Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts and, as whole-cell models become more widely built and used, aid genome design.Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional and thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction, and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the  These models can formalise processes at a specific level  or construct a trans-omic network of the relationship between different cellular processes  to represent processes such as metabolism and cytokinesis, which integrate together at every time step.Recent years have seen a significant increase in the availability of high-throughput biological data . The introcesses  and couprocesses . Whole-cnitalium  and Eschhia coli . We focuin silico data can be difficult, but large-scale analysis is possible. Tools are required to automatically process and consolidate the output so they can be viewed and clarified, even by those with little computational expertise. Existing software tools that visualise whole-cell model output  created from a model designed to map labels onto instances of the data . SnorkelM. genitalium model oscillates significantly in time  cannot be disregarded, as there may be redundancy in the genome, or unprecedented gene product interactions. The removed gene/s will not tell the full story, but zooming out to examine a large set of different genotypes through their metabolic fluxes can show us the trends across the full set of knockouts, providing a different angle than that of focusing on a single gene.The complexity of genomic interactions, even in cells as small as in silico genome design.Here, we present a novel analysis pipeline that combines whole-cell model simulations of wild-type and gene knockout cells with time series classification and network analysis. The main steps include automatic labelling of metabolic fluxes as normal or abnormal , dimensionality reduction of the reactions for visualisation, and network analysis of the reactions. This analysis—looking at intermediate steps that connect genotype to phenotype—aims to increase our understanding of cellular processes and provides foundations for M. genitalium whole-cell model on a supercomputer cluster, with each gene singly knocked out. The model requires 8 GB of RAM for each simulation and was run on BlueGem, a 900-core supercomputer at the University of Bristol, using MATLAB R2013b. It is available at https://github.com/CovertLab/WholeCell. The raw metabolic flux time series was then converted to Pandas DataFrames and stored in a pickle format to save space. The training set consisted of time series of reaction fluxes for three repetitions of every possible single knockout from the M. genitalium model, of which there are 359, plus 200 wild-type simulations. Each time series is 50,000 s in total, and we used the time series of 279 reactions from each simulation. There was 1,270 simulations in total. The dataset that we applied to the analysis consisted of 10 repetitions of all of the single gene knockouts, with the same reaction time series, and so this dataset has 3,411 simulations in total. One knockout, MG_469, consistently caused the model to crash and the simulations to terminate, and a few simulations did not complete due to errors on the supercomputer cluster. The metabolic flux data are about 200 Mb per simulation after processing, so the training dataset (three repetitions of each single knockout) is ≈200 Gb, and the analysis dataset ≈700 Gb. More repetitions of each knockout would make for a more accurate dataset, but due to the size of the data, we were limited by storage space.We began with two sets of data—one to train the machine learning models and one to apply them and analyse the output. The simulations were generated from running the Snorkel is a system that takes input data points and manually defined labelling functions and collates these into a generative model that outputs probabilistic labels for the data. The labelling functions will produce noisy labels, which are then used as weak supervision for a stronger predictive function by combining three measures—the labelling propensity (whether the data point has been assigned a label or not), the accuracy of each label, and the correlations of the multiple labelling functions. The label matrix generated from these measures is then used to define an exponential distribution that can predict probabilistic training labels. The normality of 10 reactions was manually labelled by visual inspection of the time series, comparing features of the plots such as smoothness and linearity with wild-type time series from the same reactions , and useM. genitalium model, which used the production capacity of various features from the model output to classify a simulation . With the use of the data labelled by the generative model, a neural network was trained for each reaction. Each neural network had four hidden layers and used a softmax activator function and Adam optimiser. Different combinations of hyperparameters  were tested, so that an optimal combination could be used for each network to find the highest accuracy. Generally, the combination of hyperparameters can have a significant effect on the neural network output, so these factors are important. Epoch size refers to the number of rounds of back-propagation performed by the network, batch size means the number of training data samples input before the model updates, and number of nodes refers to number of nodes of the network in each hidden layer. Epoch size will leave the data underfitted if too small and overfitted if too large; batch size is generally optimised for processing time ; and the number of nodes is usually taken to be some number between the amount of input nodes and the amount of output nodes. There is no set method for selecting hyperparameters for neural nets, and it is frequently taken to be a trial-and-error process . We tuneS, taken from the M. genitalium model knowledge base, we reduced it to its binary format (as we were focusing on the topology of the metabolic network rather than the exact stoichiometry) to form a metabolic adjacency matrix A from the relationshipAfter the neural networks were trained and fluxes classified across the dataset, we turned to network analysis. With the stoichiometric matrix for the metabolism, via the Enzyme Commission numbers from the supplementary material of the M. genitalium model . The interval under which 95% of the data were contained was found, and this value was selected as a rate parameter, which was used as the threshold of significance for whether a reaction was considered to be behaving abnormally consistently.The metabolic sub-networks were plotted in python-igraph where, for each class across the dataset, the affected reactions are shown as a sub-network with a colour gradient corresponding to how frequently that reaction behaves abnormally. A threshold for “noisy” reactions was found from wild-type simulations, where an exponential distribution was fitted to the frequencies of reactions classified as behaving abnormally by the neural networks. For a wild-type simulation, in theory, all reactions should be classified as normal, but as the We then performed principal component analysis (PCA) using the SciPy library (version 1.3.1) to reduce the data to two dimensions and plot the data on a scatter plot using Seaborn (version 0.9.0). After the reduction, 84% of the variance in the full dimensions of the data was conserved, so there was no significant information loss after this operation. After having found the driver nodes, we trained a linear support vector machine (SVM) for the normality of each one to separate the data points on the PCA plot, selecting those that could divide the data with A schematic of our pipeline is shown in M. genitalium whole-cell model has drastically varying fluxes through different reactions , and mean squared error were found , leaving a two-dimensional space over which boundaries of different thresholds could be drawn, which was much simpler and faster to visualise and compute in two dimensions than it would have been before the dimensionality reduction . Using tMaterials and Methods and Three different boundaries were defined for different labelling schemes, as different confidence thresholds performed better or worse depending on the reaction. Boundaries at the extrema and then at 99% and at 95% were selected as the three labelling functions after comparison of their performance and then combined to form the generative model. We then implemented Snorkel, leaving us with 1,270 weakly labelled time series for each reaction. Ten reactions were manually labelled as normal or abnormal to test the accuracy of Snorkel’s labels, where characteristics like smoothness or the increasing or decreasing nature of the time series were used as comparison features to decide whether the behaviour of a reaction was normal or abnormal. The majority of the Snorkel labels gave over 90% accuracy, with the lowest at 69.2% , where many reactions are behaving abnormally), these will be placed much further away from the non-essential cluster.In E. coli metabolic network corresponds to metabolic functions , due tovia the nodes. From the graphical representation, we used a maximal matching algorithm to find driver nodes. Driver nodes are the set of nodes in the network that need to be managed in order to have full control over the system, which can be found for both directed and undirected