Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection. Mycobacterium tuberculosis is the causative agent of tuberculosis disease (TB), which affects a wide range of mammals, including humans. Tuberculosis is the second cause of human infection-related death, right after COVID-19 [M. tuberculosis a major concern from an economic point of view [ of view .M. tuberculosis is composed of several phylogenetic lineages that can be broadly divided into animal- and human-associated, according to the host they are commonly isolated from. Human-associated M. tuberculosis lineages comprise L1 to L4 and L7, commonly referred to as Mycobacterium tuberculosis sensu stricto, L5 and L6, traditionally known as M. africanum, and recently described L8 and L9 [M. bovis or M. caprae, respectively [M. bovis originates host-dependent virulence, producing a mild version of tuberculosis disease in humans with human-to-human transmission being extremely rare [M. tuberculosis is attenuated in cattle [M. tuberculosis lineages or hosts, and which molecular mechanisms are involved.8 and L9 ,4,5,6,7.ectively . Host spely rare . Conversn cattle ,13. It iM. tuberculosis lineages have a high genomic identity (>99%), product of its clonal evolution and absence of horizontal gene transfer [M. tuberculosis lineages hinders the identification of genomic signatures correlated to host adaptation phenotypes. The study of metabolism, which is functionally closer to the phenotype, could reveal details about M. tuberculosis infections and host association phenotypes. However, obtaining of metabolomic or lipidomic data in the context of infection can be challenging. A suitable and alternative approach is the use of genome-scale metabolic models (GEMs).transfer ,14. Desptransfer ,16. L5, transfer . TherefoM. tuberculosis infection, as the hostile environment within the macrophages’ phagosomes restricts bacterial growth [M. tuberculosis’ virulence may be unrelated to growth maximization. An alternative approach is the use of flux sampling, where the solution space of the metabolic model is explored by random sampling without assuming any objective reaction to be maximized [GEMs are metabolic networks built from genome annotations, which are used to infer the possible reactions within the organism. After the application of constraints based on stoichiometry and experimental data, the model can be used to predict metabolic phenotypes in a determined medium composition . Flux Bal growth . Furtheraximized . M. tuberculosis, not only have they been used with similar aims [M. tuberculosis lineages could impact their fluxomic phenotypes are still lacking.GEMs have been used with success to predict phenotypes such as substrate utilization, gene essentiality, production of virulence factors or response to oxidative stress in different microorganisms ,23,24. Ilar aims ,26, but lar aims ,28,29. Tlar aims . HoweverM. tuberculosis with a combination of unsupervised and supervised methods. We determined which genomic signatures contribute more strongly to the separation of animal- versus human-associated lineages. However, most of the signatures were not exclusive of either human- or animal-associated lineages. We therefore adapted a previously available M. tuberculosis GEM, iEK1011 2.0 [M. tuberculosis lineages. With the obtained models we predicted how the genomic differences might translate into metabolic phenotypes. We observed that genomic differences apparently uncorrelated to host specificity introduce perturbations that propagate through the metabolic network, producing convergent metabolic phenotypes correlated with host association and possibly involved in host adaptation.In this study, we analyzed the genomes of ~27,000 clinical isolates spanning all the known lineages of 1011 2.0 ,30, to mM. tuberculosis: ~27,000 clinical isolates spanning all the known lineages of the M. tuberculosis  (To assess to what extent single nucleotide polymorphisms (SNPs) have a role in host specificity, we retrieved the available Illumina genomes of  and A4) . We buil and A4) . These tM. tuberculosis phylogeny by using only genomic deletions. This separation between animal- and human-associated lineages becomes more evident when using only enzymatic gene deletions . The two major components show a clear separation between the isolates belonging to either animal- or human-associated lineages A. L6 andeletions B. In thiM. tuberculosis lineages and trained a random forest classifier to determine what SNPs are more strongly correlated with either animal- or human-associated lineages, keeping the top 25 most important genes in the classification (iniA (Rv0342), which codes for an efflux pump involved in isoniazid and ethambutol tolerance and its expression is induced after treatment with isoniazid [hemB), which codes for delta-aminolevulinic acid dehydratase, involved in the synthesis of cobalamin, a cofactor of many enzymes [We then used a supervised approach to determine what are the potentially deleterious SNPs and gene deletions that are producing the separation of animal- and human-associated isolates. Regarding the SNPs, we randomly selected a set of strains from each one of fication . The modsoniazid ,33. This. In particular, the RDs that are important in the classification are RD1, RD5, RD7, RD8, RD9, RD10 , which are part of RD1, have been lost independently in A1 and A2. RD1 is the main genetic modification involved in the attenuation of the BCG vaccine strain [esxA and esxB, is only completely deleted in A1 (as well as in M. bovis BCG). A2 has a partial deletion of Rv3876 in 77.5% of the isolates. Between 15% and 50% of the members of the remaining lineages carry a partial deletion in this ORF, with the exception of L7 and L8. L8 has this gene intact in all its members, whereas 7% of L7 isolates have lost 15 to 90% of this ORF. The other deletions showing a pattern of host association are part of RD5, which spans from Rv2349c to Rv2353c. Genes in RD5 might have been deleted at least four times independently, and it is the only marker deleted almost completely in all animal lineages and not in human lineages (except for L8) [M. tuberculosis virulence and to the obtention of phosphate in the first stages of infection in the depleted environment of the phagosome [M. tuberculosis when mutated [plc genes, ppe38 and a fragment of ppe39, whereas A3 lost plcA, a fragment of plcB, ppe38 and ppe39, whereas keeping plcC intact. This indicates that these deletions occurred at least 4 times along the phylogeny, concentrating mostly in animal clades [We also fitted a random forest model to deletion data, obtaining an accuracy higher than 0.99. The most important genes in the classification fall within several D9, RD10  lost at e strain ,36. In thagosome ,38. Rv23 mutated ,40. A biM. tuberculosis lineages can grow there. The growth blockage was caused by individual gene deletions.We observed genomic differences between animal- and human-associated lineages in terms of enzymatic gene deletion and potentially deleterious SNPs. However, we could not pinpoint a set of genes whose presence was sufficient to explain host association. Consequently, we used a GEM to explore how these genomic signatures might translate into metabolic differences. We chose iEK1011 2.0 ,30 as thwbbL2). This enzyme catalyzes the first step in arabinogalactan synthesis, a component of M. tuberculosis cell wall [wbbL2 and wbbL1. It might be possible that wbbL1 (Rv3265c) is sufficient for catalyzing the reaction in vivo/in vitro. Indeed, this gene is essential for H37Rv in vitro [Deletion of Rv1525 blocks growth of iEK1011 2.0 model. This gene has more than 90% of its sequence missing in 98.87% of L1 clinical isolates. Additionally, it appears to be completely deleted in a closed PacBio genome of this lineage (Accession Number AP018033.1) . Rv1525 ell wall . The lasell wall . All theell wall ,45,46,47ell wall . It is nin vitro ,45,46,47M. tuberculosis cell wall, many of them involved in virulence [L8 model was also not able to grow. The growth-blocking gene was Rv3281. In the available PacBio genome of L8 lineage (Accession Number CP048071.1) this gene appears shorter than the reference due to an internal deletion . Rv3281 irulence ,52. The irulence ,45,53. TWe decided to keep these reactions instead of changing the model’s biomass composition in these two cases, as we do not have evidence about a different composition in these particular compounds for these two lineages. In the case of Rv1525, the fact that clinical isolates have these genes deleted suggests that there might be other genes compensating for the deletion .−1, except for L7, which had a specific growth rate of 0.21 h−1. This biomass production was considered sufficient for this lineage, as it grows at approximately half the speed of other M. tuberculosis lineages [+.We then deleted the genes affected by potentially deleterious SNPs that were prevalent within each lineage. As before, we checked if the removal of any of these genes blocked growth (see methods). Any of the models, besides L4 , were able to produce biomass. The number of genes with non-synonymous potentially deleterious SNPs that, when removed, blocked growth was: one for L1, seven for L2, four for L3, fourteen for L5, eighteen for L6, fifteen for L7, six for L8, fifteen for L9, eight for A1, seven for A2, twenty-nine for A3 and ten for A4. Again, we decided to keep all of them in the respective models. The description of what these genes code for and their PROVEAN score is included in lineages . The mod2 = 0.778, Q2 = 0.774, p-value = 0.01, Accuracy = 0.98)  A. Using  = 0.98) . The dif = 0.98) . We foun = 0.98) . The subGlycolysis/gluconeogenesis and pyruvate metabolism have a similar number of reaction fluxes positively correlated with animal-associated models and with human-associated models, indicating differential flux diverting between human- and animal-associated models B. HoweveOne reaction may be catalyzed by the product of multiple genes, and one gene product may catalyze multiple reactions. As such, gene absence does not necessarily imply reaction absence. We therefore investigated if flux differences in the altered subsystems were caused by similar numbers of absent reactions in each subsystem among lineages associated with the same host. A hierarchical clustering analysis was performed on the number of reactions removed within each subsystem per lineage . The sub−1 for all the models (−1). It is in these low growth conditions where the differences between animal- and human-associated model fluxomes are observed. These differences should manifest in conditions where the bacteria are growing slowly. We compared the fluxes of exchange reactions between sampled and FBA solutions for each