Staphylococcus aureus USA300 strain by applying independent component analysis (ICA) to 385 RNA sequencing samples. We then combined the modular TRN model with a metabolic model to study the regulation of carbon and amino acid metabolism. Our analysis showed that regulation of central carbon metabolism by CcpA and amino acid biosynthesis by CodY are closely coordinated. In general, S. aureus increases the expression of CodY-regulated genes in the presence of preferred carbon sources such as glucose. This transcriptional coordination was corroborated by metabolic model simulations that also showed increased amino acid biosynthesis in the presence of glucose. Further, we found that CodY and CcpA cooperatively regulate the expression of ribosome hibernation-promoting factor, thus linking metabolic cues with translation. In line with this hypothesis, expression of CodY-regulated genes is tightly correlated with expression of genes encoding ribosomal proteins. Together, we propose a coarse-grained model where expression of S. aureus genes encoding enzymes that control carbon flux and nitrogen flux through the system is coregulated with expression of translation machinery to modularly control protein synthesis. While this work focuses on three key regulators, the full TRN model we present contains 76 total independently modulated sets of genes, each with the potential to uncover other complex regulatory structures and interactions.The complex cross talk between metabolism and gene regulatory networks makes it difficult to untangle individual constituents and study their precise roles and interactions. To address this issue, we modularized the transcriptional regulatory network (TRN) of the IMPORTANCEStaphylococcus aureus is a versatile pathogen with an expanding antibiotic resistance profile. The biology underlying its clinical success emerges from an interplay of many systems such as metabolism and gene regulatory networks. This work brings together models for these two systems to establish fundamental principles governing the regulation of S. aureus central metabolism and protein synthesis. Studies of these fundamental biological principles are often confined to model organisms such as Escherichia coli. However, expanding these models to pathogens can provide a framework from which complex and clinically important phenotypes such as virulence and antibiotic resistance can be better understood. Additionally, the expanded gene regulatory network model presented here can deconvolute the biology underlying other important phenotypes in this pathogen. Staphylococcus aureus. Metabolic requirements specific to infection, intracellular persistence, biofilm formation, and colonization are rapidly being uncovered  in the leading human bacterial pathogen ncovered –6. Furthcovered ––9. The cractions , 10–12. tions 10–, 14. Alttions 10––17. In ttabolism , 17.E. coli and Gram-positive S. aureus, in which a balance was observed between genes regulated by stress-associated sigma factors and growth-associated translation machinery  . Models S. aureus RNA sequencing samples  and to stationary phase in chemically defined medium (CDM) did not cluster together, despite being from the same bioproject.Before applying ICA, we normalized the log-transformed transcripts per million (log-TPM) data to a project-specific control condition. This reduced batch-specific variation in the data and reduced the presence of iModulons not associated with biological signals. Principal-component analysis (PCA) of the log-TPM data showed that normalized samples tended to cluster with media types and growth phases rather than by data source . For exaS. aureus. Those iModulons that had significant overlap with other predicted regulons were named after the associated regulator  in shaping the transcriptome in the given sample. Higher iModulon activity represents a higher expression level of genes with positive weightings in the iModulon and lower expression of genes with negatively weighted genes  cycle, in the presence of high concentrations of glucose. CodY, on the other hand, globally represses the genes required for amino acid biosynthesis in response to high branched-chain amino acid (BCAA) or GTP concentrations. Lastly, the Translation iModulon almost entirely consists of ribosomal genes  and genes involved in translation, such as infA and fusA, which encode translation initiation factor IF-1 and elongation factor G, respectively , and Translation iModulons explained the most variation in the data . CcpA isectively . This iMectively , 22–25.Interestingly, the activities of these three iModulons were highly correlated across all samples  and form10.1128/msystems.00480-22.3FIG S3FIG S3, EPS file, 4.6 MB.iModulon activity clustering. The iModulon activities formed distinct clusters indicating coordinated gene regulation. The highlighted individual clusters are remapped in the smaller clustermaps. Download Copyright © 2022 Poudel et al.2022Poudel et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. aureus carefully coordinates its central carbon and nitrogen metabolism  did not lead to further change in CodY activity, creating the second phase of the trade-off plane.The correlated activity of CcpA and CodY iModulons suggested that tabolism . Close ecodY gene was interrupted showed minimal effect on CcpA iModulon activity. These samples fell well outside the CcpA-CodY trade-off line  . GEMs arS. aureus biomass production given the measured uptake and secretion rates of various amino acids and sugars in chemically defined medium (CDM) and CDM with glucose (CDMG) , can be used to calculate the metabolic flux state that optimizes a phenotype while minimizing total metabolic flux in a given condition , 31. pFBe (CDMG) . In agree (CDMG) . The tote (CDMG) . Many Cc10.1128/msystems.00480-22.4FIG S4FIG S4, EPS file, 0.6 MB.Sum of CcpA reaction fluxes in CDM and CDMG. pFBA solutions from simulating metabolism in CDM and CDMG media showed little change in total absolute flux going through CcpA reactions. Though the total magnitude of the total flux did not change much, many CcpA reactions reversed their overall flux direction in CDMG. Download Copyright © 2022 Poudel et al.2022Poudel et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the pFBA, however, gives an exact optimal solution and therefore does not account for variations or errors in input uptake data. We addressed this issue by sampling the CDM- and CDMG-specific models, which give a distribution of feasible fluxes in each of the respective conditions. We then mapped the condition-specific flux distributions to various amino acid biosynthetic pathways. For simple interpretation, we excluded amino acids that serve as intermediates for biosynthesis of other amino acids  and included only those amino acids for which unique biosynthetic pathways could be defined . Confirming pFBA analysis, 5 out of the 6 amino acid biosynthetic pathways had increased flux in CDMG compared to CDM . The res2O, NADH, etc.), we were left with 22 metabolites at the interface (N-succinyl-2-l-amino-6-oxoheptanedioate and tetrahydrofolate (THF), are less understood in the context of this trade-off. To further understand how a change in simulated flux through CcpA and CodY reactions in CDM and CDMG altered these key metabolic interfaces, we mapped the pFBA solution fluxes from each media to the reactions around two of these interfaces, glutamate and methyl-THF.CcpA and CodY iModulons contained 110 and 86 genes, respectively. Most of these genes are involved in central carbon and amino acid metabolism. Despite the large iModulon sizes and close metabolic proximity of the regulated genes, the two iModulons did not share any genes encoding metabolic enzymes. The correlation in iModulon activities, however, suggested that CcpA reactions and CodY reactions must be coordinated at a metabolic level. Using the USA300 GEM, we looked for this coordination at the metabolite interface between CcpA and CodY reactions, i.e., metabolites that are involved in both CcpA and CodY reactions. We found these metabolic interfaces by systematically identifying all metabolites in USA300 GEM that can be found in both CodY and CcpA reactions. After taking out “nonspecific” metabolites and cofactors  link is a closely studied interface in tabolism , 10. Theeactions . In gluceactions . This maeactions . pFBA soS. aureus coordinates flux through CcpA and CodY iModulon reactions at these key metabolic interfaces, despite not sharing any genes at the regulatory level.The folate cycle represents another metabolic interface of CcpA and CodY reactions. The folate cycle is required for one-carbon metabolism, nucleotide biosynthesis, and amino acid metabolism, and the pathway leading up to the cycle is the target of sulfonamide-class antibiotics . The folhpf, which encodes ribosomal hibernation-promoting factors (HPFs), is a gene found in both iModulons  from Bacillus subtilis in the same region (q] = 0.00905). This architecture, with two CodY binding sites flanking the CcpA binding site, is also found in the regulatory region of the B. subtilis BCAA operon, where both regulators contribute to the expression of the operon genes  data from our previous work found two CodY binding sites in the regulatory region of the hpf gene  38). To. Tohpf ge region . A matchon genes . The sighpf gene, CodY activity was also strongly correlated with Translation iModulon activity. In contrast, CcpA and Translation iModulon activities showed little correlation between them . Metabolic modeling in CDMG shows increased flux through amino acid biosynthetic reactions compared to CDM. The results of this TRN-agnostic metabolic model agree with the increased CodY activity in CDMG and other glucose-containing media. Additionally, we also found a feed-forward regulation whereby CcpA and CodY control the expression of the gene encoding the HPF protein, which sequesters ribosomes into inactive 100S forms, suggesting a mechanism by which translation is coordinated with the metabolic state of the cell (dashed red arrows)  , 35.Last, the activity of the Translation iModulon is also closely correlated with CodY activity, which may act as an additional layer of coordination between metabolism and translation (dashed blue arrows). However, we have yet to identify the signal or regulator controlling the Translation iModulon activity, and therefore, the source of this concomitant change in expression along with CodY is unclear. rRNA expression is regulated by ppGpp during the stringent response, which can be activated by mupirocin treatment , 45. We E. coli, Kochanowski et al. have observed similar coordination between anabolic and catabolic fractions of metabolism  of 0.5. Three milliliters of culture was harvested and immediately mixed with 6 mL of Qiagen RNAprotect bacteria reagent and incubated at room temperature for 5 min. The supernatant was decanted after the samples were centrifuged for 10 min and 17,500 rpm. The remaining cell pellets were stored at −80°C until they were prepared for RNA extraction.The 10.1128/msystems.00480-22.9TABLE S2Table S2, CSV file, 0.3 MB.Gene iModulon membership. Enriched iModulons, their gene contents, and the gene descriptions. Download Copyright © 2022 Poudel et al.2022Poudel et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Total RNA was isolated from the cell pellet in the Qiagen RNeasy minikit columns by following vendor procedures. An on-column DNase treatment was performed for 30 min at room temperature. The rRNA was removed using RiboRid protocol, as described before . RNA wasS. aureus taxonomic ID were downloaded and manually curated to obtain only the samples that were from USA300 isolates. Raw fastq files from curated samples were downloaded, trimmed with TrimGalore, and then