networks . For all driver nodes, we modelled a linear SVM across the 2D data of the analysis dataset. We then selected those that could separate the data into normal vs. abnormal behaviours with over 95% accuracy as good and simple indicators of metabolic behaviours, shown in We have shown multiple analysis methods that take a large high-dimensional dataset and distill it into visualisations that are easy to interpret. The pipeline of weak labelling followed by neural network classification is applicable to any system that outputs time series, although the features used for the initial labelled schema may have to be changed, according to what the researcher intends to look for, and the type of time series that is being analysed. As discussed previously, it is particularly useful for where “normal” behaviours for a system is not well defined, and the mechanisms that underlie the system cannot be distilled into a form that is understood. We have shown that it is applicable for black-box models, but it could also be used for data from complex physical systems where we do not understand the fundamental structure, such as meteorological phenomena. Additionally, the driver node analysis is applicable to any system where there is input, output, and internal structure, as it can highlight the most important parts of a high-dimensional system.The processing of complex data is imperative to understand whole-cell model output, and this method demonstrates how the behaviours of specific reactions can be used as a marker of a particular phenotypic class and their importance to the corresponding cellular process.Understanding the effects of single gene knockouts is a deceptively difficult task, as the domino effect of gene removal can cause large changes in the behaviour of a cell through its life cycle. Visualising and analysing thousands of time series is a challenge faced by many branches of research. These two problems come together in the context of whole-cell models. Using Snorkel and neural networks, we have been able to classify metabolic fluxes as normal or abnormal and visualise them in two dimensions, meaning that the dataset separates into groups that can be interpreted. Whole-cell model data must be understood in the context of controllable biological mechanisms to be relevant to genome design: in order to use knowledge gained from modelling in real cells, we must understand the internal operations as well as the output. The flux behaviour across different gene knockouts, and in particular the driver nodes, can show the links between genotype and phenotype, plus unprecedented effects that a gene may have on reactions seemingly unrelated to its functional annotation, on a scale that is only possible in a whole-cell model. As this analysis gives an overview of the entire metabolism, we can approach the problem of understanding gene knockouts in a way that includes the genomic context of the remaining genes and the behaviour of their associated reactions, rather than examining the phenotype with regard to the single gene that has been removed.Mycoplasma functions. Most of the driver reactions are not associated with annotated genes, as many transporter proteins are putative—however, given that M. genitalium synthesises very few compounds and gains most from its surrounding media, this is an important knowledge gap. The external media for Mycoplasma culture is generally undefined rich media, so knowledge of exactly which of the media components are essential for growth would be valuable for lab use and simplify Mycoplasma production  are less simple, as there are significantly more reactions that are consistently behaving abnormally. This is not surprising, as there are two main functions for a cell to perform: growth and replication. Growth occurs consistently through the cell cycle and requires constant synthesis and degradation of different proteins and RNAs. There is also a temporal element, as cascades of reactions that form different proteins may need to occur in a specific order. Any disruption to an aspect of this process during the life cycle will filter down to other processes, whereas if DNA replication is disrupted, it is primarily cell division that will be halted. In future studies, it would be interesting to see if dividing the proteins into functional groups and pathways for further analysis leads to a better understanding of their roles and how they interact with each other.M. genitalium is an organism where not all of the genes are classified, and the data that the model was built upon are from many different sources and organisms. In terms of the network analysis, there are some reactions that have been observed in M. genitalium but do not have known enzymes to catalyse them, which leaves gaps within the model. There may be unexpected and unusual behaviours that are not captured in the training data as well, leading to misclassifications; for example, the reactions that performed badly in the neural network classifications may be sensitive to small changes in the metabolic network, meaning that their behaviours are inconsistent and unpredictable. However, it is useful to flag these reactions and, in the future, to use different approaches to understand their behaviours. There is also the possibility that, after applying the machine learning processes, the results show more about the internal features of the model itself than the actual biology, which is a good starting point for lab work.It is hard to draw solid conclusions about cell behaviours, as E. coli (M. genitalium, with significantly more data available to validate and add to it, so this is an important development for the field. However, the complexity of models will increase hugely with the size of the genome of the organism, and as E. coli has an order of magnitude more genes than M. genitalium (As whole-cell models become more widely used, analysis software will become more important. The most recent whole-cell model is of E. coli , which initalium , analysi


Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated “metabolite repair enzymes.” Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.  •Underground metabolism can generate useful, useless, or toxic metabolites.•Damaged metabolites can be produced non-enzymatically or owing to enzyme promiscuity.•Metabolite repair enzymes eliminate damaged metabolites or pre-empt their formation.•Tools to discover underground pathways could be used to reveal hidden repair systems.•Metabolite repair is crucial to account for in disease and metabolic engineering. Non-canonical metabolitessmall molecules formed from intracellular metabolites by enzymatic side reactions or non-enzymatic reactions; they are typically not intermediates in any metabolic pathway.Substrate promiscuityproperty of an enzyme to utilize multiple substrates.Catalytic promiscuityproperty of an enzyme to catalyze more than one type of chemical reaction on a given substrate.Underground metabolismoften neglected part of cellular metabolism encompassing the side-reactions catalyzed by metabolic enzymes on endogenous substrate analogs.Damaged metabolitestoxic or useless non-canonical metabolites.Metabolite repairenzymatic transformation of a damaged metabolite back to the canonical precursor metabolite. The term “metabolite repair enzyme” can also designate enzymes that eliminate damaged metabolites or pre-empt their formation. Such enzymes are also referred to as metabolite damage-control, house-cleaning, or proofreading enzymes in literature.Metabolic engineeringprocess involving genome editing and/or (heterologous) pathway/enzyme (over)expression in host organisms, with the aim to produce novel metabolites/value-added chemicals.Credentialingused to describe analytical methods that allow identification of biologically-derived peaks in mass spectrometry-based metabolomics data generated from unlabeled and stable isotope labeled extracts.Protein moonlightingexpression used to describe proteins that have more than one physiologically relevant function .Historically, metabolic enzymes were thought to drive linear pathways by catalyzing successive, specific transformations; however, we now know that non-canonical metabolites are generated concurrently. This is due to the lack of perfect substrate and/or reaction specificity of metabolic enzymes, as well as inherent reactivity and instability of certain metabolites , 2, 3, 4Underground metabolism and metabolite repair are conceptually linked via the notion of enzyme promiscuity and overlap where the former produces toxic or wasteful metabolites (Supplementary Table S1) that need to be cleared or recycled by the latter. Apart from inherited metabolic disorders ,17, undeThe apparent paradox between the dwindling number of remaining gaps in primary metabolic pathways and the high number of remaining enzymes of unknown function , the facImportant technological advances in gas chromatography (GC) and liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) are driving the development of more and more sensitive, rapid, and comprehensive methods to analyze the cellular metabolome, including the products of underground metabolism. While untargeted metabolomic analyses generate datasets of increasing size and complexity, related data analysis method development is lagging behind. Metabolite annotation remains challenging due notably to the inability to differentiate, among the 10,000s features  detected in a single run, biologically relevant