverin vivo situation in fibroblasts and demonstrates the physiological effects of a defective α‐oxidation. Ultimately, such insights in metabolic changes may provide leads for pathophysiology and therapy.In this study, we aim to investigate the metabolic phenotype of RD at the genome scale and to study the effect of phytanate on cell metabolism. Cultured fibroblasts contain most metabolic functions present in the human body, and biochemical and functional studies in cultured skin fibroblasts are important tools for the diagnosis of patients with a peroxisomal disorder . TherefoFor this study, we used an updated version of the Recon3D model in which flavoprotein‐related metabolism was curated . This adhttps://github.com/WegrzynAB/Papers).To optimise the model, we added 25 reactions involved in the ω‐oxidation and the subsequent β‐oxidation of phytanate. Furthermore, 17 reactions involved in phytanate metabolism were deleted, because they were duplicates of other reactions in the model. Lastly, we examined the import/export reaction boundaries and blocked the flux of several drug metabolism pathways, such as those of statins, ibuprofen, paracetamol and antibiotics. These pathways were not relevant to this study but could play a role in the model outcome. All changes to the model are summarised in Table n = 6) and RD patient‐derived fibroblasts with a defect in α‐oxidation (n = 5) under standardised conditions, and harvested cells after 96 h to isolate RNA and protein. The cells were either incubated with phytol, a precursor of phytanate, or with the solvent ethanol , published transcriptomics and proteomics data obtained from fibroblasts , deleted reactions [ Recon3D_del], added metabolites [Added_mets], and information about the media constraints [HAM'sF10].Table S2. Fibroblast specific genes.Table S3. Detailed information about the ATP yields for Figs 3 and 4.Table S4. Genes and proteins significantly changed between RD and CTRL fibroblasts.Table S5. Detailed results of the metabolic tasks analysis in the fibroblast model.Table S6. Comparison between the exchanged amino acids in model prediction and in the in vitro experiments (CTRL + phyt group). For a full list of predicted uptake and secretion rates of metabolites see Table S7.Table S7. Detailed comparison at the level of secretion and uptake reactions between healthy and Refsum models forced to take up phytanate.Fig. S1. Additional data on amino acid uptake and secretion rates in the fibroblast CTRL  and RD  cultures exposed to phytol for 96 h (shown as mean ± SD). Rates were calculated based on the fresh medium measurements.Fig. S2. Additional experimental data of (A) amino acids and (B) glucose determinations in the medium of the fibroblast CTRL (n = 6) and RD (n = 5) cells after incubation at indicated time points (shown as mean ± SD). For details, see Fig. 6C. (C) Protein concentrations of cell pellets after incubation at indicated time points. (D) Growth curves of attached cells for the indicated time points (left panel), and statistical analysis of the total area under the curve per cell line after 7 days of incubation (right panel). Data are shown as bar plots.Click here for additional data file.


Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an  A key approach in Systems Biology is the elucidation of the metabolic potential of an organism and the transformation of this information into networks of metabolic reactions. Such networks are called genome-scale metabolic models and can be used in computer simulations of metabolism of cells and cellular communities. For instance, models for various eukaryotic and prokaryotic organisms and cell types have been reconstructed (e.g. as reviewed in ) and appBy mathematical approaches, such as the application of Flux Balance Analysis (FBA), we are able to optimise a predefined objective function, which is based on nutritional constraints that represent the chemical environment of cells . A widelThus, it is a common issue that there is no unanimous approach on how to model the nutritional input in constraint-based modelling. For instance, it is usually not well-defined how to proceed in representing various compounds  in the models and how to proceed in calculating their inflow values (i.e. constraints) starting from their concentrations. Researchers often unintentionally fail to provide information on their exact methodology how nutritional constraints were calculated and in numerous instances arbitrary numbers are used for nutritional constraints . As a rein-silico) that contains, besides water and NaCl, the chemically complex components tryptone and yeast extract which provide a broad range of nutrients for microbial growth. Based on the example we emphasise the importance for rationally designed nutritional constraints in FBA models in order to obtain meaningful physiological predictions.Here, we provide clear guidelines on how to design a realistic computational representation of the nutritional input for metabolic models for environments, which are initially only defined on the level of chemically complex components such as yeast extract, tryptone or milk powder. Therefore, a step-by-step procedure is proposed that incorporates publicly available databases and resources and facilitates reproducible calculations from chemically complex components to the quantities of specific molecules that are presumably available for uptake by the cell types of interest. The procedure is illustrated on the example of Lysogeny Broth (LB) medium, a commonly used microbial growth medium  Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The paper is a systematic review and guide to correctly setting up the environment (through exchange fluxes) in genome scale models of metabolic networks, with a clear