aligned to the USA300 TCH1516 genome  using Bowtie2  and their activities , 55. UnlThe USA300-specific genome-scale model (GEM) iYS854 was used for all metabolic simulations in the paper. The exchange rates of amino acids, glucose, ammonium, and acetate were adjusted to constrain the model to CDM- or CDMG-specific conditions as described in detail before . BrieflyOnce constrained, the model was then used to calculate the flux of each medium using pFBA as implemented in the COBRApy package , 31. To v) can therefore be represented as linear weightings (α) of minspan pathways (P), whereTo calculate the valid amino acid biosynthesis pathway-specific flux distribution, the solution spaces of CDM- and CDMG-specific models were sampled 10,000 times using the artificial centering hit-and-run algorithm . Next, tThe sampled fluxes were converted to pathway-specific weightings (pathway fluxes) using the minspan matrix. Pathways containing amino acid biosynthesis were manually curated, and only amino acid biosynthesis pathways that did not appear in multiple MinSpan pathways were used for analysis, as they can be easily interpreted and do not require analyzing linear combinations of multiple pathways.2, coenzyme A, H2O, hydrogen atom, sodium ion, NAD, NADH, NADP, NADPH, ammonium (NH4), and phosphate were excluded from this designation.Last, the interface metabolites were determined by comparing all metabolites that were involved in at least one CodY and one CcpA reaction. The common metabolites ADP, ATP, COhpf gene (USA300HOU_RS04065) were scanned for the CcpA motif (WTGNNARCGNWWWCAW) using find individual motif occurrence (FIMO) within the MEME suite . An interactive version of the iModulon model is also available at iModulonDB (https://imodulondb.org/).All RNA sequencing data used in this work are available publicly at Sequence Reads Archive (SRA). The accession numbers for individual samples can be found in 10.1128/msystems.00480-22.6FIG S6FIG S6, EPS file, 1.0 MB.RNA sequencing quality control metrics. (a and b) Samples with less than 500,000 reads were filtered out (a), as were samples with Pearson correlations of less than 0.9 (b). Download Copyright © 2022 Poudel et al.2022Poudel et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.00480-22.7FIG S7arlRS strain was labeled as ArlRS iModulon. Download FIG S7, EPS file, 1.1 MB.iModulon postprocessing. (a) ICA models with different dimensionalities were created with OptICA. The model using 180 dimensions, which had the maximum number of robust components while still keeping single gene components to a minimum, was chosen as the final model. (b) Some iModulons were labeled based on data from gene knockout experiments. Here, the iModulon with lowest activity in delta Copyright © 2022 Poudel et al.2022Poudel et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.00480-22.10TEXT S1S. aureus USA300 data. Detailed description of the steps taken to build the iModulon model of S. aureus USA300 strain. Download Text S1, DOCX file, 0.01 MB.Creating the iModulon model for TRN of Copyright © 2022 Poudel et al.2022Poudel et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Escherichiacoli using the latest genome-scale metabolic modeliML1515, showing highly consistent results with previous studies whilesuggesting new manipulations to boost strain performance. The sourcecode is available at https://github.com/chang88ye/OptDesign.Computationaltools have been widely adopted for strain optimizationin metabolic engineering, contributing to numerous success storiesof producing industrially relevant biochemicals. However, most ofthese tools focus on single metabolic intervention strategies  and rely on hypotheticaloptimality principles  and precise geneexpression  for phenotype prediction. This paperintroduces OptDesign, a new two-step strain design strategy. In thefirst step, OptDesign selects regulation candidates that have a noticeableflux difference between the wild type and production strains. In thesecond step, it computes optimal design strategies with limited manipulations(combining regulation and knockout), leading to high biochemical production.The usefulness and capabilities of OptDesign are demonstrated forthe production of three biochemicals in  Systemsstrain design1 has helped to improve theproduction of numerous biochemicals, including lycopene,3 malonyl-CoA,4 alkaneand alcohol,5 and hyaluronic acid.6Agrowing population and fast economical development are leadingto an increasing demand of various daily products and industrial rawmaterials, many of which are derivatives of oil and petroleum. Overthe past decades, important efforts are being made to develop sustainableproduction processes that convert biomass or other renewable resourcesto bioproducts through cell platforms.8 OptKnock,9 which was developed to blocksome reactions in metabolic networks, is one of the earliest suchtools. OptKnock identifies the knockout targets that lead to maximalbiochemical production in the context of flux balance analysis2 that is subject to mass balance and thermodynamicconstraints. This results in a bilevel optimization problem whichcan be solved through mathematical reformulation into a standard mixed-integerlinear program (MILP).9 The OptKnock modelwas later extended to consider gene up/down-regulation,10 swap of cofactor specificity,11 and introduction of heterologous pathways12 for biochemical production. It was also adapted to identifysynthetic lethal genes for anti-cancer drug development.13 Some improvement strategies, such as GDBB14 and GDLS,15 havebeen proposed to improve the efficiency of OptKnock in solving thebilevel problem. There also exist numerous approximate solutions tothe OptKnock model, including genetic algorithms16 and swarm intelligence.17 Designingstrains that couple production to growth has received increasing attentionin recent years, mainly due to the great production potential of growth-coupledstrains in adaptive laboratory evolution.18 Consequently, a number of computational tools along this directionhave been developed to design strains with various growth-coupledphenotypes.20 OptCouple20 simulates jointly gene knockouts, insertions, and mediummodifications to identify growth-coupled designs, although gene expressionregulation is not considered. In addition, game theory has been introducedinto metabolic engineering.22 NIHBA22 considers metabolic engineering design as a network interdictionproblem involving two competing players (host strain and metabolicengineer) in a max-min game enabling growth-coupled production phenotypes,and the problem is solved by an efficient mixed-integer solver. Furthermore,there are also some studies which do not rely on optimality principlesfor phenotype prediction. Among these, the minimum cut set (MCS)-basedapproach,24 which aims to find the smallest number ofinterventions blocking undesired production phenotypes, has been extensivelystudied. Despite high computational complexity, MCS-based approacheshave successfully predicted strain design strategies leading to invivo biochemical production.25 Anotherimportant approach of the same kind is OptForce, which identifiesmetabolic interventions by exploring the difference in flux distributionsbetween the wild type and the desired production strain.26 OptForce has shown good predictions for in vivomalonyl-CoA production.4A number of tools have been developed forstrain design.27 However, there are several limitations which may prevent the wideapplicability of the above-mentioned approaches. First, most of thetools focus on prediction of either knockout targets or regulationtargets alone, with a few exceptions that are capable of predictingboth interventions, such as OptForce26 andOptRAM.28 These exceptions highlight thata combination of knockout and up/down-regulation often leads to higherbiochemical production compared to a single strategy. OptForce encouragesthe use of flux measurements while identifying optimum design strategies.OptRAM considers regulatory networks from which transcriptional factorscan be optimized for biochemical production. However, both OptForceand OptRAM rely heavily on the precise expression level of regulationtargets; for example, desired production phenotypes can only be achievedat the exactly suggested flux values (OptForce) or up/down-regulationfold changes (OptRAM). It is known that gene expression is a complexprocess with many uncertainties. The underlying strict expressionrequirements in these approaches may miss theoretically non-optimalbut practically feasible design strategies. In addition, both approachesrely on a reference flux vector of the wild type, which can be incorrectlychosen from many steady-state flux distributions if it cannot be uniquelydetermined. Second, many existing strategies rely on the assumptionof optimality principles, for example, maximal growth in OptKnock9 and derivatives, in the cell metabolism. However,this assumption is not always an accurate representation of how cellsrespond to metabolic perturbations or environmental changes.29 NIHBA22 showed thatreducing unnecessary surrogate biological objectives helps to identifymany non-optimal but biologically meaningful knockout solutions.The useof computational tools is of undisputed importance to straindevelopment in metabolic engineering.This paper introduces a new computational tool, called OptDesign,that uses a two-step strategy to predict rational strain design strategiesfor biochemical production. OptDesign has the following capabilities:(C1) overcomes the uncertainty problem as there is no assumptionof exact fluxes or fold changes that cells should have for production.As a result, non-optimal but good feasible solutions are not missed.(C2) allows two types of interventions (knockout and up/down-regulation).(C3) disregards the assumption of  optimalgrowth in the production mode.(C4) can use with or withoutreference flux vectors.(C5) guarantees growth-coupled production(if desired up/down-regulationsare achievable in vivo).OptDesign is the only tool that combinesthese five capabilities,as shown in m metabolites and n reactionshas a stoichiometric matrix S that is formed by stoichiometriccoefficients of the reactions.Let J be a set of n reactions and vj the reaction rate of j ∈ J; Sv representsthe concentration change rates of the m metabolites.The flux space FS is defined as the space spanned by all possibleflux distributions v for the system subject to thermodynamicconstraints at steady state . Mathematically, FS can be described asj andubj are the lower and upper flux boundsof reaction j, respectively. We use the notationFSw for the wild type and FSm for the mutantstrain. Flux balance analysis (FBA) determines a single solution inFS when a surrogate biological objective is provided for A metabolicnetwork of v ∈ FSw denotea flux vector of the wild type and Δv denotesthe flux change needed for v to transition into adesired production state. Obviously, v + Δv represents a flux vector of the production strain, andit needs to satisfy mass balance and some production requirements,that is, v + Δv ∈ FSm. Note that flux measurements can be used to customize theflux bounds in FSw and FSm if available; otherwise,the flux bounds can be set according to flux variability analysis(FVA) predictions. For example, FSm can be constrainedby imposing production requirements on the lower bounds of the productionreaction and biomass.OptDesign recognizes metabolic changes from the wildtype to production(mutant) strains. Let DW/h) between the wild type strain and theproduction strain in reactions. OptDesign uses this concept to identifyan optimal set of manipulations leading to the production phenotypeFSm. To do so, OptDesign performs two key steps of optimization.First, OptDesign identifies a minimal set of reactions that