features from features that are not (or not directly) derived from the analyzed biological systems . Cheminformatic and other computational methods are needed to find and align peaks, perform MS/MS spectral matching, and aid unknown peak identification. Discovering non-canonical metabolites in untargeted datasets is even more challenging since their intracellular concentration is typically maintained at low levels by repair enzymes. Additionally, their chemical structures are highly diverse and may be unexpected since they arise from enzyme promiscuity and non-enzymatic reactions of canonical metabolites a. Even wCredentialing  substrates. By comparing the data derived from the unlabeled and stable isotope-labeled samples, credentialing software detects and retains features with identical retention times and a mass shift corresponding to the number of labeled atoms  is applied to data generated from pooled or separate extracts  [Plasmodium falciparum, erythrocytes infected with the parasite were credentialed using ten 13C-tracer substrates  and subjected to untargeted MS-based metabolomic analyses [The Peak Annotation and Verification Engine (PAVE) was developed for credentialing in separate extracts b and, inetected) . Of thesanalyses . The 911Buffer Modification Workflow (BMW) . Here, the LC-MS-based ow (BMW) b can be In silico Network Expansions (MINEs), the “Enzyme Commission-based” option of BioTransformer, or Extended Metabolic Models (EMM) [Genome-scale metabolic models (GEMs), although incomplete because of unknown enzymes and reactions ,41, provls (EMM) , 43, 44.ls (EMM) . Similarls (EMM) .E. coli GEM and unsupervised PSI-BLAST to predict promiscuous replacer genes that could compensate for deficient essential metabolic genes [thiG) was for example predicted  as an indirect replacer enzyme for erythronate-4-phosphate dehydrogenase (pdxB) in the E. coli pyridoxal 5′-phosphate (PLP) biosynthesis pathway. The Metabolic Disruption Workflow (MDflow) combines GEMs and EMM promiscuity prediction to evaluate the impact of heterologous enzyme expression or gene suppression/overexpression on metabolism [GEM-PROPER combines the ic genes . Thiazoltabolism . InclusiIt is likely that a substantial fraction of remaining enzymes of unknown function are involved in metabolite damage-control . MetabolHypothesized or known metabolite damages H were found to be actively degraded also in rat liver extracts [ damages a can be extracts . Proteinextracts .Inborn errors of metabolism  of unknown function  [P. falciparum Δpgp mutants indeed showed 4 PE and 2 PL accumulation, reduced glycolytic flux and interestingly, increased sensitivity to fosmidomycin, an anti-malaria drug, providing the first indication of a metabolite repair enzyme as a drug target candidate. RidA is a widely conserved damage pre-empting deaminase acting on reactive enamines/imines , which inhibit PLP-dependent enzymes. While RidA is encoded by a single-copy gene in most eukaryotes, teleost fish genomes contain two paralogs of RidA [Salmo salar RidA-1 had greater activity on 2-iminoacids derived from nonpolar amino acids, like the mammalian homolog, while RidA-2 showed higher activity on 2-iminoacids derived from glutamate and aromatic amino acids. Since gene clustering in bacterial genomes can hint at functional relationships, a particularly fruitful comparative genomics approach consists of searching bacterial chromosomes for genes clustering in a conserved manner with enzymes known to produce damaged metabolites; such genes should be considered as metabolite repair gene candidates. In this way, using the SEED database and its tools [genomics d is fruise (PGP) , a metabse (PGP) . P. falc of RidA . Imine ats tools , Niehausts tools .Phenotypic screens  [cpgA mutant, of the damaged metabolite and PPP inhibitor 4 PE (see above). While 4 PE accumulation is prevented in other species by a dedicated repair phosphatase  loss-of-function screens , can be designed to identify new enzyme functions [E. coli terpenoid biosynthesis pathway [ screens e in whic ribose) . The obsase PGP ,62), thi, thiPhenunctions . Althoug pathway . Competi pathway ,70. In h pathway .As illustrated through various examples above, inborn errors of metabolism and classical biochemistry approaches have largely contributed to original metabolite repair enzyme discoveries . The relSalmonella enterica ΔridA strain showed the anticipated 2-aminoacrylate accumulation and inhibition of PLP-dependent branched-chain amino acid and folate metabolism, but unexpectedly revealed alterations in nucleotide and SAM-dependent metabolism [S. cerevisiae NAD(P)HX dehydratase knockout strain uncovered perturbed serine metabolism, which was then shown to result from inhibition by NADHX of the serine synthesis pathway at the level of 3-phosphoglycerate oxidation [E. coli NAD(P)HX epimerase mutants suggested a moonlighting function of this repair enzyme in PLP metabolism [B. subtilis NAD(P)HX dehydratase knockout strain under osmotic and ethanol stress [Omics approaches provide a means to unravel the systems-wide  impacts of metabolite damage accumulation , often beyond what can be predicted and have already contributed to understanding the physiological relevance of selected repair systems. Transcriptomic and metabolic profiling of the tabolism . Transcrxidation . Comparatabolism . Furtherl stress . Lastly,l stress , a repail stress  and recel stress . These sen route to completing metabolic networks (The concepts of underground metabolism and metabolite repair have breathed new life into metabolic research, inspiring the discovery of new metabolites, enzymes, and entire pathways, demystifying poorly understood diseases to the point of making them treatable, and adding new parts to the toolbox of metabolic engineers. Comprehensive molecular phenotyping techniques have provided invaluable insights into the functional consequences of repair deficiencies, but the next opportunity for advancing this young field lies in a better utilization of systems biology tools to continue pulling underground and repair metabolism out of the shadows networks .None declared.


Cancer, but also its treatment, can lead to a reprogramming of cellular metabolism. These changes are observable in metabolite abundances, which can be unbiasedly measured via mass spectrometry metabolomics. However, even when the metabolome changes strongly, a (mechanistic) interpretation is difficult as metabolite levels do not necessarily directly correspond to pathway activities. Here we measure the changes of the cellular metabolome in colorectal cancer cell lines sensitive and resistant to the ruthenium-based drug BOLD-100/KP1339 and the platinum-based drug oxaliplatin. We map these changes onto a cancer-specific genome-scale metabolic model, which allows us not only to compute intracellular flux distributions, but also to disentangle drug-specific effects from growth differences from differences in metabolic adaptations due to resistance. Specifically, we find that resistance to BOLD-100/KP1339 induces more extensive reprogramming than oxaliplatin, especially with respect to fatty acid and amino acid metabolism.Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance. A reprogramming of metabolism is a hallmark of multiple diseases, including cancer . ChangesIn silico methods have the potential to integrate existing experimental data and to generate new hypotheses about the underlying mechanisms associated with metabolic reprogramming. Genome-scale metabolic models (GSMMs), which capture the known biochemical reactions of a given system, have previously been applied in various cancer studies ,6, such Metal-based drug treatments involving oxaliplatin are a standard therapy for colorectal cancer, the third most commonly diagnosed cancer ,18,19,20Acquired resistance against platinum drugs is thought to be mainly based on elevated DNA-repair mechanisms, detoxification, evading apoptosis and autophagy . HoweverMetabolomics aims to directly measure metabolite abundance from a global and unbiased perspective and has the potential to not only detect metabolic alterations but to discover diagnostic and prognostic markers and to generate hypotheses that can be validated with genetic experiments . Recent While metabolomics studies provide an effective interrogation window for the cellular changes that occur in response to a change in conditions, they do not necessarily provide mechanistic insights into the reprogramming of metabolism. Metabolite pools do not inform about pathway activity, ergo corresponding metabolic fluxes are sometimes measured. Measuring metabolic fluxes, however, also suffers from several practical limitations. For example, a prolonged time for peripheral pathways to reach isotopic steady-state, the fact that simple linear pathways can only be investigated with non-stationary labelling or an increased number of samples, and the complex data analyses required for nonstationary labelling experiments often hinder a successful and comprehensive application of isotopic labelling methods .Recent trends in metabolomics have shown it is always possible to measure more metabolites at more time points and to analyse the obtained results in combination with other ‘omics data sets ,32,33,34Typically, constraint-based modelling (CBM) is employed to study GSMMs and to explore metabolic phenotypes in the form of steady-state fluxes ,44. FluxIn this work, we integrate experimentally determined absolute concentrations