focus on FBA or FBA-like models. In my view the article is clear (didactic) and comprehensively covers the topic. The presentation is completely consistent with the scope of the paper and could indeed be useful for many applications of FBA in biotechnology. I therefore have no major criticism to raise. I would however ask authors to address the following points, all of which are in my view important (despite being related to one another).1) In the example they use throughout the paper (E coli growth on LB), the fact that something is wrong is reflected in the growth rate being nil (we know that E coli grows on LB). I understand that by adding a specific compound to the medium one cures this problem. My guess however is that one could cure this problem in a few other ways . My question is: when are exchange fluxes the most reasonable variables to adjust in such cases? This point somehow lies on top of the issues discussed in the paper but I believe it would be important to address it.2) Besides adding compounds, one could solve the zero growth problem by removing and/or replacing compounds from the medium or by changing the availability of existing compounds. For instance, because there is a lower bound on ATP hydrolysis, growth in FBA is impaired when the max glucose uptake is too low. So adding compounds does not seem like a general recipe to adjust the medium composition. The authors should address this point.3) I understand the review is focused on FBA-like models. Still in principle some of the ideas presented here could be important in other theoretical frameworks . The authors should try to highlight which parts of their recipes are general (model-independent) and which are instead specifically thought for FBA.Reviewer #2: Please see attached document. **********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Attachmentref_p1.pdfSubmitted filename: Click here for additional data file. 14 Jul 2020Response to Reviewers:REVIEWER #1"The paper is a systematic review and guide to correctly setting up the environment (through exchange fluxes) in genome scale models of metabolic networks, with a clear focus on FBA or FBA-like models. In my view the article is clear (didactic) and comprehensively covers the topic. The presentation is completely consistent with the scope of the paper and could indeed be useful for many applications of FBA in biotechnology. I therefore have no major criticism to raise. I would however ask authors to address the following points, all of which are in my view important (despite being related to one another).1) In the example they use throughout the paper (E coli growth on LB), the fact that something is wrong is reflected in the growth rate being nil (we know that E coli grows on LB). I understand that by adding a specific compound to the medium one cures this problem. My guess however is that one could cure this problem in a few other ways . My question is: when are exchange fluxes the most reasonable variables to adjust in such cases? This point somehow lies on top of the issues discussed in the paper but I believe it would be important to address it."=> We thank the reviewer for this comment and agree that adding compounds to the modelled media should not be considered as universal solution to facilitate growth of a model. We argue that this approach to achieve a desired model property (e.g. growth) should be considered as option in cases where (i) the metabolic network model underwent prior substantial curation efforts and (ii) where uncertainty exists, if a specific compound is part of the focal growth medium (e.g. molybdate in LB medium). We have added these points to the manuscript in the section for step 6 (‘troubleshooting’) on lines 332-344. "2) Besides adding compounds, one could solve the zero growth problem by removing and/or replacing compounds from the medium or by changing the availability of existing compounds. For instance, because there is a lower bound on ATP hydrolysis, growth in FBA is impaired when the max glucose uptake is too low. So adding compounds does not seem like a general recipe to adjust the medium composition. The authors should address this point."=> Yes, zero-growth can also be caused by too low maximum uptake rates. In such cases, the approach to analyse the reduced costs that are associated to the FBA solution can also point towards compounds, which are already part of the modelled medium but at too low availability. We thank the reviewer for pointing out this theoretical scenario and addressed it in the revised manuscript on lines 326-331."3) I understand the review is focused on FBA-like models. Still in principle some of the ideas presented here could be important in other theoretical frameworks . The authors should try to highlight which parts of their recipes are general (model-independent) and which are instead specifically thought for FBA."=> We agree that most steps in our proposed procedure to model the nutritional environment for metabolic modelling approaches are not limited to FBA-models. We have added a new subsection in the discussion about the limitations of our proposed procedure and outlook by highlighting the potential applicability especially to dynamic-kinetic models of cellular metabolism (lines 498-531).REVIEWER #2"In this draft, G. Marinos et al. address the problem of modeling complex, possibly undefined nutritional media for their use as input for constraint based models of metabolic networks. They propose a guideline based on 6 steps to translate computationally the nutritional information for the model and show its application to the test case of the Lysogeny broth for E. Coli, where they found it improved much more the plausibility of the FBA solution both in terms of matching experimental growth