must deviatefrom their wild-type flux with at least δ in order to achieveFSm. This set of reactions form candidate regulation targets.Second, OptDesign searches through regulation candidates, togetherwith knockout candidates, for the optimal combination of manipulationsto maximize biochemical production. The following two subsectionsare devoted to presenting these two steps in detail.OptDesign introduces the concept of noticeableflux differenceδ implies Δvj ≥δj (Δvj ≤ −δj). Constraints vjmin and Δvjmax are the lower and upper bounds of flux change Δvj, respectively. Special reactions inwhich fluxes are not allowed to be decreased (increased), for example,non-growth-associated maintenance, should have a zero value for thelower (upper) bound of their corresponding Δv components. A reaction cannot increase and decrease flux simultaneously,which implies constraint w and FSm areconstrained differently by specifying a minimum growth rate and aminimum target production rate, respectively. It is worth noting thatthe minimum target production is not a requirement for the producingstrain. Instead, it is mainly used to identify the set of reactionsthat must change their flux in order to produce the target compound.In practice, we can fulfil this purpose by setting the minimum targetproduction rate to the maximum theoretical production rate (computedby FBA with an objective to maximize the flux through the reactionacting on the target product).An MILP procedure is employed to minimize the number of reactionsthat must change their flux from the wild type to the mutant by atleast δ units. This can be expressed as the following MILP problemNote that this procedure doesnot need a known reference flux vectorfor both the wild type and the mutant; instead, it takes into accountall possible wild-type and mutant flux distributions that meet engineeringrequirements . However, it isrecommended to make use of flux measurements for the wild-type andmutant strains if possible in order to select a rational set of regulationtargets effectively.F+ (corresponding to reactions with yj+ = 1) and a flux-decrease set F– (corresponding to reactions with .yj– = 1) in addition to the suggested flux change Δv. However, these two sets are not the minimum number of manipulationsneeded for the required production state as the effects of some manipulationscan be propagated to the whole metabolic network.26 In addition, there is an engineering cost in manipulatingreactions (through gene–protein–reaction associations),and therefore, it is assumed there is a limit on the number Km of genetic manipulations, including up/down-regulationand knockout. We allow gene/reaction knockout in this step for tworeasons. First, reactions having an unnoticeable flux change (belowδj and thus not in F+ or F–) may sometimesbe good manipulation targets, especially when they are involved incompleting pathways. Taking them as potential knockout targets couldimprove biochemical production . Second, there may be reactions in the regulation candidateset that carry near-zero fluxes in the production strain. From a practicalpoint of view, completely deactivating them by gene knockout is easierthan regulating their gene expression precisely to the suggested minutefluxes. Reaction knockout candidates (denoted by set F×) can be selected by a preprocessing approach,22 which excludes reactions that are essential,irrelevant, or unlikely to be good knockout targets.The solutionto the MILP (2) results in a flux-increase set 22 that maximally forces cells to violate theirwild-type phenotypes for production, that is, to choose the optimummanipulations from F+ ∪ F– ∪ F× in favor of biochemical production regardless of what the wild-typeflux distribution is. As a result, we develop the following networkinterdiction problemcP is acoefficient vector for the target biochemical. yj× = 1 represents the knockout of reaction j, leadingto zero flux in this reaction as illustrated by constraint Here, wetreat the strain design task as a network interdiction problem9 (see reformulation in Supporting Information Data 1). The resulting MILP can be handled either by a modernMILP solver or, if numerous alternative solutions are desired in asingle run, by the hybrid Benders algorithm.22The above statement is a specialbilevel problem and can be formulatedto a standard MILP using duality theoryyj = 1, for some j ∈ F+ ∪ F–. The integer values represent the reactiontargets whose flux needs a change for biochemical production. In orderto determine how they are to be regulated in experimental implementation,the following classification rule is appliedThe solution to problem The output of the model (3) predictswhich reaction should be up-or down-regulated by at least the chosen flux change threshold. Itdoes not impose exact fluxes on the mutant strain to guarantee thehigh production of target chemicals. In this sense, the resultingmanipulations suggested by OptDesign could be experimentally morefeasible than those obtained by existing tools.31 (the detailed procedure can be found in Supporting Information Data 1), resulting in a much smaller knockout candidateset for each target product in the latest Escherichiacoli GEM iML1515.32 Theflux change threshold δj = 1 mmol/gDW/h was used throughout the paper unless otherwise stated.Algorithm 1 presents the pseudocode of OptDesign. It was implementedin MATLAB 2018b to be compatible with the Cobra Toolbox 3.0.33 All MILPs were solved by Gurobi 9.02.34 It is worth noting that a couple of minutesis enough for a modern optimization software like Gurobi to identifya reasonably small set of up/down-regulation candidates. The sourcecode is available for download at https://github.com/chang88ye/OptDesign.OptDesign relies on modelreduction and candidate selection for computational efficiency. Genome-scalemetabolic (GEM) models can be significantly simplified by compressinglinearly linked reactions and removing dead-end reactions (those carryingzero fluxes). Similarly, many reactions can be excluded from considerationwith a priori knowledge that, for example, they are vital for cellgrowth or their knockout is not likely to improve target production.We followed the model reduction and candidate selection procedure32 for E. coli. Glucose wasused as the sole carbon substrate, and its maximum uptake rate wasset to 10 mmol/gDW/h. These biochemicals include a numberof compounds that have been experimentally studied in the literature.In particular, we focussed on the native succinate and non-nativelycopene and naringenin35 in our case studies.A comparison between OptDesign predictions and experimentally validatedinterventions for another nine target biochemicals can be found in Supporting Information Data 2. The heterologousbiosynthesis pathway added to iML1515 for the production of two non-nativebiochemicals can be found in Supporting Information Data 1. The newly added reactions were charge- and mass-balanced.The growth conditions were the same as in iML1515, except that a minimalcell growth of 0.1 h–1 was imposed on mutant strainsfor biochemical production.31 All the otherparameters remained the same as the original iML1515.32 At most 10 manipulations including no more than 5 knockoutswere allowed, and the restriction on knockout is to intentionallyfavor gene expression manipulation over gene knockout. All optimizationproblems in OptDesign were solved by Gurobi 9.02 on a MacBook witha 3.3 GHz Intel Core i5 processor and 16 GB RAM. The optimizationprocess was terminated by multiple stopping criteria whichever wasmet first, including the time limit (104 seconds) and optimalitygap (5%). Indeed, we observed that the incumbent solution did notimprove either after 3000 s or when the optimality gap reached 5%.The OptDesign frameworkwas tested by identifying metabolic manipulationsfor the production of three industrially relevant biochemicals usingthe latest genome-scale metabolic model iML151537 In addition to these two mainsubsystems, reactions from the glucose metabolism, pyruvate metabolism,and cofactor conversion/formation are also possible regulation targets.For example, the glucose transporter (GLCptspp) predicted for down-regulationhere has been a deletion target in another study38 to enhance succinate production.As a startingpoint, we wondered which reactions are likely to be good regulationtargets and how they are distributed in metabolic networks. Therefore,we extracted the candidate regulation targets from the first stepof OptDesign and reorganized them into different metabolic subsystems,as shown in 40 The knockout of FAD reductase(FADRx) increases the availability of NADH, which has shown to bean effective approach to high succinate production.41 The methylglyoxal synthase (MGSA) pathway to lactate isanother primary knockout target predicted by OptDesign. The removalof this minor pathway should result in pyruvate accumulation for succinatebiosynthesis. Interestingly, this knockout has been implemented inprevious studies,40 resulting in an increased fluxin the Krebs cycle. Ribulose-phosphate 3-epimerase (RPE) is anothermanipulation target identified by OptDesign, whose knockout blocksthe conversion of ribose-5-phosphate (R5P) to d-xylulose5-phosphate (xu5p-D) in the pentose phosphate pathway.42 Therefore, it is expected that primary glycolyticflux flows into the precursors, for example, phosphoenolpyruvate andpyruvate, of succinate.E. coli, the PDH activity is either low or undetectable in order to maintainredox balance.43 However, it is observedthat an E. coli mutant with activationof PDH for extra NADH improves succinate production.40 Overexpression of CS, which is also suggested in anotherstudy,26 has been observed to increasethe flux in the Krebs cycle in a malic acid production E. coli strain.44OptDesign suggests to overexpress two enzymes in the succinatebiosynthetic pathway, that is, PDH and CS, which are intuitively straightforwardto understand. In anaerobic 36 However, the increased ATP demand due to glucosetransport via HEX1 has to be mediated by increased ATP productionby other means. For this reason, OptDesign suggests down-regulationof ATPS4rpp to reduce cleavage of ATP to ADP in order to meet metabolicenergy requirements. This prediction has been also suggested in anotherstudy.45OptDesign further predicts that high succinate production requireseither up-regulation of glucokinase (HEX1) or down-regulation of aphosphoenolpyruvate-dependent phosphotransferase system (PTS)-relatedreaction (GLCptspp), both of which have the same effect that glucosetransport is favorably through the ATP-consuming HEX1 rather thanthe more efficient but phosphoenolpyruvate-dependent PTS route. Consequently,it improves the availability of PEP, which is a precursor for biomassformation and many biochemicals including succinate. Both manipulationapproaches have been observed to improve the succinate yield.40 However, here, OptDesign suggests to down-regulate these enzymesinstead of deactivating them completely. In addition, phosphoenolpyruvatecarboxylase (PPC) and malate dehydrogenase (MDH) are also predictedas promising overexpression targets. This result is consistent withexperimental studies that show increased succinate production throughup-regulating these two enzymes.46OptDesign also identifiesa number of additional modification targets,such as pyruvate formate lyase (PFL), phosphotransacetylase (PTAr),and acetate kinase (ACKr), that have been widely used as knockoutsto increase the flux toward the Kreb cycle in succinate-focused studies.E. coli  that catalyze the oxidationof dihydroorotate to orotate in the pyrimidine biosynthesis pathway.The knockout of the underlying gene pyrD for these two reactions resultsin a reduced growth rate,47 which mightsave the carbon source for naringenin biosynthesis. The knockout