of intracellular metabolites and medium-based metabolites and growth rates of the colorectal cancer cell-line HCT116 into a cell-line specific, thermodynamic, genome-scale metabolic model (GSMM). We consider two different models of acquired resistance in colon cancer: oxaliplatin resistant (OxR) and BOLD-100/KP1339 resistant (RuR) HCT116 cells as well as their sensitive controls to generate four model instances. To identify metabolic differences between resistant and sensitive cells, we normalize the calculated flux values by their representative growth rates. As oxaliplatin and BOLD-100 are prepared in different solvents (water versus dimethylsulfoxid (DMSO)), OxR and RuR cells were grown in the same media, but RuR and its respective control were exposed to a low DMSO background equivalent to the drugs’ stock solution solvent. To account for metabolic differences that are the results of a difference in solvent, we further normalized the results obtained for the resistant cells by their sensitive controls. Eliminating both differences in growth rate and solvent background, we are able to draw drug-specific conclusions about the metabolic changes that occur upon resistance. As a result, we are able to identify specific changes in flux that are the direct result of an acquired resistance to either oxaliplatin or BOLD-100/KP1339 treatment.Mycoplasma contamination.HCT116 colon cancer cells were generously provided by Dr. Vogelstein from John Hopkins University, Baltimore. Cells were cultured in McCoy’s medium  supplemented with 10% fetal calf serum  and 2 liplatin ,53 for 2Cells were seeded at densities of HCT116 cells, HCT116 cells with acquired oxaliplatin resistance, and HCT116 cells with acquired BOLD-100/KP1339 resistance were seeded , acetyl-coenzyme A, palmitoyl-coenzyme A, malonyl-coenzyme (A below LOD) was carried out in an additional measurement series of the same samples and on the same instrumental setup but with a dedicated LC-MS method. The same separation was used with the same gradient and eluents, but flushing of the column started at 6 min instead of 7 min, shortening the total measurement time from 15 min to 14 min. The OrbiTrap MS settings were changed with regard to the mass range to 750–1100 m/z, the capillary temperature was lowered from 280 The applied extraction and centrifugation resulted in a pellet containing the high-molecular fraction and non-polar metabolites. This pellet was dissolved in 0.2 M NaOH overnight, diluted 1:10 in the same NaOH solution and determined for total protein content with the Thermo Micro BCA kit, according to manufacturer’s instructions.https://skyline.ms/project/home/software/Skyline/begin.view, accessed on 12 August 2021) extracting the [M-H]Targeted analysis of the data was done with Skyline 20.2  above 30% were removed from the dataset. This was based on the repeated injection and measurement of a pooled quality control sample. Furthermore, metabolites which had mean concentration below the determined lowest limit of quantification (LOQ) according to the validation of the LC-MS method described in  were remDatasets were combined by joining the metabolite data acquired in both positive and negative mode, as well as coenzyme data in the negative acquisition mode. A further calibration was measured in positive mode for several carnitines  with the method for metabolites, since these compounds were not contained in our original calibration mixture. Also the calibration row for coenzymes was prepared freshly in this mixture to avoid degradation by storage. The external calibration of the different coenzymes  was measured in negative mode. For every primary thiol in the dataset  its N-ethyl maleimide adduct was used for quantification after it was made sure that the conversion was quantitative.The cell dry weight measurements of HCT116 sensitive (Sen), oxaliplatin-resistant (OxR) and BOLD-100/KP1339-resistant (RuR) cells were carried out .Robinson et al.  provide soft_l1 for the loss function and Growth rates  and exchper cell . The fitsurement  and the surement  was calcAll applied model constraints are based on data obtained during the exponential growth phase and as such, all model results are specific to this growth phase.https://github.com/EPFL-LCSB/pytfa, accessed on 12 August 2021, formulates thermodynamic flux analysis (TFA) of GSMM as a mixed-integer linear programming problem that incorporates metabolite concentrations as thermodynamic constraints into a traditional flux balance analysis (FBA) model. Masid et al. [The pyTFA package , https:/d et al.  have recd et al. . Absolutf fluxes , we calcf fluxes . Becausef fluxes .find_blocked_reactions in cobrapy (Version 0.19.0) with default parameters, resulting in a model with 4530 reactions and 4492 degrees of freedom. Upon calculating flux values for each model instance using pTFVA as described, we divided each set of flux values by the outgoing flux to biomass production of that model instance, effectively normalizing for difference in growth. We checked for reactions for which both the upper and the lower bound differed by at least 15%. Furthermore, we feature-scaled all flux values to lie between 0 and 1 and divided the flux values obtained in the drug-resistant instances by the corresponding flux values obtained for their respective controls. Having thus normalized for differences in the medium composition, we were able to compare the flux profiles of the two metallodrug resistances to another, again checking for which reactions both the upper and lower bounds differed by at least 15%.We constrained four different instances of the HCT116 model: oxaliplatin-resistant cells (OxR) and their sensitive parental counterpart (HCT116) and BOLD-100/KP1339-resistant cells (RuR) and their sensitive parental counterpart in a DMSO-based medium (HCT116-DMSO). Model instances were constrained using the condition-specific exchange fluxes  and growTo investigate the metabolic changes associated with metallo-resistance in colorectal cancer, we compared the metabolic profiles of resistant and sensitive cells. Using the HCT116 colorectal cancer cell line, cells with resistance to either oxaliplatin (OxR) or BOLD-100/KP1339 (RuR) were compared to their sensitive counterparts. The two acquired resistence models are largely independent of one another: while OxR cells show moderate cross-resistance for the ruthenium-based drug, RuR cells display no cross-resistance and remain sensitive to oxaliplatin treatment . This imWith the aim of investigating whether the observed changes in cellular metabolite concentrations  translatThe maximum relative standard error observed across the metabolite measurements was less than 15%. Thus, when integrating the data into the GSMM and comparing flux differences between condition-specific instances of the model, we used a cutoff of 15% to determine whether fluxes were significantly different across conditions. Comparing resistant cells to sensitive cells, we identify pathways with the most prominent changes in flux upon acquired resistance . DiffereInitially, the oxidative phosphorylation pathway shows the highest amount of flux changes in response to OxR. Upon growth normalizing, however, it is RuR that shows a higher number of flux changes in this pathway. Furthermore, what initially appears to be significant differences in flux through the cholesterol and lipid metabolism, largely disappears upon growth normalization. Changes in the subsystem reactive oxygen species (ROS) detoxification seem minimal prior to growth normalization; the normalized results, however, indicate significant changes in flux with regards to detoxification. While the number of reactions that appear to be affected in starch and sugar and tricarboxylic acid (TCA) metabolism appears to be drug resistance-specific prior to growth normalization, this effect disappears upon growth normalization. The comparison of non-normalized and growth-normalized results in Integrating metabolite measurements into GSMMs allows for growth rate normalization of the calculated fluxes which in turn allows for a direct flux comparison between resistant and sensitive cells. The reprogramming of energy metabolism to support cell growth and proliferation is a major hallmark of cancer  and has In the growth conditions considered here, glucose acts as the primary carbon source . GlucoseWe observe that OxR cells convert less pyruvate into lactate, but in turn consume a higher relative amount of fatty acids compared to their parental sensitive cells. RuR cells, however, show a high glycolytic flux and a high oxygen consumption as well as higher fatty acid consumption than their sensitive counterparts . NotablyWith the four instances of the HCT116-specific GSMMs, further conditions encountered in the tumor environment can be simulated. Simulating the effect of hypoxic growth conditions, we first set the oxygen influx for each model instance to the minimum possible value and then observe the minimum required fatty acid influx as we iteratively increase the oxygen influx, thus plotting the growth normalized production envelope of oxygen versus minimum fatty acid influx for each of the four conditions . While RWe then set a minimum possible fatty acid influx and iteratively increased the total fatty acid influx to the model while calculating the minimum required oxygen influx . We repeCrucially, while a direct comparison between resistant and sensitive cells for each drug respectively can be made, we cannot make