lineage’s model and found similar values (2O and H+ (EX_h2o_e and EX_h_e). For all the models, sampled solutions import more H2O than the flux distributions obtained with FBA, except for L4 and L9 models. Regarding H+, import flux is always higher in FBA  in L1 and Rv3281 (accE5) in L8, both involved in the synthesis of cell wall components. These lineages must have other genes not present in H37Rv that are compensating for the deletions. The annotation of the complete genomes available of these lineages does not account for genes with the same functional category we report as missing [M. tuberculosis genes is not annotated (NC_000962.3), it is likely that the genes filling the detected metabolic gaps remain unknown. Our approach has been proven useful in the detection of such gaps. However, we might not be seeing the full array of metabolic differences between the lineages due to the absence of genes in our reference. However, the lack of complete genomes of some M. tuberculosis lineages, including almost all animal lineage and the incompleteness of its annotation, hampers the task of including these genes in our models. Nevertheless, in a species such as M. tuberculosis, where the genomic identity is higher than 99% [The process of building lineage-specific metabolic models has some limitations. The reference for the mapping of the sequencing reads is a reconstructed ancestral sequence based on the genomic content of a reference strain (H37Rv). . Therefo missing ,41. Becathan 99% , the numRegarding the impact of SNPs, premature stop codons are likely affecting protein function but assessing the functional effect of missense SNPs is difficult. In this study we used PROVEAN, one of the best performing sequence homology-based tools ,74. HoweAs the fluxomic differences we observed happen in a growth range expected in situations where the bacterium is stressed or in dormancy state, the model could be expanded to integrate processes within the host. Previous attempts to build models reproducing host-pathogen dynamics have been useful to study how the metabolic network is rewired in response to antimycobacterial drugs . This inWe designed a computational workflow  to prediM. tuberculosis strains.The input data for the workflow are a collection of genomes organized in groups. Each group should contain phylogenetically related strains with a common phenotype of interest, which in our case was the host association of the lineage. One of the groups should contain a reference strain with an available GEM. All genomes are compared with the reference to identify SNPs and deleted regions. SNPs are filtered to keep only the ones with potential deleterious effects. Within each group of related strains, metabolic genes that are frequently deleted or targets of deleterious SNPs are identified. The high frequency of alterations within the group suggests that it is a conserved event with functional consequences. The metabolic reactions associated with these frequently altered genes are blocked in the reference GEM to originate a model specific for that group of strains. Group-specific GEMs are tested for their ability to generate biomass in appropriate culture media. Some of the blocked reactions can be essential for growth, which suggests that the strains lacking the corresponding genes should have alternative genes not present in the reference genome to catalyze such reactions. In those situations, the list of blocked reactions is redefined, keeping intact the minimum number of reactions affected by potentially deleterious polymorphisms to allow the model to generate a steady-state flux distribution with biomass formation. The set of group-specific viable GEMs is used to predict metabolic phenotypes under various conditions: using different culture media, optimizing growth-rate, or sampling the solution space of each model. The aim of these variations is to find conditions where the predicted metabolic phenotypes (the steady-state metabolic fluxes) discriminate the groups of strains according to the complex phenotypes under study. The trial-and-error search for these discriminating conditions is guided by the analysis of unsupervised and supervised learning models applied to the generated metabolic flux distributions. Once the discriminating conditions are found, the supervised learning models are analyzed to extract the individual metabolic fluxes that have an important contribution to the accuracy of phenotype classification. These most important fluxes and the associated pathways can suggest new hypotheses explaining how the complex phenotype of interest can be influenced by changes in metabolic network activity. In the remaining sections of this section, we detail the methods used to apply this workflow to discover potential metabolic network determinants of host association in n = 26,972), the lineage each isolate belongs to and the details about the sequencing run are included in the The accession numbers of the Illumina sequences analyzed in this study . The resultant reads were aligned to the reconstructed ancestral sequence of M. tuberculosis obtained by Comas et al. [https://github.com/broadinstitute/picard, accessed on 23 June 2020) and excluded. Reads with an alignment score corresponding to more than 7 mismatches per 100 bp were excluded using Pysam v 0.9.0 to avoid false positives . SNPs were called with SAMtools v 1.2 mpileup [M. tuberculosis H37Rv reference annotation (NC_000962.3). SNPs within regions such as PPE and PE-PGRS, phages, insertion sequences and in regions with at least 50 bp identities to other regions in the genome were excluded from the analysis, as in the paper by Stucki et al. [Mapping and variant calling was performed as in Coscollá et al. 2021 . Brieflys et al.  using BWs et al. . Duplica mpileup  and VarS mpileup . Only SN mpileup , in accoi et al. . CustomiThe total set of SNPs observed in all the isolates was filtered to keep only SNPs that produced a premature stop codon. In parallel we kept non-synonymous SNPs and used PROVEAN to predict if the substitution will have a deleterious effect (score ≤ −2.5) . The potFor the unsupervised analysis we first counted how many times each gene was affected by potentially deleterious SNP within a lineage and obtained the average per lineage. A correspondence analysis was then performed using FactoMineR R package. For the supervised analysis we fitted a random forest model to the sampled genomes matrix obtained in 4.4. We used the caret R package for this purpose . AccuracPrincipal Component Analysis was performed with R’s base function on deletion percentage of each one of H37Rv ORFs. Results were plotted with factoextra R package. The PCAs of enzymatic genes and of genes included in iEK1011 GEM were performed analogously, previously filtering the ORFs to keep the ones that had either an entry in KEGG with an Enzyme Commission (EC) number assigned or the ones that appear in iEK1011 2.0 model. Correspondence Analysis was carried out with FactoMineR, with previous binarization of deletion data with a 15% threshold (gene is deleted if 15% or more of the sequence is lost). The input data were the counts of isolates within each lineage carrying deletions on each one of the genes included in iEK1011 2.0 model.We used the caret R package  for randp-value ≤ 0.05). The lists of differentially deleted genes were filtered to keep just the ones that are deleted in more than the 85% of the target lineage. This constituted the first list of genes to remove per lineage model. To these lists the SNPs annotated as stop gain codon and the missense SNPs predicted to be deleterious by PROVEAN that were present in more than the 85% of each lineage were added. We then removed the reactions associated with these genes and simulated the resulting models maximizing growth rate in media conditions simulating Middlebrock 7H9 supplemented with OADC [We used COBRApy for obtaining all the GEMs, curate them and to carry out the simulations . The linith OADC . None of2) = 1000, EX_btn_e (biotin) = 1, EX_pydxn_e (pyridoxine) = 1, EX_ca2_e (Ca2) = 1000, EX_mg2_e (Mg2) = 1000, EX_h_e (H+) = 1000, EX_k_e (K+) = 1000, EX_nh4_e (NH4+) = 10, EX_h2o_e (H2O) = 1000, EX_pi_e (phosphate) = 1, EX_cl_e (Cl-) = 1000, EX_o2_e (O2) = 20, EX_na1_e (Na+) = 1000, EX_so4_e (SO42−) = 1000, EX_cit_e (citrate) = 1, EX_fe3_e = 5, EX_glyc_e (glycerol) = 1, EX_glc__D_e (D-glucose) = 1, EX_ocdca_e (octadecanoate) = 1. Flux units are mmol/(gDW·h). For the simulations with cholesterol, a maximum import flux of 1 mmol/(gDW·h) was allowed. The model’s medium settings for simulating Middlebrock 7H9 supplemented with OADC medium were obtained from López-Agudelo et al. . The maxFBA was performed setting “BIOMASS__2” reaction as objective, simulating growth in Middlebrock 7H9 supplemented with OADC. PCA of the obtained flux distributions was carried out with R 3.6. Flux sampling was carried out with optGpSampler method within COBRApy (N = 1000) . The med2 and Q2, key parameters for assessing the validity of the model, were assessed with 7-fold cross validation. The significance of the model was determined by permutation test (n = 100). The p-value corresponds to the proportion of Q2perm above Q2. With a p-value below 0.05 we considered the model significant. The accuracy was computed by dividing the number of observations correctly classified by the model by the total number of observations. The loadings of the predictive component of the model were extracted to determine the correlation of each reaction flux to host association. Variable Importance in Projection (VIP) values were also obtained, to assess the contribution of each reaction flux to the classification. OPLS-DA model of sampled flux distributions was carried out with ropls R package  aiming tp-value lower or equal to 0.05) [Differential reaction fluxes between samples of human- and anima-associated GEMs were determined with VIP values. VIP is defined as the weighted sum of squares of the PLS weight, reflecting the importance of the variable to the entire model. We considered reaction fluxes with VIP ≥ 1.0 as significant ,85,86. Ato 0.05) . The subThe reactions that were blocked in each model, when the genes affected by potentially deleterious mutations were removed, were determined according to iEK1011 2.0 Gene Protein Reaction (GPR) rules. The reactions were grouped by subsystem and a count of how many reactions were lost in each subsystem per lineage model was obtained. A hierarchical clustering analysis was performed on this dataset, using Euclidean distance and Ward D aggregation method. The heatmap was obtained with gplots R package . The densities of the biomass sampled fluxes for each one of the lineage-specific GEMs were obtained with the ggplot2 R package .