ofsuccinate dehydrogenase (SUCDi) creates a surplus of the biosyntheticprecursor acetyl-CoA for naringenin, which has been experimentallyobserved in a previous study.35 Phosphoenolpyruvatecarboxylase (PPC), a metabolic shortcut for the conversion of phosphoenolpyruvateto oxaloacetate and the byproduct phosphate, is also listed as a primaryknockout. We postulate that in addition to avoid the accumulationof phosphate, its deletion could not only direct the flux throughpyruvate to acetyl-CoA but also reduce the consumption of acetyl-CoAin the Krebs cycle for the mediation of oxaloacetate. In fact, PPCmutants were found to have a flux increase from pyruvate to acetyl-CoAin a 13C-labeling experiment.48 Two linear reactions, that is, threonine synthase (THRS) and homoserinekinase (HSK), which are involved in the formation of l-threoninefrom l-homoserine, are predicted as down-regulation targets.This manipulation is expected to reduce carbon consumption in competingpathways, which therefore increases the carbon flux toward naringenin.Another down-regulation target is the inorganic diphosphatase (PPA)that catalyzes the conversion of one ion of pyrophosphate to two phosphateions. This manipulation is not intuitively straightforward and believedto create a combined effect with other manipulations to boost naringeninproduction. Since PPA down-regulation produces less phosphate whichis needed in the added naringenin biosynthesis pathway, phosphatehas to be balanced through an increase in its transport channel, thatis, the phosphate transporter (PItex).A three-step pathwayfor naringenin was introduced into the metabolic network coli see 3b, and uE. coli to use alternative conversion routes frompyruvate to acetyl-CoA without depleting coenzyme A (CoA), anotherprimary precursor for naringenin biosynthesis. The knockout of ICLprevents the malate synthase reaction from consuming acetyl-CoA. Inaddition, OptDesign also predicts that the up-regulation of acetyl-CoAcarboxylase (ACCOAC) helps to increase the production of naringenin,which has been implemented in another study.35Aside from the aboveprimary manipulations, it is also predicted that naringenin productionstrains must block at least one of the following reactions: two reactionson the Entner–Doudoroff pathway (EDD/EDA), pyruvate synthase(POR5), isocitrate lyase (ICL), and PDH. Blocking EDD/EDA might increasethe use of glucolycosis, producing more ATP which is needed in theheterologous naringenin pathway. The removal of POR5 or PDH forces E. coli  or SUCDi in this study. Each of these two knockouts has to bepaired with an additional knockout outside the Krebs cycle. This leadsto three most frequent pairs, that is, the glycine cleavage system(CLYCL) with FUM, CLYCL with SUCDi, and SUCDi with EDD/EDA. The predictedCLYCL knockout is believed to help reduce the cleavage of 3-phospho-d-glycerate into the glycine biosynthetic pathway so that morepyruvate can be accumulated. Alternatively, blocking the Entner–Doudoroffpathway allows more flux into glycolysis, leading to a higher productionof the two precursors for lycopene.A non-native lycopenebiosynthetic pathway consisting of three key reactions were addedto the metabolic network of coli see 3c.A pre+ caused by the last step of the lycopene biosyntheticpathway. Last, it is predicted that reducing the phosphate uptakerate improves lycopene production. This is probably because two outof the three reactions added for lycopene biosynthesis produce diphosphatethat can be converted to phosphate, and a flux decrease in this uptakereaction rebalances phosphate in the system.The NADPH-dependent flavodoxin reductase (FLDR2)is another primary up-regulation target predicted by OptDesign. OverexpressingFLDR2 is thought to balance the significantly increased ratio of NADPHto NADPThis paper has presented a new computational tool, called OptDesign,to aid strain development through rational identification of geneticmanipulations including reaction knockout and flux up/down-regulation.This tool has been benchmarked via three case studies of differentbiochemicals, demonstrating its capability of identifying high-qualitystrain design strategies to improve biochemical production.46 Specifically, it has been shownthat engineered E. coli strains KJ060and KJ073 produce succinate yields of 1.2–1.6 mol/mol glucoseafter removing completing pathways that lead to the byproducts ethanol,acetate, formate, and lactate.40 Thesestrains were developed through added acetate in culture media becausethe deletion of PFL causes acetate auxotrophy under anaerobic conditions.37 However, OptDesign suggests that there is noneed to completely deactivate PFL. Instead, down-regulating it avoidsacetate auxotrophy while still achieving high succinate production.52 Furthermore, the overexpressionof PPC in E. coli for increasing succinateyields has been confirmed in a previous study.46OptDesign predicts well in its first computational step a set ofcandidates that can be potentially used as experimental regulationtargets, as shown in the succinate case. In a second computationalstep, the algorithm further prunes this set to a realistically acceptablesize while optimizing biochemical production. Interestingly, manyof the predicted manipulations have been experimentally implementedin previous studies. Taking succinate production as an example, 10out of 14 manipulations  suggested by OptDesign have been employedin succinate-producing strains.44 it remains unclear whether this manipulation is also useful forsuccinate production. The suggested flux modifications on FADRx andATPS4rpp reconfirm the importance of ATP and redox balance in succinate-producingstrains.40 Deletion of RPE showed low fluxin the Krebs cycle,53 suggesting that metabolicbottlenecks may exist upstream of the Krebs cycle. The design strategiespredicted by OptDesign imply that a synergistic effect of RPE knockoutwith the other identified flux modifications can lead to a high productionof succinate. Similar observations can also be found for the productionof two non-native biochemicals, naringenin and lycopene, studied inthis paper.OptDesign also suggests a few new modifications,such as the deletionof FADRx and RPE, the up-regulation of CS, and down-regulation ofATPS4rpp, which to our best knowledge have not been experimentallyimplemented for succinate production. While up-regulation of CS hasbeen shown to increase malic acid production,Supporting Information Data 1). Consequently, the resultinglarger solution space makes it possible to identify design strategieswith a better minimum guaranteed flux for lycopene . It is observed that high succinate production is achievednear the anti-diagonal line in the 2-D parameter space. Low productionstrategies are seen when both parameters have either a small or bigvalue. This is because when the minimum required growth is high, thereis little room to adjust flux for biochemical production; on the contrary,when the minimum required growth is small, large flux changes (andmore regulation candidates to choose as indicated in Supporting Information Data 1) can be made to boost biochemicalproduction. δ impacts on production too as it not only affectsthe candidate regulation set but also the flux of candidate reactionsfor regulation on metabolic networks (see details in Supporting Information Data 1). In practice, it requires carefulselection of δ and growth threshold to yield optimum designstrategies.In addition, OptDesign can be used with a reference fluxvector v* easily by binding the flux bounds of thewild type to v* in v in v*reduces the room for flux adjustments, hence impacting less on growthrate. The production envelopes for succinate also suggest that reference-guideddesign sometimes could lead to better solutions. OptDesign has two key parameters, that is, the flux changeδand the minimum required growth rate, which influence the qualityof solutions for high production. DW/h. In contrast, some existing straindesign tools  using knockout only did not identify any strategiesleading to naringenin production. Similar to OptDesign, there alsoexist a few tools, for example, OptForce26 and OptReg,10 that can identify bothflux regulation and knockout targets. We compared OptDesign with OptForceand OptReg in terms of manipulation targets. For succinate production, OptDesign found numerous design strategiesfor naringenin, with the best having a minimum guaranteed productionflux of 1.73 mmol/gtionsee 7, itis tionsee 1 that en55 from which design strategiesconsisting of gene targets can be identified.Finally, OptDesign has been developed to identifymetabolic manipulationsregardless of whatever the wild-type flux distribution looks like,and it can be used with flux measurements if available. Indeed, ameasured wild-type flux vector can help refine the manipulation candidates,leading to a more accurate prediction of design strategies. In addition,the threshold for noticeable flux change defined in this work canbe further adjusted with measured data, and different reactions canhave distinct values for this parameter. Dedicated threshold valuesallow for a better prediction of rational flux modifications. AlthoughOptDesign has been implemented for reaction-level phenotype prediction,it can be easily modified to predict design strategies at the genelevel. For example, OptDesign can be applied to metabolic networkmodels with an advanced stoichiometric representation of gene–protein–reactionassociations,


To address this, we used a combination of isotope-labeling, substrate exclusion, and nutritional supplementation experiments using Roswell Park Memorial Institute (RPMI) medium to identify the specific nutrients used by the microbes to produce diagnostic biomarkers, and to formulate a Biomarker Enrichment Medium (BEM) as an alternative to complex undefined media for metabolomics research, clinical diagnostics, antibiotic susceptibility testing, and other applications where the analysis of stable microbial metabolic phenotypes is important.Microbes have diverse metabolic capabilities and differences in these phenotypes are critical for differentiating strains, species, and broader taxa of microorganisms. Recent advances in liquid chromatography-mass spectrometry (LC-MS) allow researchers to track the complex combinations of molecules that are taken up by each cell type and to quantify the rates that individual metabolites enter or exit the cells. This metabolomics-based approach allows complex metabolic phenotypes to be captured in a single assay, enables computational models of microbial metabolism to be constructed, and can serve as a diagnostic approach for clinical microbiology. Unfortunately, metabolic phenotypes are directly affected by the molecular composition of the culture medium and many traditional media are subject to molecular-level heterogeneity. Herein, we show that commercially sourced Mueller Hinton (MH) medium, a Clinical and Laboratory Standards Institute (CLSI) approved medium for clinical microbiology, has significant lot-to-lot and supplier-to-supplier variability in the concentrations of individual nutrients. We show that this variability does not affect microbial growth rates but does affect the metabolic phenotypes observed  Microbial phenotypes are dictated not only by their inherent metabolic capabilities (dictated by genes and protein expression), but also by their nutritional environment. The effect of medium composition on biological phenotypes glucose purchased from Cambridge Isotope Laboratories . To identify likely precursors for biomarkers that did not predominantly come from glucose  we screened the effect of exclusion of individual amino acids and other putative precursors found in RPMI on the production of these four biomarkers. RPMI was made according to the recipe in To investigate which biomarkers were derived from glucose, RPMI 1640 medium with no glucose  was supplemented with uniformly labeled + 136 m/z-> 119 m/z (23V CE), lactate [M-H]− 89 m/z-> 43 m/z (12V CE), succinate [M-H]− 117 m/z-> 73 m/z (11V CE), and glucose [M+Cl]− 215 m/z-> 35 m/z (11V CE).All metabolomics data were acquired at the Calgary Metabolomics Research Facility (CMRF) as previously described  as previously described   award and the 2017 Large Scale Applied Research Project (LSARP) competition, administered through Genome Alberta. This work was made possible in part by a research collaboration agreement with Thermo Fisher. IL was supported by an Alberta Innovates Translational Health Chair. TR was supported by an Eyes High Postdoctoral Fellowship from the University of Calgary. Metabolomics data were acquired at the Calgary Metabolomics Research Facility, which was supported by the International Microbiome Center and the Canada Foundation for Innovation (CFI-JELF 34986). This research was funded by a Genome Canada GAPP award, which is intended to enable the commercialization of research findings.IL and TR have submitted a patent  describing substrate-product relationships used to identify pathogens. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We here couple each proposed energy-metabolism to a photochemical-kinetics code and self-consistently predict the composition of Venus’s atmosphere under the scenario that life produces the observed SO2-depletion. Using this photo-bio-chemical kinetics code, we show that all three metabolisms can produce SO2-depletions, but do so by violating other observational constraints on Venus’s atmospheric chemistry. We calculate the maximum possible biomass density of sulfur-metabolising life in the clouds, before violating observational constraints, to be ~10−5 − 10−3 mg m−3. The methods employed are equally applicable to aerial biospheres on Venus-like exoplanets, planets that are optimally poised for atmospheric characterisation in the near future.Life in the clouds of Venus, if present in sufficiently high abundance, must be affecting the atmospheric chemistry. It has been proposed that abundant Venusian life could obtain energy from its environment using three possible sulfur energy-metabolisms. These metabolisms raise the possibility of Venus’s enigmatic cloud-layer SO The metabolisms proposed for hypothetical life in the clouds of Venus cannot explain the planet’s atmospheric chemistry and thus a limit can be placed on the maximum allowed biomass. Gas-phase sulfur chemistry at the cloud top is dominated by the photochemical formation of sulfuric acid, where one photon of light, one H2O molecule, and up to two SO2 molecules, are consumed in the production of one H2SO4 molecule, which then condenses and forms the bulk of the mass loading in the clouds. According to this reaction scheme, the maximum depletion of SO2 that could possibly be achieved is within a factor of 2 of the H2O mixing ratio, however this is inconsistent with the observations of ~150 ppm below-cloud SO2 and ~10 ppb above-cloud SO2, yet only ~30 ppm below-cloud H2O—a factor of five lower than the SO2 mixing ratio. Thus, Venus’s observed SO2 depletion represents a key gap in our understanding of the planet’s atmosphere4, for which no full-atmosphere model can self-consistently predict via exclusively gas-phase chemistry5. This has led to the conclusion that there must be some currently unknown chemical pathway responsible for the depletion of SO2 in the middle atmosphere. One abiotic solution has been proposed which hypothesises that the delivery of hydroxide salts to the cloud layer in the form of mineral dust can enable reduction of SO2 within the cloud droplets to reproduce the observed trend6. It is not yet known whether this in-droplet mineral chemistry is occurring in the clouds of Venus, or whether the delivery of mineral dust to the cloud droplets is sufficient for this mechanism to produce the observed SO2 depletion. This raises the possibility of an alternative hypothesis: the observed depletion of SO2 in the clouds of Venus is due to the bio-chemistry of sulfur-metabolising life in an extant aerial biosphere harboured within the cloud layer.Sulfur dioxide, SO9. It has recently been suggested that the metabolic activity of microbial life in the clouds could also be the origin of the reduced chemical species PH3 and NH3, which are biotically produced on Earth and have been tentatively observed in the Venusian atmosphere at abundances many orders of magnitude greater than those predicted given their short photochemical lifetimes13. A plausible life cycle for a strictly aerial biosphere in the clouds has been investigated11, and a suite of mission concepts for a future Venus Life Finder Mission have been recently presented to the community14. It it therefore timely to provide novel and quantitative tests of the potential metabolisms of a hypothetical aerial biosphere to better understand the requirements and limits of life in the clouds, if it exists. Given that the terrestrial biosphere has had an inexorable effect on the Earth’s atmospheric chemistry throughout geological time, investigating Venusian biochemistries self-consistently with the atmospheric chemistry is crucially important to assessing the viability of the aerial life-hypotheses.While the present day surface of Venus is uninhabitable, the lower-cloud layer lies in the region of the atmosphere where pressure-temperature conditions are suitable for life . This has led to suggestions of microbial life being a possible explanation for the existence of large and irregularly shaped (‘mode 3’) aerosol particles in the lower clouds and being the identity of the unknown UV-absorber15. Runaway greenhouse warming is instigated by an increase in the surface temperature of a planet which causes an increase in the evaporation of surface water to the atmosphere. Since H2O is a strong greenhouse gas, the increase in atmospheric H2O further exacerbates the warming effect, creating a positive climate feedback. Atmospheric H2O is eventually lost via photo-dissociation followed by hydrogen loss to space, and the record of the past water content is left imprinted in the atmospheric deuterium to hydrogen ratio (D/H). The enhanced D/H ratio measured in the atmosphere of Venus provides evidence of past water loss16, however it is unclear whether this water was ever present as liquid at the surface or always as gas in a hot steam atmosphere. If liquid water was present at the surface of Venus in its past then the planet’s slow rotation rate would have enabled a stabilising cloud-climate feedback mechanism to be established17, maintaining habitable surface conditions despite the increasing Solar luminosity over time. This phase of habitability could have lasted for up to ~900 Myrs if Venus has always been in a stagnant lid tectonic regime18, or if early Venus had Earth-like plate tectonics then this habitable period could have lasted for up to 4 Gyrs, until runaway greenhouse was instigated by voluminous magmatism17. In contrast, other modelling has suggested that, due to its proximity to the Sun, Venus could never have cooled sufficiently for liquid water to condense at the surface following its magma ocean phase, and thus a stabilising cloud feedback could never have been established19. In this case, life in the clouds would likely require an aerial origin of life scenario within cloud droplets, utilising very different bio-chemistry to terran life.It has historically been proposed that hypothetical Venusian life could have originated at the planet’s surface or been seeded from Earth, if Venus was ever habitable in its past. This surface-dwelling life would have subsequently migrated to the temperate region of the cloud layer as Venus underwent runaway greenhouse warming, residing in the clouds today as an aerial biosphere21 for three reasons: (1) the chemical energy liberated via currently proposed sulfur-based metabolisms21 is sufficient to meet the energy requirements of a cell22; (2) the relatively high abundance of sulfur species  in the Venusian atmosphere means these are the abundant feedstock molecules that could support a global biosphere; and (3) terrestrial acidophillic organisms provide a precedent for life in an acid environment which uses sulfur-metabolism as a primary source of energy (e.g. acidithiobacillus ferooxidans). Further support for sulfur-metabolising life in Venus’s clouds comes from an extensive analysis into the spectral absorptivities of proteins and cofactors used by terrestrial sulfur-metabolising microbes. Significant overlap was found between the spectral appearance of these molecules and the UV features of Venus’s spectrum, currently attributed to the unknown absorber9.Regardless of the proposed origins or adaptations of hypothetical life on Venus, the requirement of life to obtain energy and materials from its environment is universal. A stable biosphere must consume metabolic feedstock molecules and release metabolic products to liberate energy regardless of the particulars of the biochemical machinery. A Venusian aerial biosphere has been suggested to be dependent on a sulfur-based energy-metabolism21, each of which utilise the relatively abundant sulfur species in the atmosphere and can, in principle, induce the depletion of SO2 as a result of their respective biochemical effects on the atmospheric chemistry. One proposed metabolism, ‘A’, is a primitive anoxygenic photosystem used by microbes on Earth, oxidising hydrogen sulfide (H2S) to elemental sulfur (S) meanwhile fixing carbon from atmospheric CO2 into organics20:2O) represents carbon locked in organic molecules (e.g. glucose). We couple this photosystem to reaction , and investigate the parameter space of metabolic activity versus availability of metabolic inputs. While plausible metabolic pathways and survival strategies for an extant Venusian biosphere have been suggested in previous studies21 none have been investigated self-consistently with the wider atmospheric chemistry. By coupling proposed metabolic requirements with self-consistent models of atmospheric chemistry, we here produce a rigorous test of whether the proposed metabolisms could support a Venusian aerial biosphere. The results of our investigation show that, while proposed metabolic incorporation of gas-phase species can reproduce the sulfur depletion, in every case the resultant atmospheric chemistry violates separate observational constraints on the lower atmosphere of Venus.We present a framework for the coupling of each proposed energy-metabolism to a photochemical-kinetic network for modelling planetary atmospheres in 1D2-depletion biochemically in the middle atmosphere of Venus when coupled to our photochemical-kinetics network. For each metabolism, Fig. 2-depletion is shown with red contours in every panel. We take a surface boundary condition for the initial chemical composition that is consistent with past lower-atmosphere28 and full-atmosphere6 models as our fiducial initial atmospheric composition. This surface condition corresponds to species that have either been observed or are thermochemically predicted to be present at the base of the Venus atmosphere6. For the fiducial atmospheric composition, each metabolism is either rate-limited or reagent limited: metabolism A is limited by the H2S abundance; metabolism B by the H2 abundance; and metabolism C by the CO abundance. Therefore, none of the three metabolisms initially provide a significant destruction pathway for SO2.Each of the three metabolisms are capable of reproducing the observed SO2 observations, each metabolism must be in the SO2-limiting regime, and thus the abundances of the other limiting metabolic inputs must be increased from the fiducial  surface