a direct comparison between the two drug resistance models . BecauseIn order to be able to compare the metabolic profiles of the two metallodrug resistance phenotypes directly, we finally normalized the flux results obtained from the metallo-resistant model instances against their respective parental sensitive counterparts see  cells hiNotably, when comparing the OxR and RuR model instances to their respective parental HCT116 drug-sensitive counterparts, prior to growth normalization, we identified 1039 (OxR) and 1180 (RuR) fluxes that were significantly different. Upon growth normalization, these numbers reduced to 743 (OxR) and 883 (RuR), highlighting that hundreds of differences observed in the non-normalized results are simply the result of a difference in growth rate. The OxR versus RuR comparison upon growth-media DMSO-background normalization highlighted 670 different reactions, suggesting that another 73 of reactions were initially observed as significantly different because of presence of 0.5% DMSO.Genome-scale metabolic models (GSMMs) provide a platform for integrating omics data sets and for analysing them in the context of metabolic fluxes. As we have shown, GSMMs can be constrained using both extracellular and intracellular metabolite concentrations to study metallodrug resistance in colon cancer. Approximately one-hundred metabolite constraints were applied to study the effect of changes in their concentrations in thousands of reactions. Colorectal-specific GSMMs have previously been constructed ,48,58,71In this study, we investigated various pathways in silico including glycolysis, the tricarboxylic acid cycle, fatty acid and amino acid metabolism, beta-oxidation, the pentose phosphate pathway. A comprehensive stable isotope resolved metabolic flux analysis considering such a diverse set of pathways would require the application of multiple different positionally labelled isotopic tracers ,72. In aThere is no simple relationship between changes in metabolite concentrations and changes in flux . This noAccurate comparative profiling of metabolic changes observed across heterogenous conditions remains a challenge. Differences in growth rates and impact of solvent necessities will result in observed differences in metabolite concentrations but are not causal to a reprogramming of metabolism . ConsideAs a prerequisite to produce accurate, quantitative metabolomics data , we normIntegrating the metabolite data as part of a thermodynamic flux analysis allows us to normalize the calculated reaction rates by the growth rate observed under the corresponding conditions. We showed that growth-normalization reduces the number of reactions that are different between resistant and sensitive model instances and changes some of the conclusions about altered metabolic pathways entirely. Growth normalization is therefore a critical step when looking for drug-specific metabolic phenotypes. A second limitation to studying non-normalized metabolite concentrations is that data obtained from heterogeneous conditions cannot be directly compared. Using the growth normalized flux results we further normalized each resistant model against its sensitive counterpart which was grown in an identical medium and solvent composition. Hence, we were able to do a direct comparison between the two metallo-resistances and to identify drug resistance-specific responses.A limitation to our approach is that we first normalized our flux results to differences in growth rate and then normalized each resistant model against its respective counterpart. This means that we are unable to capture emergent properties that result from both differences in growth rate and solvent impact; we assume that a combined effect of the two is minimal. Furthermore our flux analyses assume metabolism to be in steady-state, such that intracellular concentrations are constant. Nonetheless, we have clearly demonstrated that a growth and medium/solvent normalization is non-trivial as it allows for comparisons across heterogeneous conditions. We expect this method to be of wider applicability in studies where the effects of medium compositions, such as the availability of carbon sources to a cell, are of interest.Time-dependent changes of metabolite profiles have previously been considered ,82, but Defense mechanisms and acquired resistance are well known phenomena when applying metal-based drugs as anticancer agents. Reduced efficacy due to acquired resistance remains a major challenge in systemic anticancer therapy. The complexity is increasingly recognized, as the contribution of epigenetic and metabolic effects will be uncovered. Drug-specific and tumor tissue specific mechanisms have been described, and more recently the tumor microenvironment has come into focus . AccordiIn this work, we consider an in vitro study of colon cancer. Gastrointestinal cancer cell lines, including colorectal cancer cell line HCT116 activate beta-oxidation as a response to oxaliplatin treatment and conversely become more sensitive to oxaliplatin upon inhibition of fatty acid catabolism . A seminThe high metabolic plasticity of cancer cells enables efficient detoxification and protection strategies . NormaliDespite shared commonalities like the production of ROS, it is expected that RuR and OxR models display different metabolic phenotypes, because of known differences in their modes of action ,92,93. OThe comparison of fluxes through key energy metabolism reactions  shows thDrastic changes in oxygen and fatty acid availability are known stress conditions in a tumor microenvironment, and are assumed to be managed with metabolic adaptations . Lipid dAdditionally, the investigation of minimum oxygen requirement at various fatty acid influxes  revealedThere are different ways to capture the metabolic phenotype of a cell. Metabolic profiling via metabolomics provides an interrogation window of the intracellular concentrations at a given point in time. Extracellular concentrations measured over time provide insight to the cellular uptake and excretion rates of cells. Together they can be integrated to constrain the solution space of a genome-scale metabolic model. The calculated flux values can then be normalized according to growth rates and environmental conditions, allowing for drug resistance specific metabolic responses to be identified across heterogenous conditions. We find the outlined normalization steps to be crucial in the interpretation of the results and show that metabolic reprogramming is more extensive in BOLD-100/KP1339 resistant cells than in oxaliplatin resistant cells. We identify pathways, such as fatty acid and amino acid metabolism, to be upregulated in response to a resistance acquired to a ruthenium-based drug when compared to a platinum-based drug. All in all, genome-scale metabolic modelling provides a valuable platform for putting observed changes in metabolite concentrations in the context of metabolic fluxes.


Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage , and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis.The pathogen IMPORTANCEClostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets. Clostridioides difficile is a Gram-positive, sporulating anaerobe that remains a critical problem in health care facilities across the developed world (C. difficile infection (CDI) is most frequently preceded by exposure to antibiotic therapy  are mathematical formalizations of metabolic reactions encoded in the genome of an organism. These models are subsequently constrained by known biological and physical parameters such as membrane transport and enzyme kinetics. GENREs can be utilized to interrogate the metabolic capability of a given organism, as well as providing a means to simulate growth and assess the impact of genotype on metabolism. GENREs have been implemented in directing genetic engineering efforts  and accus mutans , 19, 20.C. difficile including a highly characterized laboratory strain, C. difficile strain (str.) 630  which accounts for codon biases and amino acid balance and cell wall structure. Growth simulations from both GENREs were compared against in vitro gene essentiality and carbon utilization screens, which indicated significant levels of agreement across all validation data sets.We began by generating new GENREs for two strains of tr.) 630 , as welltr.) 