However, more fundamental forms of metabolic heterogeneity are seen in clonal Saccharomyces cerevisiae communities growing in nutrient-limited conditions. Cells within such communities exhibit contrasting, specialized metabolic states, and are arranged in distinct, spatially organized groups. In this study, we explain how such an organization can stem from self-organizing biochemical reactions that depend on special metabolites. These metabolites exhibit plasticity in function, wherein the same metabolites are metabolized and utilized for distinct purposes by different cells. This in turn allows cell groups to function as specialized, interdependent cross-feeding systems which support distinct metabolic processes. Exemplifying a system where cells exhibit either gluconeogenic or glycolytic states, we highlight how available metabolites can drive favored biochemical pathways to produce new, limiting resources. These new resources can themselves be consumed or utilized distinctly by cells in different metabolic states. This thereby enables cell groups to sustain contrasting, even apparently impossible metabolic states with stable transcriptional and metabolic signatures for a given environment, and divide labor in order to increase community fitness or survival. We speculate on possible evolutionary implications of such metabolic specialization and division of labor in isogenic microbial communities.In fluctuating nutrient environments, isogenic microbial cells transition into “multicellular” communities composed of phenotypically heterogeneous cells, showing functional specialization. In fungi (such as budding yeast), phenotypic heterogeneity is often described in the context of cells switching between different morphotypes ( Unicellular organisms seldom naturally exist as individuals. Rather, they live in communities with varying degrees of complexity , differences between individual hyphae of the same mycelium, or phenotypic variation between distinct, isogenic mycelia  heterogeneity arise within this clonal community, where the distinct states retain spatial organization? Emerging evidence suggests that the nature and organization of biochemical networks within these cells can explain how cells in heterogeneous states organize and function within the community.Recent studies now address how this occurs . In yeasThe explanation for the appearance and maintenance of these glycolytic cells came from emergent, self-organizing biochemical principles. Initially, within a colony, cells were highly gluconeogenic, and an outcome of gluconeogenesis is the production and accumulation of an originally limiting resource, the disaccharide trehalose - which is made of two glucose molecules. As trehalose amounts increase, this newly available resource becomes abundantly available to all cells. Some cells  increase trehalose uptake and switch to consuming and breaking-down trehalose for glucose, and this results in global, stable transcriptional changes which allow them to sustain high rates of glycolysis . SimulatFor reproductive success and survival, cellular communities must perform multiple complex biological tasks. The metabolic burden on any single cell can be large if it is required to carry out all necessary biochemical reactions to enable growth and manage survival alone, and this can lead to fitness trade-offs. Therefore, a strategy deployed by many cell communities to overcome this problem is to break down complex processes into discrete sets of steps and divide tasks among different individuals of the community . Due to i.e., the same cell cannot perform these reactions simultaneously. For example, mass-action driven metabolic fluxes ensure that the same cell cannot sustain high rates of glycolysis and gluconeogenesis at the same time. Therefore, dividing metabolic labor can become a strategy that allows microbial communities to survive fluctuating nutrient environments  and this allows the community as a whole to grow in a detoxified environment  leads to the production and accumulation of trehalose. This new (formerly limiting) resource allows some cells to stochastically switch to a trehalose-consuming state, which is glycolytic, and these glycolytic cells effectively function as auxotrophs  . This isThis highlights the concept of biochemically versatile metabolites and how their metabolic flexibility can drive, enable, or control biochemical networks that set up cross-feeding systems of specialized cell states in clonal cell communities.+), acyl transfers (acetyl-CoA and carbamoyl phosphate), and glucosyl transfers (UDP-glucose), which in turn drive multiple transcriptional and metabolic transformations across primary biochemical pathways H, and acetyl-CoA  change the availability of some of the sentinel metabolites described above. Studies from the organized, metabolically specialized cell groups within yeast colonies illustrate how this might be so. Here, the two molecules central to setting up the cross-feeding system are the disaccharide trehalose, and the amino acid aspartate . Both moWhat these studies describe are cross-feeding systems that are largely formed by self-organizing biochemical networks, which themselves have metabolically flexible molecules as the driving entities behind them. What this more broadly entails is that while sentinel metabolites power cell metabolism  that drives metabolic heterogeneity in isogenic communities is sustained over very long periods (which yeast and most microbes experience in the wild), it could potentially lead to mutagenic events where subsets of the clonal population locked into a particular metabolic state evolve auxotrophies and subsequently perform restricted biochemical tasks. Such auxotrophs will exist only as long as they are supported by the complementary population that forms the cross-feeding system. This is of course seen in other syntrophic microbial populations which have lost their ability to facultatively switch back to any alternate metabolic state. In other words, could prolonged metabolic heterogeneity as a consequence of constant environmental selective pressure therefore be imagined as an evolutionary precursor of sympatric speciation events in isogenic communities?Saccharomyces pastorianus, the source of lager beer, which is a result of a hybridization event between Saccharomyces cerevisiae and Saccharomyces eubayanus. This newly formed hybrid inherited properties from both parental strains, making it optimal for lager beer brewing  produce hybrids with reduced fitness in either parental habitat , 2010. WIt, therefore, is a plausible speculation that metabolic heterogeneity that occurs in isogenic communities as a consequence of sustained selective pressure may result in ecologically driven speciation events. Could multispecies communities that exhibit metabolic cross-feeding have originally started as isogenic groups of cells that carried out metabolic division of labor as an adaptation strategy to a specific environmental niche?