boundary conditions. Figure 2 profile (white dotted line inside the SO2-limiting region).To fit the SO2-depletion biochemically, the predicted abundance of metabolic input species for each of the three metabolisms violates a separate observational constraint in the lower atmosphere of Venus. In Fig. 2, H2S and CO as functions of altitude, for the model atmosphere that best fits the SO2 observations for each metabolic pathway. Alongside, we show the fiducial atmosphere, predicted from thermochemistry, photochemistry and chemical kinetics, which does not correctly predict the SO2 observations, and the abiotic model atmosphere of Rimmer et al. which successfully predicts the SO2 observations by hypothesising aqueous droplet chemistry6. All three proposed metabolisms fit the SO2-depletion with a similar accuracy to the droplet-chemistry hypothesis6, with metabolism A providing the closest fit of any hypothesis to the middle-atmosphere observations . Given the relative lack of observational constraints on the deep atmosphere H2S abundance, it remains a remote possibility that H2S exists with the required surface mixing ratio for SO2-depletion via metabolism A. However, assuming a volcanic source of SO2 and H2S in the Venus atmosphere37, this would require that the molar ratio of volcanically degassed H2S/SO2 is ~2. This is in disagreement with the oxidising nature of the Venus atmosphere, the observation of oxidised sulfur-containing minerals at the Venera 13, Venera 14, and Vega 2 landing sites39, and the inference that the volcanic H2S abundance should be lower than that of SO2 due to the very low water content in the atmosphere37.Metabolism A requires 300 ppm of H2-depletion in the cloud layer of Venus, but in each case they require a source of chemical reducing power roughly equal in abundance to below-cloud SO2: the atmosphere otherwise does not possess enough reducing power for life to exploit to generate the observed SO2-depletion.Our results demonstrate that the three sulfur-based metabolic pathways proposed for Venusian aerial life are capable of reproducing the observed SO2, H2O, H2S, and O2, at higher abundances than expected40, although these data are not consistent with each other without introducing spatial and temporal variability or significant systematic error. If the discrepant measurements were not made in error, then they must somehow be reconciled with the known atmospheric chemistry and thus reveal potential uncertainties in the consensus picture. However, even if these outliers represented the global atmosphere of Venus, they would still be inconsistent with these metabolic pathways explaining the sulfur depletion in Venus’s atmosphere. If, as seems likely, the standard description of the lower-atmosphere composition of Venus is broadly correct6, then we must conclude that either the SO2-depletion is due to a different metabolic pathway, or SO2-depletion on Venus is not related to life.It is possible that our understanding of the below-cloud and in-cloud atmosphere is not correct. There is a consensus picture of Venus’s atmosphere, however this picture still has several mysteries that models are not able to solve, and certain observational data are discrepant with this picture. Discrepant measurements include in situ and ground-based detections of H2-depletion is likely to be it. This is because the observed SO2-depletion does not yet have a proven abiotic solution, even though the rest of the atmospheric sulfur chemistry can be explained by abiotic models to within at least an order of magnitude accuracy6. It may be that the products of these metabolisms could in principle provide good biosignatures, but only in the case that their presence or abundances are not explained by abiotic sources and sinks. This criteria for metabolic products to be diagnostic biosignatures is not met by the three suggested metabolisms that we investigate here: the presence of CO2, H2O, H2S, OCS, and sulfur allotropes in the atmosphere of Venus is already consistent with an abiotic source from volcanic degassing37; the enhanced abundance of OCS and H2S produced via metabolisms B and C remain undetectable above the cloud layer due to efficient photochemical destruction by the Solar flux; and any H2O produced via metabolism A would very likely be retained by the hypothetical microorganism as part of its survival strategy rather than being released back to the atmosphere, given the environmental stress posed by the relative desiccation of Venus’s atmosphere compared to the Earth’s atmosphere41. Since there are limited ways to utilise SO2 as a metabolic input, and since all other reactants would seem to exist in even more limited abundances than those investigated in the present study , this strongly suggests there is no unknown sulfur-metabolism responsible for the observed SO2-depletion.If any diagnostic feature of a biosphere utilising the proposed sulfur energy-metabolisms is being imprinted in the atmospheric chemistry of Venus, then the enigmatic cloud-layer SO2 must be incorporated into the model to self-consistently predict the SO2-depletion. One alternative biologically-driven mechanism for removing SO2 has been suggested by Bains et al.42 which hypothesises that life in the clouds, fixing nitrogen from N2 and using atmospheric H2O, can produce NH3 that reacts with SO2 to form ammonium sulfites and sulfates, however this has not yet been demonstrated with fully self-consistent chemical modelling. An abiotic route to removing SO2 in the cloud layer has been suggested by Rimmer et al. by incorporating aqueous droplet chemistry. The droplet chemistry hypothesis posits that an additional source of hydrogen could be delivered to the clouds as hydroxide salts in mineral dust. This would enable aqueous reactions within the cloud droplets to provide a destruction pathway for aqueous SO2 dissolved in the droplets from the gas-phase. A dust flux to the clouds of ~16 Gt/yr containing 5 wt.% salt would be sufficient to explain the observed SO2-depletion. For more details see Rimmer et al.6. We here repeat our metabolic analysis, now coupling each metabolism in turn to the abiotic model that incorporates droplet chemistry in order to reproduce the observed SO2-depletion self-consistently6, and investigate how far we can increase the metabolic activity and abundance of metabolic input species within observational limits.Alternatively, a Venusian biosphere may exist and not be influencing the atmospheric chemistry in a diagnostic and observable way, in which case we can now place upper limits on the potential biomass density in the cloud layer before observational constraints are explicitly violated for the three proposed metabolisms. In order to calculate this limiting biomass density, an alternative removal mechanism for cloud-layer SO2 below the cloud layer43, and for metabolism C the upper end of the estimated error on the largest measured CO mixing ratio below the clouds (~60 ppm). For metabolism A, assuming that the in situ observation of 3 ppm at the surface is correct, the maximum below-cloud H2S within observational uncertainty corresponds to ~5 ppm H2S35.The first observational constraints to be exceeded as the biomass density is increased are the constraints on the abundances of metabolic inputs below the clouds. Figure Gr liberated by each metabolic reaction is already known. If we make an assumption about the wavelength of light absorbed by the photosystem of metabolism A then an effective ΔGr liberated by the net metabolic reaction can be assumed, give or take some efficiency factor and some numerical factor for the ratio of photons required per metabolic input molecule consumed in the synthesis of long-chain organic molecules . The average volumetric rate of the net metabolic reaction thus provides us with an upper limit on the net energy released via the metabolic activity. We can therefore make an estimate of the maximum biomass density as a function of cell power, under the assumption that cells are ~0.5 μm in radius9 and the density of cellular material is of the order ~1 g cm−3. The results are shown in Fig. We estimate the corresponding biomass density in the cloud layer for the three limiting metabolic models using a simple energetic argument. The reactions of metabolisms B and C are chemotrophic and therefore the Δ44. The biomass density estimates corresponding to this 5 mg m−3 for metabolism A, 3 × 106 mg m−3 for metabolism B, and 5 × 106 mg m−3 for metabolism C. These estimates are inconsistent with the mass loading estimate of ~0.1–100 mg m−3 for the Venusian lower-cloud droplets themselves9, which is problematic as these droplets would host the microbial life in the hypothesised life cycle11. This treatment must therefore be a large overestimate of the maximum possible biomass because of the minimum power assumption we have made: Life in Venus’s clouds would inevitably have far greater power requirements than the minimum estimated power requirements of terrestrial life.The possible biomass range in our model is critically dependent on the power requirements of a single cell. We can first obtain an absolute upper bound on the possible biomass density in the Venusian cloud layer for each proposed energy-metabolism, by adopting that −15 J s−1 cell−1 . For our maximum biomass density ranging between order 10−5, 6 × 10−4, and 1 × 10−3 mg m−3 for metabolism A, B, and C, respectively). We note also that these estimates have not factored in limitations on biomass from metabolic processes that are not related to energy-capture but are still necessary for life: fixation of nutrients from the atmosphere for growth, for example the energetically costly process of nitrogen fixation from atmospheric N2, and the incorporation and availability of metals for use in enzymes to catalyze reactions. This may pose a problem for the life hypothesis proposed by Bains et al. which relies on life fixing nitrogen from atmospheric N2 in a non-energy-capture metabolism to explain the SO2-depletion biochemically42. Given these additional challenges life faces, we emphasise again that the analysis we present here is the most optimistic scenario possible for the hypothetical life that we have considered, in which the only limit imposed on a hypothetical biosphere is the availability of metabolic inputs required to capture chemical energy. These arguments suggest a very high cell-power requirement should be favoured for hypothetical Venusian aerial life, with the consequence that any such sulfur-metabolising biosphere would then have to be extremely low in biomass density, at least four orders of magnitude lower than the terrestrial aerial biosphere, to remain consistent with atmospheric observations.The vast chemical challenges that the acidic environment of Venus would pose to an aerial biosphere would likely result in maintenance power requirements that are larger than any encountered on Earth, which would favour biomass density and cell concentration values below even the lower limit of our estimated biomass ranges , then, assuming the biotic matter can produce similar strength spectral properties as the suggested FeCl3 molecules, only metabolism C could possibly intersect the lower limit of the absorber’s required mass-loading range (10−3 mg m−3) and metabolisms A and B fall short by at least one order of magnitude. However, if the biological pigments can produce much stronger absorption than simple salts then the potential biomass density we have constrained in the Venusian cloud layer may possibly still be a viable explanation to the unknown UV-absorber. Future studies would be needed to compare the standard absorbance of such proposed biological pigments to the observed spectral properties of the cloud aerosol.We can compare the above estimated biomass