630 . De novontenance . AdditioC. difficile metabolism by integrating transcriptomic data collected from both laboratory culture and infection conditions where differential expression of C. difficile virulence factors was observed. Overall, during increased virulence expression both strains of C. difficile were predicted to favor increased fermentation of amino acids and decreased reliance on carbohydrate usage. Specifically in the hypervirulent strain R20291 during states of phase variation, we found efflux of the biofilm component N-acetylglucosamine in variants known to produce significantly more biofilm experimentally. Additionally, this state was predicted to have increased reliance on glucose to fuel nucleotide synthesis, instead of ATP generation. When tested in vitro, we indeed found that the colony morphology associated with this phase variant was dependent on environmental glucose availability. Alternatively, in infection-specific models of strain 630, we identified consistent patterns of proline and ornithine fermentation in states of both high and low sporulation, which agreed with metabolomic analysis of each condition. However, in instances of lower spore burden our model predicted significantly greater usage of the host-derived glycan N-acetylneuraminate (Neu5Ac) and the nucleotide precursor cytidine as primary sources of carbon. In subsequent laboratory testing we were able to show that not only can C. difficile use the substrates for growth but also both lead to lower quantities of spores, which are essential for transmission of the pathogen  cycle, and known molecular oxygen detoxification , 34Minimal medium components and known auxotrophies –37–Aminoglycan and dipeptide catabolism –40–Numerous Stickland fermentation oxidative and reductive pathways  41–52)–5241–52)–Carbohydrate fermentation and short-chain fatty acid metabolism , 53–5553–Elements of the Wood-Ljungdahl pathway Energy metabolite reversibility  Structural components including teichoic acid, peptidoglycan, and isoprenoid biosynthesisp-cresol  utilizes homologs from the genome of str. R20291). (B) Binary quantification for metabolite growth enhancement shown for both strains/GENREs in C. difficile minimal medium components across three previously published studies. Essentiality was determined for those genes and metabolites that when absent resulted in a yield of <1.0% of optimal biomass flux during growth simulation utilizing components of the corresponding media used experimentally. Download FIG S1, PDF file, 0.4 MB.Additional Copyright © 2021 Jenior et al.2021Jenior 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 in vitro growth in basal defined medium (BDM) (C. difficile is able to harvest sufficient bioavailable sulfur from excess cysteine instead of methionine (5) appears to enhance growth rate only in vitro and is not necessarily required to support low growth rates. Our results also indicated that iCdR703 was not auxotrophic for isoleucine relative to iCdG709 and indeed contained additional genes coding for synthesis of a precursor (3S)-3-methyl-2-oxopentanoate  which were not present in its counterpart GENRE  . Interesum (BDM) . Additiothionine , 68, furrt GENRE . In summC. difficile strains in a high-throughput screen   that it< 0.001)  and B. Tnt calls . This meC. difficile GENREs. The primary focus of curated C. difficile metabolic modeling efforts has been on the first fully sequenced strain of C. difficile, str. 630. The first reconstruction effort  and subTC806cdf , 72) wer (iCN900 ), a stracrobiota ; these rformance . The firformance , 77, 78,formance , which mformance , as wellpathways . Conside10.1128/mSystems.00919-21.8TABLE S3C. difficile GENREs from AGORA and those generated here. Download Table S3, XLSX file, 0.01 MB.Topology summary statistics for Copyright © 2021 Jenior et al.2021Jenior 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 in situ metabolic phenotypes that correspond with expression of known virulence traits in C. difficile. As previously stated, GENREs have provided powerful platforms for the integration of transcriptomic data, creating greater context for the shifts observed between conditions and capturing the potential influence of pathways not obviously connected  analysis utilizing a recently published unsupervised transcriptomic data integration method  rich medium for 48 h and sequenced transcriptomes from both groups. Utilizing these data, we generated context-specific versions of iCdR703 under simulated rich medium conditions and deeply sampled the resultant metabolic flux distributions to assess all possible forms of metabolism given the new constraints.. R20291 . One asp. R20291 . The colcmr-family genes does not significantly alter growth rate in vitro . These results supported that the differences in context-specific model structure seen between phase variants likely represented real variation in active metabolism.While it has been previously shown that mutation of in vitro , the conin vitro . This rein vitro . We movein vitro . We thenin vitro . C. diff proline , which iormation , in whicomponent . Observinditions . Further10.1128/mSystems.00919-21.2FIG S2P value < 0.001). (B) Analysis limited to nonbiomass reactions shared across context-specific models of iCdR703. NMDS ordination of Bray-Curtis dissimilarities for flux sampling results from core metabolic reactions. Significant difference determined by PERMANOVA. Download FIG S2, PDF file, 0.03 MB.Predicted core metabolic activity significantly differs between str. 630 phase variants. (A) Sampled biomass objective flux distributions from each context-specific model. Significance determined by Wilcoxon rank sum test . To further explore the specific differences within active metabolism between phase variants, we utilized a supervised machine learning approach with Random Forest to discriminate between rough and smooth core metabolic activity  ordination of Bray-Curtis dissimilarities . This anactivity . Severalactivity . Alanineysiology  and has ysiology . Our resBoth the network topology and metabolic activity-based analyses indicated that a large number of transporters and metabolic reactions were differentially active , especia10.1128/mSystems.00919-21.9TABLE S4Table S4, XLSX file, 0.03 MB.Rough versus smooth context-specific analysis of iCdR703 (str. R20291). Download Copyright © 2021 Jenior et al.2021Jenior 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 C. difficile str. R20291, grown anaerobically for 48 h on BHIS agar  Subcultured colonies from the indicated conditions in FIG S3, PDF file, 2.3 MB.Additional microscopy of phase variant colony morphologies. (A) Copyright © 2021 Jenior et al.2021Jenior 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 C. difficile  where cefoperazone had largely undetectable spore CFU and clindamycin had significantly higher levels at the same time point (C. difficile-colonized groups (both treated by the respective antibiotics) to extract the specific impact of the infection on the gut environment, making this data set extremely valuable.While laboratory conditions are highly informative, it is even more critical to examine metabolism for this pathogen during infection as it can more readily lead to novel therapeutic interventions. It has been previously shown that different classes of antibiotics have distinct impacts on the structure of the gut microbiota while inducing similar sensitivity to colonization by ifficile . Along tme point . These eP value = 0.001). Comparisons of overall context-specific model flux distributions highlighted substantial differences in transporter and metabolic reaction network compositions and associated activities  and cytidine only in the lower-sporulation context-specific model . (B) Mean decrease accuracy for top 10 most differentiating features/reactions from Random Forest supervised machine learning for shared nonbiomass reactions across context-specific models of iCdG709. (C) Cecal concentrations of 5-aminovalerate in mock- and C. difficile str. 630-infected mice pretreated with streptomycin, 18 h after initial colonization. Significance determined by Wilcoxon rank sum test; P values = 0.003 (high spores) and 0.0001 (low spores). Download FIG S4, PDF file, 0.01 MB.Strain 630 growth simulation and infection metabolomic results. (A) Sampled biomass objective flux distributions from each context-specific model. Significant difference tested using Wilcoxon rank sum test . Download Copyright © 2021 Jenior et al.2021Jenior 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 C. difficile str. 630 could utilize both N-acetylneuraminate and cytidine as carbon sources and if together they exerted a combined effect on growth. Both N-acetylneuraminate and cytidine were supplemented (10 mg/ml each) in liquid BDM in parallel with liquid BDM with no additional substrate and BDM + d-glucose (10 mg/ml) controls, into which C. difficile str. 630 was inoculated and incubated for 18 h and optical density at 600 nm (OD600) was measured every 5 min . Additionally, there was no discernible effect on growth when the two substrates were added simultaneously. Utilization of the nucleotide precursor cytidine as a carbon source during infection has never been previously described in C. difficile, which further supported the utility of our models as a platform for augmenting discovery.We first sought to measure if ry 5 min . This asN-acetylneuraminate and cytidine on sporulation in C. difficile str. 630, we performed a growth and sporulation assay targeting these substrates using the same defined medium conditions described previously  but no significant change compared to the glucose-added control . As was the case in the growth curve results, there was no difference between N-acetylneuraminate and cytidine when added alone versus their combined effect. Collectively, these results support that both N-acetylneuraminate and cytidine utilization by C. difficile inhibit progression through its life cycle toward spore formation. More broadly, our results support these GENREs as an advantageous discovery platform for novel elements of C. difficile metabolism and physiology.To then assess the effect of eviously . Followi control . Importa10.1128/mSystems.00919-21.5FIG S5C. difficile str. 630 spore and vegetative cell quantification in defined medium sporulation assay. Median and IQR of C. difficile str. 630 vegetative cells and spore CFU (n = 4) after 18-h incubation in rich medium or minimal medium (BDM) formulated with the indicated additional carbon sources (10 mg/ml each). Significant differences calculated with Wilcoxon rank sum test with