physiology and overall analysis on supplementation. Although it lacks a bit of generality, the article gives an original (to the best of my knowledge) contribution towards more rigorous applications of metabolic computational modeling and thus shall deserve publication in PLOS ONE but I would suggest the author to afford the following concerns:• It is pointed out by the authors that their aim is to make metabolic modeling more rigorous and quantitative, but then the authors apply and discuss their framework to FBA optimal solutions based on objective functions. The FBA approach is a very useful and wide spread approach eg to provide qualitative estimates of gene KO and/or medium growth feasibility but it misses sistematically even simple phenotypic (not even quantitative) predictions. For instance even for ideal E. coli cultures in minimal M9 medium  FBA fails to account for low : 46446-46451) and high (Microbiol. Mol. Biol. Rev. 69.1 (2005): 12-50) growth rate phenotypes (as well as intermediate in non-linear way: Appl. Environ. Microbiol. 72.2 (2006): 1164-1172). In the range of growth rate explored by the authors there shall be acetate excretion, even in rich medium (Nature 528.7580 (2015): 99-104.). Is this simple prediction verified in the authors’ framework? My general question is then why the authors limit themself to FBA? In principle their framework is more general and could be more useful in morequantitative computational modeling approaches, from dynamical kinetic models to Metabolic Flux Analysis. Although an application to these more quantitative approaches would add substantial value to their work, I invite the authors to at least acknowledge the limitations of their approach within the context of FBA."=>We thank the reviewer for pointing out the limitations of our proposed procedure in the context of FBA-models and the potential benefit for other metabolic modelling techniques.Following the suggestions to check for acetate production, we re-analysed the model-predictions and can confirm that the genome-scale E. coli model (iJO1366) as well as the core model predicted acetate production under aerobic conditions and on the basis of the modelled LB-medium. We have added these results and their discussion on lines 373-376 and lines 436-438, respectively.In addition, we extended the discussion in the revised manuscript as suggested with a new subsection for the limitations of our proposed procedure to model the nutritional environment for metabolic models and an outlook for the potential impact of the procedure for other metabolic modelling approaches, namely kinetic models of cellular metabolism (lines 498-531)."• A related issue is: how does the approach applies to smaller networks, like the core metabolism of the model iJO1366? The latter are the networks usually used for truly quantitative approaches like metabolic flux analysis. Probably the issue is non trivial since one shall reliably estimate precursors concentrations."=> We agree with the reviewer that smaller metabolic networks (e.g. of core metabolism) are powerful frameworks for quantitative and dynamic modelling approaches. Although FBA certainly is limited in possibilities to make quantitative predictions, we have applied our modelled LB-medium to the core model of E. coli to compare predictions with the genome-scale model (see lines 365-372). While also the core model, as mentioned above, can predict acetate production alongside cell growth on LB, it lacks the capabilities to utilize a large number of nutrients that are part of this chemically complex medium. Thus, we argue that our proposed procedure for in silico nutritional environment representation will have the most impact on small-scale networks for metabolic flux analysis techniques, which include the uptake/transport reactions and nutrient utilisation pathways for several resource compounds from complex growth media. We have added this consideration and outlook to the revised version of out manuscript (lines 506-524)."• The authors could spend few words more in the discussion on applications to eucaryotic growth media, that are the most complex and for which the impact of their framework could be bigger."=> The complexity of eukaryotic growth media is indeed an important topic. We added a brief discussion for the potential impact of our proposed procedure to define the nutritional environment for metabolic models of eukaryotic cell metabolism (lines 525-531). "• Minorsline 62: ions → inorganic ions line 63: prediction of biomass production → estimation of maximal biomass production line 71: the accuracy of FBA ...depends.... → I would add that the accuracy on FBA could depend, most crucially, on the extent that cell metabolism is well described by linear objective functions.line 329-332 “When the nutritional input of the models is defined as molecular quantities and not as fluxes the lower bound of each exchange reaction can be filled with the respective concentration of the metabolite”. I find this unclear. How is the conversion made? That in principle depends in non trivial way on the mechanism of uptake that could be different for each compound, with different kinetic constants, etc (eg Monod equation for glucose)."=> All points were corrected for the revised version of our manuscript. We also rephrased and clarified the mentioned sentence by elaborating on the details for the conversion of concentrations and exchange reaction bounds (lines 282-289).Attachmentresponse_to_reviewers.pdfSubmitted filename: Click here for additional data file. 16 Jul 2020Defining the nutritional input for genome-scale metabolic models: a roadmapPONE-D-20-13622R1Dear Dr. Waschina,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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