Taking these individual contributions into account, we hypothesize that the integration of bypasses and CCCs will further increase plant productivity. To test this in silico, we reconstructed a metabolic model by integrating photorespiration and photosynthesis with the synthetically designed alternative pathway 3 (AP3) enzymes and transporters. We calculated fluxes of the native plant system and those of AP3 combined with the inhibition of the glycolate/glycerate transporter by using the YANAsquare package. The activity values corresponding to each enzyme in photosynthesis, photorespiration, and for synthetically designed alternative pathways were estimated. Next, we modeled the effect of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA cycle (CETCH), which is a set of natural and synthetically designed enzymes that fix CO₂ manifold more than the native Calvin–Benson–Bassham (CBB) cycle. We compared estimated fluxes across various pathways in the native model and under an introduced CETCH cycle. Moreover, we combined CETCH and AP3-w/plgg1RNAi, and calculated the fluxes. We anticipate higher carbon dioxide–harvesting potential in plants with an AP3 bypass and CETCH–AP3 combination. We discuss the in vivo implementation of these strategies for the improvement of C3 plants and in natural high carbon harvesters.Synthetically designed alternative photorespiratory pathways increase the biomass of tobacco and rice plants. Likewise, some  The ever-increasing per capita CO₂ emissions are further limiting our options of mitigating the adverse effects of climate change. Biological processes such as photosynthesis and respiration are the two main attributes that reciprocally regulate net CO₂ concentrations in the atmosphere, but the anthropogenic emissions disturb the balance between CO₂ release and CO2 fixation by the Earth vegetation  cl cycles . Here, wArabidopsis biomass through photorespiratory bypassing in the chloroplast has already been attempted and has thus paved the way for crop improvements  by the enzyme ribulose-1,5-biphosphate carboxylase-oxygenase (Rubisco) . C4 and ovements . Followiovements  synthetieductase . Likewisectively . Besidesectively . In compin vitro implementation of the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle  cycle . This arH) cycle . The CETH) cycle . One limH) cycle  when itsttention . Despitettention . We reco2-harvesting alternatives  bypass developed by 2 in the chloroplast. It would be interesting to see which of the bypasses (AP3 vs GOC) would be more energy efficient when it comes to their co-integration with the CETCH cycle. Likewise, MOG  cycles developed by 3- instead of CO2, and thus sequester CO2 in an indirect manner. A comparison in terms of energetics and efficiency between CETCH and MOG cycles would be very intriguing for the in planta realization of these various synthetic pathways.Besides the artificial CETCH and AP3 pathways, there are also other COrnatives  which ca2 harvesting pathways into predictive models, we used structural metabolic network modeling. This is a common approach based on steady states in cellular metabolism. In contrast to dynamic metabolic modeling, the structural network modeling does not require knowledge of detailed kinetic parameters. With the structural approach, a metabolic model can be analyzed by knowing which enzymes are present and considering initial constraints. We used elementary mode analysis (EMA), a metabolic pathway analysis (MPA) method. EMA calculates the “minimal set of enzymes that can generate valid steady states”  diminishes the photorespiratory flux while increasing photosynthetic enzyme activities in the chloroplast and thereby confirming the metabolic phenotype  associatThe reconstruction of the native metabolic model was achieved by integrating different KEGG  modules Elementary modes (EM) are the non-decomposable paths in a metabolic network that can keep the network at a balanced state, in which internal metabolites are neither accumulated nor consumed, and their concentrations stay constant. In summation, they are groups of enzymes that can generate a steady state and cannot be broken down to smaller flux modes ; however, it represents the photorespiration in addition. It includes both the oxygenase and carboxylase functions of Rubisco. In this long elementary mode  revealed a new elementary mode (A-EM5). A-EM5 starts with the oxygenase activity of Rubisco to produce 3-PGA that is then used in the CBB cycle  from BB cycle . The eleBB cycle .in vitro carbon fixation cycle developed by 2 to generate glyoxylate in 14 reaction steps , which is phylogenetically closer than E. coli MAS. Elementary mode analysis resulted in eight joint EMs (J-EMs). Interestingly, J-EM8 consisting of 13 reactions of the CETCH cycle and 1 reaction from AP3 represents the connection between the two synthetic pathways. We also obtained the same EM (A-EM5) with AP3 connecting the CBB cycle and the first part of the chloroplast pathway of photorespiration to AP3 . Likewise, the CBB cycle activities increased with the integration of AP3 and CETCH combination (6.5%) as well as the integration of AP3 alone (7.5%). The integration of the CETCH cycle alone caused a slight decrease (0.8%) in the enzyme activities of the CBB cycle with a notion that the presence of CETCH caused repression in the activities of CBB enzymes. However, highest activities for all CBB cycle enzymes were observed when addition of the CETCH cycle was combined with PLGG1 deletion . In summOn the contrary, the enzyme activities of the photorespiratory pathway are repressed when PLGG1 is fully suppressed irrespective of the model tested in our analysis. The exception was the central redox buffering enzyme catalase which increased in activity. Even in the presence of PLGG1, integration of AP3 and/or CETCH decreased the activities of all photorespiration enzymes except those in the chloroplast, which increased upon the integration of the AP3 bypass. AP3 relies on the production of glycolate through the first two steps of the photorespiratory pathway . It is nInter-pathway Compatibility, CO2Harvesting Efficiency, and Prospects for In Planta Realization). Thus, these in silico results suggest that the integration of CETCH and AP3 along with a PLGG1 deletion has stimulating effects on photosynthetic reactions and diminishing effects on photorespiratorion. Furthermore, the combination of the CETCH cycle with the AP3 bypass increased the activity of malate synthase pointing to higher malate production with implications for higher biomass formation.Overall, modeling the effect of the topological changes on enzyme activities demonstrated a high stimulation of photosynthesis and elimination of photorespiration in peroxisome when CETCH integration is combined with PLGG1 deletion. However, effects of a 100% PLGG1 suppression should also be considered for possible toxicity that may arise from glycolate production in the chloroplast  or is directly released to the environment (see Supplementary text). Overall, we argue that AP3 is more suitable for co-integration with CETCH since it can deal with the glyoxlate produced in the CETCH cycle unlike the GOC cycle that does not consume glyoxylate; consequently, AP3 attains higher energy efficiency than GOC. Nevertheless, these aspects merit detailed investigation for in vivo realization.We next evaluated how robust this change is concerning the alternative pathways investigated. We integrated another recently designed alternative pathway: the GOC  bypass , which iin vitro realization of the CETCH cycle by Schwander et al., reported enzyme-specific activity kinetics. Their analysis showed highest activities for catalase  and mesaconyl-CoA hydratase , while 4-hydroxybutyryl-CoA synthetase  and methylsuccinyl-CoA oxidase (MCO. 0.1 Umg−1) proved to be less active. One may tempt to speculate that lower specific enzyme activities in the CETCH may limit the efficiency of the whole CETCH because our analysis  or longer but more energetically feasible MOG  cycles. The simplest cycles found by analysis  suggestsanalysis  from theanalysis  can be m2-fixing CCR enzyme (crotonoyl-coA carboxylase/reductase) than Rubisco  CO Rubisco . To furt Rubisco . These rin planta realization of synthetic cycles may demand the repression of endogenous cycles in order to achieve prominent CO2 fixation potential of the synthetic cycles. The artificial pathways change the elementary modes in the model and hence the distribution of fluxes across the network. CBB cycle enzymes have higher estimated activity as they are present in more EMs  than CETCH cycle enzymes (J-EM8). It is because of the fact that CBB cycle enzymes have higher connectivity with other pathways in the cellular system than the artificial CETCH cycle. It is noteworthy to mention that actual kinetic data such as enzyme and metabolite concentrations or at least gene expression data  and enzyme activity estimates. Our study utilizes a well-known method in metabolic modeling, EMA (in planta realization of these CO2-harvesting modifications, conveniently achieved in the model plant Arabidopsis thaliana. Likewise, these models can also be implemented in crop plants by exploiting conventional molecular biology tools as well as new genome-editing techniques. Besides crop plants, all our results are readily transferable to the highly conserved photorespiratory and photosynthetic cycles found in algae (2 harvesters even without engineering, they are often aimed for potential CO2-sequestration applications. Future works will integrate more experimental data and then can simulate diverse scenarios more accurately with actual kinetics and better resolution.We investigate here ing, EMA , in a noin algae , and bluin algae . As algain algae  are fast


Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline. IIl y a des systèmes vivants; il nʼy a pas de matière vivante.There are living systems; there is no living matter.These words of Nobel laureate Dr. Jacques Monod  are incr+ ILC3s, CD62L+ ILC3s, and HLA-DR+ ILC3s -paradigm . Extraceparadigm .+ PD1+ T cells are spatially restricted to the tumor-immune boundary, implicating a broader, noncheckpoint-dependent mechanism of immune exclusion in human colorectal cancers. Similarly, Levine et al. implemented mass cytometry to determine metabolic regulators of CD8+ T cell activation after Listeria monocytogenes infection in vivo . F. F125]. Box 2 Compass: an FBA tool to study immunometabolismCompass is an FBA algorithm that uses single-cell transcriptomic profiles to produce a quantitative profile for the metabolic state of single cells. Even though the mRNA expression of enzymes is not an accurate proxy for their metabolic activity, a global analysis of the metabolic network (as enabled by RNA-seq) in the context of a large sample set (as offered by single-cell genomics) coupled with strict criteria for hypothesis testing provides an effective framework for predicting cellular metabolic states.r, the maximal flux r and every cell, Compass finds a flux distribution  that minimizes the overall penalty incurred while maintaining a flux of at least ω = 0.95) through r. The additive inverse of this penalty term is the reaction score.The first step of Compass is agnostic to gene expression and computes, for every metabolic reaction k-nearest neighbors graph, similar to other scRNA-Seq algorithms [The use of genome-scale metabolic networks allows the entire metabolic transcriptome to impact the computed score for any particular reaction, rather than just the mRNA coding for the enzymes that catalyze it. This reduces the effect of instances where mRNA expression does not correlate with metabolic activity and of scRNA-Seq dropouts . Compassgorithms ,130–133.gorithms  or microgorithms ) or baseThe resulting quantitative profile of the metabolic state of a cell, represented by a reaction score matrix, is subjected to further analyses, which can be (a) hierarchical clustering of metabolic reactions as metareactions, (b) computing differential expression of reactions, (c) the correlation of reactions with phenotype under interrogation, and (d) the analysis of data-driven pathways .Genome-scale models can be useful tools to gain a better understanding of the metabolic requirements of rare immune cells, especially in vivo, and consequently are an important tool to study immune cells both under homeostasis and in disease. With the increasing availability of high-throughput data, metabolic models can have several potential applications in both preclinical and clinical settings Fig. , a few o+ T cells within an inflammatory microenvironment, such as an arthritic joint, express lactate transporters to support their survival [+ biosynthesis activity in seven mouse tissues [Caenorhabditis elegans as a model, Yilmaz et al. implemented their new computational pipeline MERGE to identify tissue-specific metabolism in the nematode. Applying tissue-specific constraints on scRNA-seq data from various tissues of C. elegans, the authors found that the metabolic properties of a nematode tissue depend on the established functions of the tissue and that metabolic similarities with analogous human tissues could be identified [Immune cells moving from blood or lymphoid tissue to the tissue of residence face a shift in microenvironment-derived metabolic input as well as oxygen concentrations. Angelin and colleagues demonstrated that Tregs maintain OXPHOS only under nutrient- and oxygen-replete conditions . Under gsurvival . Simulatsurvival , 93. In  tissues . Similarentified . These sDistinct T cell subsets are associated with autoimmunity and inflammation. In EAE, an animal model of multiple sclerosis, the balance between Th17p and Th17n/Treg cells regulates the outcome and severity of disease. Using the FBA algorithm Compass, we studied the metabolic underpinnings of T cell pathogenicity in simplified in vitro settings . Leverag13C metabolomics data. Applying this pipeline, the authors identified UDP-GlcNac and glutamine metabolism as characteristic features of M2 macrophage polarization. Furthermore, an inflammation-induced aspartate-argininosuccinate shunt was identified as a link between the metabolic rewiring of the TCA cycle and the production of nitric oxide [Arabidopsis, yeast, mice, and humans [DLD (dihydrolipoamide dehydrogenase) and BCKDHA (branched-chain keto acid dehydrogenase E1) in the brain and liver [The basis of genome-scale networks is the integration of omics data to generate draft reconstructions upon which GSMMs are built. Integration approaches that can incorporate multiple omics data can enhance the depth of network-based approaches. Jha et al. developed such a network-based approach concordant with metabolomics integrated with transcription (“CoMBI-T”) that allows the integration of transcriptional and ic oxide . The autd humans . Of noted humans . This apnd liver . Such inSLC2A5 in B cells, aldehyde dehydrogenase 1 family member A1 (ALDH1A1) in monocytes, superoxide dismutase 2 (SOD2) in neutrophils, equilibrative nucleoside transporter 1 (ENT1 or SLC29A1) and catalase (CAT) in eosinophils, and mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 2 (MT-ND2) in basophils [This increasing understanding of the human metabolic network provides an opportunity for the clinical application of GSMMs to characterize and predict the outcome of a disease. A recent attempt to increase the patient-specific understanding of diseases is the Human Pathology Atlas developed in 2017. Based on data from ~8000 patients in 17 cancer types and datasets from local and open-source data repositories such as the Cancer Genome Atlas and the Human Protein Atlas, the authors identified genes associated with survival in various cancer types . By applasophils . The recasophils  integratasophils , Recon 3asophils  and iHSAasophils , and theasophils  databaseGSMMs and FBA make simplifying assumptions due to incomplete information. For example, computational predictions by gap-filling algorithms –107 are Saccharomyces cerevisiae [supermodules to which the metabolic network is segmented. scFEA uses a neural network to allow rich modeling of non-linear dependencies of flux on gene expression but ignores reaction stoichiometry. scFEA also segments the metabolic network and prunes non-carbon molecules, resulting in less granular mechanistic predictions [The steady-state assumption in static FBA, namely that net influx through each metabolic reaction is equal to the net outflux, hinders analysis of temporal dynamics of cellular metabolism. Some insight into the system behavior if the assumption is relaxed is offered by the dual variables of the FBA constraints, sometimes called “shadow prices” , 110. Anrevisiae . Based orevisiae . uFBA inrevisiae  relaxes dictions .GSMMs also lack widely accepted standardized protocols for workflow and the validation of model outputs. Genome-scale networks require manual and laborious curation, a process that can introduce significant variability in predictions , 112. ToIn the domain of single-cell studies, future research will also address the lack of specificity in GSMMs. For example, different subsets of the metabolic network may be available to different cell types. The molecular composition of a cell’s environment, which is modeled in FBA and affects the outcomes of the computation, is uncertain even in synthetic and highly controlled growth environments. Better modeling of cellular environments will increase the predictive capability of FBA-based algorithms. The diverse protocols for single-cell sequencing also pose both challenges and promise. For example, we mainly tested the Compass algorithm on Smart-Seq single-cell data , 33. WhiThe ultimate quest of all approaches for interrogating immunometabolism is finding the link between genotype and metabolic phenotype to predict patient- and disease-specific fates of immune cells and to provide novel targets to check the progression of immunological diseases. With the increased availability of large datasets, including the Human Cell Atlas , and the


Stable isotope labelling experiments provide many opportunities for probing metabolic pathways. One goal of such experiments is to define the metabolic phenotype of an organism in terms of the fluxes supported by the entire metabolic network. Metabolic flux analysis (MFA) is used routinely in prokaryotes, but its application to plants is more challenging. Here we examine the status of MFA in plants, highlighting difficulties that hinder wider exploitation of the technique. We conclude that simulation of network fluxes using constraints-based modelling offers a more versatile approach for exploring the capabilities of a network, and that MFA might be best used to probe the interesting features that simulation reveals.13C-MFA has made significant contributions to the analysis of network-wide metabolic responses, and the provision of energy and reducing power, in heterotrophic systems such as plant cell cultures and developing embryos  In autotrophic networks involving the assimilation of a single carbon atom substrate, all metabolites become uniformly labelled at isotopic steady state, and analysis of such systems requires a transient 13C labelling experiment followed by non-stationary (INST) MFA. In principle, INST-MFA can also be used to interrogate heterotrophic networks, and this would be the appropriate strategy for investigating systems in which a metabolic steady state cannot be maintained for long enough to achieve an isotopic steady state after supply of a labelled substrate. (C) Both steady-state MFA and, to a lesser extent, INST-MFA are employed routinely for the analysis of non-compartmented metabolic networks in microbes. (D) The extension of MFA to distinguish between fluxes through equivalent reactions in different subcellular compartments of eukaryotic plant cells relies on the analysis of products that are synthesized in specific cellular locations. These products report on the labelling patterns of defined subcellular pools of network intermediates, and their usefulness depends on the extent to which the labelled intermediates exchange between different organelles. (E) Resolving the distribution of fluxes in different cell types in a complex tissue or organ similarly relies on measurement of labelling patterns of compounds that reflect the isotopomer profiles of network intermediates in individual cell types, and the ability to achieve this may be compromised by the nature and extent of exchange of metabolites between the different cell types.