Benjamini-Hochberg correction . Download FIG S5, PDF file, 0.06 MB.Raw Copyright © 2021 Jenior et al.2021Jenior 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 C. difficile’s physiology and pathogenicity is subject to a coalescence of metabolic signals from both inside and outside the cell. Historically, C. difficile research has suffered from a shortage of molecular tools and high-quality predictive models for highlighting new potential therapies. Over the previous decade, GENREs have become powerful tools for connecting genotype with phenotype and provided platforms for defining novel metabolic targets in biotechnology and improving interpretability of high-dimensional omics data. These factors make GENRE-based analyses extremely promising for directing and accelerating identification of possible therapeutic targets as well as a deeper understanding of the connections between C. difficile virulence and metabolism. Furthermore, as much of bacterial pathogenicity is now being attributed to shifts in metabolism, the analyses described here may provide large benefits to the identification of possible treatment targets in C. difficile and other recalcitrant pathogens  utilization in combination with consumption of the nucleotide precursor cytidine that corresponded with lower levels of sporulation. While not typically considered a carbon source for C. difficile, laboratory testing confirmed that C. difficile can indeed use cytidine for energy and, along with N-acetylneuraminate, decreases in sporulation. Intentional control of sporulation is an exciting prospect as spores are considered the transmissive form of C. difficile, so these results may prove valuable for downstream targeted manipulation of C. difficile virulence factor expression. Our results also supported a role for some level of amino acid fermentation across all conditions tested. This phenotype is a hallmark of C. difficile physiology and reinforced the validity of the other predictions. These results indicate a complex relationship with environmental nutrient concentrations and likely competition with the gut microbiota that all inform the regulation of C. difficile virulence expression. Additionally, in vivo context-specific gene essentiality also predicted proline racemase to be critical for growth during infection, yet it was previously found to be dispensable in an animal model using a forward genetic screen  into a form more compatible with the COBRA toolbox  medium at 37°C for 48 h to obtain isolated colonies. Rough and smooth colonies were chosen for propagation on BHI agar to ensure colony morphology maintenance (https://imagej.nih.gov/ij/).ntenance . Basal dntenance  with theC. difficile str. 630 growth was maintained in an anaerobic environment of 85% N2, 5% CO2, and 10% H2. Liquid BDM was formulated as previously described with the indicated combinations of d-glucose (10 mg/ml), N-acetylneuraminic acid (10 mg/ml), and cytidine (10 mg/ml). Overnight BHI liquid cultures of C. difficile str. 630 were back-diluted 1:3 in fresh anaerobic BHI and incubated for 1 h at 37°C, at which point 5 μl was inoculated into 1 ml of each medium condition. After 18 h anaerobic incubation at 37°C, serial dilutions in anaerobic phosphate-buffered saline of these cultures were plated on BHIS agar  plates to quantify vegetative cell abundance and then treated with 50% EtOH for 30 min (600 was measured every 5 min for 18 h (Tecan Infinite M200 Pro).r 30 min , and serC. difficile R20291 genome (FN545816.1) using the software’s default scoring penalties for mismatch, deletion, and insertion differences. All samples yielded over 22 million total reads, with over 20 million mapped to the reference . Transcript reads for each gene were normalized to the total number of reads and gene length (expressed as reads per kilobase of transcript per million mapped reads [RPKM]).For RNA isolation, rough and smooth isolates were subcultured in BHIS broth  overnight (16 to 18 h) at 37°C, and then 5 μl of the cultures was spotted on BHIS agar (1.5% agar). After 24 h, the growth was collected and suspended in 1:1 ethanol-acetone for storage at −20°C until subsequent RNA isolation. Cells stored in ethanol-acetone were pelleted by centrifugation and washed in TE  buffer. Cell pellets were suspended in 1 ml TRIsure reagent. Silica‐glass beads (0.1 mm) were added, and cells were disrupted using bead beating  for 1.5 min. Nucleic acids were extracted using chloroform, purified by precipitation in isopropanol followed by washing with cold 70% ethanol, and suspended in nuclease-free water. Samples were submitted to Genewiz, LLC , for quality control analysis, DNA removal, library preparation, and sequencing. RNA sample quantification was done using a Qubit 2.0 fluorometer (Life Technologies), and RNA quality was assessed with a 4200 TapeStation (Agilent Technologies). The Ribo Zero rRNA removal kit (Illumina) was used to deplete rRNA from the samples. RNA sequencing library preparation was done using the NEBNext Ultra RNA library prep kit for Illumina (NEB) according to the manufacturer’s protocol. Sequencing libraries were checked using the Qubit 2.0 fluorometer. The libraries were multiplexed for clustering on one lane of the Illumina HiSeq flow cell. The samples were sequenced using a 2 × 150 paired-end configuration on an Illumina HiSeq 2500 instrument. Image analyses and base calling were done using the HiSeq control software. The resulting raw sequence data files (.bcl) were converted to the FASTQ format and demultiplexed with bcl2fastq 2.17 software (Illumina). One mismatch was permitted for index sequence identification. Data were analyzed using CLC Genomics Workbench v. 20 (Qiagen). Reads were mapped to the C. difficile str. 630 and str. R20291 peptide sequences was performed using bidirectional BLASTp. RNA-Seq reads were first quality trimmed with Sickle with a cutoff ≥Q30 . Mappin Bowtie2 . MarkDup Bowtie2 . Abundan Bowtie2 .C. difficile str. 630 growth curves was determined using Dynamic Time Warping  analyses were accomplished using the vegan R package . Signifi Warping .Clostridioides difficile str. 630 (PATRIC ref. 272563.8) and Clostridioides difficile str. R20291 (PATRIC ref. 645463.3) were downloaded from the PATRIC database (PRJNA415307 and PRJNA354635) and the Gene Expression Omnibus (GSE158225). The GitHub repository for this study, with all programmatic code and GENREs described here, can be found at https://github.com/mjenior/Jenior_CdifficileGENRE_2021.Genomic and proteomic data for the strains


In 13C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy). •13C-MFA.An open-source Python package, mfapy, is developed for •13C-MFA.mfapy enables users to write Python codes for data analysis procedures of •mfapy has a flexibility and extensibility to support various data analysis procedures.•13C-MFA experiments is supported for experimental design.Computer simulations of  All scripts and example files of mfapy, detailed explanations of all functions, rules that describe the user-defined metabolic pathway, and the carbon transition network are available on the project home page (https://github.com/fumiomatsuda/mfapy). Documentation of each mfapy function is also available from GitHub (https://fumiomatsuda.github.io/mfapy-document/).mfapy is implemented in Python 3 based on external packages, including NumPy , SciPy , nlopt  of metabolites from a specified vector of metabolic flux (v) and isotopic labeling patterns of carbon sources (xinp).In v) follows the stoichiometric equation, Sv = 0, where S is the stoichiometric matrix produced from the stoichiometry of metabolic reactions. M is implemented as a MetabolicModel class in the mfapy.metabolicmodel module. An instance of the MetabolicModel class can be generated from a model definition file describing the stoichiometry and carbon transition information of each metabolic reaction  in the model fitting. The “pseudo”-type reactions are disregarded in constructing a stoichiometry matrix of its substrate . An instance of the class is automatically generated from a MetabolicModel instance by the “generate_carbon_source_template” method. The isotope labeling patterns can be set using the “set_each_isotopomer” method MDVj=skeleton . An obseskeleton b, step 52.6v is fitted to the observed mass spectrum . To solve the non-linear optimization problem, mfapy employs Scipy and nlopt packages supporting 13 global and local optimizers, such as sequential quadratic programming (SLSQP) and CRS2_LM .In mfapy, the estimation of 2.7The confidence intervals of the estimated fluxes are determined using the grid search method . Followi2.813C-MFA experiment. For a specified metabolic flux vector, v, and carbon source (such as the cs prepared above), an MDVdata instance, including simulated MDV data, can be generated by the “generate_mdv” method of the MetabolicModel instance. The MDVdata instance comprises “add_gaussian_noise” and “set_std” to add Gaussian noise to the MDV data and set standard deviation levels for the MDV measurement. A Python code example for this task is as follows:An artificial observed MDV is used for computer simulation of the 2.9(1)v is produced by minimizing the RSS(v) using the MetabolicModel.fitting_flux method, as described above.A seed metabolic flux distribution (2)th step, v, a proposal flux distribution, v’, is generated by adding random values to the flux level of three randomly selected reactions. Proposal flux distributions are iteratively generated until a v’ within a feasible flux space is obtained using the MetabolicModel.add_perturbation method.Based on the flux distribution at the j(3)p = P (RSS(v’))/P (RSS(v)) is larger than 1.0, then (v’) is accepted for the next step. P is the probability distribution of the χ2 distribution (the degree of freedom is the number of measurements). If p < 1.0, v’ is accepted with probability p. When v’ is rejected, v is used in the next step.If the acceptance probability (4)The procedure is repeated 5,000,000 times to generate a Markov chain because at least 100,000–1,000,000 steps are required to obtain a stable estimate . The dat(5)v, following a posterior distribution. From the population, 2500 data points are obtained by sampling every 1000 steps of the chain.The remaining chain with 2,500,000 steps is used as the sample population of (6)The entire procedure is performed 20 times via parallel computing.