densities to the abundance of matter required to produce the unexplained spectral properties observed at UV wavelengths in the cloud layer, which are attributed to an ‘unknown UV-absorber’. It has been suggested that the unknown UV-absorber may be explained by the presence of sulfur-metabolising microorganisms harboured in the clouds, with significant overlap found between the spectral appearance of various proteins and photosynthetic pigments used by some sulfur-metabolising microorganisms on EarthOne final consideration that could be made is that of the ecological stoichiometry of a combined ecosystem, featuring a combination of metabolisms in a steady state where the fluxes of metabolic products and inputs consumed are equally balanced. For any significant biosphere an ecosystem is needed and nutrient recycling is critical. Given that the terrestrial biosphere would collapse within a short time period in the absence of nutrient recycling between different species in a diverse ecosystem, it is also unlikely that one species of organism could exist by itself in the cloud layer of Venus, with the speculative exception perhaps of an autotrophic species using photosynthesis and only maintaining a very low biomass, such as that described by metabolism A. We have demonstrated that each of the metabolic hypotheses investigated here can be individually ruled out on the basis of violating lower-atmosphere constraints on the required metabolic input abundances, and we will now demonstrate how our results also show that a multi-metabolism ecosystem recycling the required chemical species exclusively within the cloud layer cannot explain the sulfur depletion in Venus’s clouds.2S consumed by life with metabolism A, could be produced at the same rate by life utilising metabolism B. This results in a net reaction:2 in the atmosphere via photolysis or perhaps another metabolism in a hypothetical ecosystem, the reaction inevitably still requires input of H2 to the atmosphere, inconsistent with observations. It has been suggested that H2 could be replenished in the atmosphere via hydration reactions of ferrous iron oxide minerals at the surface48. Even if there were sufficient H2O in the atmosphere to enable this, the resulting below-cloud H2 mixing ratio would violate the observational upper limit of <10 ppm30. Production of H2 via hydration of ferrous iron oxide mineral dust within the cloud layer exclusively would not be possible because this mineral dust would be dissolved in the sulfuric acid cloud droplets and thus any hydrogen in the reaction would exist as aqueous H+ ions. Mineral dust therefore cannot provide a source of H2 localised within the cloud layer as a potential metabolic input and H2 could only be replenished by surface-atmosphere processes. The observational upper limit on H2 abundance below the clouds therefore poses a strict upper limit on the productivity of any biosphere recycling nutrients in ecosystem interactions. In the absence of ecosystem interactions, the requirement for metabolic inputs to be replenished by surface-atmosphere processes extends to all chemical species and therefore below-cloud observations of any limiting input species poses a strict upper limit on the productivity of the biosphere.If we were to take, for example, the net effect of metabolism A and B together then the flux of H2 and thus be of sufficient biomass to influence the global atmospheric chemistry. We have demonstrated this result for life using each of the energy-capture metabolisms proposed in the literature so far, however our results extend generally to: (a) chemotrophs exploiting energy gradients from gaseous atmospheric species; or (b) phototrophs utilising gaseous atmospheric species as electron donors. The exception to this conclusion is phototrophic life that captures light energy not for CO2 reduction but to support general cellular demands, in which case the potential biomass density in the clouds cannot be constrained via the availability of metabolic inputs in the atmosphere, and future studies are needed to constrain this potential biomass via the available light flux. There may also still plausibly be a low mass biosphere present, the only observable effect of which is to release relatively small amounts of trace metabolic gases. The recent detections of phosphine gas in the atmosphere of Venus may be consistent with this, in the absence of any abiotic phosphine production pathway51.Ultimately there simply is not enough reducing power in the lower atmosphere of Venus for the suggested forms of life to exploit in order to deplete SO52. Interior to the habitable zone is the region where surface temperatures are too high for water to condense at the surface, and this region is more likely to be populated by Venus-like exoplanets53. For Venus-like exoplanets with permanent cloud cover in the temperate region of the atmosphere, the possibility of an aerial biosphere extends the inner edge of the habitable zone much closer to the host star, where orbital periods are shorter and transiting planetary signals have enhanced signal to noise ratios, compared to planets within the surface-water habitable zone. The presence of a biosphere at high altitude also means that biologically induced features in the atmospheric chemistry will be imprinted in the observationally accessible region of the atmosphere, compared to surface life obscured by thick cloud cover. It has been demonstrated that the metabolic inputs H2S (metabolism A) and SO2 , and metabolic products OCS (metabolism C) and H2S (metabolism B), will not be depleted photochemically in the observable region of a Venus-like planet’s atmosphere around cooler host stars, in the same way they are in Venus’s atmosphere54. This means that with high precision retrievals of the upper atmosphere, the aerial biosphere hypothesis may be possible to test on Venus-like exoplanets. The framework that we present in this paper, self-consistently coupling metabolic reaction networks with a photochemical-kinetics network for the atmosphere of Venus, is equally applicable to simulating the effect of an aerial biosphere on the atmospheric chemistry of potentially habitable Venus-like exoplanets. If the new and exotic biome of a strictly aerial biosphere is possible in principle, then given the sheer number and diversity of exoplanets, we might expect that the first detected sign of life beyond the Solar System will originate from an aerial biosphere harboured on a world interior to the surface-water habitable zone.Upcoming missions to Venus may be able to determine whether or not the cloud layer hosts a high mass biosphere using a currently unidentified metabolism, a low mass biosphere producing only trace metabolic products, or no biosphere at all. However, the prospect remains that other, possibly wetter, Venus-like exoplanets could host a habitable niche in the temperate region of their atmospheres. Aerial biospheres in general therefore have significant implications on the number and observability of potentially habitable planets beyond the Solar System. The hypothetical ‘habitable zone’ around a star has resulted in the search for Earth-sized exoplanets within the range of orbital distances for which an Earth-like atmosphere could permit condensation of liquid water at the surface6. The model is composed of a 1D Lagrangian solver, ARGO, and a network of reactions, STAND2020, that treats H/C/N/O/S/Cl chemistry accurately within a temperature range of 100–30, 000 K55. The model has been shown to reproduce the known atmosphere of Venus within approximately an order of magnitude of observations given the inclusion of a scheme of aqueous chemistry that aids the gas-phase depletion of SO2 and H2O6. The chemical network, STAND2020, is a list of reactants, products, and rate constants, for every chemical reaction considered in the atmosphere. For thermochemical reactions the reaction rates are temperature dependent, and for photochemical reactions the reaction rates are dependent on the photon flux as a function of wavelength incident at a given layer of the atmosphere. These chemical reactions determine the chemical production and loss rates of each species. At each altitude step in the atmosphere the reactions are solved by ARGO as a set of time-dependent, coupled, non-linear differential equations:z (cm) and time t (s), nX (cm−3) is the number density of species X, PX (cm−3 s−1) is the rate of production of species X, LX (s−1) is the rate constant for loss of species X, and ∂ΦX/∂z (cm−3 s−1) describes the divergence of the vertical diffusion flux, encapsulating both eddy- and molecular-diffusion.We model the atmosphere of Venus using a photochemical-diffusion model27 and middle56 atmosphere. At the top of the atmosphere, ARGO takes the incident stellar spectrum and, at each step on the journey downwards, includes photochemical reactions driven by the stellar flux irradiating that layer of the atmosphere. ARGO iterates this procedure until a convergence criteria is met and thus a global solution is found.ARGO follows a parcel of gas as it rises from the surface to the top of the atmosphere and back down again. An initial condition for the chemical composition is input at the base of the atmosphere, listed in Table k, the reaction constant in the network depends on temperature  as:2 abundance remains fixed at it’s fiducial value. For metabolism A we gradually increase the H2S abundance from 10 ppb up to 500 ppm. For metabolism B we gradually increase the H2 abundance from 3 ppb up to 10 ppm, and then increase both the H2 and CO abundances in tandem in the ratio 2:1 respectively, from 10 ppm up to 300 ppm for H2 and from 20 ppm up to 600 ppm for CO. For metabolism C we gradually increase the CO abundance from 20 ppm up to 650 ppm.We test the effect of each of the net metabolic reactions, , 4) and and4) anR (s−1 m−3) of the metabolic reaction in the model, we convert to the rate of energy liberated metabolically per unit volume in the cloud layer, ϵ (W m−3):Gr (J mol−1) is the Gibbs free energy release from the metabolic reaction and NA is Avogadro’s constant. The average cell number density in the cloud layer, ncell (cells m−3), is then given by:Pcell (W cell−1) represents the power requirement of a single microbe of mass Mcell (mg). This allows for a comparison to be made to the measured mass loading of aerosol in the atmosphere, and to the estimated mass loading of microbes in the Earth’s (transiently) aerial biosphere. We can further make comparison to measured cell concentrations in analogue terrestrial environments that are most similar to the hyperarid environment of the Venusian cloud layer. To do so, we estimate the volume of aerosol per unit volume of the atmosphere, vaero (mL m−3), within the cloud layer, from the reported aerosol mass loading maero (mg m−3) and an assumed density of aerosol material, ρaero (g mL−1):ncell, aero (cells mL−1), is then given by:Gr ~ 240 kJ mol−1 is known a priori. Photosynthetic life would obtain energy from sunlight rather than a redox disequilibrium in the atmosphere, however the phototrophic metabolism can be treated with the same analysis if an assumption is made about the wavelength of light absorbed in reaction (−1 (i.e. per NA of photons absorbed where NA is Avogadro’s constant) and so the above analysis can be repeated exactly for the phototroph (metabolism A) give or take some efficiency factor, and some numerical factor for the different possible wavelengths of light being utilised . Recent radiative transfer modelling has shown that, below ~ 59 km altitude, while biologically-damaging UV radiation is screened by the cloud layer, photosynthetically active light at longer wavelengths (>400 nm) continues to penetrate57. We therefore assume ΔGr ~ 240 kJ mol−1 for each of the three net metabolic reactions. While our choice of 500 nm for the wavelength utilised in the photosynthetic metabolism is for simplicity in calculating the biomass, this value sits well within the range of common terrestrial photosynthetic wavelengths (~400–1000 nm) including the wavelengths utilised by anoxygenic phototrophs in deep water columns58. We further assume a typical value for cell density of a microbe to be of the order ρcell ~ 1 g cm−3 and typical size of rcell ~ 0.5 μm in order to evaluate Mcell. We can then investigate how the choice of cell-power requirement, Pcell, affects our estimates for the biomass density, in Fig. For the model atmospheres that incorporate the metabolic activity of a putative Venusian biosphere at the maximum possible limit before transgressing observational constraints, we can estimate the maximum biomass density averaged over the volume of the cloud layer, under the assumption that the availability of metabolic inputs is the limiting factor on the productivity of the biosphere . For volumetric reaction rate R s− m−3 of tmbio and ncell, aero at the limiting metabolic reaction rate, by imposing that The required introduction of the cell-power requirement term provides the largest uncertainty to this energetic analysis, varying over at least 10 orders of magnitude for terrestrial microbial life alone. It is for this reason why we must evaluate a range of possible estimates for