(A) Metabolic flux analysis (MFA) is based on a set of mass balance relationships describing flux through the network at metabolic steady state. The relationships are constrained by measurements of isotopomer abundances of metabolic intermediates and/or products at isotopic steady state following supply of a substrate labelled with a stable isotope. Least-squares regression analysis of this overdetermined problem is used to infer the combination of flux values in the network that best explains the observed redistribution of label. Heterotrophic networks are commonly analysed by steady-state MFA involving the supply of a partially 13C-MFA is that the provision of energy and reducing power can only be addressed indirectly because the interconversion of pairs of coenzymes does not involve a carbon transfer. This means that 13C-MFA cannot distinguish between the activity of NAD- and NADP-dependent enzymes, and this leads to uncertainty in deducing coenzyme budgets for the network. In principle, this problem might be solved using a deuterium labelling strategy developed in mammalian cells . The stoichiometric matrix provides a mathematical description of the network in which the rows represent metabolites and the columns represent reactions. Here, column 3 indicates that reaction 3 consumes equimolar quantities of A and B to produce intermediate C. (B) If the metabolic system is in a steady state, the concentration of every intermediate will be constant, and the product of the stoichiometric matrix and the flux vector will be zero. There may be many flux distributions that correspond to a metabolic steady state, and an optimal solution for a particular set of conditions can be found by using an optimization technique such as linear programming. Operationally it is necessary to apply a set of constraints to the network, such as substrate consumption rate, biomass composition. and known limits on specific fluxes, or more generally metabolomic, transcriptomic. or proteomic data, and to assume that the network operates to achieve a specific objective. The latter would typically be an efficiency objective, such as minimization of the sum of fluxes or maximization of growth. Note that in contrast to MFA, the computational problem is underdetermined, so the optimal solution is not unique. The extent to which the specified objective function can be satisfied by alternative flux distributions can be assessed using flux variability analysis. (C) The applications of flux balance analysis to plants can be greatly extended by setting up sets of models that provide a description of the system under different conditions and solving them as a single optimization problem. In this example, the system is described by five phases that are linked by transfers of cytosolic, mitochondrial, plastidic, and vacuolar metabolites. More generally, the different models might represent different tissues in a whole plant, or different cell types in a tissue, or different time points across a developmental time scale. In the simplest application, the impact of the diel cycle on leaf metabolism can be modelled by setting up light and dark models, with linking metabolite fluxes, and solving the two models simultaneously.(A) A metabolic network interconverts a set of intermediates  and thereby converts inputs  to outputs (E) at rates characterized by fluxes (The selection of the constraints is very important in determining the validity and usefulness of the flux distributions predicted by FBA, but there has been more than a decade of experience in using this technique in plants, and the technique is now well established as a method for exploring the metabolic capacity of the plant metabolic network. Recent applications of the technique have included an analysis of leaf energy balance in the light (13C-MFA in plants.Given the difficulties that have arisen in the application of empirical MFA to plants, there is a strong case for exploiting the power of stoichiometric modelling to identify situations in which the investment in time and effort for MFA would be justified. For example, FBA models can be used routinely to predict the consequences of metabolic engineering (


Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation. Staphylococci are non-motile Gram-positive cocci, and ubiquitous commensals of the skin and mucous membranes. They are opportunistic pathogens and the most frequent cause of nosocomial infections associated with implanted medical devices.Staphylococcus aureus is probably the best-known and studied member of the genus, a number of non-aureus Staphylococci (NAS) are increasingly recognised as clinically significant pathogens [Staphylococcus epidermidis, which can colonise numerous sites throughout the body and is now the most common NAS species associated with infections of indwelling medical devices, endocarditis and neonatal infections [Although athogens ,2,3. Of fections ,5.S. epidermidis is a facultative anaerobe and possesses complete Embden–Meyerhof–Parnas (glycolytic) and pentose phosphate pathways, as well as the tricarboxylic acid (TCA) cycle (which lacks the glyoxylate shunt) [e shunt) . They mae shunt) ,7,8.S. epidermidis can survive in or on sites as diverse as skin, synovial fluid and the pericardium, suggests a great deal of flexibility in the utilisation of nutrient sources and this, in turn, implies considerable metabolic flexibility. Identifying the minimal nutrient requirements for growth and amino acid auxotrophies is a necessary first step in characterising this flexibility, although for staphylococci this has proven difficult [S. epidermidis.The fact that ifficult ,10,11 anWe propose that a better understanding of this flexibility would, in addition to the inherent contribution to basic science, have potential clinical applications, for example, in the development of prophylactic treatment against the colonisation of indwelling medical devices, in tailoring treatments to specific sites of infection, and in designing improved media for laboratory research .Genome-scale metabolic models (GSMs) describe the network of reactions that are assumed to be present in a given organism, typically derived from an annotated genome, and may be used in a variety of endeavours, including the identification of novel pathways , metabolS. epidermidis and used it to investigate substrate preferences, and understand the growth requirements and potential routes of energy production in an aerobic environment. We have combined modelling and an experimental approach to investigate the metabolic abilities of S. epidermidis to utilise amino acids, its substrate auxotrophies and the impact of amino acid removal on growth and metabolism.In this work, we have constructed the first curated genome-scale metabolic model for S. epidermidis RP62A, version 20.1, using the ScrumPy metabolic modelling package [The model was constructed on the basis of the BioCyc Pathway/Genome Database (PGDB) ,20 for S package  and in a package , Ahmad e package , and wasThe top levelmodule, which serves to import the modules listed below.Automatically generatedreactions extracted from the PGDB, automatically corrected where necessary, as described in Transport reactionsto account for the import of the various media components and export of metabolic by-products .Biomass generationS. aureus [consisting of “pseudo-transporters” to allow for the export of biomass precursors. Biomass composition, comprised of biofilm and planktonic cell composition, was defined as a modification of that described for . aureus   with minimisation of total flux as the objective function. Additional constraints of cell maintenance cost, biomass production and substrate availability in the media were included to define the linear program as:lzhütter , Holzhütlzhütter , Singh elzhütter , vi..j=b−1)- and non-growth (8 mmol ATP gDW−1 h−1)-associated maintenance cost, respectively [ectively ,34. μ vaThe effect of removing individual amino acids from the medium was identified by setting a constraint of 0 to the associated transporter and setting quations  and 2)  μ in Eququations  when groquations .Medium based on that described by Hussain et al.   and inocula were prepared as follows: a 25% glycerol stock was streaked out on BHI agar and incubated at 37 °C. After 24 h, 10 mL of fresh BHI broth were inoculated with 3 individual colonies and incubated for 18 h at 37 °C, shaking (180 rpm). Cells were recovered by centrifugation at 3000 rcf for 5 min. Finally, bacterial pellets were washed twice with sterile PBS, to minimise the potential carry over of nutrients, and re-suspended with PBS up to the original sample volume. An approximate concentration of 1.5 × 108 CFU/ml for the bacterial inoculum was estimated by serial dilution and cell counting.The Amino acids were removed from the MHHW medium one at a time to determine the effect on growth characteristics. The reduction in total available C, N and S as a result of removing the amino acid was assumed to be negligible and no other adjustments were made to the composition of the medium. Original HHW medium and BHI were used as a positive control for growth.6 CFU/mL. 150 μL of each sample was then added to three independent wells in a 96-well plate and incubated at 37 °C and shaking (180 rpm) for 48 h. Each experiment was performed in triplicate and cell growth was monitored by measuring absorbance spectrophotometrically at 600 nm (OD600) at 0, 4, 6, 8, 18, 24, 25, 26 and 48 h. No bacterial growth was observed when inoculated in an MHHW medium without any amino acids , indicating no potential nutrient carry-over. Non-inoculated MHHW medium broth and BHI broth were used as sterility controls and their OD measurements were used as background values, being subtracted from the absorbance measurements of the test samples at each timepoint.Aliquots of inoculum were added in a 1/100 proportion to each test medium (20 μL to 2 mL), to give an approximate final concentration of 1.5 × 10−1). This was achieved by multiplying the OD value by a constant factor of 0.11, which was derived from preliminary experiments measuring OD and dry weight at the same timepoints.In order to make quantitative comparisons between experimental OD observations and model flux calculations, OD must first be converted into units of biomass  and t the subject, we may also determineAs all inocula had reached stationary phase by 48 h, growth parameters were obtained by fitting the logistic (Verhulst’s) equation:S. epidermidis RP62A consists of 895 reactions and 864 internal metabolites, with an additional 95 transport reactions and 74 external metabolites  associations, 55 are spontaneous reactions, and the remaining reactions (169 reactions) have no gene–protein-reaction (GPR) associations and are inferred through the pathway prediction algorithm in the PGDB.The optimal solution to Equation , assumin2, and NH4 respectively.The import fluxes in isoleucine, leucine, phenylalanine, tryptophan, tyrosine, asparagine, histidine and methionine were equal to their