(7)A sample population of 50,000 data points (2500 with 20 replicates) is used for the following data analysis. The entire procedure is available in the MetabolicModel.posterior distribution method.The Metropolis-Hastings algorithm is performed as follows:2.1013C-MFA datasets described in our previous study, including a metabolically engineered Escherichia coli strain producing isopropanol (A toy model of the tricarboxylic acid (TCA) cycle was obtained from a previous study . Two 13Cpropanol  and breapropanol , were us33.1v) and the isotopic labeling pattern of the carbon source (xinp) . The obtained mfapy uses the framework of the EMU algorithm to determine a simulated MDV (e (xinp) . In the e (xinp)  (Antoniee (xinp) . In thise (xinp)  and a Pyterature , indicat3.213C-MFA was conducted to estimate the metabolic flux distribution of the central carbon metabolism of a metabolically engineered E. coli strain to produce isopropanol using the authentic 13C-MFA software OpenMebius (13C-labeled glucose (glucose : [1–13C]glucose : [U–13C]glucose = 2:70:28). Cells were collected in an exponential growth phase (OD600 ~1) to measure the MDV of proteinogenic amino acids by GC-MS. Furthermore, the specific rates for glucose consumption, and acetate, acetone, and isopropanol production were determined from the culture profile data. The metabolic flux distribution of central carbon metabolism was successfully estimated from the MDV of 23 fragments of proteinogenic amino acids and specific rate data using the metabolic model of E. coli, including 85 reactions. G-value parameters were included in each target fragment by applying a patch to the source code of OpenMebius to rectify the effects of inoculated unlabeled proteinogenic amino acids on the observed MDV . A metabolic model definition file (“Example_2_Ecoli_model.txt”) and status file (“Example_2_Ecoli_status.csv”) were prepared from the original data from the literature. To manage the G-value parameters, a specific reaction type, “pseudo,” can be used in mfapy (see v was optimized to minimize the difference between the observed and simulated MDVs (Eq. [3]). The metabolic flux vector v of the best-fitted optimization determined via mfapy was identical to that determined using OpenMebius in a previous study .mfapy employs Scipy and nlopt external packages to support 13 global and local non-linear optimization solvers. Various solvers with distinct algorithms have been developed owing to the compatibility between the algorithms and non-linear optimization problems. To investigate the compatibility with For example, following a generation of a population of 100 random initial metabolic flux vectors, each metabolic flux vector was optimized for 60 s using the SLSQP algorithm. The median RSS level among the 100 metabolic flux vectors was 20,896 a. The pr13C-MFA study of breast cancer (MCF-7) cells  cells  . An optimization trial comprised the global optimization by GN_CRS2_LM with 10,000 optimization steps, followed by a gradient-based local optimization (SLSQP). Optimization trials from 1000 random initial metabolic flux vectors were executed in parallel, while the optimization progression was monitored.Non-linear optimization solvers often fail to reach the global optimum because they are trapped in a local optimum or their optimization progress is slow. To avoid local optima, mfapy can execute optimization trials from many random initial metabolic flux vectors in parallel processors using a task-parallel execution environment of the joblib module. In this study, global and local optima were investigated using the A comparison between the RSS and metabolic flux level of pyruvate dehydrogenase (PDH) showed that optimizations remained incomplete after the 10,000 steps of SLSQP a. The RSFurthermore, a comparison between the RSS and metabolic flux level of the reaction of glucose 6-phosphate dehydrogenase (G6PDH) revealed a global and local optimum below the RSS threshold. Although 339 trials reached the global optimum, 20 trials were trapped in the local minimum after 1,000,000 steps d–f.13C-MFA was conducted using a metabolic network, including a reversible PGI reaction. Moreover, the reactions of fructose 1,6-bisphosphatase  and PEP synthetase  were considered based on experimental validation  was constructed in a previous study by removing the phosphoglucose isomerase (PGI) reaction and activation of the Entner–Doudoroff (ED) pathway . Howeverlidation . The metlidation . Glucoselidation g.13C]GAP and non-labeled GAP produced via the ED pathway were primarily used for glycine and phenylalanine biosynthesis because of the high metabolic flux levels of the lower EMP pathway  via the gluconeogenesis pathway and entered the ED pathway again, as shown in  pathway g. In con pathway h, glucose and non-labeled glucose at a 50:50 ratio (denoted as 50% [U–13C]glucose). The width of the 95% confidence interval of reaction v4 was 36 . The metabolic network  is lower than that of 50% [U–13C]glucose (50%), these results suggest that 100% [1–13C]glucose is an undesirable carbon source compared to 50% [U–13C]glucose for the analysis of the anaplerotic reactions. network a shows t13C]glucose:[U–13C]glucose at 50:50 is likely a better carbon source because of interactions between [1–13C]glucose and [U–13C]glucose (denoted as Mixture). Alternatively, it has been reported that the parallel execution of multiple labeling experiments, such as two labeling experiments using 100% [1–13C]glucose and 50% [U–13C]glucose, can improve the flux estimation performance   . The com13C-MFA. One approach to identify better target metabolites for measurement is to compare the simulated 95% confidence intervals using distinct sets of target metabolites. As previously stated, the 95% confidence interval of v4 was determined to be 36 (from 50 to 86) for the computer simulation of 13C-MFA using 50% [U–13C]glucose as the carbon source ), should follow a χ2 distribution (degree of freedom is the number of measurements) because the observed MDV data include an experimental error following the normal distribution. The Metropolis–Hastings method is an MCMC algorithm that generates a posterior distribution of v .The flexibility and extensibility provided by mfapy can support the development of new data analysis techniques for stable isotope labeling experiments, such as a Bayesian approach . In thisv4 was successfully obtained using the Metropolis–Hastings method, as shown in v4. v4, and that the result was identical to that of the 95% confidence interval-based method . The demonstration suggested that extendability as an open-source Python package can provide a computational basis for developing alternative methodologies for 13C-labeling experiments.To implement the Metropolis–Hastings method, we first developed an mfapy-based Python code describing the procedure . Subsequently, the method to execute the entire Metropolis–Hastings algorism was newly added to a MetabolicModel class . A generation of one posterior distribution of 3.7https://fumiomatsuda.github.io/mfapy-document/).mfapy supports INST-MFAs . The INSSynechocystis sp. PCC 6803 GT strain under photoautotrophic conditions using NaH13CO3 as the carbon source   .13C-MFA has been widely used to investigate metabolic redirection and reprogramming, such as in cancer and immune cells (13C-labeling patterns or MDV of a few metabolites. For such cases, a Monte Carlo-based approach would be useful because it can generate a probability distribution of the metabolic flux vector v using a small number of data points. The deliverables were newly implemented in the mfapy package in this study.Furthermore, the flexibility of mfapy provided a basis for new data analysis techniques, including a Monte Carlo-based data analysis method recently developed for the quantitative assessment of metabolic reprogramming  (Examplene cells . In manymfapy has been continuously developed to enrich functions, such as loading various model definition formats and data visualization . New verFumio Matsuda: Methodology, Software, Investigation, Writing – original draft, Reviewing, and Editing, Kousuke Maeda: Software, and Investigation, Takeo Taniguchi: Software, and Investigation, Yuya Kondo: Software, and Investigation, Futa Yatabe: Software, and Investigation, Nobuyuki Okahashi:, Methodology, Software, Investigation, Reviewing, and Editing, and Hiroshi Shimizu: Conceptualization and Supervision.https://github.com/fumiomatsuda/mfapy.The Python script and model definition file can be downloaded from our GitHub repository: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.