Viral myocarditis (VMC), which is defined as inflammation of the myocardium with consequent myocardial injury, may develop chronic disease eventually leading to dilated cardiomyopathy (DCM). Molecular mechanisms underlying the progression from acute VMC (aVMC), to chronic VMC (cVMC) and finally to DCM, are still unclear. Here, we established mouse models of VMC and DCM with Coxsackievirus B3 infection and conducted NMR-based metabolomic analysis of aqueous metabolites extracted from cardiac tissues of three histologically classified groups including aVMC, cVMC and DCM. We showed that these three pathological groups were metabolically distinct from their normal counterparts and identified three impaired metabolic pathways shared by these pathological groups relative to normal controls, including nicotinate and nicotinamide metabolism; alanine, aspartate and glutamate metabolism; and D-glutamine and D-glutamate metabolism. We also identified two extra impaired metabolic pathways in the aVMC group, including glycine, serine and threonine metabolism; and taurine and hypotaurine metabolism Furthermore, we identified potential cardiac biomarkers for metabolically distinguishing these three pathological stages from normal controls. Our results indicate that the metabolomic analysis of cardiac tissues can provide valuable insights into the molecular mechanisms underlying the progression from acute VMC to DCM. Myocarditis is defined as inflammation of the myocardium with consequent myocardial injury. The leading cause of myocarditis is infection with enteroviruses, especially cardiotropic Coxsackievirus B3 (CVB3). CVB3-induced acute myocarditis (aVMC) can either cause sudden death or induce chronic myocarditis (cVMC), even leading to the development of DCM, a chronic condition in which the heart becomes weakened and enlarged. Patients with DCM have only a 5-year survival rate of 55% under current heart failure treatment . Few effClinical presentations of myocarditis are diverse, including nonspecific flu-like symptoms, palpitations, arrhythmias and acute cardiogenic shock. As a gold standard for suspected myocarditis in the last three decades, endomyocardial biopsy would provide false negative results (sampling error) if only small numbers of samples (<8–10) were available . ActuallThe heart is a metabolically active organ. As one of omics technologies, metabolome is downstream of the genome and closer to the organism phenotype. Recently, metabolomic analysis has emerged as a powerful tool for exploring changes in global and cardiac-specific metabolisms occurring across a spectrum of cardiovascular disease states . CardiovIn a previous study, we established mouse models of aVMC, cVMC and DCM by infecting BALB/c mice with CVB3 and showed that NMR-based serum metabolomic analysis can be applied to mechanistically understand the pathological progression from aVMC, cVMC to DCM . As one Consistent with our previous study on sera derived from the mouse model of DCM , Ampong Due to the challenges in obtaining human heart samples, biofluids  samples are extensively used for most metabolomic studies on DCM patients. Biofluids usually reflect global pathological features of diseases ,8,9,10. Expectedly, metabolic profiling of cardiac tissues derived from aVMC, cVMC and DCM mice relative to their normal controls would provide a more accurate understanding of metabolic mechanisms underlying the progression from acute VMC to DCM. So far, few metabolomic analyses have been performed on cardiac tissues to address the metabolic mechanisms of VMC progressing into DCM in detail. Based on the established mouse models of aVMC, cVMC and DCM, we performed NMR-based metabolomic analyses of cardiac tissues, identified potential cardiac biomarkers and impaired metabolic pathways in the three pathological groups relative to normal counterparts. Our results shed light on the molecular mechanisms underlying the progression from acute VMC to DCM.In total, 48 mice were used for metabolomic analysis, including 28 MODEL mice  and 20 CON mice . The detailed histologic examination of the mouse models has been described in our previous study  with water suppression during the relaxation delay and mixing time. A fixed total spin–spin relaxation delay of 80 ms was used to attenuate broad NMR signals of slowly tumbling macromolecules with short T2 relaxation times and simultaneously to retain signals of metabolites with low molecular weights. Experimental parameters were shown as follows: spectral width = 12 KHz; number of time domain data points (TD) = 64 K; relaxation delay (RD) = 4 s; acquisition time (ACQ) = 2.73 s; number of scans (NS) = 32. These NMR spectra were multiplied by an exponential function with a line-broadening factor of 0.3 Hz prior to Fourier transformation, automatically phased and corrected for baseline distortion carefully and referenced to the methyl group of 3-(trimethylsilyl) propionate-2,2,3,3-d4 .All NMR experiments were conducted on a Bruker AVANCE III 600 MHz spectrometer at 298 K; 1D http://www.hmdb.ca/; accessed on15 July 2022) and relevant literature. The resonance assignments were confirmed by using 2D 1H-13C HSQC spectra.The water resonance regions of δ 5.2–4.65 were removed from the spectra. The spectral peaks were aligned with the Icoshift program. Then, the regions of δ 9.5–0.8 were binned by 0.001 ppm for statistical analysis. Peak integrals for each NMR spectrum were normalized by the TSP spectral integral and the weight of the tissue slice. The relative integral of each metabolite was calculated based on both the relative integral of singlet or non-overlapped peaks in each NMR spectrum and the proton number of the chemical group contained in the metabolite molecule, which was used to present the relative level of the metabolite. Resonances of aqueous metabolites derived from mouse cardiac tissues were assigned by a combination of Chenomx NMR Suite , Human Metabolome Data Base . To compensate for differences in heart tissue sizes, the spectral integrals were normalized by the weight of the tissue sample. Before multivariate statistical analysis, the data were scaled by using Pareto scaling in the SIMCA 14.0 software . The unsupervised principal component analysis (PCA) was conducted to reveal grouping trends of samples, highlight outliers, and show clustering for observations using the SIMCA 14.0 software . Then, the supervised orthogonal signal correction partial least squares discriminant analysis (OPLS-DA) was applied to maximally discriminate metabolic profiles between the groups of samples, and the response permutation test (RPT) with 200 cycles was used to evaluate the robustness of the OPLS-DA model. In addition, hierarchical clustering analysis (HCA) was performed on the binned spectral data to confirm the grouping trends revealed by PCA. In HCA, each sample was first treated as a separate cluster, and then the algorithm combined them until all the samples belonged to a cluster.t-test was performed on relative levels of the metabolites. Metabolites with p < 0.05 were identified to be differential metabolites. Finally, metabolites with VIP > 1 and p < 0.05 were identified to be characteristic metabolites.Before carrying out statistical analysis, we performed the KS test for checking statistical distributions of the relative levels of metabolites, which showed that the relative levels of metabolites basically followed normal distributions. The significance of variables (VIP) in the projection was calculated from the established OPLS-DA model, and metabolites with VIP > 1 were identified to be significant metabolites. To quantitatively analyze the levels of cardiac metabolites in each group, Student’s p < 0.05. The KEGG website was used to map the important pathways with changed metabolite levels into the metabolic network.Metabolic pathway analysis was performed by using the MetaboAnalyst 5.0 website based on relative levels of cardiac metabolites. Metabolic pathway analysis is a good integration of metabolite aggregation enrichment analysis (MESA) and pathway topology analysis. As it is known, a metabolic pattern is closely related to the levels of metabolites acting as nodes of metabolic pathways in a metabolic network. Perturbations in metabolisms would change levels of key metabolites in relevant metabolic pathways. We calculated the pathway impact value (PIV) with the centrality algorithm of path topology and identified significantly altered metabolic pathways (important pathways) using two criteria of PIV > 0.2 and Multivariate receiver operating characteristic (ROC) curve analyses were conducted to explore potential biomarkers using a logistic regression algorithm. We randomly selected 66.7% of cardiac samples for the ROC analyses of aVMC, cVMC and DCM mice compared with normal counterparts for screening potential biomarkers. Multivariate ROC curves were constructed by using the biomarker analysis module of MetaboAnalyst 5.0. Furthermore, multivariate ROC analyses were performed on the remaining 33.3% of the cardiac samples to confirm the validities of the identified potential biomarkers for predicting these pathological stages. The ROC curve area values (AUCs) were used to evaluate the predictive performances of the biomarker models. For each biomarker model with AUC > 0.7, metabolites with frequencies of being selected during cross validation (selection frequency) >0.4% were identified to be potential biomarkers for the diagnosis of a given pathological condition.We established the mouse models of aVMC, cVMC and DCM, and performed NMR-based metabolomic analyses of cardiac tissues. We showed that these three pathological groups were metabolically distinct from their normal controls and identified three impaired metabolic pathways shared by these pathological stages, including nicotinate and nicotinamide metabolism; alanine, aspartate and glutamate metabolism and D-glutamine and D-glutamate metabolism. We also identified two extra impaired metabolic pathways in the aVMC stage, including glycine, serine and threonine metabolism and taurine and hypotaurine metabolism. In addition, we identified potential cardiac biomarkers for metabolically distinguishing these three pathological stages from normal counterparts. This study may be of benefit to a mechanistic understanding of the progression from acute VMC to DCM.