respective biomass export fluxes, which is consistent with their direct incorporation into the biomass protein component and glycine, although its availability was not used. The uptake of amino acids relative to their biomass contribution is presented in Examination of the LP solution  showed that approximately 90% glucose consumption was used to satisfy the demand for biofilm, storage and structural components, with only 10% entering glycolysis.4, CO2, acetate and succinate as by-products. It is noteworthy that, although glucose was available, it was not utilised in this solution (see discussion).One explanation for the excess amino acid uptake is that these compounds are catabolised for energy generation; in order to investigate this further, Equation  was solvAs described above, Equation  was repe−1), and these were then used to determine the parameters K and Optical density measurements were first converted to biomass concentration . The time taken to reach half-maximum density , it is notable that they were not simply taken up in the proportions defined by the biomass composition, but varied considerably, between 0% in the case of glycine and 2590% in the case of proline, as shown in Figure 22, and that in an experimental setting such an observation would be taken as evidence for the operation of a conventional glycolysis/TCA/OP scheme, the model analysis suggests quite a different metabolic arrangement. This highlights the fact that a combination of modelling and experimental approaches makes it possible to identify potential modes of metabolism that could not be predicted on the basis of observation of input/output data alone.It is interesting to note that, although the overall model solution utilised glucose and oxygen while excreting CO2 as the terminal electron acceptor.In the absence of a demand for biomass, a fixed demand for ATP was satisfied utilising alanine, proline and glutamate as precursors, despite the presence of complete glycolytic and TCA cycle pathways and the availability of glucose. In the scheme described in Similar schemes for the oxidation of amino acids to pyruvate and ultimately to acetate for ATP synthesis in staphylococci were previously described by a number of authors  [trophies ,48,49,50trophies ,11,51. Germidis) . In thisA more detailed characterisation of the impact that these absences have upon flux distribution in the model, while both interesting and technically straightforward, is beyond the scope of the present investigation. The experimental observation that growth increased in the absence of certain amino acids is something that could not be explained from the model results. We propose, as a working hypothesis, that this effect results from changes in the concentration of signalling molecules in the environment and, as no simple LP method contains information about concentrations, this represents a fundamental limitation of the approach  in collagen, needs to be experimentally studied.We have shown the potential for a complex interaction between amino acid and energy metabolism, whereby amino acids are catabolised with some substrate-level phosphorylation, generating pyruvate, which then feeds the electron transport chain and oxidative phosphorylation. Further experimental investigations are now needed in clinically relevant conditions to shed light on the factors that promote the colonisation of


However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44. From their advent in pharmaceutical research, machine learning (ML) algorithms have found many successful applications, ranging from hit identification to drug delivery optimization ,2,3. ThiIn general, artificial intelligence (AI) methods can find at least three major applications focused on drug metabolism  since thNotwithstanding the above, most of the reported predictive studies are still focused on redox metabolism, with particular attention paid to CYP-450 catalyzed reactions . In contThe discrepancy between the studies focused on redox metabolic reactions and those dealing with the other metabolic reactions might be justified by considering the marked relevance of the former, which represent by far the most common biotransformations . NeverthA more pertinent cause that might explain the scarcity of comprehensive predictors is the lack of extended and truly accurate metabolic databases. Most available datasets are focused on the metabolic reactions catalyzed by CYP-450 isozymes  and ofteOn these grounds, we recently reported a novel metabolic database (MetaQSAR), which was generated by manual and critical meta-analyses of the specialized literature published during the 2005–2015 period . The colBased on these encouraging results, the MetaQSAR database is here exploited to perform an exhaustive predictive analysis of all the sets of metabolic reactions for which the database includes sufficient instances . In these predictive studies, we follow the same classification system adopted by MetaQSAR, which subdivides the metabolic reactions into three main classes , 21 classes and 101 subclasses. In detail, the reported analyses involve the 3 main classes, 18 classes and 23 populated subclasses. Details concerning the subdivision into main classes, classes and subclasses can be found elsewhere . The modBesides developing comprehensive models able to predict the occurrence of the major metabolic reactions, the study aims to offer a comparative analysis of the propensity of each metabolic reaction to be predicted. Such a propensity might depend on the chemical space covered by the extracted datasets and on the informative richness of the computed descriptors, but also on the intrinsic features that govern the predicted reactions. These comparisons should reveal which reactions are unpredictable (at least by using the available data), which can be predicted even by applying standard methods, as reported here, and which reactions require targeted approaches to enhance the resulting performance. The predictive models generated for the classes of reactions are also utilized to develop a global tool, called MetaClass, and implemented within the VEGA environment , which pThe reported comparative analysis is based on the metabolic reactions collected within the MetaQSAR database and focuses on the first-generation reactions. Overall, the study involved 3788 metabolic reactions, which could be subdivided into 3 major classes, 21 classes and 101 subclasses. Predictive models were generated for the three major classes  and for As described in the Methods, the initial predictive analyses of the three major classes were utilized in the validation phase to tune the developed models by selecting the best-performing algorithm and by optimizing the corresponding hyperparameters. Moreover, they also had a calibrating role for the following class- and subclass-specific studies since these initial analyses were carried out for evaluating (1) the performance achieved by various sets of descriptors, (2) the relevance of using balanced datasets and (3) the role of the substrate’s ionization state. The following predictive studies were then carried out by applying the derived best-performing conditions.The comparison of the results reached by the three major classes reveals that hydrolyses afford, on average, the best performance regardless of the utilized descriptors and datasets, while conjugations yield the worst results. These findings can be explained by considering that the hydrolytic reactions, while involving different enzymes, comprise a rather homogeneous set of metabolic reactions. Hence, a single predictive model can account for all the collected reactions. In contrast, the redox reactions and especially the conjugations comprise a wide variety of metabolic reactions, which involve different enzymes, different catalytic mechanisms and different substrate preferences. Thus, their occurrence cannot be properly predicted by unique models.Concerning the set of descriptors, While the overall metrics show limited differences, the comparison of the class-specific performance reached by balanced and unbalanced datasets emphasizes the beneficial effects exerted by balancing the datasets. The beneficial effect of the balanced datasets can be appreciated by considering that the capacity to recognize the instances of the minority classes  is around 0.5 in all the analyses involving the unbalanced datasets. This means that the thus obtained predictions are comparable to the random results. In contrast, the performance of the minority class increases by using the balanced datasets, reaching rather satisfactory values at least for redox reactions and hydrolyses.With regard to the role of the ionization state, The Kier–Hall indices afford also here the best models, and the performance of both sets of descriptors is similarly affected by the ionization. The effect on Kier–Hall topological indices suggests that they properly capture the structural differences between neutral and ionized molecules. The effects on stereo-electronic parameters are expected and predictable, while the impact on physicochemical descriptors can be mostly ascribed to lipophilicity-related features. Since the ionization plays a negative  role in most comparisons (4 out of 6) and considering the possible inaccuracies introduced by automatic generation of the most plausible ionized forms, the following class-specific models were developed by considering the substrates in their neutral form.Overall, The analysis of the reported average performance reveals a trend in substantial disagreement with that evidenced by The analysis of the performance reached by each specific class allows for some considerations. Concerning the redox reactions, Concerning the hydrolyses, When focusing on conjugations, With regard to the sets of descriptors, Along with the discussed heterogeneity of the involved metabolic reactions, a factor that can explain the differences in performance reached by the analyzed classes is the efficiency of the considered metabolic reaction. Stated differently, a metabolic class can be conveniently predicted if almost all the substrates that contain a given reactive group undergo the corresponding metabolic reaction. By contrast, when the presence of a given reactive moiety is a necessary but not sufficient condition and many substrates that include such a reactive center do not undergo the metabolic reaction, its occurrence can be predicted with greater difficulty. Regardless of the heterogeneity of the involved reactive moieties, In detail, the achieved performance for redox reactions confirms the difficulty in predicting the biotransformations on the carbon atoms. This is partly ascribable to the richness of substrates within these subclasses, but is especially due to the variety of factors that govern these redox reactions, which cannot be described by the simple occurrence of reactive fragments. Only the subclasses of reactions involving double bonds and carbonyls can be conveniently predicted (MCC > 0.6). In addition, O-glucoronidations on the resulting performance, while the further classification of the reactions with GSH has a negligible role on the developed predictive models.The subclasses for the hydrolyses reach remarkable performance, especially for esters, the subdivision of which into neutral and ionizable substrates enhances the resulting performance. The overall effect of the clustering into subclasses can be appreciated by analyzing the charts reported in With regard to the utilized sets of descriptors, As discussed above, the performance of the Kier–Hall indices depends on the efficacy of the considered reactions. The performance achieved by the analyzed subclasses confirms the greater efficacy of hydrolyses and conjugations compared to redox reactions. Stated differently, the performance values compiled in To explore the relevance of each descriptor included in the utilized sets, the analysis of the feature relevance was performed by Weka. Attention was here focused on the predictive analyses involving the 18 analyzed classes since they represent a comprehensive analysis of all metabolic reactions. The feature relevance was analyzed by considering together all the utilized descriptors. The results were assessed by cross-validation repeated 10 times. Only the descriptors included by at least five validation runs were considered as relevant features.O-glucuronidations to 15 for redox reactions of >NH, >NOH, and –N=O. Although there is no correlation between the number of relevant descriptors and the performance of each class, one may observe that the average number of descriptors for the three main classes is in line with the average performance , thus confirming that the best-performing models are, on average, characterized by a limited number of variables.As detailed in the Methods, the predictive models generated for the classes were deployed by a tool that was developed to predict the occurrence of the corresponding biotransformations for a given input molecule. The tool, called MetaClass, was generated by the Tree2C approach  and implTo provide a preliminary validation of its predictive performance, the MetaClass predictor was tested to predict the metabolic reactions for a set of 10 molecules not included in the database used to develop the models since their metabolic studies were published after 2015. The study involved 2787 substrates that undergo 3788 first-generation metabolic reactions, as classified into 3 main classes, 21 classes and 101 subclasses. The MetaQSAR database contains the 3D structure of the collected substrates in their neutral state . For thehttp://www.ra.cs.uni-tuebingen.de/software/bluedesc/ (accessed on 23 September 2021). In detail, the code was encapsulated into a front-end VEGA script that supports the analysis of molecular databases as well as the generation of the CSV output files.The study involved the calculation of three sets of descriptors. The first set comprised 168 various variables, including constitutional, topological and physicochemical descriptors as computed by BlueDesc. These descriptors were computed by using the Java code available at The second set comprised 25 constitutional, geometrical and physicochemical descriptors as computed by VEGA plus 12 stereo-electronic parameters as calculated by PM7 semi-empirical calculations (see above). The last set comprised 44 Kier–Hall topological indices, which are categorical values that encode for the occurrence of specific functional groups . They wehttps://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/KierHallSmartsDescriptor.html (accessed on 23 September 2021). A description of the included constitutional, geometrical, physicochemical and stereo-electronic descriptors is provided in The complete list of the Kier–Hall topological indices with the corresponding SMARTS strings can be found at As mentioned above, the presented predictive studies involved the three major classes and the classes and subclasses including at least 50 metabolic reactions. The initial predictive studies on the three major classes were exploited to select the optimal classification algorithm and to optimize the corresponding parameters. The tuning of the RF hyperparameters was performed by means of the “Experimenter” module in Weka. In detail, the following parameters were considered: (1) the batch size; (2) the number of threads; (3) the number of iterations; (4) the attribute importance. Overall, 14 runs were carried out based on 10-fold cross validation, by which each test was repeated on the dataset 10 times with different random number seeds.Based on these tests, all the reported classification models were developed by the random forest machine learning algorithm as implemented in the Weka program with the following parameters: (1) the batch size = 100; (2) the number of threads = 1; (3) number of iterations = 100; (4) the attribute importance was not evaluated.The most significant features were selected by using the Weka program according to both the BestFirst search algorithm  and the WrapperSubsetEval attribute evaluator . As described in the Results, the predictive models were developed by considering the substrates in their neutral form for the three main classes of metabolic reactions and for all the classes and the subclasses with at least 50 occurrences. All the reported predictive models were developed by using balanced datasets as obtained by random undersampling using an ad hoc script implemented in the VEGA program. In contrast, the protonated forms of the substrates and the unbalanced datasets were utilized only to predict the occurrence of the three main classes.The models generated for the classes of metabolic reactions see  were utiTo develop the MetaClass predictor, the Tree2C approach was applied to the predictive models for the metabolic classes by generating the corresponding C code for each model. The generated codes were then merged into a unique script that predicts the occurrence of all the classes for the molecule loaded in the VEGA workspace. To speed up the MetaClass predictor and to support its release, the resulting codes were assembled into a precompiled binary library, which was optimized for use within the VEGA environment.To simplify the generation of the MetaClass predictor and to support its constant updating, a script to automatize the model generation (MetaClass builder) was also developed. Based on a database of metabolic reactions structured as implemented by MetaQSAR, the script performs the following tasks: (1) extracting the datasets for the classes of metabolic reactions; (2) balancing them by random undersampling of the majority class; (3) calculating the selected descriptors for the collected substrates, which can be calculated directly by the VEGA program or can be provided externally; (4) generating the predictive models by interfacing the script with the Weka program; (5) transforming the models into the corresponding C codes by using Tree2C and (6) assembling the final precompiled library.MetaClass builder can have two major applications. First, we use this to automatically generate updated releases of the MetaClass predictor, which parallels our continuous efforts to update the MetaQSAR database. Second, any research group can use this tool to develop its own predictor system by exploiting their internal metabolic data, which should be structured as requested by the MetaQSAR database features.The study describes a set of tree-based models to predict the occurrence of specific metabolic reactions. They were developed by using a standard random forest procedure and exploiting rather limited sets of representative descriptors. It should be noted that better models might be generated by using extended sets of descriptors through an optimization procedure specifically tailored to each analyzed dataset of metabolic reactions. Nevertheless, the primary objective of the study was focused on an extensive comparison of the achieved performance when predicting the occurrence of the various metabolic reactions as classified by the MetaQSAR database. Thus, the applied procedures were similarly performed for all datasets, and the possible resulting loss of performance similarly affected all the developed models, thus enabling a reasonably unbiased comparison of all the achieved performance results.Even when considering the corresponding subclasses, the redox reactions revealed the poorest performance. These negative results underline the complexity of the factors governing these metabolic reactions and suggest that predicting these reactions requires tailored approaches involving optimized algorithms and/or specifically designed descriptors.The hydrolyses provided almost always the best models and benefited from a detailed clustering of their reactions.When subdivided into classes and subclasses, the conjugations yielded, on average, satisfactory predictive models; this suggests that these reactions can be conveniently predicted by recognizing the involved reactive groups.When considering the performance achieved by each predicted class, the obtained results can be summarized as follows:While considering the comparative aim of the study, the developed models for the considered metabolic classes provided satisfactory performance  in 10 out of 18 classes. Therefore, a global classification system, called MetaClass, was developed within the VEGA environment to predict the occurrence of the various metabolic reactions. Clearly, these predictions should be cautiously taken by considering the here described performance. Nevertheless, they can provide a reasonable picture of the overall stability of the tested molecules as well as of the most probable reactions that they can undergo. Finally, a script (MetaClass builder) was developed to automatize the development of the predictive models and of the resulting classification system. Besides allowing the constant updating of the MetaClass predictor, this can be used by any researcher to develop similar predictive tools by exploiting its own metabolic data.